---
title: "Genotype quality control with plinkQC"
author: "Hannah Meyer and Maha Syed"
date: "`r Sys.Date()`"
output: 
    pdf_document:
        fig_caption: yes
        toc: true
        toc_depth: 2
resource_files:
    
bibliography: references.bib
csl: plos-genetics.csl
vignette: >
  %\VignetteIndexEntry{GenotypeQC_with_plinkQC}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---
```{r setup, include = FALSE}
library(plinkQC)
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

# Introduction
Genotyping arrays enable the direct measurement of an individual's genotype at
thousands of markers. Subsequent analyses such as genome-wide association
studies rely on the high quality of these marker genotypes. 

*plinkQC* facilitates such genotype quality control, including a pre-trained ancestry classifier and relatedness filter optimized to retain the maximally unrelated sample set with highest quality.

*plinkQC*  assumes the genotypes have already been determined from the
 original probe intensity data of the genotype array and are available in
[plink format](https://www.cog-genomics.org/plink/1.9/input#bed). It wraps 
around PLINK [@Purcell2007,@Chang2015] basic statistics (e.g. missing
genotyping rates per individual, allele frequencies per genetic marker) and relationship functions. *plinkQC* then generates a per-individual and per-marker quality control report and individuals and markers that fail the quality control
can subsequently be removed with *plinkQC* to generate a new, clean dataset. 

The majority of functions in *plinkQC* depend on [PLINK](https://www.cog-genomics.org/plink/1.9/) 
 (**version 1.9**), which has to be manually installed prior to the usage of
 *plinkQC*. The ancestry functions depend on the newer version of [PLINK 2.0](https://www.cog-genomics.org/plink/2.0/)
 (**version 2.0**).

The protocol can be run via three main functions, the per-individual quality
control (`perIndividualQC`), the per-marker quality control (`perMarkerQC`) and
the generation of the new, quality control dataset (`cleanData`):

## Per-individual quality control
The per-individual quality control with `perIndividualQC` wraps around these
functions:

1. `check_sex`: for the identification of individuals with discordant sex information,

1. `check_heterozygosity_and_missingness`: for the identification of individuals with outlying missing genotype and/or heterozygosity rates,

1. `check_relatedness`: for the identification of related individuals

1. `ancestry_prediction`: for prediction of genomic ancestry

## Per-marker quality control
The per-marker quality control with `perMarkerQC` wraps around these
functions:

1. `check_snp_missingnes`: for the identifying markers with excessive missing genotype rates,

1. `check_hwe`: for the identifying markers showing a significant deviation from Hardy-Weinberg equilibrium (HWE),

1. `check_maf`: for the removal of markers with low minor allele
    frequency (MAF).
    
## Clean data
`cleanData` takes the results of `perMarkerQC` and `perIndividualQC` and creates
a new dataset with all individuals and markers that passed the quality control
checks.

# Workflow
In the following, genotype quality control with *plinkQC* is applied on a small
example dataset with 200 individuals and 10,000 markers (provided within this
package). The  quality control is demonstrated in three easy steps,
per-individual and per-marker quality control followed by the generation of the
new dataset. In addition, the functionality of each of the functions underlying
`perMarkerQC` and `perIndividualQC` is demonstrated at the end of this vignette.

To run the ancestry functionality of the package, [Plink v2](https://www.cog-genomics.org/plink/2.0/) is needed. 
Before use, the study data should be in the new hg38 annotation. USCS's [liftOver](https://genome.ucsc.edu/cgi-bin/hgLiftOver) tool
may be needed to map variants from one annotation to another. More details on
how to use the tool can be found on the processing [HapMap III reference data vignette](https://meyer-lab-cshl.github.io/plinkQC/articles/HapMap.html). We provide an example dataset that is in the hg38 annotation. 

Additional loading matrices are needed for the PCA projection used in the model.
This is hosted on the plinkQC github repo under the inst/extdata folder located 
[here](https://github.com/meyer-lab-cshl/plinkQC/tree/ancestry_update/inst/extdata/load_mat). Alternatively, the whole github repo can downloaded with
```{bash installing github, eval=FALSE}
git clone git@github.com:meyer-lab-cshl/plinkQC.git
```

The name of the files (before the .acount or .eigenvec.allele) for the loading matrices must be included in the `path2load_mat` variable.

