Dynamic Scan Procedure for Detecting Rare-Variant Association Regions in Whole-Genome Sequencing Studies

Li, Zilin; Li, Xihao; Liu, Yaowu; Shen, Jincheng; Chen, Han; Zhou, Hufeng; Morrison, Alanna C.; Boerwinkle, Eric; Lin, Xihong

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Author

Li, Zilin

Li, Xihao

Liu, Yaowu HARVARD

Shen, Jincheng

Chen, Han

Zhou, Hufeng HARVARD

Morrison, Alanna C.

Boerwinkle, Eric

Lin, Xihong HARVARD

Published Version

https://doi.org/10.1016/j.ajhg.2019.03.002

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Citation

Li, Zilin, Xihao Li, Yaowu Liu, Jincheng Shen, Han Chen, Hufeng Zhou, Alanna C. Morrison, Eric Boerwinkle, and Xihong Lin. “Dynamic Scan Procedure for Detecting Rare-Variant Association Regions in Whole-Genome Sequencing Studies.” The American Journal of Human Genetics 104, no. 5 (May 2019): 802–14. https://doi.org/10.1016/j.ajhg.2019.03.002.

Abstract

Large-scale whole genome sequencing (WGS) studies have enabled the analysis of rare variants (RVs) associated with complex phenotypes. Commonly used RV association tests (RVATs) have limited scope to leverage variant functions. We propose STAAR (variant-Set Test for Association using Annotation infoRmation), a scalable and powerful RVAT method by effectively incorporating both variant categories and multiple complementary annotations using a dynamic weighting scheme. For the latter, we introduce “annotation Principal Components”, multi-dimensional summaries of in-silico variant annotations. STAAR accounts for population structure and relatedness, and is scalable for analyzing very large cohort and biobank WGS studies of continuous and dichotomous traits. We applied STAAR to identify RVs associated with four lipid traits in 12,316 discovery samples and 17,822 replication samples from the Trans-Omics for Precision Medicine program. We discovered and replicated novel RV associations, including disruptive missense RVs of NPC1L1 and an intergenic region near APOC1P1 associated with low-density lipoprotein cholesterol.