pTest {GPA} | R Documentation |
Hypothesis testing for pleiotropy.
pTest( fit, fitH0, vDigit=1000 )
fit |
Fit of the GPA model of interest. |
fitH0 |
GPA model fit under the null hypothesis of pleiotropy test. |
vDigit |
Number of digits for reporting parameter estimates and standard errors. For example, setting it to 1000 means printing out values up to three digits below zero. |
pTest
implements the hypothesis testing for pleiotropy.
It requires two GPA model fits, one of interest and one under the null hypothesis
(obtained by setting pleiotropyH0=TRUE
when running GPA
function),
and evaluates genetical correlation among multiple phenotypes using the likelihood ratio test.
Returns a list with components:
pi |
pi estimates. |
piSE |
Standard errors for pi estimates. |
statistics |
Statistics of the pleiotropy test. |
pvalue |
p-value of the pleiotropy test. |
Dongjun Chung
Chung D*, Yang C*, Li C, Gelernter J, and Zhao H (2014), "GPA: A statistical approach to prioritizing GWAS results by integrating pleiotropy information and annotation data," PLoS Genetics, 10: e1004787. (* joint first authors)
# simulator function simulator <- function( risk.ind, nsnp=20000, alpha=0.6 ) { m <- length(risk.ind) p.sig <- rbeta( m, alpha, 1 ) pvec <- runif(nsnp) pvec[ risk.ind ] <- p.sig return(pvec) } # run simulation set.seed(12345) nsnp <- 1000 alpha <- 0.3 pmat <- matrix( NA, nsnp, 5 ) pmat[,1] <- simulator( c(1:200), nsnp=nsnp, alpha=alpha ) pmat[,2] <- simulator( c(51:250), nsnp=nsnp, alpha=alpha ) pmat[,3] <- simulator( c(401:600), nsnp=nsnp, alpha=alpha ) pmat[,4] <- simulator( c(451:750), nsnp=nsnp, alpha=alpha ) pmat[,5] <- simulator( c(801:1000), nsnp=nsnp, alpha=alpha ) # GPA without annotation data fit.GPA.noAnn <- GPA( pmat, NULL, maxIter = 100 ) # GPA under the null hypothesis of pleiotropy test fit.GPA.pleiotropy.H0 <- GPA( pmat, NULL, pleiotropyH0=TRUE, maxIter = 100 ) # hypothesis testing for pleiotropy test.pleiotropy <- pTest( fit.GPA.noAnn, fit.GPA.pleiotropy.H0 )