README.md

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

gxgRC

Installation

install.packages("devtools") # devtools must be installed first and only once

devtools::install_github("SharonLutz/gxgRC") #install for new updates

Input

For n subjects, SNP X1 is generated from a binomial distribution with a mean specified by the user (input: MAF1). The second SNP X2 is generated from a logistic regression such that

logit[P(X2)] = γ0 + γ1 X1

(input: gamma0, gammaX1). The outcome Y is generated from a normal distribution with variance (input: varY) and mean as follows:

E[Y] = β0 + β1 X1 + β2 X2 + βI X1 X2

(input: beta0, betaX1, betaX2, betaI). See the manpage for more detail regarding the input of the gxgRC function.

library(gxgRC)
?gxgRC # For details on this function

Simulation Scenario

For 1,000 subjects, we generated X1 to have a mean of 0.5. X2 is generated with γ0 =0 and γ1 X1=0.3. The outcome Y is generated with β0=0, β1 X1=0.3, β1 X2=0.3 and β1 XI vaires from 0.3 to 1 by 0.5. The code is as follows:

gxgRC(n=1000,nSim=1000,MAF1=0.5,gamma0=0,gammaX1=0.3,
beta0=0,betaX1=0.3,betaX2=0.3,betaI=seq(from=0.3,to=1,by=0.05),varY=1,
alpha_level=0.00000005,plot.pdf=T,plot.name="gxgRCexample.pdf",SEED=1)

Simulation Scenario Output

For this example, we get the following matrix and corresponding plot:

      scenario1 scenario2 scenario3 scenario4 scenario5
 [1,]     0.000     0.956         0     0.027     0.017
 [2,]     0.000     0.991         0     0.008     0.001
 [3,]     0.010     0.983         0     0.002     0.005
 [4,]     0.027     0.973         0     0.000     0.000
 [5,]     0.059     0.941         0     0.000     0.000
 [6,]     0.133     0.867         0     0.000     0.000
 [7,]     0.214     0.786         0     0.000     0.000
 [8,]     0.330     0.670         0     0.000     0.000
 [9,]     0.505     0.495         0     0.000     0.000
[10,]     0.650     0.350         0     0.000     0.000
[11,]     0.803     0.197         0     0.000     0.000
[12,]     0.878     0.122         0     0.000     0.000
[13,]     0.933     0.067         0     0.000     0.000
[14,]     0.976     0.024         0     0.000     0.000
[15,]     0.990     0.010         0     0.000     0.000

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