```{r set parameters}
package.dir <- find.package('plinkQC')
indir <- file.path(package.dir, 'extdata')
qcdir <- tempdir()
name <- 'data.hg38'
path2plink <- "/path/to/plink"
path2plink2 <- "/path/to/plink2"
path2load_mat <- "path/to/load_mat/merged_chrs.postQC.train.pca"
```

## Per-individual quality control
For `perIndividualQC`, one simply specifies the directory where the data is
stored (qcdir) and the prefix of the plink files (i.e. prefix.bim, prefix.bed,
prefix.fam). Per default, all quality control checks will be conducted. 

In addition to running each check, `perIndividualQC` writes a list of all fail
individual IDs to the qcdir. These IDs will be removed in the computation of the
`perMarkerQC`. If the list is not present, `perMarkerQC` will send a message
about conducting the quality control on the entire dataset.

The ancestry portion of the package requires data in PLINK 2.0 format (i.e pgen, pvar, psam files), and the variant identifiers should be formatted similar to
the following example: 1:12345[hg38]. `perIndividualQC` will run the functions `convert_to_plink2()` and `rename_variant_identifiers()` to convert the data
into PLINK 2.0 format and rename the variant identifiers respectively. 

*NB*: There are limitations on CRAN file sizes. To reduce the data size of the
example data in `plinkQC`, data.genome  has already been reduced to the
individuals that are related. Thus the relatedness plots in C only counts for related individuals only. 
For ancestry identification, the example dataset is very small (10k genotypes)
and contains markers and individuals failing qc controls to showcase the
*plinkQC*'s functionality. In practice this means that the overlap in genotypes between the study and  reference data is only 597 SNPs (roughly 0.2%) of the reference dataset. Thus, additional noise is expected and visible in the
ancestry plot below.

`perIndividualQC` displays the results of the quality control steps in a
multi-panel plot. 

```{r individual QC,  eval=FALSE, fig.height=12, fig.width=9}
fail_individuals <- perIndividualQC(indir=indir, qcdir=qcdir, name=name,
                            path2plink=path2plink, 
                            path2load_mat = path2load_mat,
                            path2plink2=path2plink2, 
                            excludeAncestry = c("Africa"),
                            interactive=TRUE, verbose=TRUE)

```
```{r load individualQC, echo=FALSE,out.width = "500px", fig.align='center'}
#| fig.alt: >
#|   Individual quality control report
par(mfrow=c(2,1), las=1)
knitr::include_graphics("individualQC.png")
```


`overviewperIndividualQC` depicts overview plots of quality control failures and
the intersection of quality control failures.
```{r overview individual QC,fig.width=7, fig.height=7, eval=FALSE}
overview_individuals <- overviewPerIndividualQC(fail_individuals,
                                                interactive=TRUE)
```

```{r load overviewIndividualQC, out.width = "500px", echo=FALSE}
#| fig.alt: >
#|   Showing intersection of fails for individual quality 
#|   control with an upset plot
par(mfrow=c(2,1), las=1)
knitr::include_graphics("overviewQC.png")
```


## Per-marker quality control
`perMarkerQC` applies its checks to data in the specified directory (qcdir),
starting with the specified prefix of the plink files (i.e. prefix.bim,
prefix.bed, prefix.fam). Optionally, the user can specify different thresholds
for the quality control checks and which check to conduct. Per default, all
quality control checks will be conducted. `perMarkerQC` displays the results of
the QC step in a multi-panel plot.
```{r marker QC, eval=FALSE}
fail_markers <- perMarkerQC(indir=indir, qcdir=qcdir, name=name,
                            path2plink=path2plink,
                            verbose=TRUE, interactive=TRUE,
                            showPlinkOutput=FALSE)
```

```{r load markerQC, echo=FALSE, out.width = "500px", fig.align='center'}
#| fig.alt: >
#|   Marker quality control reports
par(mfrow=c(2,1), las=1)
knitr::include_graphics("markerQC.png")
```

`overviewPerMarkerQC` depicts an overview of the marker quality control
failures and their overlaps.
```{r overview marker QC, eval=FALSE}
#| fig.alt: >
#|   Upset plot showing intersection of markers that fail 
overview_marker <- overviewPerMarkerQC(fail_markers, interactive=TRUE)
```

