Key result
The proposed hierarchical generalized linear model framework simultaneously analyzed multiple groups of rare and common variants, demonstrating higher power and better type I error control than existing methods.
The development of the BhGLM framework provides a stable and flexible tool for jointly estimating group and individual-variant effects in genetic association studies.
May advance multi-variant genetic analysis; leaves open validation in cardiovascular cohorts before research adoption.
Complex diseases and traits are likely influenced by many common and rare genetic variants and environmental factors. Detecting disease susceptibility variants is a challenging task, especially when their frequencies are low and/or their effects are small or moderate. We propose here a comprehensive hierarchical generalized linear model framework for simultaneously analyzing multiple groups of rare and common variants and relevant covariates. The proposed hierarchical generalized linear models introduce a group effect and a genetic score (i.e., a linear combination of main-effect predictors for genetic variants) for each group of variants, and jointly they estimate the group effects and the weights of the genetic scores. This framework includes various previous methods as special cases, and it can effectively deal with both risk and protective variants in a group and can simultaneously estimate the cumulative contribution of multiple variants and their relative importance. Our computational strategy is based on extending the standard procedure for fitting generalized linear models in the statistical software R to the proposed hierarchical models, leading to the development of stable and flexible tools. The methods are illustrated with sequence data in gene ANGPTL4 from the Dallas Heart Study. The performance of the proposed procedures is further assessed via simulation studies. The methods are implemented in a freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/).
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Yi et al. (2011) studied Genetic association with triglyceride levels (n=3,551). Hierarchical Generalized Linear Models (BhGLM) vs. Simple-Sum, Weighted-Sum, and single-variant methods was evaluated on Power and Type I error for detecting genetic associations. The proposed hierarchical generalized linear model framework simultaneously analyzed multiple groups of rare and common variants, demonstrating higher power and better type I error control than existing methods.
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