| Title: | Optimizing and benchmarking polygenic risk scores with GWAS summary statistics |
| Journal: | Genome Biology |
| Published: | 8 Oct 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39379999/ |
| DOI: | https://doi.org/10.1186/s13059-024-03400-w |
Publication 13262
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
Abstract</p>
Background</p>
Polygenic risk score (PRS) is a major research topic in human genetics. However, a significant gap exists between PRS methodology and applications in practice due to often unavailable individual-level data for various PRS tasks including model fine-tuning, benchmarking, and ensemble learning.ResultsWe introduce an innovative statistical framework to optimize and benchmark PRS models using summary statistics of genome-wide association studies. This framework builds upon our previous work and can fine-tune virtually all existing PRS models while accounting for linkage disequilibrium. In addition, we provide an ensemble learning strategy named PUMAS-ensemble to combine multiple PRS models into an ensemble score without requiring external data for model fitting. Through extensive simulations and analysis of many complex traits in the UK Biobank, we demonstrate that this approach closely approximates gold-standard analytical strategies based on external validation, and substantially outperforms state-of-the-art PRS methods.ConclusionsOur method is a powerful and general modeling technique that can continue to combine the best-performing PRS methods out there through ensemble learning and could become an integral component for all future PRS applications.</p>
8 Keywords
- Benchmarking
- Genetic Predisposition to Disease
- Genetic Risk Score
- Genome-Wide Association Study
- Humans
- Linkage Disequilibrium
- Models, Genetic
- Multifactorial Inheritance
9 Authors
- Zijie Zhao
- Tim Gruenloh
- Meiyi Yan
- Yixuan Wu
- Zhongxuan Sun
- Jiacheng Miao
- Yuchang Wu
- Jie Song
- Qiongshi Lu
1 Application
| Application ID | Title |
|---|---|
| 42148 | Dissect the genetic architecture of various sociological traits through integrative analysis of GWAS and functional annotations |
Enabling scientific discoveries that improve human health