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<p>Polygenic scores can improve atrial fibrillation risk prediction. However, limited accuracy and cross-ancestry transferability hinder clinical translation. Here, we explore several ensemble approaches to generate ancestry-optimized polygenic scores, with development in diverse participants from the All of Us Research Program, BioBank Japan, and three additional cohorts. Our ancestry-specific multi-trait approach particularly improves prediction in South-Asian (odds-ratio/standard deviation 1.5–1.8; area under curve 0.60-0.64; relative <i>R²</i> +71%), Admixed-American (1.5; 0.60; +34%) and African ancestry groups (1.4; 0.57; +56%). Nevertheless, performance remains highest in European and East-Asian ancestries (1.8–2.2; 0.65–0.68), where &gt;50% of SNP-heritability is explained. Improved risk stratification is also observed at the extremes, identifying European and East-Asian ancestry individuals with risk comparable to rare <i>TTN</i> variants (e.g., 6–11% with &gt;4-fold odds). Finally, our scores improve incident risk prediction alongside clinical models. Together, we show that our ancestry-tailored multi-trait polygenic scores advance atrial fibrillation risk prediction and stratification, providing an equitable foundation for implementation.</p>

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Multi-trait polygenic risk scores improve genomic prediction of atrial fibrillation across diverse ancestries

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月5日

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  • Poeya Haydarlou,
  • Daria R. Kramarenko,
  • Nobuyuki Enzan,
  • Marie Klevjer,
  • Oliver B. Vad,
  • Marre E. Corver,
  • Dominic S. Zimmerman,
  • Koichi Matsuda,
  • Takayuki Morisaki,
  • Yukinori Okada,
  • Yoichiro Kamatani,
  • Kaori Muto,
  • Akiko Nagai,
  • Yoji Sagiya,
  • Natsuhiko Kumasaka,
  • Yoichi Furukawa,
  • Yuji Yamanashi,
  • Yoshinori Murakami,
  • Yusuke Nakamura,
  • Wataru Obara,
  • Ken Yamaji,
  • Kazuhisa Takahashi,
  • Satoshi Asai,
  • Yasuo Takahashi,
  • Shinichi Higashiue,
  • Shuzo Kobayashi,
  • Hiroki Yamaguchi,
  • Yasunobu Nagata,
  • Satoshi Wakita,
  • Chikako Nito,
  • Yu-ki Iwasaki,
  • Shigeo Murayama,
  • Kozo Yoshimori,
  • Yoshio Miki,
  • Daisuke Obata,
  • Masahiko Higashiyama,
  • Akihide Masumoto,
  • Yoshinobu Koga,
  • Yukihiro Koretsune,
  • Koichi Matsuda,
  • Søren Z. Diederichsen,
  • Anja Bye,
  • Jesper H. Svendsen,
  • Kaoru Ito,
  • Patrick T. Ellinor,
  • Connie R. Bezzina,
  • Sean J. Jurgens

摘要

Polygenic scores can improve atrial fibrillation risk prediction. However, limited accuracy and cross-ancestry transferability hinder clinical translation. Here, we explore several ensemble approaches to generate ancestry-optimized polygenic scores, with development in diverse participants from the All of Us Research Program, BioBank Japan, and three additional cohorts. Our ancestry-specific multi-trait approach particularly improves prediction in South-Asian (odds-ratio/standard deviation 1.5–1.8; area under curve 0.60-0.64; relative R² +71%), Admixed-American (1.5; 0.60; +34%) and African ancestry groups (1.4; 0.57; +56%). Nevertheless, performance remains highest in European and East-Asian ancestries (1.8–2.2; 0.65–0.68), where >50% of SNP-heritability is explained. Improved risk stratification is also observed at the extremes, identifying European and East-Asian ancestry individuals with risk comparable to rare TTN variants (e.g., 6–11% with >4-fold odds). Finally, our scores improve incident risk prediction alongside clinical models. Together, we show that our ancestry-tailored multi-trait polygenic scores advance atrial fibrillation risk prediction and stratification, providing an equitable foundation for implementation.