eQTLs和顺式调节元件的综合建模表明了eQTLs细胞类型特异性的潜在机制
Integrative modeling of eQTLs and cis-regulatory elements suggest mechanisms underlying cell type specificity of eQTLs
摘要：
顺式调控元件或反式调控元件的遗传变异通常会影响基因转录的数量和时空分布。近年来，人们对表达数量性状基因座（eQTL）定位的兴趣促使人们采用全基因组关联研究（GWAS）来分析人类的复杂性状和疾病。在许多GWAS关联标记非编码SNP的小的影响，这些SNP通过修饰基因表达发挥表型控制的假设下，它已成为共同使用eQTL数据解释GWAS关联。为了利用eQTL-GWAS比较的机制解释性，需要对eQTL的遗传结构和细胞类型特异性有更好的理解。我们通过四个部分进行eQTL分析来满足这一需求：首先，我们从7种细胞类型的11项研究中确定了eQTL;其次，我们量化了这些研究中细胞类型特异性的eQTL;然后，我们将eQTL数据与ENCODE项目的顺式调控元件（CRE）数据集整合在一起;最后，我们建立了一个分类器来预测细胞类型特异性的eQTL。与先前的研究一致，我们证明了等位基因异质性在顺式eQTL中是普遍存在的，并且顺式eQTL通常是细胞类型特异的。在细胞类型内和细胞类型之间，eQTL复制与eQTL SNP重叠相关，eQTL SNP与数百种细胞类型特异性CRE元件类别重叠，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。使用包括526个CRE数据集作为特征的随机森林分类器，我们成功地预测了eQTL SNP的细胞类型特异性，在缺乏来自感兴趣的细胞类型的基因表达数据的情况下。我们预计，这种综合性的预测模型将提高我们理解人类复杂表型变异的机制基础的能力。
英文摘要：
Genetic variants in cis-regulatory elements or trans-acting regulators commonly 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 exploit the mechanistic interpretability of eQTL-GWAS comparisons, an improved understanding of the genetic architecture and cell type specificity of eQTLs is required. We address this need by performing an eQTL analysis in four parts: first we identified eQTLs from eleven studies on seven cell types; next we quantified cell type specific eQTLs across the studies; then we integrated eQTL data with cis-regulatory element (CRE) data sets from the ENCODE project; finally we built a classifier to predict cell type specific eQTLs. Consistent with prior studies, we demonstrate that allelic heterogeneity is pervasive at cis-eQTLs and that cis-eQTLs are often cell type specific. Within and between cell type eQTL replication is associated with eQTL SNP overlap with hundreds of cell type specific CRE element classes, including enhancer, promoter, and repressive chromatin marks, regions of open chromatin, and many classes of DNA binding proteins. Using a random forest classifier including 526 CRE data sets as features, we successfully predict the cell type specificity of eQTL SNPs in the absence of gene expression data from the cell type of interest. We anticipate that such integrative, predictive modeling will improve our ability to understand the mechanistic basis of human complex phenotypic variation.
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