Type: | Package |
Title: | The W-Test for Genetic Interactions Testing |
Version: | 3.2 |
Author: | Rui Sun, Maggie Haitian Wang |
Maintainer: | Rui Sun <rsunzju@gmail.com> |
Description: | Perform the calculation of W-test, diagnostic checking, calculate minor allele frequency (MAF) and odds ratio. |
License: | GPL-2 |
LazyData: | TRUE |
RoxygenNote: | 6.1.1 |
NeedsCompilation: | yes |
Encoding: | UTF-8 |
Packaged: | 2019-09-03 06:49:04 UTC; rui |
Repository: | CRAN |
Date/Publication: | 2019-09-03 07:10:02 UTC |
Position of CpG Sites for the methylation Data
Description
This dataset contains pseudo positions of 200 CpG sites.
Usage
data(CpG.pos)
Format
The format is: CpG sites by rows; column 1: names of CpG sites; column 2: positions of CpG sites.
Pseudo Position of SNPs for the genotype Data
Description
Positions of 200 SNPs.
Usage
data(SNP.pos)
Format
The format is: SNPs by rows; column 1: names of SNPs, column 2: positions of SNPs.
Genotype Data of Candidate Diabetes Genes
Description
A data frame contains 23 SNPs for 115 individuals.
Usage
data(diabetes.geno)
Format
The format is: subjects by rows and genotypes by columns.
References
Wang, M. H., Li, J., Yeung, V. S. Y., Zee, B. C. Y., Yu, R. H. Y., Ho, S., & Waye, M. M. Y. (2014). Four pairs of gene-gene interactions associated with increased risk for type 2 diabetes (CDKN2BAS-KCNJ11), obesity (SLC2A9-IGF2BP2, FTO-APOA5), and hypertension (MC4R-IGF2BP2) in Chinese women. Meta Gene, 2, 384-391. http://doi.org/10.1016/j.mgene.2014.04.010
Example Genotype Data
Description
This simulated data frame contains 300 individuals and 200 SNPs.
Usage
data(genotype)
Format
The format is: subjects by rows, and genotype by columns.
Patameter Estimation for W-test Probability Distribution
Description
Estimate parameters (h and f) for W-test
.
Usage
hf(data, w.order, B = 400, n.sample = nrow(data),
n.marker = "default.nmarker")
Arguments
data |
a data frame or matrix containing genotypes in the columns and subjects in the rows. Genotypes should be coded as (0, 1, 2) or (0, 1). |
w.order |
a numeric number. |
B |
a numeric number specifying the number of replicates. Default is 400. |
n.sample |
a numeric number specifying the number of samples to be used for estimating parameters. Default is the total number of samples in the data. |
n.marker |
a numeric value, the number of biomarkers to include in bootstrapping. For |
Value
a set of h and f values indexed by k, estimated automatically. For main effect, k is the number of levels of a predictor variable. For interactions, k is the number of categorical combinations of a variable pair.
Author(s)
Rui Sun, Maggie Haitian Wang
References
Maggie Haitian Wang, Rui Sun, Junfeng Guo, Haoyi Weng, Jack Lee, Inchi Hu, Pak Sham and Benny C.Y. Zee (2016). A fast and powerful W-test for pairwise epistasis testing. Nucleic Acids Research. doi:10.1093/nar/gkw347.
See Also
Examples
data(diabetes.geno)
# Please note that parameter B is recommended to be greater than 400.
# For high order interaction analysis (w.order > 2), it is recommended to use default n.sample.
hf1 <- hf(data = diabetes.geno, w.order = 1, B = 100)
hf2 <- hf(data = diabetes.geno, w.order = 2, B = 80)
Parameter Estimation for W-test Probability Distribution in Gene-methylation Data
Description
Estimate parameters (h and f) for W-test
.
