pleiotropySNP: pleiotropySNP

In SharonLutz/pleiotropy: This Package Tests For Pleiotropy

pleiotropySNP

Description

This function tests for pleiotropy using 2 different SNP based approaches

Usage

pleiotropySNP(X, Y, Ydist, Z = NULL, covariates = FALSE, nPerm = 5000)

Arguments

X	X is the vector of the SNP of interest
Y	Y is a matrix of the traits of interest where the number of rows equal the number of subjects and the number of columns equal the number of traits.
Ydist	Ydist is a vector that specifies the distribution of the each trait. For example, for one normally distributed trait and the second binary trait, then Ydist<-c("gaussian","binomial"). Other choices for Ydist can be found by looking at the choice of family for the glm function (i.e. ?glm).
Z	Z is a matrix of covariates where the number of rows equal the number of subjects and the number of columns equal the number of covariates.
covariates	If covariates=FALSE, then the models will not be adjusted for covariates. If covariates=TRUE, then the model will be adjusted for covariates.
nPerm	nPerm is the number of permutations. Default is 5,000.

Details

For SNP based tests of pleiotropy, this function computes the p-values obtained from both the cut-off based permutation approach and the Hausdorff based permutation approach.

Value

cutoffPvalue is the p-value from the cut-off based permutation approach

hausdorffPvalue is the p-value from the Hausdorff based permutation approach

Author(s)

Sharon Lutz

Examples

set.seed(1)
X<-rbinom(1000,2,0.2) #generate the SNP X=0,1,2
Z<-matrix(rnorm(1000),nrow=1000,ncol=1)
Y1<-rnorm(1000,0.2*X+0.1*Z,1)
Y2<-rnorm(1000,0.2*X+0.1*Z,1)
Y<-cbind(Y1,Y2)
Ydist<-c("gaussian","gaussian")

pleiotropySNP(X,Y,Ydist) # NOT adjusting for covariates Z
pleiotropySNP(X,Y,Ydist,Z,covariates=TRUE) # Adjusting for covariates Z

SharonLutz/pleiotropy documentation built on Jan. 21, 2025, 10:03 p.m.