Large-scale pathway specific polygenic risk and transcriptomic community network analysis identifies novel functional pathways in Parkinson disease.
Large-scale pathway specific polygenic risk and transcriptomic community network analysis identifies novel functional pathways in Parkinson disease.
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DOI：
10.1007/s00401-020-02181-3
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发表时间：
2020-09
影响因子：
12.7
通讯作者：
International Parkinson Disease Genomics Consortium
中科院分区：
文献类型：
--
作者：
Bandres-Ciga S;Saez-Atienzar S;Kim JJ;Makarious MB;Faghri F;Diez-Fairen M;Iwaki H;Leonard H;Botia J;Ryten M;Hernandez D;Gibbs JR;Ding J;Gan-Or Z;Noyce A;Pihlstrom L;Torkamani A;Soltis AR;Dalgard CL;American Genome Center;Scholz SW;Traynor BJ;Ehrlich D;Scherzer CR;Bookman M;Cookson M;Blauwendraat C;Nalls MA;Singleton AB;International Parkinson Disease Genomics Consortium
多基因遗传在帕金森病(PD)发病中起核心作用。阐明帕金森病病因学的首要任务是确定遗传风险的生物学基础。解开风险是如何导致破坏的，将产生可能有效的疾病修改治疗目标。在这里，我们使用高通量和无假设的方法来确定帕金森病潜在的生物学过程，使用了国际帕金森病遗传学联合会(IPDGC)和加速药物伙伴关系-帕金森病倡议(AMP-PD)等来源目前可用的最大遗传和基因表达数据队列。我们应用大规模的基因集特异性多基因风险评分(PRS)分析来评估共同变异在PD风险中的作用，重点放在代表被管理的通路的公开注释的基因集上。我们认为蛋白质错误折叠和聚集、翻译后蛋白修饰、免疫反应、膜和细胞内转运、脂质和维生素代谢、突触传递、内体溶酶体功能障碍、染色质重塑和caspase介导的细胞凋亡是帕金森病的主要致病因素。我们在一组独立的全基因组测序数据中评估了罕见变异对帕金森病风险的影响，并发现了在一系列过程中存在罕见破坏性等位基因负担的证据，包括神经传递相关途径和免疫反应。我们使用单细胞基因表达数据探索了与表达细胞特异性模式相关的丰富，并展示了多巴胺能神经元、5-羟色胺能神经元、下丘脑GABA能神经元和神经前体细胞的显著风险模式。随后，我们利用PD患者血液中的转录数据构建了一张网络表达社区图，从而创建了一种新的构建新途径的方法，该图揭示了炎症信号通路、细胞死亡机制相关过程和线粒体稳态失调的功能丰富。我们的分析突出了功能优先排序的几个特定的有希望的途径和基因，并提供了应该完成此类工作的细胞环境。本文的在线版本(10.1007/s00401-020-020-3)包含向授权用户提供的补充材料。
Polygenic inheritance plays a central role in Parkinson disease (PD). A priority in elucidating PD etiology lies in defining the biological basis of genetic risk. Unraveling how risk leads to disruption will yield disease-modifying therapeutic targets that may be effective. Here, we utilized a high-throughput and hypothesis-free approach to determine biological processes underlying PD using the largest currently available cohorts of genetic and gene expression data from International Parkinson’s Disease Genetics Consortium (IPDGC) and the Accelerating Medicines Partnership-Parkinson’s disease initiative (AMP-PD), among other sources. We applied large-scale gene-set specific polygenic risk score (PRS) analyses to assess the role of common variation on PD risk focusing on publicly annotated gene sets representative of curated pathways. We nominated specific molecular sub-processes underlying protein misfolding and aggregation, post-translational protein modification, immune response, membrane and intracellular trafficking, lipid and vitamin metabolism, synaptic transmission, endosomal–lysosomal dysfunction, chromatin remodeling and apoptosis mediated by caspases among the main contributors to PD etiology. We assessed the impact of rare variation on PD risk in an independent cohort of whole-genome sequencing data and found evidence for a burden of rare damaging alleles in a range of processes, including neuronal transmission-related pathways and immune response. We explored enrichment linked to expression cell specificity patterns using single-cell gene expression data and demonstrated a significant risk pattern for dopaminergic neurons, serotonergic neurons, hypothalamic GABAergic neurons, and neural progenitors. Subsequently, we created a novel way of building de novo pathways by constructing a network expression community map using transcriptomic data derived from the blood of PD patients, which revealed functional enrichment in inflammatory signaling pathways, cell death machinery related processes, and dysregulation of mitochondrial homeostasis. Our analyses highlight several specific promising pathways and genes for functional prioritization and provide a cellular context in which such work should be done. The online version of this article (10.1007/s00401-020-02181-3) contains supplementary material, which is available to authorized users.
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影响因子：
5.8
作者：
Liberzon, Arthur;Subramanian, Aravind;Mesirov, Jill P.
通讯作者：
Mesirov, Jill P.
DOI：
10.1093/bioinformatics/btu848
发表时间：
2015-05-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Euesden J;Lewis CM;O'Reilly PF
通讯作者：
O'Reilly PF
影响因子：
9.2
作者：
Chang CC;Chow CC;Tellier LC;Vattikuti S;Purcell SM;Lee JJ
通讯作者：
Lee JJ
DOI：
10.1073/pnas.0911175107
发表时间：
2010-05-25
影响因子：
11.1
作者：
Hao, Ling-Yang;Giasson, Benoit I.;Bonini, Nancy M.
通讯作者：
Bonini, Nancy M.
影响因子：
30.8
作者：
Satake, Wataru;Nakabayashi, Yuko;Toda, Tatsushi
通讯作者：
Toda, Tatsushi