```{r load overviewMarkerQC, out.width = "500px", echo=FALSE}
#| fig.alt: >
#|   Overview of the marker quality control ran 
par(mfrow=c(2,1), las=1)
knitr::include_graphics("overviewMarkerQC.png")
```

## Create QC-ed dataset
After checking results of the per-individual and per-marker quality control,
individuals and markers that fail the chosen criteria can automatically be
removed from the dataset  with `cleanData`, resulting in the new dataset qcdir/data.clean.bed,qcdir/data.clean.bim, qcdir/data.clean.fam. For
convenience, `cleanData` returns a list of all individuals in the study split
into keep and remove individuals.
```{r clean data, eval=FALSE}
Ids  <- cleanData(indir=indir, qcdir=qcdir, name=name, path2plink=path2plink,
                            verbose=TRUE, showPlinkOutput=FALSE)
```

\newpage
# Step-by-step

## Individuals with discordant sex information
The identification of individuals with discordant sex information helps to detect
sample mix-ups and samples with very poor genotyping rates. For each sample, the
homozygosity rates across all X-chromosomal genetic markers are computed and
compared with the expected rates (typically $<0.2$ for females and $>0.8$ for
males). For samples where the assigned sex (PEDSEX in the .fam file) contradicts
the sex inferred from the homozygosity rates (SNPSEX), it should be checked that
the sex was correctly recorded (genotyping often occurs at different locations
as phenotyping and misrecording might occur). Samples with discordant sex
information that is not accounted for should be removed from the study.
Identifying individuals with discordant sex information is implemented in 
`check_sex`. It finds individuals  whose SNPSEX != PEDSEX. Optionally, an extra
data.frame with sample IDs and sex can be provided to double check if external
and PEDSEX data (often processed at different centers) match. If a mismatch
between PEDSEX and SNPSEX was detected, by SNPSEX == Sex, PEDSEX of these
individuals can optionally be updated. `check_sex` depicts the X-chromosomal
heterozygosity (SNPSEX) of the samples split by their (PEDSEX).

```{r check sex, eval=FALSE, out.width = "500px", fig.align='center'}
fail_sex <- check_sex(indir=indir, qcdir=qcdir, name=name, interactive=TRUE,
                      verbose=TRUE, path2plink=path2plink)
```
```{r load checkSex, out.width = "500px", echo=FALSE, fig.align='center'}
#| fig.alt: >
#|   Graph showing predicted sex versus labeled sex
knitr::include_graphics("checkSex.png")
```


## Individuals with outlying missing genotype and/or heterozygosity rates
The identification of individuals with outlying missing genotype and/or
heterozygosity rates helps to detect samples with poor DNA quality and/or
concentration that should be excluded from the study. Typically, individuals
with more than 3-7\% of their genotype calls missing are removed. Outlying
heterozygosity rates are judged relative to the overall heterozygosity rates in
the study, and individuals whose rates are more than a few standard deviations
(sd) from the mean heterozygosity rate are removed. A typical quality control
for outlying heterozygosity rates would remove individuals who are three sd away
from the mean rate.
Identifying related individuals with outlying missing genotype and/or
heterozygosity rates is implemented in `check_het_and_miss`.
It finds individuals that have genotyping and heterozygosity rates that fail the
set thresholds and depicts the results as a scatter plot with the samples'
missingness rates on x-axis and their heterozygosity rates on the y-axis.


```{r check het miss, eval=FALSE, fig.height=3, fig.width=5, fig.align='center'}
fail_het_imiss <- check_het_and_miss(indir=indir, qcdir=qcdir, name=name,
                                     interactive=TRUE, path2plink=path2plink)
```
```{r load checkHetImiss, out.width = "500px", echo=FALSE, fig.align='center'}
#| fig.alt: >
#|   Showing the missingness on x-axis and heterzyogisity rate on the
#|   y-axis for individuals
knitr::include_graphics("checkHetImiss.png")
```


## Related individualis
Depending on downstream analyses, it might be required to remove related
individuals from the study. Related individuals can be identified by
their proportion of shared alleles at the genotyped markers (identity by
descend, IBD). Standardly, individuals with second-degree relatedness or higher
will be excluded. 
Identifying related individuals is implemented in `check_relatedness`. It finds
pairs of samples whose proportion of IBD is larger than the specified `highIBDTh`. 
Subsequently, for pairs of individual that do not have additional relatives in
the dataset, the individual with the greater genotype missingness rate is
selected and returned as the individual failing the relatedness check. For more
complex family structures, the unrelated individuals per family are selected
(e.g. in a parents-offspring trio, the offspring will be marked as fail, while 
the parents will be kept in the analysis).

NB: To reduce the data size of the example data in `plinkQC`,
data.genome  has already been reduced to the individuals that are related. Thus
the relatedness plots in C only show counts for related individuals only.

