| Title: | Improving polygenic risk prediction in admixed populations by explicitly modeling ancestral-differential effects via GAUDI |
| Journal: | Nature Communications |
| Published: | 3 Feb 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38310129/ |
| DOI: | https://doi.org/10.1038/s41467-024-45135-z |
| URL: | https://www.nature.com/articles/s41467-024-45135-z.pdf |
| Citations: | 7 (7 in last 2 years) as of 8 Aug 2024 |
Publication 8793
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Abstract
Polygenic risk scores (PRS) have shown successes in clinics, but most PRS methods focus only on participants with distinct primary continental ancestry without accommodating recently-admixed individuals with mosaic continental ancestry backgrounds for different segments of their genomes. Here, we develop GAUDI, a novel penalized-regression-based method specifically designed for admixed individuals. GAUDI explicitly models ancestry-differential effects while borrowing information across segments with shared ancestry in admixed genomes. We demonstrate marked advantages of GAUDI over other methods through comprehensive simulation and real data analyses for traits with associated variants exhibiting ancestral-differential effects. Leveraging data from the Women's Health Initiative study, we show that GAUDI improves PRS prediction of white blood cell count and C-reactive protein in African Americans by > 64% compared to alternative methods, and even outperforms PRS-CSx with large European GWAS for some scenarios. We believe GAUDI will be a valuable tool to mitigate disparities in PRS performance in admixed individuals.</p>
18 Authors
- Quan Sun
- Bryce T. Rowland
- Jiawen Chen
- Anna V. Mikhaylova
- Christy Avery
- Ulrike Peters
- Jessica Lundin
- Tara Matise
- Steve Buyske
- Ran Tao
- Rasika A. Mathias
- Alexander P. Reiner
- Paul L. Auer
- Nancy J. Cox
- Charles Kooperberg
- Timothy A. Thornton
- Laura M. Raffield
- Yun Li
1 Application
| Application ID | Title |
|---|---|
| 25953 | Transcriptome-wide Association Study of Cardiometabolic Traits in the UK Biobank Cohort |
Enabling scientific discoveries that improve human health