| Title: | Extending genome-wide association studies to admixed cohorts with high degrees of relatedness |
| Journal: | Nature Genetics |
| Published: | 20 Jul 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/42477114/ |
| DOI: | https://doi.org/10.1038/s41588-026-02689-6 |
| URL: | https://www.medrxiv.org/content/medrxiv/early/2025/06/09/2025.05.27.25328444.full.pdf |
Publication 21302
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Abstract
Admixed populations comprise a large portion of the human population worldwide, but are often excluded from genome-wide association studies (GWASs) due to analytic challenges. Our group developed Tractor, a local-ancestry-informed GWAS tool designed for admixed samples that produces accurate ancestry-specific effect sizes and boosts the discovery power to identify ancestry-enriched loci. However, Tractor operates under an assumption of unrelated samples. Here, to address this gap, we propose Tractor-Mix, which allows for well-calibrated association studies in datasets containing admixed samples with relatedness. Extensive simulations show that this method is competitive with other state-of-the-art approaches that do not produce ancestry-specific results. Empirical testing of Tractor-Mix on admixed samples from the UK Biobank, Yale-Penn cohort and Mexico City Prospective Study highlight the value of this method, identifying ancestry-specific associations. In summary, Tractor-Mix extends the capabilities of current models and enables well-calibrated GWASs for related samples with admixture.</p>
8 Keywords
- Cohort Studies
- Computer Simulation
- Genetics, Population
- Genome-Wide Association Study
- Humans
- Models, Genetic
- Polymorphism, Single Nucleotide
- UK Biobank
15 Authors
- Taotao Tan
- Alejandra Vergara-Lope
- José Jaime Martínez-Magaña
- Nirav N. Shah
- Yi-Sian Lin
- Kai Yuan
- Jaime Berumen
- Jesus Alegre-Díaz
- Pablo Kuri-Morales
- Roberto Tapia-Conyer
- Joel Gelenter
- Janitza L. Montalvo-Ortiz
- Wei Zhou
- Jason M. Torres
- Elizabeth G. Atkinson
1 Application
| Application ID | Title |
|---|---|
| 95179 | Advancing Genomics for Diverse and Admixed Populations |
Enabling scientific discoveries that improve human health