gxgRC: gxgRC

In SharonLutz/gxgRC: Examines the effect of a SNP on the outcome given another SNP both the outcome and the first SNP

Description

Tests for the effect of a SNP on the outcome in the presence of epistasis.

Usage

gxgRC(n=1000,nSim=1000,MAF1=0.2,gamma0=0,gammaX1=0.2,
beta0=0,betaX1=0.1,betaX2=0.1,betaI=seq(from=0.1,to=0.5,by=0.1),varY=1,
alpha_level=0.05,plot.pdf=T,plot.name="gxgRC.pdf",SEED=1)

Arguments

n	is the sample size
nSim	is the number of simulations
MAF1	is the MAF for X1
gamma0	is the intercept for the association between the SNP X1 and the SNP X2 where logit(X2)=gamma0+gammaX1*X1
gammaX1	is the effect of SNP X1 on SNP X2 where logit(X2)=gamma0+gammaX1*X1
beta0	is the intercept for E[Y]=beta0+betaX1*X1+betaX2*X2+betaI*X1*X2
betaX1	is the association between X1 and Y for E[Y]=beta0+betaX1*X1+betaX2*X2+betaI*X1*X2
betaX2	is the association between X2 and Y for E[Y]=beta0+betaX1*X1+betaX2*X2+betaI*X1*X2
betaI	is the interaction between X1 and X2 on Y for E[Y]=beta0+betaX1*X1+betaX2*X2+betaI*X1*X2
varY	is the variance for Y
alpha_level	is the significance level, default 0.05
plot.pdf	equals T to output a plot, equals F to not output a plot
plot.name	is the name of the plot
SEED	sets the seed

Details

Tests for the effect of a SNP on the outcome in the presence of epistasis.

Value

a matrix of estimates and p-values

Author(s)

Sharon Lutz

SharonLutz/gxgRC documentation built on Dec. 21, 2020, 4:26 p.m.