Accurate identification of genes associated with brain disorders by integrating heterogeneous genomic data into a Bayesian framework

He, Dan, Li, Ling, Zhang, Huasong, Liu, Feiyi, Li, Shaoying, Xiu, Xuehao, Fan, Cong, Qi, Mengling, Meng, Meng, Ye, Junping, Mort, Matthew ORCID: https://orcid.org/0000-0002-3986-0935, Stenson, Peter D., Cooper, David N. ORCID: https://orcid.org/0000-0002-8943-8484 and Zhao, Huiying 2024. Accurate identification of genes associated with brain disorders by integrating heterogeneous genomic data into a Bayesian framework. EBioMedicine 107 , 105286. 10.1016/j.ebiom.2024.105286

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Abstract

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 Here, we present an unsupervised Bayesian framework to identify disease-associated genes by integrating risk SNPs with long-range chromatin interactions (iGOAT), 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. Findings The application of iGOAT to three psychiatric disorders and three neurodegenerative/neurological diseases predicted sets of high-risk (HRGs) and low-risk (LRGs) genes for each disorder. The HRGs were enriched in drug targets, and exhibited 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 the eQTL-based method, the TWAS-based method, and the gene-level GWAS method indicated that the genes identified by our method are more enriched in known brain disorder-related genes, and exhibited higher precision. Finally, the method predicted 205 risk genes not previously reported to be associated with any brain disorder, of which one top-risk gene, MLH1, was experimentally validated as being schizophrenia-associated. Interpretation iGOAT can successfully leverage epigenomic data, phenotype–genotype associations, and protein–protein interactions to advance our understanding of brain disorders, thereby facilitating the development of new therapeutic approaches.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Medicine
Publisher:	Elsevier
ISSN:	2352-3964
Date of First Compliant Deposit:	1 November 2024
Date of Acceptance:	1 August 2024
Last Modified:	01 Nov 2024 14:46
URI:	https://orca.cardiff.ac.uk/id/eprint/173492

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