| 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: | https://pmc.ncbi.nlm.nih.gov/articles/PMC10645553/pdf/main.pdf |
| Citations: | 3 (3 in last 2 years) as of 8 Aug 2024 |
Publication 9311
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
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>
7 Keywords
- Asian People
- Black People
- Genome-Wide Association Study
- Humans
- Multifactorial Inheritance
- Phenotype
- White People
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