Department of Biostatistics and Bioinformatics at the Duke University School of Medicine.
Innovation Center for Biomedical Informatics (ICBI) at Georgetown University.
Brief Bioinform. 2019 May 21;20(3):842-856. doi: 10.1093/bib/bbx157.
Mental illness is increasingly recognized as both a significant cost to society and a significant area of opportunity for biological breakthrough. As -omics and imaging technologies enable researchers to probe molecular and physiological underpinnings of multiple diseases, opportunities arise to explore the biological basis for behavioral health and disease. From individual investigators to large international consortia, researchers have generated rich data sets in the area of mental health, including genomic, transcriptomic, metabolomic, proteomic, clinical and imaging resources. General data repositories such as the Gene Expression Omnibus (GEO) and Database of Genotypes and Phenotypes (dbGaP) and mental health (MH)-specific initiatives, such as the Psychiatric Genomics Consortium, MH Research Network and PsychENCODE represent a wealth of information yet to be gleaned. At the same time, novel approaches to integrate and analyze data sets are enabling important discoveries in the area of mental and behavioral health. This review will discuss and catalog into an organizing framework the increasingly diverse set of MH data resources available, using schizophrenia as a focus area, and will describe novel and integrative approaches to molecular biomarker discovery that make use of mental health data.
精神疾病越来越被认为是对社会的重大成本，也是生物学突破的重大机会领域。随着组学和成像技术使研究人员能够探究多种疾病的分子和生理基础，探索行为健康和疾病的生物学基础的机会也随之出现。从个体研究人员到大型国际合作组织，研究人员在精神健康领域生成了丰富的数据集，包括基因组、转录组、代谢组、蛋白质组、临床和成像资源。一般的数据存储库，如基因表达综合数据库 (GEO) 和基因型和表型数据库 (dbGaP) 以及精神健康 (MH)-特定的计划，如精神疾病基因组学联盟、MH 研究网络和 PsychENCODE，代表了尚未挖掘的丰富信息。与此同时，整合和分析数据集的新方法正在精神和行为健康领域实现重要发现。本综述将讨论并将越来越多样化的 MH 数据资源分类到一个组织框架中，以精神分裂症为重点领域，并将描述利用精神健康数据发现分子生物标志物的新颖和综合方法。
