| Title: | Improving on polygenic scores across complex traits using select and shrink with summary statistics (S4) and LDpred2 |
| Journal: | BMC Genomics |
| Published: | 18 Sep 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39294559/ |
| DOI: | https://doi.org/10.1186/s12864-024-10706-3 |
Publication 13472
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Abstract
BackgroundAs precision medicine advances, polygenic scores (PGS) have become increasingly important for clinical risk assessment. Many methods have been developed to create polygenic models with increased accuracy for risk prediction. Our select and shrink with summary statistics (S4) PGS method has previously been shown to accurately predict the polygenic risk of epithelial ovarian cancer. Here, we applied S4 PGS to 12 phenotypes for UK Biobank participants, and compared it with the LDpred2 and a combined S4 + LDpred2 method.ResultsThe S4 + LDpred2 method provided overall improved PGS accuracy across a variety of phenotypes for UK Biobank participants. Additionally, the S4 + LDpred2 method had the best estimated PGS accuracy in Finnish and Japanese populations. We also addressed the challenge of limited genotype level data by developing the PGS models using only GWAS summary statistics.ConclusionsTaken together, the S4 + LDpred2 method represents an improvement in overall PGS accuracy across multiple phenotypes and populations.</p>
7 Keywords
- Female
- Genome-Wide Association Study
- Humans
- Models, Genetic
- Multifactorial Inheritance
- Phenotype
- Polymorphism, Single Nucleotide
6 Authors
- Jonathan P. Tyrer
- Pei-Chen Peng
- Amber A. DeVries
- Simon A. Gayther
- Michelle R. Jones
- Paul D. Pharoah
1 Application
| Application ID | Title |
|---|---|
| 28126 | Validating risk prediction models for common hormonal cancers |
Enabling scientific discoveries that improve human health