Gene Level Meta-Analysis of Quantitative Traits by Functional Linear Models.

ABSTRACT: Meta-analysis of genetic data must account for differences among studies including study designs, markers genotyped, and covariates. The effects of genetic variants may differ from population to population, i.e., heterogeneity. Thus, meta-analysis of combining data of multiple studies is difficult. Novel statistical methods for meta-analysis are needed. In this article, functional linear models are developed for meta-analyses that connect genetic data to quantitative traits, adjusting for covariates. The models can be used to analyze rare variants, common variants, or a combination of the two. Both likelihood-ratio test (LRT) and F-distributed statistics are introduced to test association between quantitative traits and multiple variants in one genetic region. Extensive simulations are per

SUBMITTER: Fan R 

PROVIDER: S-EPMC4574252 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature