Description Usage Arguments Details Value Author(s) Examples

View source: R/pleiotropySNP.R

This function tests for pleiotropy using 2 different SNP based approaches

1 | ```
pleiotropySNP(X, Y, Ydist, Z = NULL, covariates = FALSE, nPerm = 5000)
``` |

`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. |

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.

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

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
``` |

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