Statistical models to investigate long-distance QTL transcription regulation

描述（由申请人提供）：数千项全基因组关联研究将特定疾病或复杂表型与人类基因组中的单个突变联系起来。但是，将这些结果转化为医学治疗需要精确了解这种突变如何导致疾病的机制。目前，单核苷酸多态性（SNP）的调节作用在很大程度上限于少数人类组织中的局部或顺式表达数量性状基因座（eQTL）。但并非所有疾病或复杂表型都由顺式eQTL介导。尽管反式eQTL在一些复杂的表型中发挥着重要作用，但在人体组织中很少发现和验证长距离或反式eQTL。选择性剪接也被证明可以调节某些表型;然而，对调节选择性剪接的SNP知之甚少。拟议的K99/R 00研究旨在设计统计方法，建立基因和转录网络，以识别调节基因和mRNA亚型转录的SNP，包括本地和远距离，并验证这些发现，以深入了解复杂表型和疾病的机制。 我们建议利用我们目前工作中确定的人类顺式eQTL和基因表达数据，在基因组规模上建立精确的定向基因网络。我们将使用贝叶斯统计模型来构建这些网络，以计算特定网络与网络中每个基因联合的概率，相关的eQTL提供有关受调控基因是否位于其他网络基因上游或下游的信息。我们将使用马尔可夫链蒙特卡罗和线性规划松弛方法，已被证明可以找到这类问题的近最佳解决方案。我们将使用这些网络来识别trans-eQTL，并使用我们实验室开发的贝叶斯统计测试来量化每个trans-eQTL在特定过程中的影响。随后，我们建议利用新的RNA测序技术和非参数统计模型的机会，以确定每个转录基因的转录异构体，同时，通过扩展稀疏因子分析模型的个人特定的转录水平。这将使我们能够通过扩展eQTL鉴定方法来鉴定通过选择性剪接事件调节特定转录本异构体（tQTL）转录的QTL。我们将使用我们为eQTL开发的方法来构建转录本异构体网络（转录本网络）。最后，我们将使用转录本网络来识别和量化tQTL，这些tQTL在本地和长遗传距离上调节转录本异构体的个体特异性水平，就像eQTL一样。我们将公开我们所有的方法和结果。 公共卫生相关性：数以千计的全基因组关联研究将特定疾病或复杂性状与人类基因组中的单个突变联系起来，但这些结果还不能转化为医学治疗，因为知道突变与疾病有关，事实上，并不能让我们深入了解突变如何有助于疾病的机制。我们提出的研究将设计和验证统计方法，提供一个全面的路线图，以了解在这些关联研究中确定的突变的生物学作用。掌握了数千种可能与疾病相关的突变的作用，研究人员可以开始拼凑疾病的机制，并将他们的发现更快地转化为疾病的治疗方法。

DESCRIPTION (provided by applicant): Thousands of genome-wide association studies link specific diseases or complex phenotypes to single mutations in the human genome. But translating these results to medical treatments requires a precise understanding of how that mutation contributes to the mechanism of disease. Currently, the regulatory role of single nucleotide polymorphisms (SNPs) is, for the most part, confined to local, or cis-, expression quantitative trait loci (eQTLs) in a small number of human tissues. But not all diseases or complex phenotypes are mediated by cis-eQTLs. Very few long-distance, or trans-, eQTLs have been identified and validated in human tissues, although trans-eQTLs play an important role in some complex phenotypes. Alternative splicing has also been shown to modulate certain phenotypes; however, little is known about SNPs that regulate alternative splicing. The proposed K99/R00 research seeks to design statistical methods that build gene and transcript networks to identify SNPs that regulate gene and mRNA isoform transcription, both locally and over long distances, and to validate those findings, for the purpose of providing insight into mechanisms for complex phenotypes and disease. We propose to leverage cis-eQTLs and gene expression data in humans identified in our current work to build precise, directed gene networks on a genome-scale. We will build these networks using Bayesian statistical models to compute the probability of a particular network with respect to each gene in the network jointly, with associated eQTLs providing information about whether regulated genes are upstream or downstream of other network genes. We will use Markov chain Monte Carlo and linear programming relaxation methods that have been shown to find near-optimal solutions to this type of problem. We will use these networks to identify trans-eQTLs, and quantify the effect of each trans-eQTL in a particular process using Bayesian statistical tests developed in our lab. Subsequently, we propose to exploit the opportunities of novel RNA sequencing techniques and nonparametric statistical models to identify transcript isoforms for each transcribed gene and, simultaneously, individual-specific transcript levels by extending sparse factor analysis models. This will enable us to identify QTLs that regulate the transcription of specific transcript isoforms (tQTLs) via alternative splicing events by extending the methods we have for eQTL identification. We will use the methodology we developed for eQTLs to build networks for transcript isoforms (transcript networks). Finally, we will use transcript networks to identify and quantify tQTLs that regulate individual-specific levels of transcript isoforms both locally and over long genetic distances, as with eQTLs. We will make all of our methods and results publicly available. PUBLIC HEALTH RELEVANCE: Thousands of genome-wide association studies link specific diseases or complex traits to single mutations in the human genome, but these results cannot yet be translated to medical treatments because knowing that a mutation is associated with a disease does not, in fact, give us insight into how that mutation contributes to the mechanism of disease. Our proposed research will design and validate statistical methods that provide a comprehensive road map to understanding the biological role of the mutations that are identified in these association studies. With the role of thousands of possibly disease-related mutations in hand, researchers can begin to piece together the mechanism of a disease and translate their findings into treatments for the disease much more quickly.