Testing Rare Variants for Association with Diseases: A Bayesian Marker Selection Approach

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Abstract

Summary

It has been a research focus to uncover the genetic determination of complex diseases caused by rare variants. As the vast majority of genomic variants represent background variation, highlighting potentially causal mutations through a weighting scheme is critical to the success of association studies aimed at identifying rare variants. In this study, we propose a novel Bayesian marker selection approach to perform a weighting-based association test. In this approach, an individual association signal and its direction are used to weight variants. In addition, the predicted biological function of variants is taken as prior information to direct the selection of likely causal variants. Simulation studies show that the proposed method has improved power over several existing methods in certain conditions. Analyses of two empirical datasets demonstrate its applicability.

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