Multiple platform build/check report for BioC 3.22 - CHECK results for MungeSumstats on lconway

Hostname	OS	Arch (*)	R version	Installed pkgs
nebbiolo2	Linux (Ubuntu 24.04.3 LTS)	x86_64	4.5.1 Patched (2025-08-23 r88802) -- "Great Square Root"	4901
lconway	macOS 12.7.6 Monterey	x86_64	4.5.1 Patched (2025-09-10 r88807) -- "Great Square Root"	4691
kjohnson3	macOS 13.7.7 Ventura	arm64	4.5.1 Patched (2025-09-10 r88807) -- "Great Square Root"	4637
Click on any hostname to see more info about the system (e.g. compilers) () as reported by 'uname -p', except on Windows and Mac OS X*

CHECK results for MungeSumstats on lconway

To the developers/maintainers of the MungeSumstats package:
- Allow up to 24 hours (and sometimes 48 hours) for your latest push to git@git.bioconductor.org:packages/MungeSumstats.git to reflect on this report. See Troubleshooting Build Report for more information.
- Use the following Renviron settings to reproduce errors and warnings.
- If 'R CMD check' started to fail recently on the Linux builder(s) over a missing dependency, add the missing dependency to 'Suggests:' in your DESCRIPTION file. See Renviron.bioc for more information.

raw results

Summary

Package: MungeSumstats

Version: 1.18.0

Command: /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:MungeSumstats.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings MungeSumstats_1.18.0.tar.gz

StartedAt: 2025-10-30 22:39:53 -0400 (Thu, 30 Oct 2025)

EndedAt: 2025-10-30 22:59:34 -0400 (Thu, 30 Oct 2025)

EllapsedTime: 1180.2 seconds

RetCode: 0

Status: OK

CheckDir: MungeSumstats.Rcheck

Warnings: 0

Command output

##############################################################################
##############################################################################
###
### Running command:
###
###   /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:MungeSumstats.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings MungeSumstats_1.18.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/Users/biocbuild/bbs-3.22-bioc/meat/MungeSumstats.Rcheck’
* using R version 4.5.1 Patched (2025-09-10 r88807)
* using platform: x86_64-apple-darwin20
* R was compiled by
    Apple clang version 14.0.0 (clang-1400.0.29.202)
    GNU Fortran (GCC) 14.2.0
* running under: macOS Monterey 12.7.6
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘MungeSumstats/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘MungeSumstats’ version ‘1.18.0’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... NOTE
Found the following hidden files and directories:
  .BBSoptions
These were most likely included in error. See section ‘Package
structure’ in the ‘Writing R Extensions’ manual.
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘MungeSumstats’ can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking whether startup messages can be suppressed ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... NOTE
checkRd: (-1) check_no_chr_bp.Rd:64-65: Lost braces
    64 | \item \code{sumstats_dt}{
       |                         ^
checkRd: (-1) check_no_chr_bp.Rd:66-67: Lost braces
    66 | \item \code{rsids}{
       |                   ^
checkRd: (-1) check_no_chr_bp.Rd:68-69: Lost braces
    68 | \item \code{log_files}{
       |                       ^
checkRd: (-1) check_on_ref_genome.Rd:73-74: Lost braces
    73 | \item \code{sumstats_dt}{
       |                         ^
checkRd: (-1) check_on_ref_genome.Rd:75-76: Lost braces
    75 | \item \code{rsids}{
       |                   ^
checkRd: (-1) check_on_ref_genome.Rd:77-78: Lost braces
    77 | \item \code{log_files}{
       |                       ^
checkRd: (-1) compute_nsize.Rd:32: Lost braces in \itemize; meant \describe ?
checkRd: (-1) compute_nsize.Rd:33-36: Lost braces in \itemize; meant \describe ?
checkRd: (-1) compute_nsize.Rd:37-38: Lost braces in \itemize; meant \describe ?
checkRd: (-1) compute_nsize.Rd:39-40: Lost braces in \itemize; meant \describe ?
checkRd: (-1) compute_nsize.Rd:41-42: Lost braces in \itemize; meant \describe ?
checkRd: (-1) compute_nsize.Rd:43-44: Lost braces in \itemize; meant \describe ?
checkRd: (-1) compute_sample_size.Rd:21-28: Lost braces in \itemize; meant \describe ?
checkRd: (-1) compute_sample_size.Rd:30-34: Lost braces in \itemize; meant \describe ?
checkRd: (-1) compute_sample_size.Rd:36-40: Lost braces in \itemize; meant \describe ?
checkRd: (-1) compute_sample_size.Rd:42-46: Lost braces in \itemize; meant \describe ?
checkRd: (-1) compute_sample_size.Rd:48-52: Lost braces in \itemize; meant \describe ?
checkRd: (-1) compute_sample_size_n.Rd:16-23: Lost braces in \itemize; meant \describe ?
checkRd: (-1) compute_sample_size_n.Rd:25-29: Lost braces in \itemize; meant \describe ?
checkRd: (-1) compute_sample_size_n.Rd:31-35: Lost braces in \itemize; meant \describe ?
checkRd: (-1) compute_sample_size_n.Rd:37-41: Lost braces in \itemize; meant \describe ?
checkRd: (-1) compute_sample_size_n.Rd:43-47: Lost braces in \itemize; meant \describe ?
checkRd: (-1) compute_sample_size_neff.Rd:21-28: Lost braces in \itemize; meant \describe ?
checkRd: (-1) compute_sample_size_neff.Rd:30-34: Lost braces in \itemize; meant \describe ?
checkRd: (-1) compute_sample_size_neff.Rd:36-40: Lost braces in \itemize; meant \describe ?
checkRd: (-1) compute_sample_size_neff.Rd:42-46: Lost braces in \itemize; meant \describe ?
checkRd: (-1) compute_sample_size_neff.Rd:48-52: Lost braces in \itemize; meant \describe ?
checkRd: (-1) read_sumstats.Rd:29: Lost braces in \itemize; meant \describe ?
checkRd: (-1) read_sumstats.Rd:30: Lost braces in \itemize; meant \describe ?
checkRd: (-1) read_sumstats.Rd:31-32: Lost braces in \itemize; meant \describe ?
checkRd: (-1) read_vcf.Rd:64: Lost braces in \itemize; meant \describe ?
checkRd: (-1) read_vcf.Rd:65: Lost braces in \itemize; meant \describe ?
checkRd: (-1) read_vcf.Rd:66-67: Lost braces in \itemize; meant \describe ?
checkRd: (-1) read_vcf_parallel.Rd:40: Lost braces in \itemize; meant \describe ?
checkRd: (-1) read_vcf_parallel.Rd:41: Lost braces in \itemize; meant \describe ?
checkRd: (-1) read_vcf_parallel.Rd:42-43: Lost braces in \itemize; meant \describe ?
checkRd: (-1) select_vcf_fields.Rd:27: Lost braces in \itemize; meant \describe ?
checkRd: (-1) select_vcf_fields.Rd:28: Lost braces in \itemize; meant \describe ?
checkRd: (-1) select_vcf_fields.Rd:29-30: Lost braces in \itemize; meant \describe ?
checkRd: (-1) sort_coords.Rd:19-21: Lost braces in \itemize; meant \describe ?
checkRd: (-1) sort_coords.Rd:22-24: Lost braces in \itemize; meant \describe ?
* checking Rd metadata ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking R/sysdata.rda ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
Examples with CPU (user + system) or elapsed time > 5s
                    user system elapsed
get_genome_builds 94.737  5.635 100.931
format_sumstats   90.753  4.600  96.402
liftover           3.570  0.041   8.302
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘testthat.R’
 OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE

Status: 2 NOTEs
See
  ‘/Users/biocbuild/bbs-3.22-bioc/meat/MungeSumstats.Rcheck/00check.log’
for details.

