PolyGEE: a generalized estimating equation approach to the efficient and robust estimation of polygenic effects in large-scale association studies.

ABSTRACT: To quantify polygenic effects, i.e. undetected genetic effects, in large-scale association studies, we propose a generalized estimating equation (GEE) based estimation framework. We develop a marginal model for single-variant association test statistics of complex diseases that generalizes existing approaches such as LD Score regression and that is applicable to population-based designs, to family-based designs or to arbitrary combinations of both. We extend the standard GEE approach so that the parameters of the proposed marginal model can be estimated based on working-correlation/linkage-disequilibrium (LD) matrices from external reference panels. Our method achieves substantial efficiency gains over standard approaches, while it is robust against misspecification of the LD structure, i.

SUBMITTER: Hecker J 

PROVIDER: S-EPMC5991211 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature