Identification of putative causal loci in whole-genome sequencing data via knockoff statistics

Zihuai He (), Linxi Liu, Chen Wang, Yann Guen, Justin Lee, Stephanie Gogarten, Fred Lu, Stephen Montgomery, Hua Tang, Edwin K. Silverman, Michael H. Cho, Michael Greicius and Iuliana Ionita-Laza ()
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Zihuai He: Stanford University
Linxi Liu: Columbia University
Chen Wang: Columbia University
Yann Guen: Stanford University
Justin Lee: Stanford University
Stephanie Gogarten: University of Washington
Fred Lu: Stanford University
Stephen Montgomery: Stanford University
Hua Tang: Stanford University
Edwin K. Silverman: Brigham and Women’s Hospital, Harvard Medical School
Michael H. Cho: Brigham and Women’s Hospital, Harvard Medical School
Michael Greicius: Stanford University
Iuliana Ionita-Laza: Columbia University

Nature Communications, 2021, vol. 12, issue 1, 1-18

Abstract: Abstract The analysis of whole-genome sequencing studies is challenging due to the large number of rare variants in noncoding regions and the lack of natural units for testing. We propose a statistical method to detect and localize rare and common risk variants in whole-genome sequencing studies based on a recently developed knockoff framework. It can (1) prioritize causal variants over associations due to linkage disequilibrium thereby improving interpretability; (2) help distinguish the signal due to rare variants from shadow effects of significant common variants nearby; (3) integrate multiple knockoffs for improved power, stability, and reproducibility; and (4) flexibly incorporate state-of-the-art and future association tests to achieve the benefits proposed here. In applications to whole-genome sequencing data from the Alzheimer’s Disease Sequencing Project (ADSP) and COPDGene samples from NHLBI Trans-Omics for Precision Medicine (TOPMed) Program we show that our method compared with conventional association tests can lead to substantially more discoveries.

Date: 2021
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DOI: 10.1038/s41467-021-22889-4

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