Usage
hf.snps.meth(B = 400, geno, meth, y, geno.pos, meth.pos, window.size,
n.sample = nrow(geno), n.pair = 1000)
Arguments
B |
a numeric number specifying the number of bootstrapping times. Default is 400. |
geno |
a data frame or matrix containing genotypes in the columns. Genotypes should be coded as (0, 1, 2) or (0, 1). SNP names should be stored as column names. |
meth |
a data frame or matrix containing methylation data in the columns. Methylation data should be recoded as (0, 1, 2) or (0, 1). Names of CpG sites should be stored as column names. |
y |
a numeric vector of 0 or 1. |
geno.pos |
a data frame containing SNP names and positions in two columns. |
meth.pos |
a data frame containing CpG names and positions in two columns. |
window.size |
a numeric number specifying the size of genome distance. Interaction of the SNPs and CpG sites located within the size of genome distance will be evaluated exhaustively. |
n.sample |
a numeric number specifying the number of samples to be included for estimating parameters. Default is the total number of samples. |
n.pair |
a numeric value, the number of SNP-CpG pairs to use in bootstrapping. Default = min(P, 1000). P is the total number of pairs within the |
Value
a set of h and f values indexed by k, estimated automatically. Variable k is the number of categorical combinations of a variable pair.
Author(s)
Rui Sun, Maggie Haitian Wang
References
Maggie Haitian Wang, Rui Sun, Junfeng Guo, Haoyi Weng, Jack Lee, Inchi Hu, Pak Sham and Benny C.Y. Zee (2016). A fast and powerful W-test for pairwise epistasis testing. Nucleic Acids Research. doi:10.1093/nar/gkw347.
Examples
data(SNP.pos)
data(CpG.pos)
data(genotype)
data(methylation)
data(phenotype2)
# Please note that parameter B is recommended to be greater than 400.
hf.pair <- hf.snps.meth(B = 80, geno = genotype, meth = methylation, y = phenotype2,
geno.pos = SNP.pos, meth.pos = CpG.pos, window.size = 1000)
Minor Allele Frequency
Description
Calculate minor allele frequency.
Usage
maf(data, which.snp = NULL)
Arguments
data |
a data frame or matrix containing genotypes in the columns. Genotypes should be coded as (0, 1, 2) or (0, 1). |
which.snp |
a numeric value, indicating which SNP to calculate. When which.snp = NULL, MAF of all the markers is calculated. Default is NULL. |
Value
The MAF of one marker.
Examples
data(diabetes.geno)
result <- maf(diabetes.geno, which.snp=10)
Example Methylation Data
Description
This data frame contains 300 samples and 200 CpG sites.
Usage
data(methylation)
Format
The format is: subjects by rows and methylation by columns.
Recode Methylation Data
Description
Code a CpG variable into two levels (high and low) by the two-mean clustering method.
Usage
methylation.recode(data)
Arguments
data |
a data frame or matrix contains methylation data in the columns. |
Examples
data(methylation)
data.recoded <- methylation.recode(methylation)
Odds Ratio
Description
Calculate odds ratio for a single SNP or a pair of SNPs. Single marker odds ratio is computed by contigency table as the odds of disease at minor allele vs the odds of diseases at major allele. Odds ratio of a pair of SNPs is calculated by the Logistic Regression.
Usage
odds.ratio(data, y, w.order, which.marker)
Arguments
data |
a data frame or matrix containing genotypes in the columns. Genotypes should be coded as (0, 1, 2) or (0, 1), according to minor allele count. |
y |
binary values. |
w.order |
a numeric number taking values 1 or 2. If w.order = 1, odds ratio of main effect is calculated. If w.order = 2, odds ratio of pairwise interaction is calculated. |
which.marker |
a numeric vector, when w.order = 1, a single value indicating the column index of the variable to calculate; when w.order = 2, a vector indicating the column index of a SNP-pair to calculate. |
Value
The odds ratio of a SNP or a SNP-pair.
Examples
data(diabetes.geno)
data(phenotype1)
y <- as.numeric(phenotype1)
OR.snp4.snp8 <- odds.ratio(diabetes.geno, y, w.order=2, which.marker = c(4,8))
OR.snp4 <- odds.ratio(diabetes.geno, y, w.order = 1, which.marker = 4)
Phenotype of the diabetes.geno Data
Description
A binary variable indicate the status of 115 individuals (1 = affacted, 0 = unaffacted).
Usage
data(phenotype1)
Format
The format is: 115 rows and 1 column.
References
Wang, M. H., Li, J., Yeung, V. S. Y., Zee, B. C. Y., Yu, R. H. Y., Ho, S., & Waye, M. M. Y. (2014). Four pairs of gene-gene interactions associated with increased risk for type 2 diabetes (CDKN2BAS-KCNJ11), obesity (SLC2A9-IGF2BP2, FTO-APOA5), and hypertension (MC4R-IGF2BP2) in Chinese women. Meta Gene, 2, 384-391. http://doi.org/10.1016/j.mgene.2014.04.010
Simulated Phenotype of the genotype-methylation Data
Description
The phenotype of the 300 individuals (1 = affacted, 0 = unaffacted).
