LDpred2 is a widely used Bayesian method for building polygenic scores (PGSs). LDpred2-auto can infer the two parameters from the LDpred model, the SNP heritability $h^2$ and polygenicity p, so that it does not require an additional validation dataset to choose best-performing parameters. The main aim of this paper is to properly validate the use of LDpred2-auto for inferring multiple genetic parameters. Here, we present a new version of LDpred2-auto that adds an optional third parameter α to its model, for modeling negative selection. We then validate the inference of these three parameters (or two, when using the previous model). We also show that LDpred2-auto provides per-variant probabilities of being causal that are well calibrated and can therefore be used for fine-mapping purposes. We also introduce a formula to infer the out-of-sample predictive performance $r^2$ of the resulting PGS directly from the Gibbs sampler of LDpred2-auto. Finally, we extend the set of HapMap3 variants recommended to use with LDpred2 with 37% more variants to improve the coverage of this set, and we show that this new set of variants captures 12% more heritability and provides 6% more predictive performance, on average, in UK Biobank analyses.

LDpred2 是一种广泛使用的贝叶斯方法，用于构建多基因评分 (PGS)。LDpred2-auto可以从LDpred模型中推断出两个参数，即SNP遗传力$H^2$和多基因性p，因此不需要额外的验证数据集来选择性能最佳的参数。本文的主要目的是正确验证 LDpred2-auto 推断多个遗传参数的使用。在这里，我们提出了 LDpred2-auto 的新版本，它在其模型中添加了可选的第三个参数α，用于对负选择进行建模。然后，我们验证这三个参数（或两个，当使用之前的模型时）的推论。我们还表明，LDpred2-auto 提供了经过良好校准的每个变量的因果概率，因此可用于精细映射目的。我们还引入了一个公式来推断样本外预测性能$r^2$直接从 LDpred2-auto 的 Gibbs 采样器生成 PGS。最后，我们扩展了建议与 LDpred2 一起使用的 HapMap3 变体集，增加了 37% 的变体，以提高该集的覆盖范围，并且我们表明，这组新变体的遗传力提高了 12%，预测性能提高了 6%。英国生物银行分析中的平均值。