The accuracy of polygenic risk scores (PRSs) to predict complex diseases increases with the training sample size. PRSs are generally derived based on summary statistics from large meta-analyses of multiple genome-wide association studies (GWAS). However, it is now common for researchers to have access to large individual-level data as well, such as the UK biobank data. To the best of our knowledge, it has not yet been explored how to best combine both types of data (summary statistics and individual-level data) to optimize polygenic prediction. The most widely used approach to combine data is the meta-analysis of GWAS summary statistics (Meta-GWAS), but we show that it does not always provide the most accurate PRS. Through simulations and using twelve real case-control and quantitative traits from both iPSYCH and UK Biobank along with external GWAS summary statistics, we compare Meta-GWAS with two alternative data-combining approaches, stacked clumping and thresholding (SCT) and Meta-PRS. We find that, when large individual-level data is available, the linear combination of PRSs (Meta-PRS) is both a simple alternative to Meta-GWAS and often more accurate.

中文翻译：

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利用个人水平的遗传数据和GWAS汇总统计信息，可以提高多基因预测

多基因风险评分（PRS）预测复杂疾病的准确性随训练样本量的增加而增加。PRS通常是基于对多个全基因组关联研究（GWAS）进行的大型荟萃分析得出的摘要统计信息得出的。但是，现在，研究人员通常也可以访问大型的个人数据，例如英国生物库数据。就我们所知，尚未探索如何最佳地组合两种类型的数据（摘要统计数据和个人数据）以优化多基因预测。组合数据最广泛使用的方法是GWAS摘要统计数据（Meta-GWAS）的荟萃分析，但是我们证明了它并不总是提供最准确的PRS。通过模拟，并使用来自iPSYCH和UK Biobank的十二种实际病例控制和定量特征以及外部GWAS汇总统计数据，我们将Meta-GWAS与两种替代数据组合方法（堆积聚类和阈值（SCT）和Meta-PRS）进行了比较。我们发现，当可获得大量个人数据时，PRS的线性组合（Meta-PRS）既是Meta-GWAS的简单替代方式，又往往更准确。