Integrative modeling of eQTLs and cis-regulatory elements suggests mechanisms underlying cell type specificity of eQTLs.
Integrative modeling of eQTLs and cis-regulatory elements suggests mechanisms underlying cell type specificity of eQTLs.
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DOI：
10.1371/journal.pgen.1003649
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发表时间：
2013
期刊：
影响因子：
4.5
通讯作者：
Engelhardt BE
中科院分区：
文献类型：
--
作者：
Brown CD;Mangravite LM;Engelhardt BE
顺式调节元件或反式调节元件中的遗传变异经常影响基因转录的数量和时空分布。最近对表达数量性状基因座(EQTL)定位的兴趣与采用全基因组关联研究(Gwas)来分析人类的复杂性状和疾病是平行的。在假设许多GWAS关联标记非编码SNPs的影响很小，并且这些SNP通过改变基因表达来发挥表型控制作用的假设下，使用eQTL数据来解释GWAS关联已经变得很常见。为了充分利用eQTL-GWAS比较的机械可解释性，需要更好地理解eQTL的细胞类型特异性的遗传结构和原因机制。我们通过对eQTL进行三个部分的分析来满足这一需求：首先，我们从七种细胞类型的11项研究中确定了eQTL；然后，我们将eQTL数据与ENCODE项目中的顺式调节元件(CRE)数据进行了整合；最后，我们构建了一组分类器来预测eQTL的细胞类型特异性。EQTL的细胞类型特异性与eQTL SNP与数百个细胞类型特异性Cre类重叠有关，包括增强子、启动子和抑制性染色质标记、开放染色质区域和许多类别的DNA结合蛋白。这些关联提供了对产生eQTL细胞类型特异性的分子机制以及相应eQTL的调节模式的洞察。使用以细胞特异性Cre-SNP重叠为特征的随机森林分类器，我们论证了预测eQTL细胞类型特异性的可行性。然后，我们证明了在缺乏该细胞类型的eQTL数据的情况下，可以使用来自与特征相关的细胞类型的CRES来注释GWAS关联。我们预计，这种对细胞特异性的综合、预测性建模将提高我们理解人类复杂表型变异的机制基础的能力。当解释全基因组关联研究表明特定的遗传变异与疾病风险相关时，科学家们寻找遗传变异与疾病背后的生物机制之间的联系。一种作用机制是，遗传变异可能通过共定位的基因组调控元件影响基因转录，例如开放染色质区域内的转录因子结合部位。通常，这种类型的调控发生在某些类型的细胞中，而不是其他类型的细胞。在这项研究中，我们研究了七种细胞类型的11种基因表达研究，并考虑了遗传转录调节因子或eQTL如何在细胞类型内和细胞类型之间复制。我们通过多个独立的eQTL识别普遍存在的等位基因异质性，或单个基因的转录控制。我们整合了来自ENCODE的关于细胞类型特定调控元件的大量数据，以通过丰富调控元件中的eQTL来确定转录调控的一般方法。我们还构建了一个分类器来预测eQTL跨细胞类型的复制。本文的结果提供了一条通向综合的、可预测的方法的途径，以提高我们理解人类表型变异的机制基础的能力。
Genetic variants in cis-regulatory elements or trans-acting regulators frequently influence the quantity and spatiotemporal distribution of gene transcription. Recent interest in expression quantitative trait locus (eQTL) mapping has paralleled the adoption of genome-wide association studies (GWAS) for the analysis of complex traits and disease in humans. Under the hypothesis that many GWAS associations tag non-coding SNPs with small effects, and that these SNPs exert phenotypic control by modifying gene expression, it has become common to interpret GWAS associations using eQTL data. To fully exploit the mechanistic interpretability of eQTL-GWAS comparisons, an improved understanding of the genetic architecture and causal mechanisms of cell type specificity of eQTLs is required. We address this need by performing an eQTL analysis in three parts: first we identified eQTLs from eleven studies on seven cell types; then we integrated eQTL data with cis-regulatory element (CRE) data from the ENCODE project; finally we built a set of classifiers to predict the cell type specificity of eQTLs. The cell type specificity of eQTLs is associated with eQTL SNP overlap with hundreds of cell type specific CRE classes, including enhancer, promoter, and repressive chromatin marks, regions of open chromatin, and many classes of DNA binding proteins. These associations provide insight into the molecular mechanisms generating the cell type specificity of eQTLs and the mode of regulation of corresponding eQTLs. Using a random forest classifier with cell specific CRE-SNP overlap as features, we demonstrate the feasibility of predicting the cell type specificity of eQTLs. We then demonstrate that CREs from a trait-associated cell type can be used to annotate GWAS associations in the absence of eQTL data for that cell type. We anticipate that such integrative, predictive modeling of cell specificity will improve our ability to understand the mechanistic basis of human complex phenotypic variation. When interpreting genome-wide association studies showing that specific genetic variants are associated with disease risk, scientists look for a link between the genetic variant and a biological mechanism behind that disease. One functional mechanism is that the genetic variant may influence gene transcription via a co-localized genomic regulatory element, such as a transcription factor binding site within an open chromatin region. Often this type of regulation occurs in some cell types but not others. In this study, we look across eleven gene expression studies with seven cell types and consider how genetic transcription regulators, or eQTLs, replicate within and between cell types. We identify pervasive allelic heterogeneity, or transcriptional control of a single gene by multiple, independent eQTLs. We integrate extensive data on cell type specific regulatory elements from ENCODE to identify general methods of transcription regulation through enrichment of eQTLs within regulatory elements. We also build a classifier to predict eQTL replication across cell types. The results in this paper present a path to an integrative, predictive approach to improve our ability to understand the mechanistic basis of human phenotypic variation.
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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.
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