Interpreting human enhancer variants with a network-regularized composite model

 描述(申请人提供)：用于解释人类基因组非蛋白质编码区序列变异的计算方法的发展已经落后于产生大量全基因组相关研究(GWAS)和全基因组测序(WGS)数据的能力。在这个项目中，我们将开发基于严格统计建模的创新计算方法，以整合来自不同来源的大量不同来源的基因组数据集，以识别可能影响机体功能并导致疾病风险或其他特征的非编码变体。鉴于它们的基因组流行率和功能重要性，我们将把这项拟议的研究重点放在被称为增强子的特定类别的基因组位点上。通过专注于增强剂，我们能够开发出可以通过实验方法广泛验证的严格的统计方法。长期目标是准确预测赋予表型效应的序列变异。这一特定应用的目标是开发分析基因组数据的计算方法，以识别一组非编码变体，这些变体是影响机体功能并导致疾病风险或其他特征的候选变量。虽然我们的方法旨在处理人类基因组中识别的不同类型位点中的非编码变体，但在本应用中，我们将基于我们的中心假设关注增强子中变体的表型效应：i)非编码区中大多数功能重要的、与疾病和性状相关的变体发生在增强子区内，以及ii)这些变体不仅改变了邻近编码靶基因上的增强子作用，而且还扰乱了增强子相互作用的调控网络，导致更广泛的转录调控程序的变化。这些假说是基于我们自己在9p21基因沙漠中产生的初步数据制定的，9p21基因沙漠与特定类型的癌症、心血管疾病和2型糖尿病有关，并且我们已经在9p21基因沙漠中做出了贡献，将Gwas数据与对特定增强子功能的机制理解联系在一起。在强大的初步数据的指导下，这一假说将通过追求两个具体目标来检验：1)通过生物网络的统计建模来预测因果增强剂的变异；2)通过实验验证计算预测。这种方法是创新的，因为我们的计算方法不同于其他用于分析序列变体的软件工具，例如RegulomeDB和FunSeq，因为它集成了来自不同来源的大量不同的基因组数据集，并结合了生物网络的严格统计建模。这项拟议的研究意义重大，因为通过结合遗传疾病和性状的基因和表型信息，我们的方法将能够识别非编码变体和表型之间的潜在功能联系，并促进对全基因组序列数据的有针对性的分析，以进行疾病风险评估。

 DESCRIPTION (provided by applicant): The development of Computational methods for interpreting sequence variants in the non-protein coding regions of the human genome has lagged behind the ability to generate large volumes of genome-wide associated study (GWAS) and whole-genome sequencing (WGS) data. In this project, we will develop innovative computational methods based on rigorous statistical modeling to integrate a large number of heterogeneous genomic data sets from diverse sources to identify non-coding variants that are candidates for affecting organismal function and leading to disease risk or other traits. Due to their genomic prevalence and functional importance, we will focus this proposed research on the specific class of genomic sites known as enhancers. By focusing on enhancers, we are able to develop rigorous statistical methodologies that can be extensively validated via experimental methods. The long-term goal is to accurately predict the sequence variants that confer a phenotypic effect. The objective in this particular application is to develop computational methods that analyze genomic data to identify a set of non-coding variants that are candidates for affecting organismal function and leading to disease risk or other traits. While our methods are intended to handle non- coding variants in different classes of sites identified in human genomes, in this application we will focus on phenotypic effects of variants in enhancers based on our central hypotheses are i) the majority of functionally- important, disease- and trait-associated variants in non-coding regions occur within enhancer regions, and ii) these variants not only alter enhancer actions on adjacent coding target genes, but also disrupt regulatory networks of enhancer interactions, leading to changes in broader programs of transcriptional regulation. These hypotheses have been formulated on the basis of our own preliminary data produced in the 9p21 gene desert, which is linked to specific types of cancer, cardiovascular disease, and type 2 diabetes, and is a locus where we have already made contributions linking GWAS data to a mechanistic understanding of specific enhancer functions. Guided by strong preliminary data, this hypothesis will be tested by pursuing two specific aims: 1) To predict causal enhancers variant by statistical modeling with biological networks; 2) To experimentally validate the computational predictions. The approach is innovative, because our computational approach is different from other software tools for analyzing sequence variants - e.g., RegulomeDB and FunSeq - as it integrates a large number of heterogeneous genomic data sets from diverse sources and incorporates rigorous statistical modeling of biological networks. The proposed research is significant, because by incorporating both genotypic and phenotypic information of genetic diseases and traits, our methods will be able to identify potential functional connections between non-coding variants and phenotypes, and facilitate a targeted analysis of whole-genome sequence data for disease risk assessment.