美国哈佛大学Alkes L. Price、Omer Weissbrod等研究人员合作利用精细绘图和多人群训练数据来改善跨人群的多基因风险评分。相关论文于2022年4月7日在线发表在《自然—遗传学》杂志上。

研究人员提出了PolyPred，一种通过结合两种预测因子来改善跨人群多基因风险分数的方法：一种新的预测因子，利用功能知情的精细映射来估计因果效应（而不是标记效应），解决连接不平衡的差异，以及BOLT-LMM，一个已发表的预测因子。当非欧洲目标人群中有大量训练样本时，研究人员提出了PolyPred⁺，它进一步纳入了非欧洲的训练数据。研究人员将PolyPred应用于四个英国生物库人群的49种疾病/特征，使用英国生物库的英国训练数据，观察到相对于BOLT-LMM的改进，范围从南亚人的+7%到非洲人的+32%，与模拟结果一致。

研究人员利用英国生物库和日本生物库的训练数据，将PolyPred⁺应用于英国生物库东亚人的23种疾病/特征，观察到相对于BOLT-LMM的改进为+24%，相对于PolyPred为+12%。基于汇总统计的PolyPred和PolyPred⁺的类似物获得了相近的改进。

据了解，多基因风险评分在非欧洲人口中的准确性降低，加剧了健康差异。

附：英文原文

Title: Leveraging fine-mapping and multipopulation training data to improve cross-population polygenic risk scores

Author: Weissbrod, Omer, Kanai, Masahiro, Shi, Huwenbo, Gazal, Steven, Peyrot, Wouter J., Khera, Amit V., Okada, Yukinori, Martin, Alicia R., Finucane, Hilary K., Price, Alkes L.

Issue&Volume: 2022-04-07

Abstract: Polygenic risk scores suffer reduced accuracy in non-European populations, exacerbating health disparities. We propose PolyPred, a method that improves cross-population polygenic risk scores by combining two predictors: a new predictor that leverages functionally informed fine-mapping to estimate causal effects (instead of tagging effects), addressing linkage disequilibrium differences, and BOLT-LMM, a published predictor. When a large training sample is available in the non-European target population, we propose PolyPred+, which further incorporates the non-European training data. We applied PolyPred to 49 diseases/traits in four UK Biobank populations using UK Biobank British training data, and observed relative improvements versus BOLT-LMM ranging from +7% in south Asians to +32% in Africans, consistent with simulations. We applied PolyPred+ to 23 diseases/traits in UK Biobank east Asians using both UK Biobank British and Biobank Japan training data, and observed improvements of +24% versus BOLT-LMM and +12% versus PolyPred. Summary statistics-based analogs of PolyPred and PolyPred+ attained similar improvements. PolyPred and PolyPred+ methods that leverage fine-mapping and non-European training data significantly improve cross-population polygenic prediction accuracy when applied to diseases and complex traits in UK Biobank populations.

DOI: 10.1038/s41588-022-01036-9

Source: https://www.nature.com/articles/s41588-022-01036-9