Motivation: Polygenic scores (PGS) aim to genetically predict complex traits at an individual level. PGS are typically trained on genome-wide association summary statistics and require an independent test dataset to tune parameters. More recent methods allow parameters to be tuned on the training data, removing the need for independent test data, but approaches are computationally intensive. Based on fine-mapping principles, we present RapidoPGS, a flexible and fast method to compute PGS requiring summary-level GWAS datasets only, with little computational requirements and no test data required for parameter tuning. Results: We show that RapidoPGS performs slightly less well than two out of three other widely-used PGS methods (LDpred2, PRScs, and SBayesR) for case-control datasets, with median r2 difference: -0.0092, -0.0042, and 0.0064, respectively, but up to 17,000-fold faster with reduced computational requirements. RapidoPGS is implemented in R and can work with user-supplied summary statistics or download them from the GWAS catalog. Availability and implementation: Our method is available with a GPL license as an R package from GitHub.

中文翻译：

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RapidoPGS：快速的多基因评分计算器，用于汇总GWAS数据而无需测试数据集

动机：多基因评分（PGS）旨在从个人角度对复杂性状进行遗传预测。PGS通常在全基因组关联摘要统计方面接受培训，并且需要独立的测试数据集来调整参数。最近的方法允许在训练数据上调整参数，从而不需要独立的测试数据，但是方法需要大量的计算。基于精细映射原理，我们提出了RapidoPGS，这是一种灵活而快速的方法，用于计算仅需要摘要级GWAS数据集的PGS，几乎不需要计算，也不需要参数调整的测试数据。结果：我们显示，对于病例对照数据集，RapidoPGS的性能略低于其他三种广泛使用的PGS方法（LDpred2，PRScs和SBayesR）中的两种，其中r2的中位数分别为-0.0092，-0.0042和0.0064，分别提高了17,000倍，同时减少了计算需求。RapidoPGS在R中实现，可以与用户提供的摘要统计信息一起使用，也可以从GWAS目录中下载它们。可用性和实施​​：我们的方法可通过GPL许可证从GitHub作为R包获得。