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Leveraging Fine-mapping and Multipopulation Training Data to Improve Cross-population Polygenic Risk Scores by Omer Weissbrod; Masahiro Kanai; Huwenbo Shi; Steven Gazal; Wouter J. Peyrot; Amit V. Khera; Yukinori Okada; Koichi Matsuda; Yuji Yamanashi; Yoichi Furukawa; Takayuki Morisaki; Yoshinori Murakami; Yoichiro Kamatani; Kaori Muto; Akiko Nagai; Wataru Obara; Ken Yamaji; Kazuhisa Takahashi; Satoshi Asai; Yasuo Takahashi; Takao Suzuki; Nobuaki Sinozaki; Hiroki Yamaguchi; Shiro Minami; Shigeo Murayama; Kozo Yoshimori; Satoshi Nagayama; Daisuke Obata; Masahiko Higashiyama; Akihide Masumoto; Yukihiro Koretsune; Alicia R. Martin; Hilary K. Finucane; Alkes L. Price; The Biobank Japan Project is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is Leveraging Fine-mapping and Multipopulation Training Data to Improve Cross-population Polygenic Risk Scores about?

olygenic risk scores (PRSs) can identify individuals at elevated risk of complex diseases, providing opportunities for preventive action [1] . However, many studies have shown that PRSs based on European training data attain lower accuracy when applied to populations of non-European ancestry . This loss of accuracy is primarily driven by linkage disequilibrium (LD) differences , allele frequency differences (including population-specific SNPs) and causal effect-size differences , although differences in heritability also play a minor role . PRSs based on non-European training data do not suffer from these limitations, but are currently limited by much smaller training sample sizes 1, . The development of new methods to reduce this gap in cross-population PRS accuracy has the potential to ameliorate health disparities .In the present study, we propose PolyPred, which linearly combines two complementary predictors derived from European training data: (1) PolyFun-pred, a new predictor that circumvents LD differences by applying genome-wide, functionally informed fine-mapping to precisely estimate causal effects (instead of tagging effects); and (2) BOLT-LMM , a published predictor tha

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Author

Omer Weissbrod; Masahiro Kanai; Huwenbo Shi; Steven Gazal; Wouter J. Peyrot; Amit V. Khera; Yukinori Okada; Koichi Matsuda; Yuji Yamanashi; Yoichi Furukawa; Takayuki Morisaki; Yoshinori Murakami; Yoichiro Kamatani; Kaori Muto; Akiko Nagai; Wataru Obara; Ken Yamaji; Kazuhisa Takahashi; Satoshi Asai; Yasuo Takahashi; Takao Suzuki; Nobuaki Sinozaki; Hiroki Yamaguchi; Shiro Minami; Shigeo Murayama; Kozo Yoshimori; Satoshi Nagayama; Daisuke Obata; Masahiko Higashiyama; Akihide Masumoto; Yukihiro Koretsune; Alicia R. Martin; Hilary K. Finucane; Alkes L. Price; The Biobank Japan Project

Publisher

Springer Science and Business Media LLC

Published

2022

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)