Installation output

MungeSumstats.Rcheck/00install.out

##############################################################################
##############################################################################
###
### Running command:
###
###   /Library/Frameworks/R.framework/Resources/bin/R CMD INSTALL MungeSumstats
###
##############################################################################
##############################################################################


* installing to library ‘/Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/library’
* installing *source* package ‘MungeSumstats’ ...
** this is package ‘MungeSumstats’ version ‘1.18.0’
** using staged installation
** R
** data
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** installing vignettes
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (MungeSumstats)

Tests output

MungeSumstats.Rcheck/tests/testthat.Rout


R version 4.5.1 Patched (2025-09-10 r88807) -- "Great Square Root"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(testthat)
> library(MungeSumstats)
> 
> test_check("MungeSumstats")


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca418df5ee.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca662bbd21
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A0	A1	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A0	A1	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca418df5ee.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.08 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca313be8ac.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca662bbd21
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca313be8ac.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.053 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca766ac6b3.tsv.gz
Log data to be saved to ==>  /tmp/RtmpNksM9m
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca7ca7f39c
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicate SNPs from SNP ID.
Checking for SNPs with duplicated base-pair positions.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
Checking for bi-allelic SNPs.
Loading SNPlocs data for build 144 on GRCH37.
Loading reference genome data.
Preprocessing RSIDs.
Validating RSIDs of 93 SNPs using BSgenome::snpsById...
BSgenome::snpsById done in 56 seconds.
1 SNPs are non-biallelic. These will be removed.
Writing in tabular format ==> /tmp/RtmpNksM9m/snp_bi_allelic.tsv.gz
46 SNPs (50%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca766ac6b3.tsv.gz
Summary statistics report:
   - 92 rows (98.9% of original 93 rows)
   - 92 unique variants
   - 69 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 1.007 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca2fb3dda5.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca7ca7f39c
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicate SNPs from SNP ID.
Checking for SNPs with duplicated base-pair positions.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
Checking for bi-allelic SNPs.
Loading SNPlocs data for build 144 on GRCH37.
Loading reference genome data.
Preprocessing RSIDs.
Validating RSIDs of 93 SNPs using BSgenome::snpsById...
BSgenome::snpsById done in 34 seconds.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca2fb3dda5.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.644 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Sorting coordinates with 'data.table'.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca5da96bf.tsv.gz
Log data to be saved to ==>  /tmp/RtmpNksM9m
Found 1 Indels. These will be removed from the sumstats. 
WARNING If you want to keep Indels, set the drop_indel param to FALSE & rerun MungeSumstats::format_sumstats()
Writing in tabular format ==> /tmp/RtmpNksM9m/indel.tsv.gz
Loading required namespace: GenomicFiles
Using local VCF.
bgzip-compressing VCF file.
Finding empty VCF columns based on first 10,000 rows.
Dropping 1 duplicate column(s).
1 sample detected: EBI-a-GCST005647
Constructing ScanVcfParam object.
VCF contains: 39,630,630 variant(s) x 1 sample(s)
Reading VCF file: single-threaded
Converting VCF to data.table.
Expanding VCF first, so number of rows may increase.
Dropping 1 duplicate column(s).
Checking for empty columns.
Unlisting 3 columns.
Dropped 314 duplicate rows.
Time difference of 0.1 secs
VCF data.table contains: 101 rows x 11 columns.
Time difference of 0.6 secs
Renaming ID as SNP.
sumstats has -log10 P-values; these will be converted to unadjusted p-values in the 'P' column.
No INFO (SI) column detected.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca5029f51.tsv.gz
Log data to be saved to ==>  /tmp/RtmpNksM9m
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca1c0ea34b
Checking for empty columns.
sumstats has -log10 P-values; these will be converted to unadjusted p-values in the 'P' column.
Infer Effect Column
First line of summary statistics file: 
SNP	chr	BP	end	REF	ALT	FILTER	AF	ES	LP	SE	P	N	
Standardising column headers.
First line of summary statistics file: 
SNP	chr	BP	end	REF	ALT	FILTER	AF	ES	LP	SE	P	N	
Summary statistics report:
   - 101 rows
   - 101 unique variants
   - 0 genome-wide significant variants (P<5e-8)
   - 1 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Reordering so first three column headers are SNP, CHR and BP in this order.
Reordering so the fourth and fifth columns are A1 and A2.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
2 SNPs (2%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca5029f51.tsv.gz
Summary statistics report:
   - 101 rows (100% of original 101 rows)
   - 101 unique variants
   - 0 genome-wide significant variants (P<5e-8)
   - 1 chromosomes
Done munging in 0.054 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
           SNP   CHR    BP     A1     A2   END FILTER    FRQ    BETA       LP
        <char> <int> <int> <char> <char> <int> <char>  <num>   <num>    <num>
1:  rs58108140     1 10583      G      A 10583   PASS 0.1589  0.0312 0.369267
2:    rs806731     1 30923      G      T 30923   PASS 0.7843 -0.0114 0.126854
3: rs116400033     1 51479      T      A 51479   PASS 0.1829  0.0711 1.262410
4: rs146477069     1 54421      A      G 54421   PASS 0.0352 -0.0240 0.112102
       SE          P      N
    <num>      <num>  <int>
1: 0.0393 0.42730011 293723
2: 0.0353 0.74669974 293723
3: 0.0370 0.05464998 293723
4: 0.0830 0.77249913 293723
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca3166b181.tsv.gz
Log data to be saved to ==>  /tmp/RtmpNksM9m
sumstats has -log10 P-values; these will be converted to unadjusted p-values in the 'P' column.
Infer Effect Column
First line of summary statistics file: 
SNP	chr	BP	end	REF	ALT	FILTER	AF	ES	LP	SE	P	N	Beta	
Standardising column headers.
First line of summary statistics file: 
SNP	chr	BP	end	REF	ALT	FILTER	AF	ES	LP	SE	P	N	Beta	
Summary statistics report:
   - 101 rows
   - 101 unique variants
   - 0 genome-wide significant variants (P<5e-8)
   - 1 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Reordering so first three column headers are SNP, CHR and BP in this order.
Reordering so the fourth and fifth columns are A1 and A2.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
2 SNPs (2%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca3166b181.tsv.gz
Summary statistics report:
   - 101 rows (100% of original 101 rows)
   - 101 unique variants
   - 0 genome-wide significant variants (P<5e-8)
   - 1 chromosomes
Done munging in 0.057 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
           SNP   CHR    BP     A1     A2   END FILTER    FRQ      ES       LP
        <char> <int> <int> <char> <char> <int> <char>  <num>   <num>    <num>
1:  rs58108140     1 10583      G      A 10583   PASS 0.1589  0.0312 0.369267
2:    rs806731     1 30923      G      T 30923   PASS 0.7843 -0.0114 0.126854
3: rs116400033     1 51479      T      A 51479   PASS 0.1829  0.0711 1.262410
4: rs146477069     1 54421      A      G 54421   PASS 0.0352 -0.0240 0.112102
       SE          P      N    BETA
    <num>      <num>  <int>   <num>
1: 0.0393 0.42730011 293723  0.0312
2: 0.0353 0.74669974 293723 -0.0114
3: 0.0370 0.05464998 293723  0.0711
4: 0.0830 0.77249913 293723 -0.0240
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca60100bd5.tsv.gz
Log data to be saved to ==>  /tmp/RtmpNksM9m
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca1c0ea34b
Checking for empty columns.
sumstats has -log10 P-values; these will be converted to unadjusted p-values in the 'P' column.
Infer Effect Column
First line of summary statistics file: 
SNP	chr	BP	end	REF	ALT	FILTER	AF	ES	LP	P	N	
Standardising column headers.
First line of summary statistics file: 
SNP	chr	BP	end	REF	ALT	FILTER	AF	ES	LP	P	N	
Summary statistics report:
   - 101 rows
   - 101 unique variants
   - 0 genome-wide significant variants (P<5e-8)
   - 1 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Reordering so first three column headers are SNP, CHR and BP in this order.
Reordering so the fourth and fifth columns are A1 and A2.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
The sumstats SE column is not present...Deriving SE from Beta and P
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
2 SNPs (2%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca60100bd5.tsv.gz
Summary statistics report:
   - 101 rows (100% of original 101 rows)
   - 101 unique variants
   - 0 genome-wide significant variants (P<5e-8)
   - 1 chromosomes
Done munging in 0.052 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
           SNP   CHR    BP     A1     A2   END FILTER    FRQ    BETA       LP
        <char> <int> <int> <char> <char> <int> <char>  <num>   <num>    <num>
1:  rs58108140     1 10583      G      A 10583   PASS 0.1589  0.0312 0.369267
2:    rs806731     1 30923      G      T 30923   PASS 0.7843 -0.0114 0.126854
3: rs116400033     1 51479      T      A 51479   PASS 0.1829  0.0711 1.262410
4: rs146477069     1 54421      A      G 54421   PASS 0.0352 -0.0240 0.112102
            P      N         SE IMPUTATION_SE
        <num>  <int>      <num>        <lgcl>
1: 0.42730011 293723 0.03930361          TRUE
2: 0.74669974 293723 0.03529477          TRUE
3: 0.05464998 293723 0.03699948          TRUE
4: 0.77249913 293723 0.08301411          TRUE
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca77cf95dc.tsv.gz
Log data to be saved to ==>  /tmp/RtmpNksM9m
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca1c0ea34b
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
SNP	CHR	BP	A1	A2	FRQ	Z	SE	P	N	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
SNP	CHR	BP	A1	A2	FRQ	Z	SE	P	N	
Summary statistics report:
   - 25 rows
   - 25 unique variants
   - 0 genome-wide significant variants (P<5e-8)
   - 1 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
The sumstats BETA column is not present...Deriving BETA from Z and SE
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
13 SNPs (52%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca77cf95dc.tsv.gz
Summary statistics report:
   - 25 rows (100% of original 25 rows)
   - 25 unique variants
   - 0 genome-wide significant variants (P<5e-8)
   - 1 chromosomes
Done munging in 0.052 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
           SNP   CHR     BP     A1     A2       FRQ      Z          SE      P
        <char> <int>  <int> <char> <char>     <num>  <num>       <num>  <num>
1:  rs12184267     1 715265      C      T 0.9591931 -0.916 0.007518884 0.3598
2:  rs12184277     1 715367      A      G 0.9589313 -0.656 0.007491601 0.5116
3:  rs12184279     1 717485      C      A 0.9594241 -1.050 0.007534860 0.2938
4: rs116801199     1 720381      G      T 0.9578380 -0.300 0.007391344 0.7644
        N         BETA IMPUTATION_BETA
    <int>        <num>          <lgcl>
1: 225955 -0.006887298            TRUE
2: 226215 -0.004914490            TRUE
3: 226224 -0.007911603            TRUE
4: 226626 -0.002217403            TRUE
Returning path to saved data.
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Sorting coordinates with 'data.table'.
Filtering SNPs based on INFO score.
46 SNPs are below the INFO threshold of 0.9 and will be removed.
Writing in tabular format ==> /tmp/RtmpNksM9m/info_filter.tsv.gz
INFO_filter==0. Skipping INFO score filtering step.
Filtering SNPs based on INFO score.
All rows have INFO>=0.9
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Sorting coordinates with 'data.table'.
3 p-values are >1 which LDSC/MAGMA may not be able to handle. These will be converted to 1.
5 p-values are <0 which LDSC/MAGMA may not be able to handle. These will be converted to 0.
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Sorting coordinates with 'data.table'.
8 p-values are <=5e-324 which LDSC/MAGMA may not be able to handle. These will be converted to 0.
Reading header.
Tabular format detected.
Reading header.
Tabular format detected.
Reading header.
Tabular format detected.
Reading header.
VCF format detected.This will be converted to a standardised table format.
Importing tabular file: /Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/library/MungeSumstats/extdata/eduAttainOkbay.txt
Checking for empty columns.
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Computing Z-score from P using formula: `sign(BETA)*sqrt(stats::qchisq(P,1,lower=FALSE)`
Standardising column headers.
First line of summary statistics file: 
SNP	CHR	BP	A1	A2	FRQ	BETA	SE	P	Z	newZ	
Computing Z-score from BETA ans SE using formula: `BETA/SE`


