Shared activity patterns arising at genetic susceptibility loci reveal underlying genomic and cellular architecture of human disease.
Shared activity patterns arising at genetic susceptibility loci reveal underlying genomic and cellular architecture of human disease.
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DOI：
10.1371/journal.pcbi.1005934
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发表时间：
2018-03
影响因子：
4.3
通讯作者：
Hume DA
中科院分区：
文献类型：
--
作者：
Baillie JK;Bretherick A;Haley CS;Clohisey S;Gray A;Neyton LPA;Barrett J;Stahl EA;Tenesa A;Andersson R;Brown JB;Faulkner GJ;Lizio M;Schaefer U;Daub C;Itoh M;Kondo N;Lassmann T;Kawai J;IIBDGC Consortium;Mole D;Bajic VB;Heutink P;Rehli M;Kawaji H;Sandelin A;Suzuki H;Satsangi J;Wells CA;Hacohen N;Freeman TC;Hayashizaki Y;Carninci P;Forrest ARR;Hume DA
复杂性状（包括疾病易感性）的遗传变异在转录调控元件、启动子和增强子中富集。有新的证据表明，与特定性状或疾病相关的调控元件具有相似的转录活性模式。因此，共享的转录活性（共表达）可能有助于优先考虑与给定性状相关的基因座，并有助于识别潜在的生物过程。使用帽分析的基因表达（CAGE）的启动子和增强子衍生的RNA在1824人样本的配置文件，我们已经分析了共同表达的RNA起源于性状相关的调控区使用一种新的定量方法（网络密度分析; NDA）。对于大多数研究的性状，表型相关的变体在调控区被连接到紧密共表达的网络，可能共享重要的功能特性。共表达提供了一个新的信号，独立于表型关联，使致病变异的精细定位。NDA共表达方法鉴定了与特定性状相关的新遗传变异，包括OCT 1阳离子转运蛋白的调节与循环胆固醇水平相关的遗传变异之间的关联。NDA强烈暗示疾病发病机制中的特定细胞类型和组织。例如，疾病相关调节区的不同分组在溃疡性结肠炎的发病机制中涉及两个不同的生物过程;另外两个独立的过程涉及克罗恩病。因此，我们对疾病遗传易感性的功能分析定义了新的不同的疾病内在型。我们预测，各组中易感性变异占优势的患者对药物治疗的反应可能不同。总之，这些发现使人们能够对复杂性状的因果基础有更深入的生物学理解。我们发现，与特定疾病相关的遗传变异彼此之间的共同点比我们以前看到的更多。具体来说，与同一疾病相关的变异往往存在于基因组的某些部分，这些部分在许多不同的细胞类型中以类似的复杂模式打开或关闭。我们发现，与特定疾病相关的遗传变异存在于共享表达模式的调控元件中。具体来说，与同一疾病相关的变异往往存在于基因组的某些部分，这些部分在许多不同的细胞类型中以类似的复杂模式一起打开或关闭。了解这一点有助于我们发现与某些疾病相关的新变异，并更好地了解其他疾病的遗传原因。此外，我们发现炎症性肠病的遗传原因分为两种不同的模式，表明这种疾病存在两种病因学上不同的内在型。与其他从遗传信息中了解疾病机制的方法不同，我们的方法不需要任何关于基因本身的知识或假设，它只依赖于基因组的某些部分在不同细胞类型中被激活的模式。
Genetic variants underlying complex traits, including disease susceptibility, are enriched within the transcriptional regulatory elements, promoters and enhancers. There is emerging evidence that regulatory elements associated with particular traits or diseases share similar patterns of transcriptional activity. Accordingly, shared transcriptional activity (coexpression) may help prioritise loci associated with a given trait, and help to identify underlying biological processes. Using cap analysis of gene expression (CAGE) profiles of promoter- and enhancer-derived RNAs across 1824 human samples, we have analysed coexpression of RNAs originating from trait-associated regulatory regions using a novel quantitative method (network density analysis; NDA). For most traits studied, phenotype-associated variants in regulatory regions were linked to tightly-coexpressed networks that are likely to share important functional characteristics. Coexpression provides a new signal, independent of phenotype association, to enable fine mapping of causative variants. The NDA coexpression approach identifies new genetic variants associated with specific traits, including an association between the regulation of the OCT1 cation transporter and genetic variants underlying circulating cholesterol levels. NDA strongly implicates particular cell types and tissues in disease pathogenesis. For example, distinct groupings of disease-associated regulatory regions implicate two distinct biological processes in the pathogenesis of ulcerative colitis; a further two separate processes are implicated in Crohn’s disease. Thus, our functional analysis of genetic predisposition to disease defines new distinct disease endotypes. We predict that patients with a preponderance of susceptibility variants in each group are likely to respond differently to pharmacological therapy. Together, these findings enable a deeper biological understanding of the causal basis of complex traits. We discover that genetic variants associated with specific diseases have more in common with each other than we have previously seen. Specifically, variants associated with the same disease tend to be in parts of the genome that are turned on or off in similar complex patterns across many different cell types. We discover that genetic variants associated with specific diseases are found within regulatory elements that share patterns of expression. Specifically, variants associated with the same disease tend to be in parts of the genome that are turned on or off together in similar complex patterns across many different cell types. Knowing this helps us to find new variants associated with some diseases, and to better understand the genetic causes of other diseases. Furthermore, we discover that the genetic causes of inflammatory bowel disease fall into two distinct patterns, indicating that two aetiologically-distinct endotypes of this condition exist. Unlike other methods to learn about disease mechanisms from genetic information, our approach does not require any knowledge or assumptions about the genes themselves–it depends only on the patterns in which parts of the genome are activated in different cell types.
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影响因子：
64.8
作者：
Ernst, Jason;Kheradpour, Pouya;Mikkelsen, Tarjei S.;Shoresh, Noam;Ward, Lucas D.;Epstein, Charles B.;Zhang, Xiaolan;Wang, Li;Issner, Robbyn;Coyne, Michael;Ku, Manching;Durham, Timothy;Kellis, Manolis;Bernstein, Bradley E.
通讯作者：
Bernstein, Bradley E.
影响因子：
56.9
作者：
Carninci, P;Kasukawa, T;Hayashizaki, Y
通讯作者：
Hayashizaki, Y
影响因子：
4.3
作者：
Lamparter D;Marbach D;Rueedi R;Kutalik Z;Bergmann S
通讯作者：
Bergmann S
影响因子：
4.5
作者：
Baillie JK;Arner E;Daub C;De Hoon M;Itoh M;Kawaji H;Lassmann T;Carninci P;Forrest AR;Hayashizaki Y;FANTOM Consortium;Faulkner GJ;Wells CA;Rehli M;Pavli P;Summers KM;Hume DA
通讯作者：
Hume DA
影响因子：
30.8
作者：
Finucane HK;Bulik-Sullivan B;Gusev A;Trynka G;Reshef Y;Loh PR;Anttila V;Xu H;Zang C;Farh K;Ripke S;Day FR;ReproGen Consortium;Schizophrenia Working Group of the Psychiatric Genomics Consortium;RACI Consortium;Purcell S;Stahl E;Lindstrom S;Perry JR;Okada Y;Raychaudhuri S;Daly MJ;Patterson N;Neale BM;Price AL
通讯作者：
Price AL