Gene-based genetic association test with adaptive optimal weights

ABSTRACT

It is well known that using proper weights for genetic variants is crucial in enhancing the power of gene- or pathway-based association tests. To increase the power, we propose a general approach that adaptively selects weights among a class of weight families and apply it to the popular sequencing kernel association test. Through comprehensive simulation studies, we demonstrate that the proposed method can substantially increase power under some conditions. Applications to real data are also presented. This general approach can be extended to all current set-based rare variant association tests whose performances depend on variant's weight assignment.

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