fgamut: Fast implementation of GAMuT (Gene Association with Multiple...

In baolinwu/MSKAT: Various multi-trait variant-set association test methods

Description

Return the multi-trait variant set association test p-values from the Broadaway et. al (2016) approach. Both tests from linear kernel and projection matrix are computed. This is a much faster implementation following the approach of Wu and Pankow (2016) using the multiple linear regression based score test. We also provide modified (and more accurate) p-values following the approach of Wu et al. (2016).

Usage

fgamut(Y, G, X = NULL, W = NULL, W.beta = c(1, 25))

Arguments

Y	outcome matrix. sample in rows.
G	genotype matrix. sample in rows.
X	covariate matrix to be adjusted. sample in rows.
W	variant weight; use Beta dist weight if NULL
W.beta	Beta distribution parameters for the variant weight. Default to Beta(1,25).

Value

a p-value vector p.value with four components

proj

p-value for the GAMuT with projection matrix kernel

Cproj

corrected p-value for the GAMuT with projection matrix kernel

linear

p-value for the GAMuT with linear kernel

Clinear

corrected p-value for the GAMuT with linear kernel

References

Broadaway, K.A., Cutler, D.J., Duncan, R., Moore, J.L., Ware, E.B., Jhun, M.A., Bielak, L.F., Zhao, W., Smith, J.A., Peyser, P.A., Kardia, S.L.R., Ghosh, D., Epstein, M.P. (2016). A Statistical Approach for Testing Cross-Phenotype Effects of Rare Variants. The American Journal of Human Genetics, 98, 525–540.

Wu,B. and Pankow,J.S. (2016) Sequence kernel association test of multiple continuous phenotypes. Genetic Epidemiology, 40(2), 91-100.

Wu,B., Guan,W., Pankow,J.S. (2016) On efficient and accurate calculation of significance p-values for sequence kernel association test of variant set. Annals of human genetics, 80(2), 123-135.

Wu,B. and Qin,H. (2018). On testing cross-phenotype effects of rare variants. AHG, under revision. http://www.umn.edu/~baolin/research/gamut

baolinwu/MSKAT documentation built on May 28, 2019, 6:37 p.m.