Leveraging Functional Genomic Annotations and Genome Coverage to Improve Polygenic Prediction of Complex Traits Within and Between Ancestries Publisher Pubmed



Zheng Z^{1, 2, 3} ; Liu S¹ ; Sidorenko J¹ ; Wang Y¹ ; Lin T¹ ; Yengo L¹ ; Turley P^{4, 5} ; Ani A^{6, 7} ; Wang R⁶ ; Nolte IM⁶ ; Snieder H⁶ ; Wijmenga C¹⁴ ; Vonk JM⁶ ; Swertz MA¹⁴ Show All Authors
Source: Nature Genetics Published:2024

Abstract

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. © The Author(s) 2024.