Biobank-wide association scan identifies risk factors for late-onset Alzheimer’s disease and endophenotypes-Reference-Cited by-同舟云学术

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

Published:2023-12-12 Issue: Volume: Page:
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Author:

Yan Donghui¹,Hu Bowen²,Darst Burcu F.³,Mukherjee Shubhabrata⁴^ORCID,Kunkle Brian W.⁵,Deming Yuetiva³^ORCID,Dumitrescu Logan⁶,Wang Yunling¹,Naj Adam⁷^ORCID,Kuzma Amanda⁷,Zhao Yi⁷,Kang Hyunseung²,Johnson Sterling C.⁸⁹¹⁰,Cruchaga Carlos¹¹^ORCID,Hohman Timothy J.⁶^ORCID,Crane Paul K.⁴,Engelman Corinne D.³⁸¹⁰^ORCID,Alzheimer’s Disease Genetics Consortium (ADGC) ,Lu Qiongshi²¹²^ORCID

Affiliation:

1. University of Wisconsin-Madison, Madison, WI, 53706, USA

2. Department of Statistics, University of Wisconsin-Madison, Madison, WI, 53706, USA

3. Department of Population Health Sciences, University of Wisconsin-Madison, Madison, WI, 53726, USA

4. Division of General Internal Medicine, Department of Medicine, University of Washington, Seattle, WA, 98195, USA

5. University of Miami Miller School of Medicine, Miami, FL, 33136, USA

6. Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Vanderbilt University School of Medicine, Nashville, TN, 37212, USA

7. School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA

8. Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53719, USA

9. Geriatric Research Education and Clinical Center, Wm. S. Middleton Memorial VA Hospital, Madison, WI, 53705, USA

10. Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53792, USA

11. Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, 63110, USA

12. Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53792, USA

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 1,738 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. Further, 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.

Publisher

eLife Sciences Publications, Ltd