Leveraging functional genomic annotations and genome coverage to improve polygenic prediction of complex traits within and between ancestries

Journal article

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.

Authors: Zheng, Z; Liu, S; Sidorenko, J; Wang, Y; Lin, T

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Nature Research
• Journal: Nature Genetics
• Volume: 56
• Issue: 5
• Pages: 767-777
• Publication date: 2024-04-30
• Acceptance date: 2024-03-05
• DOI: 10.1038/s41588-024-01704-y
• EISSN: 1546-1718
• ISSN: 1061-4036
• Language: English