Details

Autor(en) / Beteiligte

• Zheng, Zhili
• Liu, Shouye
• Sidorenko, Julia
• Wang, Ying
• Lin, Tian
• Yengo, Loic
• Turley, Patrick
• Ani, Alireza
• Wang, Rujia
• Nolte, Ilja M
• Snieder, Harold
• Yang, Jian
• Wray, Naomi R
• Goddard, Michael E
• Visscher, Peter M
• Zeng, Jian

Titel

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

Ist Teil von

• Nature genetics, 2024-05, Vol.56 (5), p.767-777

Ort / Verlag

United States: Nature Publishing Group

Erscheinungsjahr

2024

Link zum Volltext

Quelle

MEDLINE

Beschreibungen/Notizen

• 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.