Genetic fine-mapping from summary data using a nonlocal prior improves the detection of multiple causal variants.
Bioinformatics
; 39(7)2023 07 01.
Article
em En
| MEDLINE
| ID: mdl-37348543
ABSTRACT
MOTIVATION Genome-wide association studies (GWAS) have been successful in identifying genomic loci associated with complex traits. Genetic fine-mapping aims to detect independent causal variants from the GWAS-identified loci, adjusting for linkage disequilibrium patterns. RESULTS:
We present "FiniMOM" (fine-mapping using a product inverse-moment prior), a novel Bayesian fine-mapping method for summarized genetic associations. For causal effects, the method uses a nonlocal inverse-moment prior, which is a natural prior distribution to model non-null effects in finite samples. A beta-binomial prior is set for the number of causal variants, with a parameterization that can be used to control for potential misspecifications in the linkage disequilibrium reference. The results of simulations studies aimed to mimic a typical GWAS on circulating protein levels show improved credible set coverage and power of the proposed method over current state-of-the-art fine-mapping method SuSiE, especially in the case of multiple causal variants within a locus. AVAILABILITY AND IMPLEMENTATION https//vkarhune.github.io/finimom/.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Genômica
/
Estudo de Associação Genômica Ampla
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article