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62cabc8d812.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca16ac5427
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	EAF	Beta	SE	Pval	CHR_BP_A2_A1	
Standardising column headers.
First line of summary statistics file: 
MarkerName	EAF	Beta	SE	Pval	CHR_BP_A2_A1	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Summary statistics file does not have obvious CHR/BP columns. Checking to see if they are joined in another column.
Column CHR_BP_A2_A1 has been separated into the columns CHR, BP, A2, A1
If this is the incorrect format for the column, update the column name to the correct format e.g.`CHR:BP:A2:A1` and format_sumstats().
Standardising column headers.
First line of summary statistics file: 
SNP	FRQ	BETA	SE	P	CHR	BP	A2	A1	
Checking for incorrect base-pair positions
Reordering so first three column headers are SNP, CHR and BP in this order.
Reordering so the fourth and fifth columns are A1 and A2.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62cabc8d812.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.099 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca7e38384f.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca16ac5427
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca7e38384f.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.052 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca77e64c3.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62cae49aef9
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	EAF	Beta	SE	Pval	CHR_BP_A2_A1	
Standardising column headers.
First line of summary statistics file: 
MarkerName	EAF	Beta	SE	Pval	CHR_BP_A2_A1	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Summary statistics file does not have obvious CHR/BP columns. Checking to see if they are joined in another column.
Column CHR_BP_A2_A1 has been separated into the columns CHR, BP, A2, A1
If this is the incorrect format for the column, update the column name to the correct format e.g.`CHR:BP:A2:A1` and format_sumstats().
Standardising column headers.
First line of summary statistics file: 
SNP	FRQ	BETA	SE	P	CHR	BP	A2	A1	
Checking for incorrect base-pair positions
Reordering so first three column headers are SNP, CHR and BP in this order.
Reordering so the fourth and fifth columns are A1 and A2.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca77e64c3.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.107 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca78b26e8d.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62cae49aef9
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca78b26e8d.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.054 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62cadc070d2.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca7cdee5ca
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	EAF	Beta	SE	Pval	alleles	allele	
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	EAF	Beta	SE	Pval	alleles	allele	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Warning: Multiple columns in the sumstats file seem to relate to alleles A1>A2.
The column ALLELES will be kept whereas the column(s) ALLELE will be removed.
If this is not the correct column to keep, please remove all incorrect columns from those listed here before 
running `format_sumstats()`.
Column ALLELES has been separated into the columns A1, A2
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Reordering so first three column headers are SNP, CHR and BP in this order.
Reordering so the fourth and fifth columns are A1 and A2.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62cadc070d2.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.05 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca79b34b56.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca7cdee5ca
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca79b34b56.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.056 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca76382e9d.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca8b83753
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	A1	A2	EAF	Beta	SE	Pval	CHR_BP	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	A1	A2	EAF	Beta	SE	Pval	CHR_BP	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Summary statistics file does not have obvious CHR/BP columns. Checking to see if they are joined in another column.
Column CHR_BP has been separated into the columns CHR, BP
Standardising column headers.
First line of summary statistics file: 
SNP	A1	A2	FRQ	BETA	SE	P	CHR	BP	
Checking for incorrect base-pair positions
Reordering so first three column headers are SNP, CHR and BP in this order.
Reordering so the fourth and fifth columns are A1 and A2.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca76382e9d.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.094 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca5e32ec7.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca8b83753
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca5e32ec7.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.061 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca63bba3f0.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca1ab6fd7b
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	A1	A2	EAF	Beta	SE	Pval	CHR_BP	CHR_BP_2	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	A1	A2	EAF	Beta	SE	Pval	CHR_BP	CHR_BP_2	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Summary statistics file does not have obvious CHR/BP columns. Checking to see if they are joined in another column.
Warning: Multiple columns in the sumstats file seem to relate to Chromosome:Base Pair position.
The column CHR_BP_2 will be kept whereas the column(s) CHR_BP will be removed.
If this is not the correct column to keep, please remove all incorrect columns from those listed here before 
running `format_sumstats()`.
Column CHR_BP_2 has been separated into the columns CHR, BP
Standardising column headers.
First line of summary statistics file: 
SNP	A1	A2	FRQ	BETA	SE	P	CHR	BP	
Checking for incorrect base-pair positions
Reordering so first three column headers are SNP, CHR and BP in this order.
Reordering so the fourth and fifth columns are A1 and A2.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca63bba3f0.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.115 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca13a292d0.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca1ab6fd7b
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca13a292d0.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.058 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca198b531d.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca311bd24b
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca198b531d.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.055 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca6670ccde.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca246cda50
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca6670ccde.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.059 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.
Setting sorted=FALSE (required when formatted=FALSE).


