ArticleeLife2024
Biobank-wide association scan identifies risk factors for late-onset Alzheimer's disease and endophenotypes.
Article in eLife, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Testing for causality between systematically identified risk factors and glioma: a Mendelian randomization study.BMC cancer · 2020Pooled it
- Integrative machine learning approach to risk prediction for dementia and Alzheimer's disease.GeroScience · 2026Article
- Multi-ancestry GWAS of age-related hearing loss identifies 140 loci and key cellular mechanisms.Nature communications · 2026Article
- Association of whole-person eigen-polygenic risk scores with Alzheimer's disease.Human molecular genetics · 2024Article
- PUMAS: fine-tuning polygenic risk scores with GWAS summary statistics.Genome biology · 2021Article
- Polygenic Risk Score for Alzheimer's Disease in Caribbean Hispanics.Annals of neurology · 2021Article
- Integrating brain imaging endophenotypes with GWAS for Alzheimer's disease.Quantitative biology (Beijing, China) · 2021Article
- Cerebrospinal fluid metabolomics identifies 19 brain-related phenotype associations.Communications biology · 2021Article
- Penalized regression and model selection methods for polygenic scores on summary statistics.PLoS computational biology · 2020Article
- Genetic variants and functional pathways associated with resilience to Alzheimer's disease.Brain : a journal of neurology · 2020Article
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Authors and funding
19 authors.
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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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