Biobank-wide association scan identifies risk factors for late-onset Alzheimer’s disease and endophenotypes

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Abstract

Rich data from large biobanks, coupled with increasingly accessible association statistics from genome-wide association studies (GWAS), provide great opportunities to dissect the complex relationships among human traits and diseases. We introduce BADGERS, a powerful method to perform polygenic score-based biobank-wide association scans. Compared to traditional approaches, BADGERS uses GWAS summary statistics as input and does not require multiple traits to be measured in the same cohort. We applied BADGERS to two independent datasets for late-onset Alzheimer’s disease (AD; n=61,212). Among 1738 traits in the UK biobank, we identified 48 significant associations for AD. Family history, high cholesterol, and numerous traits related to intelligence and education showed strong and independent associations with AD. Furthermore, we identified 41 significant associations for a variety of AD endophenotypes. While family history and high cholesterol were strongly associated with AD subgroups and pathologies, only intelligence and education-related traits predicted pre-clinical cognitive phenotypes. These results provide novel insights into the distinct biological processes underlying various risk factors for AD.

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Fitting Linear Mixed-Effects Models Usinglme4

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The variant call format and VCFtools

Petr Danecek, Adam Auton, Gonçalo R Abecasis … (2011)

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Author and article information

Contributors

Qiongshi Lu:

ORCID: https://orcid.org/0000-0002-4514-0969

Nicholas Mancuso: Role: Reviewing Editor

Timothy E Behrens: Role: Senior Editor

Journal

Journal ID (nlm-ta): eLife

Journal ID (iso-abbrev): Elife

Journal ID (publisher-id): eLife

Title: eLife

Publisher: eLife Sciences Publications, Ltd

ISSN (Electronic): 2050-084X

Publication date (Electronic, pub): 24 May 2024

Publication date (Electronic, collection): 2024

Volume: 12

Electronic Location Identifier: RP91360

Affiliations

[1 ] University of Wisconsin-Madison ( https://ror.org/01y2jtd41) Madison United States

[2 ] Department of Statistics, University of Wisconsin-Madison ( https://ror.org/01y2jtd41) Madison United States

[3 ] Department of Population Health Sciences, University of Wisconsin-Madison ( https://ror.org/01y2jtd41) Madison United States

[4 ] Division of General Internal Medicine, Department of Medicine, University of Washington ( https://ror.org/00cvxb145) Seattle United States

[5 ] University of Miami Miller School of Medicine ( https://ror.org/02dgjyy92) Miami United States

[6 ] Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Vanderbilt University School of Medicine ( https://ror.org/05dq2gs74) Nashville United States

[7 ] School of Medicine, University of Pennsylvania ( https://ror.org/00b30xv10) Philadelphia United States

[8 ] Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health ( https://ror.org/01y2jtd41) Madison United States

[9 ] Geriatric Research Education and Clinical Center, Wm. S. Middleton Memorial VA Hospital ( https://ror.org/01nh3sx96) Madison United States

[10 ] Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health ( https://ror.org/01y2jtd41) Madison United States

[11 ] Department of Psychiatry, Washington University in St. Louis ( https://ror.org/01yc7t268) St. Louis United States

[12 ] Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison ( https://ror.org/01y2jtd41) Madison United States

University of Southern California ( https://ror.org/03taz7m60) United States

University of Oxford ( https://ror.org/052gg0110) United Kingdom

University of Southern California United States