A scalable variational approach to characterize pleiotropic components across thousands of human diseases and complex traits using GWAS summary statistics
A scalable variational approach to characterize pleiotropic components across thousands of human diseases and complex traits using GWAS summary statistics
Abstract Genome-wide association studies (GWAS) across thousands of traits have revealed the pervasive pleiotropy of trait-associated genetic variants. While methods have been proposed to characterize pleiotropic components across groups of phenotypes, scaling these approaches to ultra large-scale biobanks has been challenging. Here, we propose FactorGo, a scalable variational factor analysis model to identify and characterize pleiotropic components using biobank GWAS summary data. In extensive simulations, we observe that FactorGo outperforms the state-of-the-art (model-free) approach tSVD in capturing latent pleiotropic factors across phenotypes, while maintaining a similar computational cost. We apply FactorGo to estimate 100 latent pleiotropic factors from GWAS summary data of 2,483 phenotypes measured in European-ancestry Pan-UK BioBank individuals (N=420,531). Next, we find that factors from FactorGo are more enriched with relevant tissue-specific annotations than those identified by tSVD (P=2.58E-10), and validate our approach by recapitulating brain-specific enrichment for BMI and the height-related connection between reproductive system and muscular-skeletal growth. Finally, our analyses suggest novel shared etiologies between rheumatoid arthritis and periodontal condition, in addition to alkaline phosphatase as a candidate prognostic biomarker for prostate cancer. Overall, FactorGo improves our biological understanding of shared etiologies across thousands of GWAS.
Zhang Zixuan、Mancuso Nicholas、Gazal Steven、Jung Junghyun、Suboc Noah、Kim Artem
Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern CaliforniaCenter for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California||Department of Quantitative and Computational Biology, University of Southern California||Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern CaliforniaCenter for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California||Department of Quantitative and Computational Biology, University of Southern California||Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern CaliforniaCenter for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern CaliforniaCenter for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern CaliforniaCenter for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California
医学研究方法基础医学生物科学研究方法、生物科学研究技术
Zhang Zixuan,Mancuso Nicholas,Gazal Steven,Jung Junghyun,Suboc Noah,Kim Artem.A scalable variational approach to characterize pleiotropic components across thousands of human diseases and complex traits using GWAS summary statistics[EB/OL].(2025-03-28)[2025-04-29].https://www.medrxiv.org/content/10.1101/2023.03.27.23287801.点此复制
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