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<p>Leveraging multi-ancestry data can improve fine-mapping power. We propose MultiSuSiE, an extension of Sum of Single Effects (SuSiE), to multiple ancestries that allows causal effect sizes to vary across ancestries. We evaluated MultiSuSiE using whole-genome sequencing data from 47,000 African-ancestry, 36,000 Latino-ancestry and 116,000 European-ancestry individuals from All of Us. In simulations, MultiSuSiE applied to Afr36k + Lat36k + Eur36k was well-calibrated and attained higher power than SuSiE applied to Eur109k; compared to recent multi-ancestry methods (SuSiEx and MESuSiE), MultiSuSiE attained higher power and lower computational cost. In analyses of 14 quantitative traits, MultiSuSiE applied to Afr47k + Lat36k + Eur116k identified 348 fine-mapped variants with posterior inclusion probability (PIP) &gt; 0.9, and MultiSuSiE applied to Afr36k + Lat36k + Eur36k identified 59% more PIP &gt; 0.9 variants than SuSiE applied to Eur109k; MultiSuSiE identified 29% more PIP &gt; 0.9 variants than SuSiEx, and MESuSiE was not included due to its high computational cost. We validated these findings through functional enrichment of fine-mapped variants and highlighted examples implicating biologically plausible fine-mapped variants.</p>

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MultiSuSiE improves multi-ancestry fine-mapping in All of Us whole-genome sequencing data

  • 期刊论文

  • 发表日期：

    2026年1月5日

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  • Jordan Rossen,
  • Huwenbo Shi,
  • Benjamin J. Strober,
  • Martin Jinye Zhang,
  • Masahiro Kanai,
  • Zachary R. McCaw,
  • Liming Liang,
  • Omer Weissbrod,
  • Alkes L. Price

摘要

Leveraging multi-ancestry data can improve fine-mapping power. We propose MultiSuSiE, an extension of Sum of Single Effects (SuSiE), to multiple ancestries that allows causal effect sizes to vary across ancestries. We evaluated MultiSuSiE using whole-genome sequencing data from 47,000 African-ancestry, 36,000 Latino-ancestry and 116,000 European-ancestry individuals from All of Us. In simulations, MultiSuSiE applied to Afr36k + Lat36k + Eur36k was well-calibrated and attained higher power than SuSiE applied to Eur109k; compared to recent multi-ancestry methods (SuSiEx and MESuSiE), MultiSuSiE attained higher power and lower computational cost. In analyses of 14 quantitative traits, MultiSuSiE applied to Afr47k + Lat36k + Eur116k identified 348 fine-mapped variants with posterior inclusion probability (PIP) > 0.9, and MultiSuSiE applied to Afr36k + Lat36k + Eur36k identified 59% more PIP > 0.9 variants than SuSiE applied to Eur109k; MultiSuSiE identified 29% more PIP > 0.9 variants than SuSiEx, and MESuSiE was not included due to its high computational cost. We validated these findings through functional enrichment of fine-mapped variants and highlighted examples implicating biologically plausible fine-mapped variants.