Deep learning predicts DNA methylation regulatory variants in specific brain cell types and enhances fine mapping for brain disorders.
Deep learning predicts DNA methylation regulatory variants in specific brain cell types and enhances fine mapping for brain disorders.
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深度学习可预测特定脑细胞类型中的 DNA 甲基化调控变异，并增强大脑疾病的精细定位。
DOI：
10.1101/2024.01.18.576319
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发表时间：
2024
期刊：
影响因子：
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通讯作者：
Han,Shizhong
中科院分区：
文献类型：
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作者：
Zhou,Jiyun;Weinberger,DanielR;Han,Shizhong
DNA甲基化（DNAm）对大脑发育和功能至关重要，并可能介导大脑疾病的遗传风险变体的影响。我们提出了INTERACT，这是一种基于transformer的深度学习模型，利用人脑中现有的单核DNAm数据，预测影响特定脑细胞类型中DNAm水平的调控变体。我们表明，INTERACT准确地预测细胞类型特异性DNAm的配置文件，实现了0.99跨细胞类型的受试者工作特征曲线下的平均面积。此外，INTERACT预测细胞类型特异性DNAm调节变体，其反映了细胞环境并丰富了相关细胞类型中脑相关性状的遗传性。我们证明，将预测的变异效应和DNA m水平的CpG位点增强了精细映射为三个大脑疾病-精神分裂症，抑郁症和阿尔茨海默氏病，并有利于映射因果基因特定的细胞类型。我们的研究强调了深度学习在识别细胞类型特异性调控变体方面的力量，这将增强我们对复杂性状遗传学的理解。
DNA methylation (DNAm) is essential for brain development and function and potentially mediates the effects of genetic risk variants underlying brain disorders. We present INTERACT, a transformer-based deep learning model to predict regulatory variants affecting DNAm levels in specific brain cell types, leveraging existing single-nucleus DNAm data from the human brain. We show that INTERACT accurately predicts cell type–specific DNAm profiles, achieving an average area under the receiver operating characteristic curve of 0.99 across cell types. Furthermore, INTERACT predicts cell type–specific DNAm regulatory variants, which reflect cellular context and enrich the heritability of brain-related traits in relevant cell types. We demonstrate that incorporating predicted variant effects and DNAm levels of CpG sites enhances the fine mapping for three brain disorders—schizophrenia, depression, and Alzheimer’s disease—and facilitates mapping causal genes to particular cell types. Our study highlights the power of deep learning in identifying cell type–specific regulatory variants, which will enhance our understanding of the genetics of complex traits.