GhostKnockoff inference empowers identification of putative causal variants in genome-wide association studies

Zihuai He; Linxi Liu; Michael E. Belloy; Yann Le Guen; Aaron Sossin; Xiaoxia Liu; Xinran Qi; Shiyang Ma; Prashnna K. Gyawali; Tony Wyss-Coray; Hua Tang; Chiara Sabatti; Emmanuel Candès; Michael D. Greicius; Iuliana Ionita-Laza

doi:10.1038/s41467-022-34932-z

Nature Communications (Nov 2022)

GhostKnockoff inference empowers identification of putative causal variants in genome-wide association studies

Zihuai He,
Linxi Liu,
Michael E. Belloy,
Yann Le Guen,
Aaron Sossin,
Xiaoxia Liu,
Xinran Qi,
Shiyang Ma,
Prashnna K. Gyawali,
Tony Wyss-Coray,
Hua Tang,
Chiara Sabatti,
Emmanuel Candès,
Michael D. Greicius,
Iuliana Ionita-Laza

Affiliations

Zihuai He: Department of Neurology and Neurological Sciences, Stanford University
Linxi Liu: Department of Statistics, University of Pittsburgh
Michael E. Belloy: Department of Neurology and Neurological Sciences, Stanford University
Yann Le Guen: Department of Neurology and Neurological Sciences, Stanford University
Aaron Sossin: Department of Biomedical Data Science, Stanford University
Xiaoxia Liu: Department of Neurology and Neurological Sciences, Stanford University
Xinran Qi: Department of Neurology and Neurological Sciences, Stanford University
Shiyang Ma: Department of Biostatistics, Columbia University
Prashnna K. Gyawali: Department of Neurology and Neurological Sciences, Stanford University
Tony Wyss-Coray: Department of Neurology and Neurological Sciences, Stanford University
Hua Tang: Department of Genetics, Stanford University
Chiara Sabatti: Department of Biomedical Data Science, Stanford University
Emmanuel Candès: Department of Statistics, Stanford University
Michael D. Greicius: Department of Neurology and Neurological Sciences, Stanford University
Iuliana Ionita-Laza: Department of Biostatistics, Columbia University

DOI: https://doi.org/10.1038/s41467-022-34932-z
Journal volume & issue: Vol. 13, no. 1
pp. 1 – 16

Abstract

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The authors present GhostKnockoff, a method for genome-wide association studies which can be applied to enhance existing and future studies to identify functional variants with weaker statistical effects that might be missed by conventional association tests.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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