Identification of Putative Causal Loci in Whole-genome Sequencing Data Via Knockoff Statistics

Overview

Journal Nat Commun

Specialty Biology

Date 2021 May 26

PMID 34035245

Citations 19

Authors

Zihuai He

Linxi Liu

Chen Wang

Yann Le Guen

Justin Lee

Stephanie Gogarten

Fred Lu

Stephen Montgomery

Hua Tang

Edwin K Silverman

Michael H Cho

Michael Greicius

Iuliana Ionita-Laza

Affiliations

Soon will be listed here.

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.

Citing Articles

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Second-order group knockoffs with applications to genome-wide association studies.

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Summary statistics knockoffs inference with family-wise error rate control.

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