pleiotropySNP: pleiotropySNP

Description Usage Arguments Details Value Author(s) Examples

View source: R/pleiotropySNP.R

Description

This function tests for pleiotropy using 2 different SNP based approaches

Usage

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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

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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 Dec. 10, 2019, 7:41 a.m.