```{r check related, eval=FALSE, fig.height=3, fig.width=5, fig.align='center'}
exclude_relatedness <- check_relatedness(indir=indir, qcdir=qcdir, name=name,
                                         interactive=TRUE,
                                         path2plink=path2plink)
```
```{r load checkRelatedness, out.width = "500px", echo=FALSE, fig.align='center'}
#| fig.alt: >
#|   bar graph with a histogram og IBD measurements
knitr::include_graphics("checkRelatedness.png")
```

## Ancestry Predictions of Data
For ancestry estimation, accessory functions from [Plink v2](https://www.cog-genomics.org/plink/2.0/) are needed, as are pre-computed
loading matrices for the PCA projection used in the model.
These are hosted on the _plinkQC_ github repo under the inst/extdata folder
located [here](https://github.com/meyer-lab-cshl/plinkQC/tree/ancestry_update/inst/extdata/load_mat). Alternatively, the whole github repo can downloaded with

```{bash installing github for step by step, eval=FALSE}
git clone git@github.com:meyer-lab-cshl/plinkQC.git
```

For this, it is important that the data is in hg38 annotation. USCS's [liftOver](https://genome.ucsc.edu/cgi-bin/hgLiftOver) tool may be needed to map variants from one annotation to another. More details on how to use the tool can
be found on the processing [HapMap III reference data vignette](https://meyer-lab-cshl.github.io/plinkQC/articles/HapMap.html).

Additionally, the ancestry prediction function requires that the data is in plink 2 format and has variant identifiers in the format: 1:12345[hg38]. The function 
`ancestry_prediction` will call helper functions, `convert_to_plink2()` and `rename_variant_identifiers()` to format the data as directed by the parameters.
The function returns: 
  (i) prediction_prob: Dataframe of family ids, sample ids, and model probability of the each ancestry.
  (ii) prediction_majority: Dataframe of family ids, sample ids, and ancestry with highest model probability.
  (iii) exclude_ancestry: A list of ids to be excluded based on user-specified ancestries
to the ancestral predictions of the samples
  (iv) p_ancestry: Bar graph of the ancestry model probabilities. 

```{r ancestry prediction, eval=FALSE}
path2plink2 <- "/Users/syed/bin/plink2"
path2load_mat <- "path/to/load_mat/merged_chrs.postQC.train.pca"

anc_check <- ancestry_prediction(indir=indir, qcdir=qcdir, name=name,
                                         interactive=TRUE,
                                         path2plink2=path2plink2, 
                                         path2load_mat = path2load_mat)
```

```{r load anc graph, out.width = "500px", echo=FALSE, fig.align='center'}
#| fig.alt: >
#|   marker missingness
knitr::include_graphics("ancestral_prediction.png")
```

## Markers with excessive missingness rate
Markers with excessive missingness rate are removed as they are considered 
unreliable. Typically, thresholds for marker exclusion based on missingness
range from 1\%-5\%. 
Identifying markers with high missingness rates is implemented in
`snp_missingness`. It calculates the rates of missing genotype calls and
frequency for all variants in the individuals that passed the `perIndividualQC`.

```{r check snp missing, eval=FALSE}
fail_snpmissing <- check_snp_missingness(indir=indir, qcdir=qcdir, name=name,
                                         interactive=TRUE,
                                         path2plink=path2plink, 
                                         showPlinkOutput=FALSE)
```

```{r load snp missing, out.width = "500px", echo=FALSE, fig.align='center'}
#| fig.alt: >
#|   marker missingness
knitr::include_graphics("snpmissingness.png")
```

## Markers with deviation from HWE
Markers with strong deviation from HWE might be indicative of genotyping or
genotype-calling errors. As serious genotyping errors often yield
very low p-values (in the order of $10^{-50}$), it is recommended to choose a 
reasonably low threshold to avoid filtering too many variants (that might have
slight, non-critical deviations).
Identifying markers with deviation from HWE is implemented in `check_hwe`.
It calculates the observed and expected heterozygote frequencies per SNP in the
individuals that passed the `perIndividualQC` and computes the deviation of the
frequencies from Hardy-Weinberg equilibrium (HWE) by HWE exact test.

```{r check hwe, eval=FALSE}
fail_hwe <- check_hwe(indir=indir, qcdir=qcdir, name=name, interactive=TRUE,
                      path2plink=path2plink, showPlinkOutput=FALSE)
```
```{r load hwe, out.width = "500px", echo=FALSE, fig.align='center'}
#| fig.alt: >
#|   p-values of deviations from HWE histogram 
knitr::include_graphics("hwe.png")
```


## Markers with low minor allele frequency
Markers with low minor allele count are often removed as the actual genotype
calling (via the calling algorithm) is very difficult due to the small sizes of
the heterozygote and rare-homozygote clusters.
Identifying markers with low minor allele count is implemented in `check_maf`.
It calculates the minor allele frequencies for all variants in the individuals
that passed the `perIndividualQC`.

```{r check maf, eval=FALSE}
fail_maf <- check_maf(indir=indir, qcdir=qcdir, name=name, interactive=TRUE,
                      path2plink=path2plink, showPlinkOutput=FALSE)
```
```{r load  maf, out.width = "500px", echo=FALSE, fig.align='center'}
#| fig.alt: >
#|   Histogram of minor allele frequencies
knitr::include_graphics("maf.png")
```


# References 
