Genome Analysis: Data Accuracy, Haplotyping and Mapping

描述(由申请人提供)： 我们建议开发一系列新的统计方法，用于分析来自遗传学和基因组学中高通量测序分析的功能表型，特别是来自RNA-SEQ、CHIP-SEQ和DNase-SEQ分析的数据。我们建议的方法是将沿基因组映射到每个碱基的读数视为高度多变量的，但也是高度结构化的表型。使用信号处理(小波)的方法，我们将开发方法来识别基因组中这些表型在样本或样本组(例如细胞类型、处理组或基因类别)之间存在显著差异的区域。与基于滑动窗口的方法不同，这些方法将能够识别在多个不同尺度上发生的差异。统计方法 我们的开发将促进小规模比较(例如，确定两个样本或条件之间在结合或组蛋白修饰方面的差异)和较大规模的分析，例如旨在识别与这些表型(表达QTL、结合QTL、dsQTL)相关的遗传变异的遗传关联分析。作为一个重要的特例，我们的方法将解决从RNA-SEQ数据中识别差异表达基因的常见问题，包括剪接或替代转录本的变异。这些方法将建立在R01当前供资周期内开发的关联分析方法的基础上，并在很大程度上扩展这些方法。我们的研究结果将是一套统计工具，将极大地促进对涉及功能表型的广泛的遗传和基因组研究的分析。我们将制作和分发实施这些方法的用户友好的软件。我们将使用我们的方法来分析我们的合作者产生的现有数据，以及来自NIH资助的GTEx项目的公开可用数据，以便将它们与现有的分析方法进行比较，并确定导致表型变异的调控基因变异。总体目标是为遗传学和基因组学研究界提供软件和统计工具，促进生物学发现和洞察，并最终了解常见疾病的遗传基础。

DESCRIPTION (provided by applicant): We propose to develop an array of novel statistical methods for association analysis of functional phenotypes arising from high-throughput sequencing assays in genetics and genomics, specifically data from RNA-seq, ChIP-seq, and DNase-seq assays. Our proposed approach is to treat the number of reads mapping to each base along the genome as a highly-multivariate, but also highly-structured, phenotype. Using methods from signal processing (wavelets), we will develop methods to identify regions of the genome where these phenotypes differ significantly between samples, or groups of samples (e.g. cell types, treatment groups, or genotype classes). In contrast to approaches based on sliding windows, the methods will be capable of identifying differences that occur at multiple different scales. The statistical methods we develop will facilitate both small-scale comparisons (e.g. identifying differences in binding, o histone modifications, between two samples or conditions), and larger-scale analyses, such as genetic association analyses that aim to identify genetic variants associated with these phenotypes (expression QTLs, binding QTLs, dsQTLs). As an important special case, our methods will tackle the commonly- encountered problem of identifying differentially expressed genes, including variations in splicing or alternative transcripts, from RNA-seq data. These methods will build on and substantially extend methods for association analyses developed during the current funding cycle of this R01. The result of our research will be a suite of statistical tools that will greatly facilitate the analysis of the wide range of genetic and genomi studies that involve functional phenotypes. We will produce and distribute user-friendly software implementing these methods. We will use our methods to analyze existing data generated by our collaborators, and publicly-available data from the NIH-funded GTeX project, both to compare them with existing analysis methods and to identify regulatory genetic variants responsible for phenotypic variation. The overall objective is for the work to provide software and statistical tools for the genetics and genomics research community, facilitating biological discoveries and insights, and, ultimately, understanding of the genetic basis of common disease.