The heritability of common psychiatric disorders has motivated global efforts to identify risk-associated genetic variants and elucidate molecular pathways connecting DNA sequence to disease-associated brain dysfunction. The overrepresentation of risk variants among gene regulatory loci instead of protein-coding loci, however, poses a unique challenge in discerning which among the many thousands of variants identified contribute functionally to disease etiology. Defined broadly, psychiatric epigenomics seeks to understand the effects of disease-associated genetic variation on functional readouts of chromatin in an effort to prioritize variants in terms of their impact on gene expression in the brain. Here, we provide an overview of epigenomic mapping in the human brain and highlight findings of particular relevance to psychiatric genetics. Computational methods, including convolutional neuronal networks, and other machine learning approaches hold great promise for elucidating the functional impact of both common and rare genetic variants, thereby refining the epigenomic architecture of psychiatric disorders and enabling integrative analyses of regulatory noncoding variants in the context of large population-level genome and phenome databases.

中文翻译：

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解析大脑表观基因组中非编码遗传变异的功能影响

常见精神疾病的遗传性促使全球努力识别与风险相关的遗传变异，并阐明将 DNA 序列与疾病相关的脑功能障碍联系起来的分子途径。然而，基因调控位点而不是蛋白质编码位点中风险变异的过度代表性，对辨别数千个已识别的变异中哪些对疾病病因学有功能性贡献提出了独特的挑战。从广义上讲，精神表观基因组学旨在了解与疾病相关的遗传变异对染色质功能读数的影响，以便根据变异对大脑基因表达的影响来确定变异的优先顺序。在这里，我们概述了人脑表观基因组图谱，并重点介绍了与精神遗传学特别相关的发现。计算方法，包括卷积神经网络和其他机器学习方法，对于阐明常见和罕见遗传变异的功能影响具有很大的希望，从而完善精神疾病的表观基因组结构，并能够在大背景下对调节性非编码变异进行综合分析。群体水平的基因组和表型数据库。