表达数量性状基因座
生物
计算生物学
染色质
全基因组关联研究
精神分裂症(面向对象编程)
人口
增强子
特质
数量性状位点
RNA剪接
遗传学
基因调控网络
遗传关联
疾病
进化生物学
基因
单核苷酸多态性
基因型
基因表达
计算机科学
心理学
核糖核酸
精神科
医学
病理
程序设计语言
环境卫生
作者
Daifeng Wang,Shuang Liu,Jonathan Warrell,Hyejung Won,Xu Shi,Fábio C. P. Navarro,Declan Clarke,Mengting Gu,Prashant S. Emani,Yucheng Yang,Min Xu,Michael J. Gandal,Shaoke Lou,Jing Zhang,Jonathan J. Park,Chengfei Yan,Suhn K. Rhie,Kasidet Manakongtreecheep,Holly Zhou,Aparna Nathan
出处
期刊:Science
[American Association for the Advancement of Science]
日期:2018-12-13
卷期号:362 (6420)
被引量:880
标识
DOI:10.1126/science.aat8464
摘要
Despite progress in defining genetic risk for psychiatric disorders, their molecular mechanisms remain elusive. Addressing this, the PsychENCODE Consortium has generated a comprehensive online resource for the adult brain across 1866 individuals. The PsychENCODE resource contains ~79,000 brain-active enhancers, sets of Hi-C linkages, and topologically associating domains; single-cell expression profiles for many cell types; expression quantitative-trait loci (QTLs); and further QTLs associated with chromatin, splicing, and cell-type proportions. Integration shows that varying cell-type proportions largely account for the cross-population variation in expression (with >88% reconstruction accuracy). It also allows building of a gene regulatory network, linking genome-wide association study variants to genes (e.g., 321 for schizophrenia). We embed this network into an interpretable deep-learning model, which improves disease prediction by ~6-fold versus polygenic risk scores and identifies key genes and pathways in psychiatric disorders.
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