pCAInteraction: Epistasis Test by Principal Component Analysis

In FRGEpistasis: Epistasis Analysis for Quantitative Traits by Functional Regression Model

Description

Test the epistasis between two genes (or genomic regions) with the principal components analysis method.

Usage

pCAInteraction(phenoData, x_A, x_B)

Arguments

phenoData	Vector of phenotype data which can be quantitative trait or binary trait.
x_A	Matrix of genotype of gene A.
x_B	Matrix of genotype of gene B.

Details

This function takes phenotype vector and genotype matrices as input and tests the epistasis using PCA method. The number of principal components is determined by PCA to explain 80 percent of the genetic variation. The interaction between gene A and gene B is tested with chi-squared test.

Value

It returns the p value of chi-squared test for epistasis detection between gene A and gene B.

Author(s)

Futao Zhang

Examples

smp_num=1000
number_snp_A=25
number_snp_B=20
pheno<-sample(c(0:500),smp_num,replace=TRUE)
smpl=rep(0,number_snp_A*smp_num)
idx_1=sample(c(1:(number_snp_A*smp_num)),ceiling(number_snp_A*smp_num/100))
idx_2=sample(c(1:(number_snp_A*smp_num)),ceiling(number_snp_A*smp_num/200))
smpl[idx_1]=1
smpl[idx_2]=2
geno_A=matrix(smpl,smp_num,number_snp_A)

smpl=sample(c(0,1,2),number_snp_B*smp_num,replace=TRUE)
geno_B=matrix(smpl,smp_num,number_snp_B)
pCAInteraction(pheno,geno_A,geno_B)

FRGEpistasis documentation built on Nov. 8, 2020, 5:51 p.m.