We develop a method, SBayesRC, that integrates genome-wide association study (GWAS) summary statistics with functional genomic annotations to improve polygenic prediction of complex traits. Our method is scalable to whole-genome variant analysis and refines signals from functional annotations by allowing them to affect both causal variant probability and causal effect distribution. We analyze 50 complex traits and diseases using ∼7 million common single-nucleotide polymorphisms (SNPs) and 96 annotations. SBayesRC improves prediction accuracy by 14% in European ancestry and up to 34% in cross-ancestry prediction compared to the baseline method SBayesR, which does not use annotations, and outperforms other methods, including LDpred2, LDpred-funct, MegaPRS, PolyPred-S and PRS-CSx. Investigation of factors affecting prediction accuracy identifies a significant interaction between SNP density and annotation information, suggesting whole-genome sequence variants with annotations may further improve prediction. Functional partitioning analysis highlights a major contribution of evolutionary constrained regions to prediction accuracy and the largest per-SNP contribution from nonsynonymous SNPs.

我们开发了一种方法 SBayesRC，它将全基因组关联研究 (GWAS) 摘要统计与功能基因组注释相结合，以改进复杂性状的多基因预测。我们的方法可扩展到全基因组变异分析，并通过允许功能注释影响因果变异概率和因果效应分布来细化信号。我们使用约700 万个常见的单核苷酸多态性 (SNP) 和 96 个注释来分析 50 种复杂性状和疾病。与不使用注释的基线方法 SBayesR 相比，SBayesRC 在欧洲血统中的预测精度提高了 14%，在跨血统预测中提高了 34%，并且优于其他方法，包括 LDpred2、LDpred-funct、MegaPRS、PolyPred-S和 PRS-CSx。对影响预测准确性的因素的调查发现，SNP 密度和注释信息之间存在显着的相互作用，这表明带有注释的全基因组序列变异可能会进一步改善预测。功能分区分析强调了进化受限区域对预测准确性的主要贡献以及非同义 SNP 对每个 SNP 的最大贡献。