Usage
data(phenotype2)
Format
The format is: 300 rows and 1 column.
W-test Probability Distribution Diagnostic Plot
Description
Diagnostic checking of W-test probability distribution estimation.
Usage
w.diagnosis(data, w.order = c(1, 2), n.rep = 10,
n.sample = nrow(data), n.marker = ncol(data), hf1 = "default.hf1",
hf2 = "default.hf2", ...)
Arguments
data |
a data frame or matrix containing genotypes in the columns. Genotypes should be coded as (0, 1, 2) or (0, 1). |
w.order |
an integer value of 0 or 1. |
n.rep |
a numeric value, the number of bootstrapping times. |
n.sample |
a numeric value, the number of samples to use in bootstrapping. Default is the total number of samples in the data. |
n.marker |
a numeric value, the number of markers to use in bootstrapping. Default is the total number of markers. |
hf1 |
h and f values to calculate main effect, organized as a matrix, with columns (k, h, f), k = 2 to 3. Needed when |
hf2 |
h and f values to calculate interaction associations, organized as a matrix, with columns (k, h, f), k = 2 to 9. Needed when |
... |
graphical parameters. |
Details
This function evaluates the input W values of main or interaction effects using a set of null Y by the W-test
, and the evaluation is performed in several bootstrap samples to achieve fast and stable output. The W histogram and its theoretical Chi-squared distribution density with f degrees of freedom are plotted indexed by k. Close overlaying of the histogram and the probability density curve indicates that the estimated h and f give a good test statistic probability distribution.
Author(s)
Rui Sun, Maggie Haitian Wang
References
Maggie Haitian Wang, Rui Sun, Junfeng Guo, Haoyi Weng, Jack Lee, Inchi Hu, Pak Sham and Benny C.Y. Zee (2016). A fast and powerful W-test for pairwise epistasis testing. Nucleic Acids Research. doi:10.1093/nar/gkw347.
See Also
Examples
data(diabetes.geno)
# Please note that parameter B is recommended to be greater than 400.
hf1 <- hf(data = diabetes.geno, w.order = 1, B = 100)
hf2 <- hf(data = diabetes.geno, w.order = 2, B = 50)
w.diagnosis(diabetes.geno, w.order = 1, n.rep = 100, hf1 = hf1, main=NULL, xlab=NULL, ylab=NULL)
w.diagnosis(diabetes.geno, w.order = 2, n.rep = 100, hf2 = hf2, main=NULL, xlab=NULL, ylab=NULL)
W P-values Diagnosis by Q-Q Plot
Description
Draw a Q-Q plot for W-test
Usage
w.qqplot(data, y, w.order = c(1, 2), input.poolsize = 200,
hf1 = "default.hf1", hf2 = "default.hf2", ...)
Arguments
data |
a data frame or matrix containing genotypes in the columns. Genotypes should be coded as (0, 1, 2) or (0, 1). |
y |
a numeric vector of 0 or 1. |
w.order |
a numeric number taking values 1 or 2. |
input.poolsize |
a numeric number; The maximum number of SNPs to calculate the Q-Q plot. Default is 200. The |
hf1 |
h and f values to calculate main effect, organized as a matrix, with columns (k, h, f), k = 2 to 3. Needed when |
hf2 |
h and f values to calculate interaction associations, organized as a matrix, with columns (k, h, f), k = 2 to 9. Needed when |
... |
graphical parameters. |
Details
With a given data and y, the p-value of W-test is calculated at given h and f values, which are plotted against the theoretical distribution.
Value
Q-Q plot
Examples
data(diabetes.geno)
data(phenotype1)
## Step 1. HF Calculation
# Please note that parameter B is recommended to be greater than 400.
hf1<-hf(data = diabetes.geno, w.order = 1, B = 200)
## Step 2. Q-Q Plot
w.qqplot(data = diabetes.geno, y = phenotype1, w.order = 1, hf1 = hf1, cex =.5)
abline(0,1)
W-test
Description
This function performs the W-test
to calculate main effect or pairwise interactions in case-control studies
for categorical data sets. The test measures target variables' distributional difference between cases and controls via a combined
log of odds ratio. It follows a Chi-squared probability distribution with data-adaptive degrees of freedom. For pairwise interaction
calculation, the user has 3 options: (1) calculate a single pair's W-value, (2) calculate pairwise interaction for a list of variables,
which p-values are smaller than a threshold (input.pval
); (3) calculate the pairwise interaction exhaustively for all variables.
