Leveraging Gene-Level Prediction as Informative Covariate in Hypothesis Weighting Improves Power for Rare Variant Association Studies

Leveraging Gene-Level Prediction as Informative Covariate in Hypothesis Weighting Improves Power for Rare Variant Association Studies

Gene-based rare variant association studies (RVASs) have low power due to the infrequency of rare variants and the large multiple testing burden. To correct for multiple testing, traditional false discovery rate (FDR) procedures which depend solely on P-values are often used. Recently, Independent H...

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Bibliographic Details
Main Authors: Ying Ji, Rui Chen, Quan Wang, Qiang Wei, Ran Tao, Bingshan Li
Format: Article
Language:English
Published: MDPI AG 2022-02-01
Series:Genes
Subjects:
rare variant association study
gene prioritization
multiple hypothesis testing
false discovery rate
GWAS
machine learning
Online Access:https://www.mdpi.com/2073-4425/13/2/381

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