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
en 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.
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Herencia Multifactorial
/
Polimorfismo de Nucleótido Simple
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Estudio de Asociación del Genoma Completo
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Anotación de Secuencia Molecular
Límite:
Humans
Idioma:
En
Revista:
Nat Genet
Asunto de la revista:
GENETICA MEDICA
Año:
2024
Tipo del documento:
Article
País de afiliación:
Australia