For both main and interaction calculation, the output can be filtered by p-values, such that only sets with smaller p-value
than a threshold (output.pval
) will be returned. An extension of the W-test for rare variant analysis is available in zfa
package.
Usage
wtest(data, y, w.order = c(1, 2), hf1 = "default.hf1",
hf2 = "default.hf2", which.marker = NULL, output.pval = NULL,
sort = TRUE, input.pval = 0.1, input.poolsize = 150)
Arguments
data |
a data frame or matrix containing genotypes in the columns. Genotypes should be coded as (0, 1, 2) or (0, 1). |
y |
a numeric vector of 0 or 1. |
w.order |
an integer value of 0 or 1. |
hf1 |
h and f values to calculate main effect, organized as a matrix, with columns (k, h, f), k = 2 to 3. Needed when |
hf2 |
h and f values to calculate interaction associations, organized as a matrix, with columns (k, h, f), k = 2 to 9. Needed when |
which.marker |
a numeric vector, when |
output.pval |
a p-value threshold for filtering the output. If NULL, all the results will be listed; otherwise, the function will only output the results with p-values smaller than the |
sort |
a logical value indicating whether or not to sort the output by p-values in ascending order. Default = TRUE. |
input.pval |
a p-value threshold to select markers for pairwise calculation, used only when |
input.poolsize |
an integer, with value less than the number of input variables. It is an optional filter to control the maximum number of variables to include in pairwise calculation, used only when |
Details
W-test is a model-free statistical test to measure main effect or pairwise interactions in case-control studies with categorical variables. Theoretically, the test statistic follows a Chi-squared distribution with f degrees of freedom. The data-adaptive degree of freedom f, and a scalar h in the test statistics allow the W-test to correct for distributional bias due to sparse data and small sample size. Let k be the number of columns of the 2 by k contingency table formed by a single variable or a variable pair. When the sample size is large and there is no population stratification, the h and f will approximate well to the theoretical value h = (k-1)/k, and f = k-1. When sample size is small and there is population stratification, the h and f will vary to correct for distributional bias caused by the data structure.
When w.order
=2, the wtest()
will automatically calculate the main effect first and then do a pre-filter before calculating interactions.
This filtering is to avoid overloading the memory before having a better understanding of the data. User can specify a smaller input.pval such as 0.05 or 0.001
for less output, or input.pval
=1 or NULL for exhaustive pairwise calculation. Another optional filter is input.poolsize
. It will take the top input.poolsize
number of variables to calculated pairwise effect exhaustively, selected by smallest p-value; when used together with input.pval
, the smaller set will be passed to pairwise calculation.
Value
An object "wtest"
containing:
order |
the "w.order" specified. |
results |
When |
hf1 |
The h and f values used in main effect calculation. |
hf2 |
The h and f values used in pairwise interaction calculation. |
Author(s)
Rui Sun, Maggie Haitian Wang
References
Maggie Haitian Wang, Rui Sun, Junfeng Guo, Haoyi Weng, Jack Lee, Inchi Hu, Pak Sham and Benny C.Y. Zee (2016). A fast and powerful W-test for pairwise epistasis testing. Nucleic Acids Research. doi:10.1093/nar/gkw347.
Maggie Haitian Wang, Haoyi Weng, Rui Sun, Jack Lee, William K.K. Wu, Ka Chun Chong, Benny C.Y. Zee. (2017). A Zoom-Focus algorithm (ZFA) to locate the optimal testing region for rare variant association tests. Bioinformatics, 33(15), 2330-2336.
