# pleiotropySNP: pleiotropySNP In SharonLutz/pleiotropy: This Package Tests For Pleiotropy

## Description

This function tests for pleiotropy using 2 different SNP based approaches

## Usage

 `1` ```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

Sharon Lutz

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10``` ```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.