cmats: Pleiotropy informed adaptive association test across multiple...

In baolinwu/MTAR: Various Multi-Trait Association tests in R

Description

Multi-trait association test based on GWAS summary statistics under common genetic effect assumption

Usage

cmats(Z, Sig, rho = 0:10/10)

Arguments

Z	summary Z-statistics across multiple traits
Sig	estimated trait correlation matrix
rho	sequence of weights assigned to the PT. Default to 0:10/10

Details

Under pleiotropy of common effects across traits, an 1-DF pleiotropy test (PT) can be constructed. The omnibus K-DF chi-square test (OT) is generally robust and powerful. We then define the adaptive test (AT) as the minium p-values of weighted sums of PT (ρ) and OT (1-ρ). An efficient algorithm is developed to exactly compute the analytical p-value of AT. We recommend using the LD score regression (Bulik-Sullivan et al.) to accurately estimate the trait correlation matrix.

Value

p.value

vector of p-values for: AT, OT and PT

pvals

the list of all p-values

rho.est

estimated optimal ρ value

References

Bulik-Sullivan B et al. (2015) LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nature Genetics, 47(3):291–295.

Bulik-Sullivan B et al. (2015) An atlas of genetic correlations across human diseases and traits. Nature Genetics, 47(11):1236–1241.

Massoti M*, Guo B* and Wu B. (2018) Pleiotropy informed adaptive association test of multiple traits using GWAS summary data. Biometrics, to appear (* contribute equallly).

Examples

Sig = diag(3)
Sig[c(2:3,6, 4,7,8)] = c(0.2646,-0.2317, 0.7451)
Z = c(1,2,4)
cmats(Z,Sig)

baolinwu/MTAR documentation built on May 14, 2019, 6:03 a.m.