A general framework for association tests with multivariate traits in large-scale genomics studies.

ABSTRACT: Genetic association studies often collect data on multiple traits that are correlated. Discovery of genetic variants influencing multiple traits can lead to better understanding of the etiology of complex human diseases. Conventional univariate association tests may miss variants that have weak or moderate effects on individual traits. We propose several multivariate test statistics to complement univariate tests. Our framework covers both studies of unrelated individuals and family studies and allows any type/mixture of traits. We relate the marginal distributions of multivariate traits to genetic variants and covariates through generalized linear models without modeling the dependence among the traits or family members. We construct score-type statistics, which are computationally fast a

SUBMITTER: He Q 

PROVIDER: S-EPMC3926135 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature