Background: Genome-wide association studies (GWAS) have revealed many brain-disorder associated SNPs residing in the noncoding genome, rendering it a challenge to decipher the underlying pathogenic mechanisms.
Methods: To identify disease-associated genes, we present an unsupervised Bayesian framework that integrated risk SNPs with long-range chromatin interactions, including SNP-SNP interactions extracted from ~500,000 patients and controls from the UK Biobank, and enhancer-promoter interactions derived from multiple brain cell types at different developmental stages.
Results: The method has been used to three psychiatric disorders and three neurodegenerative/neurological diseases, and predicted sets of high-risk (HRGs) and low-risk (LRGs) genes for each disorder. The HRGs exhibit higher expression during prenatal brain developmental stages than postnatal stages, indicating their potential to affect brain development at an early stage. The HRGs associated with Alzheimer’s disease were found to share genetic architecture with schizophrenia, bipolar disorder, and major depressive disorder according to gene co-expression module analysis and rare variants analysis. Comparisons of this method to eQTL-based method, TWAS-based method, and gene-level GWAS method indicated that the genes identified by our method are more enriched in known brain disorder-related genes, and has shown higher precision. Finally, the method predicted 205 novel risk genes not hitherto reported to be associated with any brain disorder, among which one top-risk gene was experimentally validated as schizophrenia associated.
Conclusion: The method presented by this study can successfully leverage epigenomic data, phenotype-genotype associations, and protein-protein interactions to advance our understanding of brain disorders, thereby facilitating the development of novel therapeutic approaches.