| Title: | Leveraging local ancestry and cross-ancestry genetic architecture to improve genetic prediction of complex traits in admixed populations |
| Journal: | American Journal of Human Genetics |
| Published: | 8 Jul 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40633541/ |
| DOI: | https://doi.org/10.1016/j.ajhg.2025.06.010 |
Publication 16581
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Abstract
The broader application of polygenic risk score (PRS) is hindered by the limited transferability of PRS developed in Europeans to non-European populations. While many statistical methods have been developed to improve the performance of PRS in non-European populations, most of them focused on discrete genetic ancestry clusters and did not consider admixed individuals. Admixed individuals pose a unique challenge for PRS calculation due to the complexity of local ancestry and cross-ancestry effect sizes. Here, we present a statistical method called SDPR_admix for calculating PRS in admixed individuals. SDPR_admix characterizes the joint distribution of the effect sizes of a genetic variant with two ancestries to be both zero, ancestry enriched, or shared with correlation. SDPR_admix outperformed other methods in simulations and improved the prediction of real traits in European-African admixed individuals in UK Biobank when trained on the Population Architecture using Genomics and Epidemiology (PAGE) dataset (N = 13,000). Deployment of SDPR_admix on All of Us (N = 52,000) further increased the prediction accuracy by approximately 5-fold on average compared with training on PAGE. This enhancement was achieved with manageable computational time and cost, demonstrating the feasibility of training PRS models on large-scale All of Us data. We provided several examples demonstrating that both ancestral-enriched and shared effects, as included in the SDPR_admix prediction model, are helpful for improving polygenic prediction in admixed populations. We also applied SDPR_admix to construct PRS for admixed Americans with mixture of European and Amerindigenous ancestries and showed that SDPR_admix overall outperformed other methods.</p>
10 Keywords
- Black People
- Computer Simulation
- Genetics, Population
- Genome-Wide Association Study
- Humans
- Models, Genetic
- Multifactorial Inheritance
- Phenotype
- Polymorphism, Single Nucleotide
- White People
4 Authors
- Geyu Zhou
- Israel Yolou
- Yuhan Xie
- Hongyu Zhao
1 Application
| Application ID | Title |
|---|---|
| 29900 | Defining physical activity phenotypes based on objective measures and dissecting the genetic and phenotypic relationships of physical activity with diseases and health outcomes |
Enabling scientific discoveries that improve human health