格雷戈尔：使用系统的数据驱动方法评估表观基因组特征中与性状相关变体的全局富集情况。
GREGOR: evaluating global enrichment of trait-associated variants in epigenomic features using a systematic, data-driven approach.
作者信息
Schmidt Ellen M, Zhang Ji, Zhou Wei, Chen Jin, Mohlke Karen L, Chen Y Eugene, Willer Cristen J
机构信息
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109.
Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI 48109 and.
出版信息
Bioinformatics. 2015 Aug 15;31(16):2601-6. doi: 10.1093/bioinformatics/btv201. Epub 2015 Apr 16.
MOTIVATION
The majority of variation identified by genome wide association studies falls in non-coding genomic regions and is hypothesized to impact regulatory elements that modulate gene expression. Here we present a statistically rigorous software tool GREGOR (Genomic Regulatory Elements and Gwas Overlap algoRithm) for evaluating enrichment of any set of genetic variants with any set of regulatory features. Using variants from five phenotypes, we describe a data-driven approach to determine the tissue and cell types most relevant to a trait of interest and to identify the subset of regulatory features likely impacted by these variants. Last, we experimentally evaluate six predicted functional variants at six lipid-associated loci and demonstrate significant evidence for allele-specific impact on expression levels. GREGOR systematically evaluates enrichment of genetic variation with the vast collection of regulatory data available to explore novel biological mechanisms of disease and guide us toward the functional variant at trait-associated loci.
AVAILABILITY AND IMPLEMENTATION
GREGOR, including source code, documentation, examples, and executables, is available at http://genome.sph.umich.edu/wiki/GREGOR.
CONTACT
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
动机
全基因组关联研究识别出的大部分变异位于非编码基因组区域，据推测会影响调控基因表达的调控元件。在此，我们展示了一种统计严谨的软件工具GREGOR（基因组调控元件与全基因组关联研究重叠算法），用于评估任何一组遗传变异与任何一组调控特征的富集情况。利用来自五种表型的变异，我们描述了一种数据驱动的方法，以确定与感兴趣的性状最相关的组织和细胞类型，并识别可能受这些变异影响的调控特征子集。最后，我们通过实验评估了六个脂质相关位点的六个预测功能变异，并证明了等位基因对表达水平有特异性影响的显著证据。GREGOR系统地评估遗传变异与大量可用调控数据的富集情况，以探索疾病的新生物学机制，并引导我们找到性状相关位点的功能变异。
可用性与实现
GREGOR包括源代码、文档、示例和可执行文件，可在http://genome.sph.umich.edu/wiki/GREGOR获取。
联系方式
补充信息
补充数据可在《生物信息学》在线获取。
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