Department of Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510006, China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, 510006, China.
School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, 510006, China.
EBioMedicine. 2024 Sep;107:105286. doi: 10.1016/j.ebiom.2024.105286. Epub 2024 Aug 20.
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.
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.
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.
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.
The work was funded by the National Key Research and Development Program of China (2024YFF1204902), the Natural Science Foundation of China (82371482), Guangzhou Science and Technology Research Plan (2023A03J0659) and Natural Science Foundation of Guangdong (2024A1515011363).
全基因组关联研究（GWAS）揭示了许多位于非编码基因组中的与脑疾病相关的单核苷酸多态性（SNP），这使得破译潜在的致病机制具有挑战性。
在这里，我们提出了一种无监督的贝叶斯框架，通过整合风险 SNP 与长程染色质相互作用（iGOAT）来识别疾病相关基因，包括从英国生物库中约 50 万名患者和对照中提取的 SNP-SNP 相互作用，以及从不同发育阶段的多种脑细胞类型中提取的增强子-启动子相互作用。
将 iGOAT 应用于三种精神疾病和三种神经退行性/神经疾病，预测了每种疾病的高风险（HRG）和低风险（LRG）基因集。HRG 富集于药物靶点，并且在产前脑发育阶段的表达高于产后阶段，表明它们有可能在早期影响大脑发育。通过基因共表达模块分析和罕见变异分析，与阿尔茨海默病相关的 HRG 与精神分裂症、双相情感障碍和重度抑郁症具有遗传结构相似性。与基于 eQTL 的方法、基于 TWAS 的方法和基于基因水平的 GWAS 方法的比较表明，我们的方法鉴定的基因在已知的与脑疾病相关的基因中更为丰富，并且具有更高的精度。最后，该方法预测了 205 个以前未报道与任何脑疾病相关的风险基因，其中一个顶级风险基因 MLH1 被实验验证与精神分裂症相关。
iGOAT 可以成功地利用表观基因组数据、表型-基因型关联和蛋白质-蛋白质相互作用来加深我们对脑疾病的理解，从而促进新的治疗方法的开发。
这项工作得到了中国国家重点研发计划（2024YFF1204902）、中国自然科学基金（82371482）、广州科技计划（2023A03J0659）和广东省自然科学基金（2024A1515011363）的资助。