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca58096b89.tsv.gz
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Assigning N=1000 for all SNPs.
N already exists within sumstats_dt.
[1] "Testing: compute_n='ldsc'"
Computing effective sample size using the LDSC method:
 Neff = (N_CAS+N_CON) * (N_CAS/(N_CAS+N_CON)) / mean((N_CAS/(N_CAS+N_CON))[(N_CAS+N_CON)==max(N_CAS+N_CON)]))
[1] "Testing: compute_n='giant'"
Computing effective sample size using the GIANT method:
 Neff = 2 / (1/N_CAS + 1/N_CON)
[1] "Testing: compute_n='metal'"
Computing effective sample size using the METAL method:
 Neff = 4 / (1/N_CAS + 1/N_CON)
[1] "Testing: compute_n='sum'"
Computing sample size using the sum method:
 N = N_CAS + N_CON


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca2d4e3ce2.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca68be6aaf
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca2d4e3ce2.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.068 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca3a3f7f94.tsv.gz
Log data to be saved to ==>  /tmp/RtmpNksM9m
Saving output messages to:
/tmp/RtmpNksM9m/file62ca3a3f7f94_log_msg.txt
Any runtime errors will be saved to:
/tmp/RtmpNksM9m/file62ca3a3f7f94_log_output.txt
Messages will not be printed to terminal.
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca2a823711.tsv.gz
Log data to be saved to ==>  /tmp/RtmpNksM9m
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca696648f3
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca2a823711.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.054 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca6cb4609e.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca15950dab
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 186 rows
   - 93 unique variants
   - 140 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
93 sumstat rows are duplicated. These duplicates will be removed.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca6cb4609e.tsv.gz
Summary statistics report:
   - 93 rows (50% of original 186 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.052 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca78e55311.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca15950dab
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca78e55311.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.054 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca7d7f1edf.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca15950dab
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 94 rows
   - 94 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicate SNPs from SNP ID.
Checking for SNPs with duplicated base-pair positions.
1 base-pair positions are duplicated in the sumstats file. These duplicates will be removed.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
Checking for bi-allelic SNPs.
Loading SNPlocs data for build 144 on GRCH37.
Loading reference genome data.
Preprocessing RSIDs.
Validating RSIDs of 93 SNPs using BSgenome::snpsById...
BSgenome::snpsById done in 31 seconds.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca7d7f1edf.tsv.gz
Summary statistics report:
   - 93 rows (98.9% of original 94 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.579 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca4198daf.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca20e0c2de
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Filtering effect columns, ensuring none equal 0.
5 SNPs have effect values = 0 and will be removed
Ensuring all SNPs have N<5 std dev above mean.
44 SNPs (50%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca4198daf.tsv.gz
Summary statistics report:
   - 88 rows (94.6% of original 93 rows)
   - 88 unique variants
   - 65 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.055 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca7cbc1285.tsv.gz
Log data to be saved to ==>  /tmp/RtmpNksM9m
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca7beaee31
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	FRQ	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	FRQ	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs based on FRQ.
38 SNPs are below the FRQ threshold of 0.9 and will be removed.
Writing in tabular format ==> /tmp/RtmpNksM9m/frq_filter.tsv.gz
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
55 SNPs (100%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca7cbc1285.tsv.gz
Summary statistics report:
   - 55 rows (59.1% of original 93 rows)
   - 55 unique variants
   - 41 genome-wide significant variants (P<5e-8)
   - 16 chromosomes
Done munging in 0.051 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     EAF   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
2: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
3:  rs1008078     1 91189731      T      C 0.37310 -0.016 0.003 6.005e-10
4: rs61787263     1 98618714      T      C 0.76120  0.016 0.003 5.391e-08
        FRQ
      <num>
1: 1.863269
2: 1.169733
3: 1.401423
4: 1.873332
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca43999b5a.tsv.gz
Log data to be saved to ==>  /tmp/RtmpNksM9m
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca7beaee31
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	FRQ	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	FRQ	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs based on FRQ.
38 SNPs are below the FRQ threshold of 0.9 and will be removed.
Writing in tabular format ==> /tmp/RtmpNksM9m/frq_filter.tsv.gz
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
55 SNPs (100%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=FALSE, the FRQ column will be renamed MAJOR_ALLELE_FRQ to differentiate the values from 
minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca43999b5a.tsv.gz
Summary statistics report:
   - 55 rows (59.1% of original 93 rows)
   - 55 unique variants
   - 41 genome-wide significant variants (P<5e-8)
   - 16 chromosomes
Done munging in 0.048 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     EAF   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
2: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
3:  rs1008078     1 91189731      T      C 0.37310 -0.016 0.003 6.005e-10
4: rs61787263     1 98618714      T      C 0.76120  0.016 0.003 5.391e-08
   MAJOR_ALLELE_FRQ
              <num>
1:         1.863269
2:         1.169733
3:         1.401423
4:         1.873332
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca9cf6e0a.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca30cba545
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
SNP	CHR	BP	A1	A2	FRQ	BETA	SE	P	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
SNP	CHR	BP	A1	A2	FRQ	BETA	SE	P	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca9cf6e0a.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.053 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca4d537c70.tsv
Converting full summary stats file to tabix format for fast querying...
Reading header.
Ensuring file is bgzipped.
Tabix-indexing file.
Removing temporary .tsv file.
Reading header.
Reading entire file.
Sorting coordinates with 'GenomicRanges'.
Converting summary statistics to GenomicRanges.
Sorting coordinates with 'data.table'.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca4afeef6f.tsv.gz
Log data to be saved to ==>  /tmp/RtmpNksM9m
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca59a4af83
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	INFO	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	INFO	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
Filtering SNPs based on INFO score.
38 SNPs are below the INFO threshold of 0.9 and will be removed.
Writing in tabular format ==> /tmp/RtmpNksM9m/info_filter.tsv.gz
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
28 SNPs (50.9%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca4afeef6f.tsv.gz
Summary statistics report:
   - 55 rows (59.1% of original 93 rows)
   - 55 unique variants
   - 41 genome-wide significant variants (P<5e-8)
   - 16 chromosomes
Done munging in 0.05 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
2: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
3:  rs1008078     1 91189731      T      C 0.37310 -0.016 0.003 6.005e-10
4: rs61787263     1 98618714      T      C 0.76120  0.016 0.003 5.391e-08
       INFO
      <num>
1: 1.863269
2: 1.169733
3: 1.401423
4: 1.873332
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62cad4f1468.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca1fe56514
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62cad4f1468.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.052 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62caa24947e.tsv.gz
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62caa24947e.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.047 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.
[1] "/tmp/RtmpNksM9m/data/file1/file62ca1451a9a3.tsv.gz"
[1] "/tmp/RtmpNksM9m/data/file2/file62ca7d5816c0.tsv.gz"
[1] "/tmp/RtmpNksM9m/data/file3/file62ca223dd78a.tsv.gz"
[1] "/tmp/RtmpNksM9m/data/file4/file62caa11b696.tsv.gz"
[1] "/tmp/RtmpNksM9m/data/file5/file62ca10ea3704.tsv.gz"
[1] "/tmp/RtmpNksM9m/data/file6/file62cac1f049.tsv.gz"
[1] "/tmp/RtmpNksM9m/data/file7/file62ca3c864902.tsv.gz"
[1] "/tmp/RtmpNksM9m/data/file8/file62ca14234159.tsv.gz"
[1] "/tmp/RtmpNksM9m/data/file9/file62ca16974463.tsv.gz"
[1] "/tmp/RtmpNksM9m/data/file10/file62ca250acb2b.tsv.gz"
10 file(s) found.
Parsing info from 10 log file(s).