See Also
Examples
data(diabetes.geno)
data(phenotype1)
## Step 1. HF Calculation
# Please note that parameter B is recommended to be greater than 400.
hf1 <- hf(data = diabetes.geno, w.order = 1, B = 100)
hf2 <- hf(data = diabetes.geno, w.order = 2, B = 50)
## Step 2. W-test Calculation
w1 <- wtest(diabetes.geno, phenotype1, w.order = 1, hf1 = hf1)
w2 <- wtest(diabetes.geno, phenotype1, w.order = 2, input.pval = 0.3,
input.poolsize = 50, output.pval = 0.01, hf1 = hf1, hf2 = hf2)
w.pair <- wtest(diabetes.geno, phenotype1, w.order = 2, which.marker = c(10,13), hf2 = hf2)
W-test for High Order Interaction Analysis
Description
This function performs the W-test
to calculate high-order interactions in case-control studies
for categorical data sets. The test measures target variables' distributional difference between cases and controls via a combined
log of odds ratio. It follows a Chi-squared probability distribution with data-adaptive degrees of freedom. For high-order interaction
calculation, the user has 3 options: (1) calculate W-test of a set of SNPs, (2) calculate high-order interaction for a list of variables,
which p-values are smaller than a threshold (input.pval
); (3) calculate high-order interaction exhaustively for all variables.
Output can be filtered by p-values, such that only sets with smaller p-value than a threshold (output.pval
) will be returned.
Usage
wtest.high(data, y, w.order = 3, hf1 = "default.hf1",
hf.high.order = "default.high", which.marker = NULL,
output.pval = NULL, sort = TRUE, input.pval = 0.1,
input.poolsize = 10)
Arguments
data |
a data frame or matrix containing genotypes in the columns. Genotypes should be coded as (0, 1, 2) or (0, 1). |
y |
a numeric vector of 0 or 1. |
w.order |
an integer value, indicating the order of high-way interactions. For example, |
hf1 |
h and f values to calculate main effect, organized as a matrix, with columns (k, h, f), k = 2 to 3. |
hf.high.order |
h and f values to calculate high-order interactions, organized as a matrix, with columns (k, h, f), where k is the number of genotype combinations of a set of SNPs. |
which.marker |
a numeric vector indicating the column index of a set of SNPs to calculate. Default |
output.pval |
a p-value threshold for filtering the output. If NULL, all the results will be listed; otherwise, the function will only output the results with p-values smaller than the |
sort |
a logical value indicating whether or not to sort the output by p-values in ascending order. Default = TRUE. |
input.pval |
a p-value threshold to select markers for high-order interaction calculation, used only when |
input.poolsize |
an integer, with value less than the number of input variables. It is an optional filter to control the maximum number of variables to include in high-order interaction calculation, used only when |
Details
W-test is a model-free statistical test orginally proposed to measure main effect or pairwise interactions in case-control studies with categorical variables. It can be extended to high-order interaction detection by the wtest.high() function. Theoretically, the test statistic follows a Chi-squared distribution with f degrees of freedom. The data-adaptive degree of freedom f, and a scalar h in the test statistics allow the W-test to correct for distributional bias due to sparse data and small sample size. Let k be the number of columns of the 2 by k contingency table formed by a single variable or a variable pair. When the sample size is large and there is no population stratification, the h and f will approximate well to the theoretical value h = (k-1)/k, and f = k-1. When sample size is small and there is population stratification, the h and f will vary to correct for distributional bias caused by the data structure.
When w.order
> 2, the wtest()
will automatically calculate the main effect first and then do a pre-filter before calculating interactions.
This filtering is to avoid overloading the memory before having a better understanding of the data. User can specify a smaller input.pval such as 0.05 or 0.001
for less output, or input.pval
=1 or NULL for exhaustive high-order interaction calculation. Another optional filter is input.poolsize
. It will select the top input.poolsize
number of variables, ranked by p-values, to calculate high-order interactions. When used together with input.pval
, the algorithm selects the smaller set in the high-order calculation.
Value
An object "wtest"
containing:
order |
the "w.order" specified. |
results |
When order > 2 and which.marker = NULL, the test results include: (information of a set) [SNPs name, W-value, k, p-value]; (Information of the first variable in the set) [W-value, k, p-value]; (Information of the second variable in the set) [W-value, k, p-value] ... |
hf1 |
The h and f values used in main effect calculation. |
hf2 |
The h and f values used in high-order interaction calculation. |
Author(s)
Rui Sun, Maggie Haitian Wang
References
Maggie Haitian Wang, Rui Sun, Junfeng Guo, Haoyi Weng, Jack Lee, Inchi Hu, Pak Sham and Benny C.Y. Zee (2016). A fast and powerful W-test for pairwise epistasis testing. Nucleic Acids Research. doi:10.1093/nar/gkw347.
