| Title: | A scalable approach to characterize pleiotropy across thousands of human diseases and complex traits using GWAS summary statistics |
| Journal: | American Journal of Human Genetics |
| Published: | 24 Oct 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/37879338/ |
| DOI: | https://doi.org/10.1016/j.ajhg.2023.09.015 |
| Citations: | 2 (2 in last 2 years) as of 8 Aug 2024 |
Publication 9351
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Abstract
Genome-wide association studies (GWASs) 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 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 GWASs.</p>
6 Authors
- Zixuan Zhang
- Junghyun Jung
- Artem Kim
- Noah Suboc
- Steven Gazal
- Nicholas Mancuso
1 Application
| Application ID | Title |
|---|---|
| 68459 | Statistical methods to investigate the regulatory and evolutionary forces shaping the genetics of human traits in diverse UK Biobank populations |
Enabling scientific discoveries that improve human health