Multivariate simulation framework reveals performance of multi-trait GWAS methods.

ABSTRACT: Burgeoning availability of genome-wide association study (GWAS) results and national biobank data has led to growing interest in performing multi-trait genetic analyses. Numerous multi-trait GWAS methods that exploit either summary statistics or individual-level data have been developed, but their relative performance is unclear. Here we develop a simulation framework to model the complex networks underlying multivariate genetic epidemiology, enabling the vast model space of genetic effects on multiple correlated traits to be explored systematically. We perform a comprehensive comparison of the leading multi-trait GWAS methods, finding: (1) method performance is highly sensitive to the specific combination of genetic effects and phenotypic correlations, (2) most of the current multivariate

SUBMITTER: Porter HF 

PROVIDER: S-EPMC5347376 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature