Effect of non-normality and low count variants on cross-phenotype association tests in GWAS.

ABSTRACT: Many complex human diseases, such as type 2 diabetes, are characterized by multiple underlying traits/phenotypes that have substantially shared genetic architecture. Multivariate analysis of correlated traits has the potential to increase the power of detecting underlying common genetic loci. Several cross-phenotype association methods have been proposed-some require individual-level data on traits and genotypes, while the others require only summary-level data. In this article, we explore whether non-normality of multivariate trait distribution affects the inference from some of the existing multi-trait methods and how that effect is dependent on the allele count of the genetic variant being tested. We find that most of these tests are susceptible to biases that lead to spurious associati

SUBMITTER: Ray D 

PROVIDER: S-EPMC7028917 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature