Department of Population Health Sciences, University of Wisconsin-Madison, Madison, United States
Logan Dumitrescu
Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Vanderbilt University School of Medicine, Nashville, United States
Yunling Wang
University of Wisconsin-Madison, Madison, United States
Adam Naj
School of Medicine, University of Pennsylvania, Philadelphia, United States
Amanda Kuzma
School of Medicine, University of Pennsylvania, Philadelphia, United States
Yi Zhao
School of Medicine, University of Pennsylvania, Philadelphia, United States
Hyunseung Kang
Department of Statistics, University of Wisconsin-Madison, Madison, United States
Sterling C Johnson
Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, United States; Geriatric Research Education and Clinical Center, Wm. S. Middleton Memorial VA Hospital, Madison, United States; Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, United States
Cruchaga Carlos
Department of Psychiatry, Washington University in St. Louis, St. Louis, United States
Timothy J Hohman
Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Vanderbilt University School of Medicine, Nashville, United States
Paul K Crane
Division of General Internal Medicine, Department of Medicine, University of Washington, Seattle, United States
Corinne D Engelman
Department of Population Health Sciences, University of Wisconsin-Madison, Madison, United States; Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, United States; Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, United States
Department of Statistics, University of Wisconsin-Madison, Madison, United States; Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, United States
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.
