Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Summary statistics knockoff 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, Tony Wyss-Coray, Hua Tang, Chiara Sabatti, Emmanuel Candès, Michael D. Greicius, Iuliana Ionita-Laza
doi: https://doi.org/10.1101/2021.12.06.471440
Zihuai He
1Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA
2Quantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, CA 94305, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: zihuai@stanford.edu
Linxi Liu
3Department of Statistics, University of Pittsburgh, Pittsburgh, PA 15260, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Michael E. Belloy
1Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Yann Le Guen
1Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA
4Institut du Cerveau - Paris Brain Institute - ICM, Paris, 75013, France
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Aaron Sossin
5Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Xiaoxia Liu
1Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Xinran Qi
1Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Shiyang Ma
6Department of Biostatistics, Columbia University, New York, NY 10032, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Tony Wyss-Coray
1Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Hua Tang
7Department of Genetics, Stanford University, Stanford, CA 94305, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Chiara Sabatti
5Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Emmanuel Candès
8Department of Statistics, Stanford University, Stanford, CA 94305, USA
9Department of Mathematics, Stanford University, Stanford, CA 94305, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Michael D. Greicius
1Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Iuliana Ionita-Laza
6Department of Biostatistics, Columbia University, New York, NY 10032, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Recent advances in genome sequencing and imputation technologies provide an exciting opportunity to comprehensively study the contribution of genetic variants to complex phenotypes. However, our ability to translate genetic discoveries into mechanistic insights remains limited at this point. In this paper, we propose an efficient knockoff-based method, GhostKnockoff, for genome-wide association studies (GWAS) that leads to improved power and ability to prioritize putative causal variants relative to conventional GWAS approaches. The method requires only Z-scores from conventional GWAS and hence can be easily applied to enhance existing and future studies. The method can also be applied to meta-analysis of multiple GWAS allowing for arbitrary sample overlap. We demonstrate its performance using empirical simulations and two applications: (1) analysis of 1,403 binary phenotypes from the UK Biobank data in 408,961 samples of European ancestry, and (2) a meta-analysis for Alzheimer’s disease (AD) comprising nine overlapping large-scale GWAS, whole-exome and whole-genome sequencing studies. The UK Biobank analysis demonstrates superior performance of the proposed method compared to conventional GWAS in both statistical power (2.05-fold more discoveries) and localization of putative causal variants at each locus (46% less proxy variants due to linkage disequilibrium). The AD meta-analysis identified 55 risk loci (including 31 new loci) with ~70% of the proximal genes at these loci showing suggestive signal in downstream single-cell transcriptomic analyses. Our results demonstrate that GhostKnockoff can identify putatively functional variants with weaker statistical effects that are missed by conventional association tests.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted December 07, 2021.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Summary statistics knockoff inference empowers identification of putative causal variants in genome-wide association studies
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Summary statistics knockoff 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, Tony Wyss-Coray, Hua Tang, Chiara Sabatti, Emmanuel Candès, Michael D. Greicius, Iuliana Ionita-Laza
bioRxiv 2021.12.06.471440; doi: https://doi.org/10.1101/2021.12.06.471440
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Summary statistics knockoff 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, Tony Wyss-Coray, Hua Tang, Chiara Sabatti, Emmanuel Candès, Michael D. Greicius, Iuliana Ionita-Laza
bioRxiv 2021.12.06.471440; doi: https://doi.org/10.1101/2021.12.06.471440

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Genetics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4383)
  • Biochemistry (9602)
  • Bioengineering (7097)
  • Bioinformatics (24869)
  • Biophysics (12623)
  • Cancer Biology (9961)
  • Cell Biology (14359)
  • Clinical Trials (138)
  • Developmental Biology (7960)
  • Ecology (12111)
  • Epidemiology (2067)
  • Evolutionary Biology (15990)
  • Genetics (10929)
  • Genomics (14745)
  • Immunology (9871)
  • Microbiology (23680)
  • Molecular Biology (9486)
  • Neuroscience (50887)
  • Paleontology (369)
  • Pathology (1540)
  • Pharmacology and Toxicology (2683)
  • Physiology (4019)
  • Plant Biology (8657)
  • Scientific Communication and Education (1510)
  • Synthetic Biology (2397)
  • Systems Biology (6440)
  • Zoology (1346)