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca121ab1ed.tsv.gz
sumstats has -log10 P-values; these will be converted to unadjusted p-values in the 'P' column.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval_org	LP	P	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval_org	LP	P	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca121ab1ed.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.05 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE  PVAL_ORG
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
          LP         P
       <num>     <num>
1:  7.746178 1.794e-08
2:  9.627272 2.359e-10
3: 13.424581 3.762e-14
4:  7.745452 1.797e-08
Returning data directly.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca3f9700f9.tsv.gz
sumstats has -log10 P-values; these will be converted to unadjusted p-values in the 'P' column.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval_org	LP	P	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval_org	LP	P	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca3f9700f9.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.048 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE  PVAL_ORG
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
           LP         P
        <num>     <num>
1:  -7.746178 1.794e-08
2:  -9.627272 2.359e-10
3: -13.424581 3.762e-14
4:  -7.745452 1.797e-08
Returning data directly.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca1b403cf9.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca16c3f5d4
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 92 unique variants
   - 69 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
WARNING: 1 rows in sumstats file are missing data and will be removed.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
46 SNPs (50%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca1b403cf9.tsv.gz
Summary statistics report:
   - 92 rows (98.9% of original 93 rows)
   - 92 unique variants
   - 69 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.048 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2    FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>  <num>  <num> <num>     <num>
1: rs10061788     5 87934707      A      G 0.2164  0.021 0.004 2.464e-09
2:  rs1007883    16 51163406      T      C 0.3713 -0.015 0.003 5.326e-08
3:  rs1008078     1 91189731      T      C 0.3731 -0.016 0.003 6.005e-10
4:  rs1043209    14 23373986      A      G 0.6026  0.018 0.003 1.816e-11
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca4d01436f.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca16c3f5d4
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca4d01436f.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.05 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2    FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>  <num>  <num> <num>     <num>
1: rs10061788     5 87934707      A      G 0.2164  0.021 0.004 2.464e-09
2:  rs1007883    16 51163406      T      C 0.3713 -0.015 0.003 5.326e-08
3:  rs1008078     1 91189731      T      C 0.3731 -0.016 0.003 6.005e-10
4:  rs1043209    14 23373986      A      G 0.6026  0.018 0.003 1.816e-11
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62caf1e2c13.tsv.gz
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 21 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Loading SNPlocs data for build 144 on GRCH37.
Loading reference genome data.
Preprocessing RSIDs.
Validating RSIDs of 1 SNPs using BSgenome::snpsById...
BSgenome::snpsById done in 8 seconds.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62caf1e2c13.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.201 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2    FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>  <num>  <num> <num>     <num>
1: rs10061788     5 87934707      A      G 0.2164  0.021 0.004 2.464e-09
2:  rs1007883    16 51163406      T      C 0.3713 -0.015 0.003 5.326e-08
3:  rs1008078     1 91189731      T      C 0.3731 -0.016 0.003 6.005e-10
4:  rs1043209    14 23373986      A      G 0.6026  0.018 0.003 1.816e-11
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca4284dd49.tsv.gz
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	extra	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	extra	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 21 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Loading SNPlocs data for build 144 on GRCH37.
Loading reference genome data.
Preprocessing RSIDs.
Validating RSIDs of 1 SNPs using BSgenome::snpsById...
BSgenome::snpsById done in 2 seconds.
Checking for missing data.
WARNING: 93 rows in sumstats file are missing data and will be removed.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca4cfccc25.tsv.gz
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	extra	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	extra	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 21 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Loading SNPlocs data for build 144 on GRCH37.
Loading reference genome data.
Preprocessing RSIDs.
Validating RSIDs of 1 SNPs using BSgenome::snpsById...
BSgenome::snpsById done in 2 seconds.
Checking for missing data.
WARNING: None of the inputted columns:
CHRA APOS
To be checked for missing data were found in the sumstats. Sumstats columns:
SNP CHR BP A1 A2 FRQ BETA SE P EXTRA
This check will not be run.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca4cfccc25.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.102 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2    FRQ   BETA    SE         P  EXTRA
       <char> <int>    <int> <char> <char>  <num>  <num> <num>     <num> <lgcl>
1: rs10061788     5 87934707      A      G 0.2164  0.021 0.004 2.464e-09     NA
2:  rs1007883    16 51163406      T      C 0.3713 -0.015 0.003 5.326e-08     NA
3:  rs1008078     1 91189731      T      C 0.3731 -0.016 0.003 6.005e-10     NA
4:  rs1043209    14 23373986      A      G 0.6026  0.018 0.003 1.816e-11     NA
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca42d4ea73.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca1edd0e49
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
chromosome	rs_id	markername	position_hg18	Effect_allele	Other_allele	EAF_HapMapCEU	N_SMK	Effect_SMK	StdErr_SMK	P_value_SMK	N_NONSMK	Effect_NonSMK	StdErr_NonSMK	P_value_NonSMK	
Standardising column headers.
First line of summary statistics file: 
chromosome	rs_id	markername	position_hg18	Effect_allele	Other_allele	EAF_HapMapCEU	N_SMK	Effect_SMK	StdErr_SMK	P_value_SMK	N_NONSMK	Effect_NonSMK	StdErr_NonSMK	P_value_NonSMK	
Summary statistics report:
   - 5 rows
   - 5 unique variants
   - 1 chromosomes
Checking for multi-GWAS.
WARNING: Multiple traits found in sumstats file only one of which can be analysed: 
SMK, NONSMK
Standardising column headers.
First line of summary statistics file: 
CHR	SNP	MARKERNAME	POSITION_HG18	A2	A1	EAF_HAPMAPCEU	N	EFFECT	STDERR	P_VALUE	N_NONSMK	EFFECT_NONSMK	STDERR_NONSMK	P_VALUE_NONSMK	
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
1 SNP IDs are not correctly formatted and will be removed.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Summary statistics file does not have obvious CHR/BP columns. Checking to see if they are joined in another column.
Column MARKERNAME has been separated into the columns CHR, BP
Standardising column headers.
First line of summary statistics file: 
CHR	SNP	POSITION_HG18	A2	A1	EAF_HAPMAPCEU	N	BETA	SE	P	N_NONSMK	EFFECT_NONSMK	STDERR_NONSMK	P_VALUE_NONSMK	BP	
Checking for incorrect base-pair positions
Reordering so first three column headers are SNP, CHR and BP in this order.
Reordering so the fourth and fifth columns are A1 and A2.
Checking for missing data.
Checking for duplicate columns.
Ensuring that the N column is all integers.
The sumstats N column is not all integers, this could effect downstream analysis. These will be converted to integers.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca42d4ea73.tsv.gz
Summary statistics report:
   - 4 rows (80% of original 5 rows)
   - 4 unique variants
   - 0 genome-wide significant variants (P<5e-8)
   - 1 chromosomes
Done munging in 0.151 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
         SNP    CHR        BP     A1     A2 POSITION_HG18 EAF_HAPMAPCEU     N
      <char> <char>     <int> <char> <char>         <int>         <num> <int>
1: rs1000050   chr1 161003087      C      T     161003087        0.9000 36257
2: rs1000073   chr1 155522020      G      A     155522020        0.3136 36335
3: rs1000075   chr1  94939420      C      T      94939420        0.3583 38959
4: rs1000085   chr1  66630503      G      C      66630503        0.1667 38761
      BETA     SE      P N_NONSMK EFFECT_NONSMK STDERR_NONSMK P_VALUE_NONSMK
     <num>  <num>  <num>    <int>         <num>         <num>          <num>
1:  0.0001 0.0109 0.9931   127514        0.0058        0.0059         0.3307
2:  0.0046 0.0083 0.5812   126780        0.0038        0.0045         0.3979
3: -0.0013 0.0082 0.8687   147567       -0.0043        0.0044         0.3259
4:  0.0053 0.0095 0.5746   147259       -0.0034        0.0052         0.5157
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca52d7e599.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca6f537f80
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	N	N_fixed	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	N	N_fixed	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Ensuring that the N column is all integers.
The sumstats N column is not all integers, this could effect downstream analysis. These will be converted to integers.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca52d7e599.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.052 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P     N
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num> <int>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08     5
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10     1
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14     1
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08     7
   N_FIXED
     <int>
1:       5
2:       1
3:       1
4:       7
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca176bc7b8.tsv.gz
Log data to be saved to ==>  /tmp/RtmpNksM9m
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca3a258ad
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	N	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	N	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
The sumstats N column is not all integers, this could effect downstream analysis.These will NOT be converted to integers.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
1 SNPs have N values 5 standard deviations above the mean and will be removed
Writing in tabular format ==> /tmp/RtmpNksM9m/n_large.tsv.gz
47 SNPs (51.1%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca176bc7b8.tsv.gz
Summary statistics report:
   - 92 rows (98.9% of original 93 rows)
   - 92 unique variants
   - 69 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.053 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P     N
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num> <int>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08     3
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10     5
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14     3
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08     3
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca5e70f69a.tsv.gz
Log data to be saved to ==>  /tmp/RtmpNksM9m
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca3a258ad
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	N	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	N	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
The sumstats N column is not all integers, this could effect downstream analysis.These will NOT be converted to integers.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
1 SNPs have N values 5 standard deviations above the mean and will be removed
Writing in tabular format ==> /tmp/RtmpNksM9m/n_large.tsv.gz
47 SNPs (51.1%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca5e70f69a.tsv.gz
Summary statistics report:
   - 92 rows (98.9% of original 93 rows)
   - 92 unique variants
   - 69 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.052 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P     N
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num> <int>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08     3
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10     5
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14     3
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08     3
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca498ce34d.tsv.gz
Log data to be saved to ==>  /tmp/RtmpNksM9m
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca3a258ad
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	N	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	N	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
The sumstats N column is not all integers, this could effect downstream analysis.These will NOT be converted to integers.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
1 SNPs have N values 5 standard deviations above the mean and will be removed
Writing in tabular format ==> /tmp/RtmpNksM9m/n_large.tsv.gz
Removing rows where is.na(N)
0 SNPs have N values that are NA and will be removed.
Writing in tabular format ==> /tmp/RtmpNksM9m/n_null.tsv.gz
47 SNPs (51.1%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca498ce34d.tsv.gz
Summary statistics report:
   - 92 rows (98.9% of original 93 rows)
   - 92 unique variants
   - 69 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.072 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P     N
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num> <int>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08     3
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10     5
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14     3
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08     3
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca7d873fbc.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca5158525c
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Summary statistics file does not have obvious CHR/BP columns. Checking to see if they are joined in another column.
Standardising column headers.
First line of summary statistics file: 
SNP	BP	A1	A2	FRQ	BETA	SE	P	
Loading SNPlocs data for build 144 on GRCH37.
Loading reference genome data.
Preprocessing RSIDs.
Validating RSIDs of 93 SNPs using BSgenome::snpsById...
BSgenome::snpsById done in 31 seconds.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca7d873fbc.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.634 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca3bf34135.tsv.gz
Log data to be saved to ==>  /tmp/RtmpNksM9m
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca5c9313b6
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Summary statistics file does not have obvious CHR/BP columns. Checking to see if they are joined in another column.
Standardising column headers.
First line of summary statistics file: 
SNP	A1	A2	FRQ	BETA	SE	P	
Loading SNPlocs data for build 144 on GRCH37.
Loading reference genome data.
Preprocessing RSIDs.
Validating RSIDs of 93 SNPs using BSgenome::snpsById...
BSgenome::snpsById done in 31 seconds.
Writing in tabular format ==> /tmp/RtmpNksM9m/chr_bp_not_found_from_snp.tsv.gz
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca3bf34135.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.639 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca65a3f5b3.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca19abea4f
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
1 SNP IDs are not correctly formatted. These will be corrected from the reference genome.
Loading SNPlocs data for build 144 on GRCH37.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Coercing BP column to numeric.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca65a3f5b3.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.053 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca60d15aab.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca19abea4f
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca60d15aab.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.05 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca69e119f9.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62caa85b9de
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
1 SNP IDs appear to be made up of chr:bp, these will be replaced by their SNP ID from the reference genome
Loading SNPlocs data for build 144 on GRCH37.
1 SNP IDs are not correctly formatted and will be removed.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Summary statistics file does not have obvious CHR/BP columns. Checking to see if they are joined in another column.
Standardising column headers.
First line of summary statistics file: 
SNP	A1	A2	FRQ	BETA	SE	P	
Loading SNPlocs data for build 144 on GRCH37.
Loading reference genome data.
Preprocessing RSIDs.
Validating RSIDs of 92 SNPs using BSgenome::snpsById...
BSgenome::snpsById done in 59 seconds.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
46 SNPs (50%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca69e119f9.tsv.gz
Summary statistics report:
   - 92 rows (98.9% of original 93 rows)
   - 92 unique variants
   - 69 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 1.157 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca20df387e.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca516da337
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
1 SNP IDs are not correctly formatted. These will be corrected from the reference genome.
Loading SNPlocs data for build 144 on GRCH37.
1 SNP IDs appear to be made up of chr:bp, these will be replaced by their SNP ID from the reference genome
Loading SNPlocs data for build 144 on GRCH37.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Coercing BP column to numeric.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca20df387e.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.121 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca1daf725d.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca74f699a4
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca1fb958d7.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62caa85b9de
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca1fb958d7.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.071 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca1ec0024e.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca19917d67
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Loading SNPlocs data for build 144 on GRCH37.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca1ec0024e.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.104 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca1972e9c3.tsv.gz
Log data to be saved to ==>  /tmp/RtmpNksM9m
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62cad0e1c87
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 23 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
1 SNPs have been removed as their BP column is not in the range of 1 to the length of the chromosome
Writing in tabular format ==> /tmp/RtmpNksM9m/bad_bp.tsv.gz
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
2 SNPs are on chromosomes X, Y, MT and will be removed.
Writing in tabular format ==> /tmp/RtmpNksM9m/chr_excl.tsv.gz
45 SNPs (50%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca1972e9c3.tsv.gz
Summary statistics report:
   - 90 rows (96.8% of original 93 rows)
   - 90 unique variants
   - 67 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.077 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca1516de45.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62cad0e1c87
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca1516de45.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.047 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.
Reading header.
Reading entire file.
Reading header.
Reading header.
Reading header.
Reading header.
Reading header.
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca7c6939aa
Checking for empty columns.
Standardising column headers.
First line of summary statistics file: 
SNP	CHR	BP	A1	A2	FRQ	BETA	SE	P	
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca5a992d75
Checking for empty columns.
Standardising column headers.
First line of summary statistics file: 
SNP	CHR	BP	A1	A2	FRQ	BETA	SE	P	


