Leveraging functional genomic annotations and genome coverage to improve polygenic prediction of complex traits within and between ancestries.
Nat Genet
; 56(5): 767-777, 2024 May.
Article
de En
| MEDLINE
| ID: mdl-38689000
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 â¼7 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.
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Hérédité multifactorielle
/
Polymorphisme de nucléotide simple
/
Étude d'association pangénomique
/
Annotation de séquence moléculaire
Limites:
Humans
Langue:
En
Journal:
Nat Genet
Sujet du journal:
GENETICA MEDICA
Année:
2024
Type de document:
Article
Pays d'affiliation:
Australie