A Bayesian framework that integrates multi-omics data and gene networks predicts risk genes from schizophrenia GWAS data.
Nat Neurosci
; 22(5): 691-699, 2019 05.
Article
em En
| MEDLINE
| ID: mdl-30988527
ABSTRACT
Genome-wide association studies (GWAS) have identified more than 100 schizophrenia (SCZ)-associated loci, but using these findings to illuminate disease biology remains a challenge. Here we present integrative risk gene selector (iRIGS), a Bayesian framework that integrates multi-omics data and gene networks to infer risk genes in GWAS loci. By applying iRIGS to SCZ GWAS data, we predicted a set of high-confidence risk genes, most of which are not the nearest genes to the GWAS index variants. High-confidence risk genes account for a significantly enriched heritability, as estimated by stratified linkage disequilibrium score regression. Moreover, high-confidence risk genes are predominantly expressed in brain tissues, especially prenatally, and are enriched for targets of approved drugs, suggesting opportunities to reposition existing drugs for SCZ. Thus, iRIGS can leverage accumulating functional genomics and GWAS data to advance our understanding of SCZ etiology and potential therapeutics.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Esquizofrenia
/
Predisposição Genética para Doença
/
Genômica
/
Redes Reguladoras de Genes
Tipo de estudo:
Etiology_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Animals
/
Humans
Idioma:
En
Revista:
Nat Neurosci
Assunto da revista:
NEUROLOGIA
Ano de publicação:
2019
Tipo de documento:
Article
País de afiliação:
Estados Unidos