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca5a3914a3.vcf.bgz
Sorting coordinates with 'data.table'.
Converting summary statistics to GenomicRanges.
Converting summary statistics to VRanges.
Writing in VCF format ==>  /tmp/RtmpNksM9m/file62ca5a3914a3.vcf.bgz
Using local VCF.
Finding empty VCF columns based on first 10,000 rows.
1 sample detected: GWAS
Constructing ScanVcfParam object.
Reading VCF file: single-threaded
Converting VCF to data.table.
Expanding VCF first, so number of rows may increase.
Checking for empty columns.
Time difference of 0.1 secs
VCF data.table contains: 93 rows x 11 columns.
Time difference of 0.5 secs
No INFO (SI) column detected.
Standardising column headers.
First line of summary statistics file: 
ID	chr	BP	end	REF	ALT	SNP	FRQ	BETA	SE	P	
Using local VCF.
bgzip-compressing VCF file.
Finding empty VCF columns based on first 10,000 rows.
Dropping 1 duplicate column(s).
1 sample detected: EBI-a-GCST005647
Constructing ScanVcfParam object.
VCF contains: 39,630,630 variant(s) x 1 sample(s)
Reading VCF file: single-threaded
Converting VCF to data.table.
Expanding VCF first, so number of rows may increase.
Dropping 1 duplicate column(s).
Checking for empty columns.
Unlisting 3 columns.
Dropped 314 duplicate rows.
Time difference of 0.1 secs
VCF data.table contains: 101 rows x 11 columns.
Time difference of 0.6 secs
Renaming ID as SNP.
sumstats has -log10 P-values; these will be converted to unadjusted p-values in the 'P' column.
No INFO (SI) column detected.
Standardising column headers.
First line of summary statistics file: 
SNP	chr	BP	end	REF	ALT	FILTER	AF	ES	LP	SE	P	


