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<p>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>

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Extending genome-wide association studies to admixed cohorts with high degrees of relatedness

  • 期刊论文

  • 发表日期：

    2026年7月20日

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  • 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

摘要

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.