A Powerful Adaptive Cauchy-Variable Combination Method for Rare-Variant Association Analysis

Tang, Yuan; Zhou, Yajing; Chen, L.; Bao, Yunlong; Zhang, R.

doi:10.1134/s1022795421020125

Cited by 1 publication

(1 citation statement)

References 22 publications

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“…We then consider different weighting factors for the score vector, where the different weights are tailored to different alternatives. To aggregate information across the different weighting factors, we use the recent Cauchy method of Liu & Xie (2020), which has since been used for genetic studies in different settings (Tang et al, 2015; Liu et al, 2019). We also discuss the connection of our adaptive method with that of Wu, Xu & Pan (2019).…”

Section: Introductionmentioning

confidence: 99%

A stable and adaptive polygenic signal detection method based on repeated sample splitting

Zhao

Sun

2023

Can J Statistics

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Focusing on polygenic signal detection in high‐dimensional genetic association studies of complex traits, we develop a stable and adaptive test for generalized linear models to accommodate different alternatives. To facilitate valid post‐selection inference for high‐dimensional data, our study here adheres to the original sample‐splitting principle but does so repeatedly to increase stability of the inference. We show the asymptotic null distribution of the proposed test for both fixed and diverging numbers of variants. We also show the asymptotic properties of the proposed test under local alternatives, providing insights on why power gain attributed to variable selection and weighting can compensate for efficiency loss due to sample splitting. We support our analytical findings through extensive simulation studies and two applications. The proposed procedure is computationally efficient and has been implemented as the R package DoubleCauchy.

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