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca7096b6a.vcf.bgz
Sorting coordinates with 'data.table'.
Converting summary statistics to GenomicRanges.
Converting summary statistics to VRanges.
Writing in VCF format ==>  /tmp/RtmpNksM9m/file62ca7096b6a.vcf.bgz
Using local VCF.
Finding empty VCF columns based on first 10,000 rows.
1 sample detected: GWAS
Constructing ScanVcfParam object.
Reading VCF file: single-threaded
Converting VCF to data.table.
Expanding VCF first, so number of rows may increase.
Checking for empty columns.
Time difference of 0.1 secs
VCF data.table contains: 101 rows x 13 columns.
Time difference of 0.4 secs
sumstats has -log10 P-values; these will be converted to unadjusted p-values in the 'P' column.
No INFO (SI) column detected.
Standardising column headers.
First line of summary statistics file: 
ID	chr	BP	end	REF	SNP	END	FILTER	FRQ	BETA	LP	SE	P	


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca6b838be.tsv.gz
Log data to be saved to ==>  /tmp/RtmpNksM9m
Infer Effect Column
First line of summary statistics file: 
SNP	P	FRQ	BETA	CHR	BP	
Standardising column headers.
First line of summary statistics file: 
SNP	P	FRQ	BETA	CHR	BP	
Summary statistics report:
   - 5 rows
   - 5 unique variants
   - 0 genome-wide significant variants (P<5e-8)
   - 1 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
5 SNP IDs contain other information in the same column. These will be separated.
Checking for merged allele column.
Column SNP_INFO has been separated into the columns A1, A2
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Coercing BP column to numeric.
Reordering so first three column headers are SNP, CHR and BP in this order.
Reordering so the fourth and fifth columns are A1 and A2.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
SE is not present but can be imputed with BETA & P. Set impute_se=TRUE and rerun to do this.
Ensuring all SNPs have N<5 std dev above mean.
3 SNPs (60%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca6b838be.tsv.gz
Summary statistics report:
   - 5 rows (100% of original 5 rows)
   - 5 unique variants
   - 0 genome-wide significant variants (P<5e-8)
   - 1 chromosomes
Done munging in 0.051 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
           SNP   CHR    BP     A1     A2           P       FRQ      BETA
        <char> <int> <int> <char> <char>       <num>     <num>     <num>
1: rs140052487     1 54353      C      A 0.037219838 0.3000548 0.8797957
2: rs558796213     1 54564      G      T 0.004382482 0.5848666 0.7068747
3: rs561234294     1 54591      A      G 0.070968402 0.3334671 0.7319726
4:   rs2462492     1 54676      C      T 0.065769040 0.6220120 0.9316344
Returning data directly.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

******::NOTE::******
 - Log results will be saved to `tempdir()` by default.
 - This means all log data from the run will be  deleted upon ending the R session.
 - To keep it, change `log_folder` to an actual directory  (e.g. log_folder='./').
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca5ffebd0c.tsv.gz
Log data to be saved to ==>  /tmp/RtmpNksM9m
Infer Effect Column
First line of summary statistics file: 
SNP	P	FRQ	BETA	CHR	BP	A1	A2	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
SNP	P	FRQ	BETA	CHR	BP	A1	A2	
Summary statistics report:
   - 5 rows
   - 5 unique variants
   - 0 genome-wide significant variants (P<5e-8)
   - 1 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Coercing BP column to numeric.
Reordering so first three column headers are SNP, CHR and BP in this order.
Reordering so the fourth and fifth columns are A1 and A2.
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
SE is not present but can be imputed with BETA & P. Set impute_se=TRUE and rerun to do this.
Ensuring all SNPs have N<5 std dev above mean.
3 SNPs (60%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca5ffebd0c.tsv.gz
Summary statistics report:
   - 5 rows (100% of original 5 rows)
   - 5 unique variants
   - 0 genome-wide significant variants (P<5e-8)
   - 1 chromosomes
Done munging in 0.05 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
           SNP   CHR    BP     A1     A2           P       FRQ      BETA
        <char> <int> <int> <char> <char>       <num>     <num>     <num>
1: rs140052487     1 54353      C      A 0.037219838 0.3000548 0.8797957
2: rs558796213     1 54564      G      T 0.004382482 0.5848666 0.7068747
3: rs561234294     1 54591      A      G 0.070968402 0.3334671 0.7319726
4:   rs2462492     1 54676      C      T 0.065769040 0.6220120 0.9316344
Returning data directly.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca1ffa3635.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca184bea29
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca2677c81c.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca6747fa25
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca2677c81c.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.048 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca3f107903.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca6747fa25
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca3f107903.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.058 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca5fb691a1.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca12371aa6
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca5fb691a1.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.073 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca7e4214af.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca2977e49d
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
47 SNPs (50.5%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca7e4214af.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.05 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca783968c8.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca14152524
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
5 SNPs have SE values <= 0 and will be removed
Ensuring all SNPs have N<5 std dev above mean.
44 SNPs (50%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca783968c8.tsv.gz
Summary statistics report:
   - 88 rows (94.6% of original 93 rows)
   - 88 unique variants
   - 65 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.051 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	Support	
Returning unmapped column names without making them uppercase.
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	Support	
Returning unmapped column names without making them uppercase.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca1a23fd8b.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca4b96cfca
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 85 rows
   - 85 unique variants
   - 63 genome-wide significant variants (P<5e-8)
   - 19 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
Checking for strand ambiguous SNPs.
43 SNPs (50.6%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca1a23fd8b.tsv.gz
Summary statistics report:
   - 85 rows (100% of original 85 rows)
   - 85 unique variants
   - 63 genome-wide significant variants (P<5e-8)
   - 19 chromosomes
Done munging in 0.05 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca48f8cfbc.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca4b96cfca
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
Checking for strand ambiguous SNPs.
8 SNPs are strand-ambiguous alleles including 4 A/T and 4 C/G ambiguous SNPs. These will be removed
43 SNPs (50.6%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency.
The FRQ column was mapped from one of the following from the inputted  summary statistics file:
FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.B, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, ALL_AF
As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency,
set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca48f8cfbc.tsv.gz
Summary statistics report:
   - 85 rows (91.4% of original 93 rows)
   - 85 unique variants
   - 63 genome-wide significant variants (P<5e-8)
   - 19 chromosomes
Done munging in 0.054 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     FRQ   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning path to saved data.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca12ba3deb.tsv.gz


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca729bee31.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca5047b112
Checking for empty columns.
Non-standard mapping file detected.Making sure all entries in `Uncorrected` are in upper case.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Checking for missing data.
Checking for duplicate columns.
Checking for duplicated rows.
INFO column not available. Skipping INFO score filtering step.
Filtering SNPs, ensuring SE>0.
Ensuring all SNPs have N<5 std dev above mean.
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca729bee31.tsv.gz
Summary statistics report:
   - 93 rows (100% of original 93 rows)
   - 93 unique variants
   - 70 genome-wide significant variants (P<5e-8)
   - 20 chromosomes
Done munging in 0.058 minutes.
Successfully finished preparing sumstats file, preview:
Reading header.
          SNP   CHR       BP     A1     A2     EAF   BETA    SE         P
       <char> <int>    <int> <char> <char>   <num>  <num> <num>     <num>
1:   rs301800     1  8490603      T      C 0.17910  0.019 0.003 1.794e-08
2: rs11210860     1 43982527      A      G 0.36940  0.017 0.003 2.359e-10
3: rs34305371     1 72733610      A      G 0.08769  0.035 0.005 3.762e-14
4:  rs2568955     1 72762169      T      C 0.23690 -0.017 0.003 1.797e-08
Returning data directly.
Converting summary statistics to GenomicRanges.