See Also
Examples
data(diabetes.geno)
data(phenotype1)
## Step 1. HF Calculation
# Please note that parameter B is recommended to be greater than 400 for w.order = 1 or 2.
# For high order interaction analysis (w.order > 2), it is recommended to use default n.sample.
hf1 <- hf(data = diabetes.geno, w.order = 1, B = 100)
hf.high <- hf(data = diabetes.geno, w.order = 3, B = 30, n.marker = 10)
## Step 2. W-test Calculation
w1 <- wtest.high(diabetes.geno, phenotype1, w.order = 1, hf1 = hf1)
w3 <- wtest.high(diabetes.geno, phenotype1, w.order = 3, input.pval = 0.3,
input.poolsize = 50, output.pval = 0.5, hf1 = hf1, hf.high.order = hf.high)
w.set <- wtest.high(diabetes.geno, phenotype1, w.order = 3, which.marker = c(10,13,20),
hf.high.order = hf.high)
W-test for Gene-methylation Interaction Analysis
Description
Calculate cis-gene-methylation interaction of a (SNP, CpG) pair in user-defined window, and can run in a genome-wide manner. The output can be filtered by p-values, such that only sets with smaller
p-value than the threshold (output.pval
) will be returned.
Usage
wtest.snps.meth(geno, meth, y, geno.pos, meth.pos, window.size = 10000,
hf = "default.hf", output.pval = NULL, sort = TRUE,
which.marker = NULL)
Arguments
geno |
a data frame or matrix containing genotypes in the columns and subjects in the rows. Genotypes should be coded as (0, 1, 2) or (0, 1). SNP names should be stored as column names of the data. |
meth |
a data frame or matrix containing methylation data in the columns. Methylation data should be recoded as (0, 1, 2) or (0, 1). Names of CpG sites should be stored as column names of the data. |
y |
a numeric vector of 0 or 1. |
geno.pos |
a data frame containing SNP names and positions in two columns. |
meth.pos |
a data frame containing CpG names and positions in two columns. |
window.size |
a numeric number specifying the size of genome distance. Interaction effects of the SNPs and CpG sites located within the size of genome distance will be evaluated exhaustively. |
hf |
h and f values to calculate gene-methylation interaction associations, organized as a matrix, with columns (k, h, f), k = 2 to 6. |
output.pval |
a p-value threshold for filtering the output. If NULL, all results will be listed; otherwise, the function will only output the results with p-values smaller than |
sort |
a logical value indicating whether or not to sort the output by p-values in ascending order. Default = TRUE. |
which.marker |
a vector indicating the column index of a SNP-CpG pair to calculate. Default |
Details
Calculate cis-gene-methylation interaction of a (SNP, CpG) pair in user-defined window, and can run in a genome-wide manner. The output can be filtered by p-values, such that only sets with smaller
p-value than the threshold (output.pval
) will be returned.
Value
An object "wtest.snps.meth"
containing:
results |
The test results include: SNP name, CpG name, SNP position, CpG position, W value, k, and p-value. |
hf |
The h and f values used for each k in pairwise calculation, where k = 2 to 6. |
Author(s)
Rui Sun, Maggie Haitian Wang
References
Maggie Haitian Wang, Rui Sun, Junfeng Guo, Haoyi Weng, Jack Lee, Inchi Hu, Pak Sham and Benny C.Y. Zee (2016). A fast and powerful W-test for pairwise epistasis testing. Nucleic Acids Research. doi:10.1093/nar/gkw347.
See Also
Examples
data(SNP.pos)
data(CpG.pos)
data(genotype)
data(methylation)
data(phenotype2)
w <- 13000
# Recode methylation data
methylation <- methylation.recode(methylation)
## Step 1. HF Calculation.
# Please note that parameter B is recommended to be greater than 400.
hf.pair <- hf.snps.meth(B = 80, geno = genotype, meth = methylation, y = phenotype2,
geno.pos = SNP.pos, meth.pos = CpG.pos, window.size = w)
## Step 2. Application
result <- wtest.snps.meth(geno = genotype, meth = methylation, y = phenotype2, geno.pos = SNP.pos,
meth.pos = CpG.pos, window.size = w, hf = hf.pair, output.pval = 0.1)