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体表型之间的鸿沟提供了第一步。迄今为止进行的研究中几乎无处不在的观察是，主要位于非编码区域的顺式调控变异是普遍存在的，并且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dna足迹已成为鉴定体内dna -蛋白质相互作用的有力方法。为此，在Aim 1中，我们将利用数字dna足迹的力量，通过在40个遗传多样化的酵母菌株和物种（38株酿酒酵母菌，1株悖论酵母菌和1株bayanus酵母菌）中开发一个全面的核苷酸水平的体内dna -蛋白质相互作用分辨率图，系统地探究非编码变异的功能意义。这些数据将对体内蛋白质结合位点变异的自然变异以及物种内部和物种之间形成调节序列变异模式的进化力量产生根本性的见解。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性模式关联起来，提供迄今为止生成的最大的功能调节等位基因纲要之一。重要的是，我们还将使用这个独特的数据集来开发预测功能显著的非编码变异的统计方法。该项目的成功完成将为更有原则地理解非编码变异提供基础，促进将静态基因组信息转化为转录丰度的预测和定量模型，使个人基因组学倡议背景下的序列变异能够得到解释，并对基因表达水平的进化产生新的见解。

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.