Prioritization of schizophrenia risk genes from GWAS results by integrating multi-omics data

Dan He,Cong Fan,David N Cooper,Mengling Qi,Yuedong Yang,Huiying Zhao

doi:10.1038/s41398-021-01294-x

Abstract

Schizophrenia (SCZ) is a polygenic disease with a heritability approaching 80%. Over 100 SCZ-related loci have so far been identified by genome-wide association studies (GWAS). However, the risk genes associated with these loci often remain unknown. We present a new risk gene predictor, rGAT-omics, that integrates multi-omics data under a Bayesian framework by combining the Hotelling and Box–Cox transformations. The Bayesian framework was constructed using gene ontology, tissue-specific protein–protein networks, and multi-omics data including differentially expressed genes in SCZ and controls, distance from genes to the index single-nucleotide polymorphisms (SNPs), and de novo mutations. The application of rGAT-omics to the 108 loci identified by a recent GWAS study of SCZ predicted 103 high-risk genes (HRGs) that explain a high proportion of SCZ heritability (Enrichment = 43.44 and p = 9.30 times 10^{ - 9}). HRGs were shown to be significantly (p_{mathrm{adj}} = 5.35 times 10^{ - 7}) enriched in genes associated with neurological activities, and more likely to be expressed in brain tissues and SCZ-associated cell types than background genes. The predicted HRGs included 16 novel genes not present in any existing databases of SCZ-associated genes or previously predicted to be SCZ risk genes by any other method. More importantly, 13 of these 16 genes were not the nearest to the index SNP markers, and them would have been difficult to identify as risk genes by conventional approaches while ten out of the 16 genes are associated with neurological functions that make them prime candidates for pathological involvement in SCZ. Therefore, rGAT-omics has revealed novel insights into the molecular mechanisms underlying SCZ and could provide potential clues to future therapies.

Highlights

Schizophrenia (SCZ) is a mental condition with a very complex etiology and highly variable clinical manifestations[1]
Results rGAT-omics identified highrisk genes (HRGs) by integrating multi-omics data with networks rGAT-omics was developed by integrating multi-omics features (differential expression (DE), de novo mutations (DNM), gross deletions, distal regulatory elements (DRE) promoters, distance to index single-nucleotide polymorphisms (SNPs) (DTS), and Reads Per Kilobase of transcript per Million (RPKM) in adolescence and adulthood from BrainSpan) with the gene interaction networks including the gene ontology (GO) network, BioGRID network[19], and tissue-specific network from TissueNet[20] (Fig. 1)
The application of rGAT-omics to 108 loci associated with SCZ provided by the previous genome-wide association studies (GWAS) study[2] yielded 103 HRGs and 849 low-risk background genes (LBGs)

Summary

Introduction

Schizophrenia (SCZ) is a mental condition with a very complex etiology and highly variable clinical manifestations[1]. Two genome-wide association studies (GWAS) were performed on SCZ in an attempt to explore the etiology of the disease; together, they successfully identified over 100 SCZ-related loci[2,3], the identified GWAS loci mostly failed to identify any SCZ risk genes. To identify risk genes regulated by GWAS loci, many methods have been proposed. Most of these approaches have attempted to define candidate genes by setting a fixed distance around each index SNP and subsequently identifying SCZ risk genes by integrating genomic functions[7,8], or considering topologically associated domains that are generated by prior chromatin interaction experiments[9,10]. A recent study has explored the gene regulatory mechanisms underlying SCZ by integrating functional genomics and position weight matrix (PWM)[7]

Methods

Results

Conclusion