A Bayesian framework that integrates multi-omics data and gene networks predicts risk genes from schizophrenia GWAS data

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...

Full description

Saved in:
Bibliographic Details
Published inNature neuroscience Vol. 22; no. 5; pp. 691 - 699
Main Authors Wang, Quan, Chen, Rui, Cheng, Feixiong, Wei, Qiang, Ji, Ying, Yang, Hai, Zhong, Xue, Tao, Ran, Wen, Zhexing, Sutcliffe, James S, Liu, Chunyu, Cook, Edwin H, Cox, Nancy J, Li, Bingshan
Format Journal Article
LanguageEnglish
Published United States Nature Publishing Group 01.05.2019
Subjects
Analysis
Animals
Bayes Theorem
Bayesian analysis
Brain
Disease Models, Animal
Drug approval
Drugs
Etiology
Etiology (Medicine)
Gene Regulatory Networks
Genes
Genetic aspects
Genetic Predisposition to Disease
Genetic research
Genome-wide association studies
Genome-Wide Association Study
Genomes
Genomics
Genomics - methods
Health aspects
Heritability
Humans
Linkage disequilibrium
Loci
Medical research
Mental disorders
Mice
Risk
Risk Factors
Schizophrenia
Schizophrenia - genetics
Therapeutics
Online AccessGet full text

Cover

More Information
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
B.L. conceived the overall design of the study, with input from Q.Wang and R.C.. Q.Wang and R.C. implemented the algorithm and performed the most of the analyses. F.C., Q.Wei, Y.J., H.Y, X.Z, and R.T. provided data integration and analysis. Z.W, J.S., C.L, E.C., and N.C. contributed to the interpretation of the results. Q.Wang, R.C., F.C., and B.L. wrote the manuscript, and all authors participated in the manuscript review and revision.
These authors contribute equally to this study.
Author contributions
ISSN:1097-6256
1546-1726
DOI:10.1038/s41593-019-0382-7