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新方法改善对脑疾病风险基因的预测
作者:小柯机器人 发布时间:2020/3/10 15:03:33

美国北卡罗莱纳大学Hyejung Won小组建立了一种新的分析方法(H-MAGMA),其可通过整合脑细胞染色质的相互作用谱来改善对大脑疾病风险基因的预测。相关论文3月9日在线发表于《自然—神经科学》。

研究人员建立了一个Hi-C耦合-基因组数据注释和功能分析(H-MAGMA)方法,该方法通过整合人类脑组织在两个发育时期和两种脑细胞类型中的染色质相互作用谱来提高MAGMA。

通过分析疾病相关组织中基因调控的关系,H-MAGMA鉴定了与神经生物学相关的靶基因。研究人员将H-MAGMA应用于5种精神疾病和4种神经退行性疾病,以探究与每种疾病有关的生物学途径、发育突破口和细胞类型。

精神疾病风险基因倾向于在妊娠中期和兴奋性神经元中表达,而神经退行性疾病风险基因的表达则随着时间的流逝而增加,并且具有更高的细胞类型特异性。

H-MAGMA增加了现有的分析模型,有助于揭示脑部疾病的神经生物学原理。

研究人员介绍,通过全基因组关联分析确定的大多数与脑部疾病相关的风险变异都存在于非编码基因组中,这使得难以破解其生物学机制。常用工具MAGMA,通过将单核苷酸多态性关联到最近的基因来解决这个问题。

附:英文原文

Title: A computational tool (H-MAGMA) for improved prediction of brain-disorder risk genes by incorporating brain chromatin interaction profiles

Author: Nancy Y. A. Sey, Benxia Hu, Won Mah, Harper Fauni, Jessica Caitlin McAfee, Prashanth Rajarajan, Kristen J. Brennand, Schahram Akbarian, Hyejung Won

Issue&Volume: 2020-03-09

Abstract: Most risk variants for brain disorders identified by genome-wide association studies reside in the noncoding genome, which makes deciphering biological mechanisms difficult. A commonly used tool, multimarker analysis of genomic annotation (MAGMA), addresses this issue by aggregating single nucleotide polymorphism associations to nearest genes. Here we developed a platform, Hi-C-coupled MAGMA (H-MAGMA), that advances MAGMA by incorporating chromatin interaction profiles from human brain tissue across two developmental epochs and two brain cell types. By analyzing gene regulatory relationships in the disease-relevant tissue, H-MAGMA identified neurobiologically relevant target genes. We applied H-MAGMA to five psychiatric disorders and four neurodegenerative disorders to interrogate biological pathways, developmental windows and cell types implicated for each disorder. Psychiatric-disorder risk genes tended to be expressed during mid-gestation and in excitatory neurons, whereas neurodegenerative-disorder risk genes showed increasing expression over time and more diverse cell-type specificities. H-MAGMA adds to existing analytic frameworks to help identify the neurobiological principles of brain disorders.

DOI: 10.1038/s41593-020-0603-0

Source: https://www.nature.com/articles/s41593-020-0603-0

期刊信息

Nature Neuroscience:《自然—神经科学》,创刊于1998年。隶属于施普林格·自然出版集团,最新if:21.126
官方网址:https://www.nature.com/neuro/
投稿链接:https://mts-nn.nature.com/cgi-bin/main.plex