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，用于遗传学和基因组学中高通量测序测定产生的功能表型的关联分析，特别是来自RNA-seq，ChIP-seq和DNase-seq测定的数据。我们提出的方法是将沿着基因组沿着映射到每个碱基的读段的数量视为高度多变量的，但也是高度结构化的表型。使用来自信号处理（小波）的方法，我们将开发方法来识别基因组区域，其中这些表型在样品或样品组（例如细胞类型，处理组或基因型类别）之间存在显著差异。与基于滑动窗口的方法相比，这些方法将能够识别在多个不同尺度上发生的差异。的统计方法 我们开发的新方法将有助于小规模比较（例如，鉴定两个样品或条件之间的结合、组蛋白修饰的差异）和大规模分析，例如旨在鉴定与这些表型相关的遗传变异（表达QTL、结合QTL、dsQTL）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物的变化。这些方法将建立在本R 01当前资助周期内开发的关联分析方法的基础上，并对其进行大幅扩展。我们的研究结果将是一套统计工具，这将大大促进涉及功能表型的广泛遗传和基因组研究的分析。我们将生产和分发实现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生成的现有数据，以及来自NIH资助的GTeX项目的公开数据，将它们与现有的分析方法进行比较，并确定负责表型变异的调控遗传变异。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，促进生物学发现和见解，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.