RT Journal Article
SR Electronic
T1 Identification of putative causal loci in whole-genome sequencing data via knockoff statistics
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.03.08.434451
DO 10.1101/2021.03.08.434451
A1 Zihuai He
A1 Linxi Liu
A1 Chen Wang
A1 Yann Le Guen
A1 Justin Lee
A1 Stephanie Gogarten
A1 Fred Lu
A1 Stephen Montgomery
A1 Hua Tang
A1 Edwin K. Silverman
A1 Michael H. Cho
A1 Michael Greicius
A1 Iuliana Ionita-Laza
YR 2021
UL http://biorxiv.org/content/early/2021/03/09/2021.03.08.434451.abstract
AB 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.Competing Interest StatementThe authors have declared no competing interest.