Polygenic risk score prediction accuracy convergence.

IF 3.3 Q2 GENETICS & HEREDITY
Léo Henches, Jihye Kim, Zhiyu Yang, Simone Rubinacci, Gabriel Pires, Clara Albiñana, Christophe Boetto, Hanna Julienne, Arthur Frouin, Antoine Auvergne, Yuka Suzuki, Sarah Djebali, Olivier Delaneau, Andrea Ganna, Bjarni Vilhjálmsson, Florian Privé, Hugues Aschard
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Abstract

Polygenic risk scores (PRSs) models trained from genome-wide association study (GWAS) results are set to play a pivotal role in biomedical research addressing multifactorial human diseases. The prospect of using these risk scores in clinical care and public health is generating both enthusiasm and controversy, with varying opinions among experts about their strengths and limitations. The performance of existing polygenic scores is still limited but is expected to improve with increasing GWAS sample sizes and the development of new, more powerful methods. Theoretically, the variance explained by PRS can be as high as the total additive genetic variance, but it is unclear how much of that variance has already been captured by PRS. Here, we conducted a retrospective analysis to assess progress in PRS prediction accuracy since the publication of the first large-scale GWASs, using data from six common human diseases with sufficient GWAS information. We show that although PRS accuracy has grown rapidly over the years, the pace of improvement from recent GWAS has decreased substantially, suggesting that merely increasing GWAS sample sizes may lead to only modest improvements in risk discrimination. We next investigated the factors influencing the maximum achievable prediction using whole-genome sequencing data from 125K UK Biobank participants and state-of-the-art modeling of polygenic outcomes. Our analyses suggest that increasing the variant coverage of PRS-using either more imputed variants or sequencing data-is a key component for future improvements in prediction accuracy.

多基因风险评分预测精度收敛。
从全基因组关联研究(GWAS)结果中训练出来的多基因风险评分(prs)模型将在解决多因素人类疾病的生物医学研究中发挥关键作用。在临床护理和公共卫生中使用这些风险评分的前景既引起了热情,也引起了争议,专家们对其优点和局限性的看法不一。现有的多基因评分的性能仍然有限,但随着GWAS样本量的增加和新的、更强大的方法的发展,有望得到改善。从理论上讲,PRS解释的方差可以与总加性遗传方差一样高,但目前尚不清楚PRS已经捕获了多少方差。在这里,我们进行了回顾性分析,以评估自第一次大规模GWAS发表以来PRS预测准确性的进展,使用了具有足够GWAS信息的六种常见人类疾病的数据。我们表明,尽管PRS的准确性在过去几年里快速增长,但从最近的GWAS改进的速度已经大大下降,这表明仅仅增加GWAS样本量可能只会导致风险辨别的适度改善。接下来,我们利用来自125K UK Biobank参与者的全基因组测序数据和最先进的多基因结果建模,研究了影响最大可实现预测的因素。我们的分析表明,增加prs的变异覆盖率——使用更多的输入变异或测序数据——是未来提高预测准确性的关键组成部分。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
HGG Advances
HGG Advances Biochemistry, Genetics and Molecular Biology-Molecular Medicine
CiteScore
4.30
自引率
4.50%
发文量
69
审稿时长
14 weeks
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