| Title: | Power of inclusion: Enhancing polygenic prediction with admixed individuals |
| Journal: | American Journal of Human Genetics |
| Published: | 27 Oct 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/37890495/ |
| DOI: | https://doi.org/10.1016/j.ajhg.2023.09.013 |
| URL: | http://www.cell.com/article/S0002929723003518/pdf |
| Citations: | 3 (3 in last 2 years) as of 8 Aug 2024 |
Publication 9311
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Abstract
Admixed individuals offer unique opportunities for addressing limited transferability in polygenic scores (PGSs), given the substantial trans-ancestry genetic correlation in many complex traits. However, they are rarely considered in PGS training, given the challenges in representing ancestry-matched linkage-disequilibrium reference panels for admixed individuals. Here we present inclusive PGS (iPGS), which captures ancestry-shared genetic effects by finding the exact solution for penalized regression on individual-level data and is thus naturally applicable to admixed individuals. We validate our approach in a simulation study across 33 configurations with varying heritability, polygenicity, and ancestry composition in the training set. When iPGS is applied to n = 237,055 ancestry-diverse individuals in the UK Biobank, it shows the greatest improvements in Africans by 48.9% on average across 60 quantitative traits and up to 50-fold improvements for some traits (neutrophil count, R2 = 0.058) over the baseline model trained on the same number of European individuals. When we allowed iPGS to use n = 284,661 individuals, we observed an average improvement of 60.8% for African, 11.6% for South Asian, 7.3% for non-British White, 4.8% for White British, and 17.8% for the other individuals. We further developed iPGS+refit to jointly model the ancestry-shared and -dependent genetic effects when heterogeneous genetic associations were present. For neutrophil count, for example, iPGS+refit showed the highest predictive performance in the African group (R2 = 0.115), which exceeds the best predictive performance for the White British group (R2 = 0.090 in the iPGS model), even though only 1.49% of individuals used in the iPGS training are of African ancestry. Our results indicate the power of including diverse individuals for developing more equitable PGS models.</p>
2 Authors
- Yosuke Tanigawa
- Manolis Kellis
1 Application
| Application ID | Title |
|---|---|
| 21942 | Integrated models of complex traits in the UK Biobank |
Enabling scientific discoveries that improve human health