The functional significance of non-coding variation

描述(由申请人提供)：基因表达是一种重要的分子表型，为弥合静态基因组信息和动态生物表型之间的鸿沟提供了第一步。到目前为止，几乎无处不在的研究发现，主要位于非编码区的顺式调节变异是普遍存在的，是可遗传基因表达变异的重要来源。然而，非编码变异的功能后果很难在全基因组范围内进行评估。最近，数字DNAseI足迹已经成为识别体内DNA-蛋白质相互作用的一种强有力的方法。为此，在目标1中，我们将利用数字DNAseI足迹的力量，通过开发40个遗传不同的酵母菌株和物种(38株酿酒酵母，1株矛盾链霉菌和1株巴扬链霉菌)体内DNA-蛋白质相互作用的全面和核苷酸水平的分辨率图，系统地询问非编码变异的功能意义。这些数据将产生对体内蛋白质结合位点变异的自然变异以及物种内和物种之间调节序列变异模式的进化力量的基本见解。在目标2中，我们将对所有40个菌株和物种进行深度RNA-Seq，并将导致体内DNA-蛋白质相互作用与基因表达水平变化的多态模式关联起来，提供迄今产生的最大的功能调节等位基因之一。重要的是，我们还将使用这个独特的数据集来开发统计方法，以预测功能上显著的非编码变异。拟议项目的成功完成将为更原则性地理解非编码变异奠定基础，促进将静态基因组信息转化为转录本丰度的预测和定量模型，能够在个人基因组学倡议的背景下解释序列变异，并对基因表达水平的演变产生新的见解。

DESCRIPTION (provided by applicant): Gene expression is an important molecular phenotype, providing the initial step in bridging the divide between static genomic information and dynamic organismal phenotypes. A nearly ubiquitous observation of studies performed to date is that cis-regulatory variation, primarily located in non-coding regions, is pervasive and a significant source of heritable gene expression variation. However, the functional consequences of non-coding variation have been difficult to assess on a genome-wide scale. Recently, digital DNAseI footprinting has emerged as a powerful approach to identify in vivo DNA-protein interactions. To this end, in Aim 1 we will leverage the power of digital DNAseI footprinting to systematically interrogate the functional significance of non-coding variation by developing a comprehensive and nucleotide level resolution map of in vivo DNA-protein interactions in 40 genetically diverse yeast strains and species (38 strains of Saccharomyces cerevisae, one strain of S. paradoxus, and one strain of S. bayanus). These data will yield fundamental insights into natural variation of in vivo protein binding site variation and the evolutionary forces shaping patterns of regulatory sequence variation within and between species. In Aim 2, we will perform deep RNA-Seq on all 40 strains and species and correlate patterns of polymorphisms that lead to variation in in vivo DNA- protein interactions with gene expression levels, providing one of the largest compendiums of functional regulatory alleles generated to date. Importantly, we will also use this unique dataset to develop statistical methods for predicting functionally significant non-coding variation. The successful completion of the proposed project will provide the foundation for a more principled understanding of non-coding variation, facilitate the translation of static genomic information into predictive and quantitative models of transcript abundance, enable the interpretation of sequence variation in the context of personal genomics initiatives, and yield new insights into the evolution of gene expression levels.