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

Optimizing and benchmarking polygenic risk scores with GWAS summary statistics

View ORCID ProfileZijie Zhao, Tim Gruenloh, Yixuan Wu, Zhongxuan Sun, View ORCID ProfileJiacheng Miao, Yuchang Wu, Jie Song, View ORCID ProfileQiongshi Lu
doi: https://doi.org/10.1101/2022.10.26.513833
Zijie Zhao
1Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, WI
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Zijie Zhao
Tim Gruenloh
1Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, WI
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Yixuan Wu
1Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, WI
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Zhongxuan Sun
1Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, WI
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jiacheng Miao
1Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, WI
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Jiacheng Miao
Yuchang Wu
1Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, WI
2Center for Demography of Health and Aging, University of Wisconsin-Madison, Madison, WI
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jie Song
3Department of Statistics, University of Wisconsin-Madison, WI
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Qiongshi Lu
1Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, WI
2Center for Demography of Health and Aging, University of Wisconsin-Madison, Madison, WI
3Department of Statistics, University of Wisconsin-Madison, WI
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Qiongshi Lu
  • For correspondence: qlu@biostat.wisc.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

We introduce an innovative statistical framework to optimize and benchmark polygenic risk score (PRS) models using summary statistics of genome-wide association studies. This framework builds upon our previous work and can fine-tune virtually all existing PRS models while accounting for linkage disequilibrium. In addition, we provide an ensemble learning strategy named PUMA-CUBS to combine multiple PRS models into an ensemble score without requiring external data for model fitting. Through extensive simulations and analysis of many complex traits in the UK Biobank, we demonstrate that this approach closely approximates gold-standard analytical strategies based on external validation, and substantially outperforms state-of-the-art PRS methods. We argue that PUMA-CUBS is a powerful and general modeling technique that can continue to combine the best-performing PRS methods out there through ensemble learning and could become an integral component for all future PRS applications.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Notations in statistical methodology revised; Minor revision to main texts; Minor revision to figures, supplementary figures and supplementary tables

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 November 07, 2022.
Download PDF

Supplementary Material

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.
Optimizing and benchmarking polygenic risk scores with GWAS summary statistics
(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
Optimizing and benchmarking polygenic risk scores with GWAS summary statistics
Zijie Zhao, Tim Gruenloh, Yixuan Wu, Zhongxuan Sun, Jiacheng Miao, Yuchang Wu, Jie Song, Qiongshi Lu
bioRxiv 2022.10.26.513833; doi: https://doi.org/10.1101/2022.10.26.513833
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Optimizing and benchmarking polygenic risk scores with GWAS summary statistics
Zijie Zhao, Tim Gruenloh, Yixuan Wu, Zhongxuan Sun, Jiacheng Miao, Yuchang Wu, Jie Song, Qiongshi Lu
bioRxiv 2022.10.26.513833; doi: https://doi.org/10.1101/2022.10.26.513833

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 (4100)
  • Biochemistry (8804)
  • Bioengineering (6501)
  • Bioinformatics (23421)
  • Biophysics (11777)
  • Cancer Biology (9184)
  • Cell Biology (13304)
  • Clinical Trials (138)
  • Developmental Biology (7426)
  • Ecology (11396)
  • Epidemiology (2066)
  • Evolutionary Biology (15133)
  • Genetics (10424)
  • Genomics (14032)
  • Immunology (9159)
  • Microbiology (22138)
  • Molecular Biology (8802)
  • Neuroscience (47492)
  • Paleontology (350)
  • Pathology (1426)
  • Pharmacology and Toxicology (2488)
  • Physiology (3723)
  • Plant Biology (8074)
  • Scientific Communication and Education (1436)
  • Synthetic Biology (2220)
  • Systems Biology (6029)
  • Zoology (1252)