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca272c71b5.tsv.gz


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca56dd362a.tsv.gz


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca550f2bf3.tsv.gz


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca570e8a25.tsv.gz


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca7b8fafc9.tsv.gz


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca165df47f.tsv.gz


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca725ec951.tsv.gz


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca2cf31f80.tsv.gz


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62cad99238e.tsv.gz


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca40ed469b.tsv.gz


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca19b68b6a.tsv.gz


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca5c06fffe.tsv.gz
Reading header.
Tabular format detected.
Importing tabular file: /tmp/RtmpNksM9m/file62ca147c0c3b
Checking for empty columns.
Infer Effect Column
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	
Allele columns are ambiguous, attempting to infer direction
Can't infer allele columns from sumstats
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	
Summary statistics report:
   - 93 rows
   - 93 unique variants
   - 20 chromosomes
Checking for multi-GWAS.
Checking for multiple RSIDs on one row.
Checking SNP RSIDs.
Checking for merged allele column.
Checking A1 is uppercase
Checking A2 is uppercase
Checking for incorrect base-pair positions
Standardising column headers.
First line of summary statistics file: 
MarkerName	CHR	POS	A1	A2	EAF	Beta	SE	Pval	
Sorting coordinates with 'data.table'.
.tsv
=== write tests ===
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca1ea14bb8.tsv

=== read tests ===
Importing tabular file: /tmp/RtmpNksM9m/file62ca1ea14bb8.tsv
Checking for empty columns.
.tsv.gz
=== write tests ===
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca49929152.tsv.gz

=== read tests ===
Importing tabular file: /tmp/RtmpNksM9m/file62ca49929152.tsv.gz
Checking for empty columns.
.tsv.bgz
=== write tests ===
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca58da5783.tsv.bgz

=== read tests ===
Importing tabular bgz file: /tmp/RtmpNksM9m/file62ca58da5783.tsv.bgz
Checking for empty columns.
.tsv.gz
=== write tests ===
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca4da07197.tsv
Writing uncompressed instead of gzipped to enable tabix indexing.
Converting full summary stats file to tabix format for fast querying...
Reading header.
Ensuring file is bgzipped.
Tabix-indexing file.
Removing temporary .tsv file.

=== read tests ===
Importing tabular bgz file: /tmp/RtmpNksM9m/file62ca4da07197.tsv.bgz
Checking for empty columns.
.tsv.bgz
=== write tests ===
Sorting coordinates with 'data.table'.
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca43fb7da0.tsv
Writing uncompressed instead of gzipped to enable tabix indexing.
Converting full summary stats file to tabix format for fast querying...
Reading header.
Ensuring file is bgzipped.
Tabix-indexing file.
Removing temporary .tsv file.

=== read tests ===
Importing tabular bgz file: /tmp/RtmpNksM9m/file62ca43fb7da0.tsv.bgz
Checking for empty columns.
.csv
=== write tests ===
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca13337c94.csv

=== read tests ===
Importing tabular file: /tmp/RtmpNksM9m/file62ca13337c94.csv
Checking for empty columns.
.csv.gz
=== write tests ===
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca40c1de93.csv.gz

=== read tests ===
Importing tabular file: /tmp/RtmpNksM9m/file62ca40c1de93.csv.gz
Checking for empty columns.
.vcf
=== write tests ===


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

save_path suggests VCF output but write_vcf=FALSE. Switching output to tabular format (.tsv.gz).
Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca5e2086f9.tsv.gz
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca5e2086f9.tsv.gz

=== read tests ===
Importing tabular file: /tmp/RtmpNksM9m/file62ca5e2086f9.tsv.gz
Checking for empty columns.
.vcf.gz
=== write tests ===


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

save_path suggests VCF output but write_vcf=FALSE. Switching output to tabular format (.tsv.gz).
Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca1f96b95c.tsv.gz
Writing in tabular format ==> /tmp/RtmpNksM9m/file62ca1f96b95c.tsv.gz

=== read tests ===
Importing tabular file: /tmp/RtmpNksM9m/file62ca1f96b95c.tsv.gz
Checking for empty columns.
.vcf
=== write tests ===
Sorting coordinates with 'data.table'.
Converting summary statistics to GenomicRanges.
Converting summary statistics to VRanges.
Writing in VCF format ==>  /tmp/RtmpNksM9m/file62ca6043af6b.vcf

=== read tests ===
Using local VCF.
bgzip-compressing VCF file.
Finding empty VCF columns based on first 10,000 rows.
1 sample detected: GWAS
Constructing ScanVcfParam object.
Reading VCF file: single-threaded
Converting VCF to data.table.
Expanding VCF first, so number of rows may increase.
Checking for empty columns.
Time difference of 0.1 secs
VCF data.table contains: 93 rows x 11 columns.
Time difference of 0.7 secs
No INFO (SI) column detected.
.vcf.gz
=== write tests ===
Sorting coordinates with 'data.table'.
Converting summary statistics to GenomicRanges.
Converting summary statistics to VRanges.
Writing in VCF format ==>  /tmp/RtmpNksM9m/file62ca7788ea72.vcf.gz

=== read tests ===
Using local VCF.
Finding empty VCF columns based on first 10,000 rows.
1 sample detected: GWAS
Constructing ScanVcfParam object.
Reading VCF file: single-threaded
Converting VCF to data.table.
Expanding VCF first, so number of rows may increase.
Checking for empty columns.
Time difference of 0.2 secs
VCF data.table contains: 93 rows x 11 columns.
Time difference of 1.1 secs
No INFO (SI) column detected.
.vcf
=== write tests ===
Sorting coordinates with 'data.table'.
Converting summary statistics to GenomicRanges.
Converting summary statistics to VRanges.
Writing in VCF format ==>  /tmp/RtmpNksM9m/file62ca6a226778.vcf
.vcf
=== write tests ===


******::NOTE::******
 - Formatted results will be saved to `tempdir()` by default.
 - This means all formatted summary stats will be deleted upon ending the R session.
 - To keep formatted summary stats, change `save_path`  ( e.g. `save_path=file.path('./formatted',basename(path))` ),   or make sure to copy files elsewhere after processing  ( e.g. `file.copy(save_path, './formatted/' )`.
 ******************** 

Formatted summary statistics will be saved to ==>  /tmp/RtmpNksM9m/file62ca5ba86ed4.vcf.bgz
Sorting coordinates with 'data.table'.
Converting summary statistics to GenomicRanges.
Converting summary statistics to VRanges.
Writing in VCF format ==>  /tmp/RtmpNksM9m/file62ca5ba86ed4.vcf.bgz

=== read tests ===
Using local VCF.
File already tabix-indexed.
Finding empty VCF columns based on first 10,000 rows.
1 sample detected: GWAS
Constructing ScanVcfParam object.
Reading VCF file: single-threaded
Converting VCF to data.table.
Expanding VCF first, so number of rows may increase.
Checking for empty columns.
Time difference of 0.3 secs
VCF data.table contains: 93 rows x 11 columns.
Time difference of 0.8 secs
No INFO (SI) column detected.
.vcf.bgz
=== write tests ===
Sorting coordinates with 'data.table'.
Converting summary statistics to GenomicRanges.
Converting summary statistics to VRanges.
Writing in VCF format ==>  /tmp/RtmpNksM9m/file62ca2a2ae86b.vcf.bgz

=== read tests ===
Using local VCF.
File already tabix-indexed.
Finding empty VCF columns based on first 10,000 rows.
1 sample detected: GWAS
Constructing ScanVcfParam object.
Reading VCF file: single-threaded
Converting VCF to data.table.
Expanding VCF first, so number of rows may increase.
Checking for empty columns.
Time difference of 0.2 secs
VCF data.table contains: 93 rows x 11 columns.
Time difference of 0.9 secs
No INFO (SI) column detected.
[ FAIL 0 | WARN 3 | SKIP 0 | PASS 158 ]

[ FAIL 0 | WARN 3 | SKIP 0 | PASS 158 ]
Warning message:
In names(xx) :
  closing unused connection 4 (/tmp/RtmpNksM9m/file62ca3a3f7f94_log_msg.txt)
> 
> proc.time()
   user  system elapsed 
554.241  27.542 624.381

Example timings

MungeSumstats.Rcheck/MungeSumstats-Ex.timings

name	user	system	elapsed
compute_nsize	4.302	0.173	4.488
download_vcf	0.000	0.000	0.001
find_sumstats	0.001	0.001	0.002
format_sumstats	90.753	4.600	96.402
formatted_example	3.082	0.227	3.316
get_genome_builds	94.737	5.635	100.931
import_sumstats	0.002	0.001	0.002
index_tabular	2.933	0.380	3.325
index_vcf	3.625	0.058	3.689
infer_effect_column	2.794	0.009	2.809
liftover	3.570	0.041	8.302
list_sumstats	0.001	0.001	0.002
parse_logs	0.011	0.001	0.012
read_header	0.004	0.002	0.006
read_sumstats	0.006	0.001	0.006
read_vcf	1.727	0.022	1.755
standardise_header	2.970	0.010	3.011
vcf2df	0.617	0.007	0.627
write_sumstats	0.007	0.002	0.009