Statistical models to investigate long-distance QTL transcription regulation

项目摘要/摘要 数以千计的全基因组关联研究将特定疾病或复杂表型与单一 人类基因组中的突变。但将这些结果转化为医疗需要 准确地理解这种突变是如何导致疾病机制的。目前， 单核苷酸多态(SNPs)的调节作用在很大程度上与 局部或顺式表达的数量性状基因座(EQTL)在少量人体组织中表达。但 并不是所有的疾病或复杂的表型都是由cis-eQTL介导的。很少有长途电话，或者 已经在人体组织中识别和验证了反式eQTL，尽管反式eQTL发挥着 在一些复杂的表型中起着重要作用。另一种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SNPs知之甚少。这个 拟议的K99/R00研究旨在设计统计方法，建立基因和 转录网络用于识别调控基因和mRNA异构体转换的SNPs 脚本，无论是本地的还是远距离的，并为 目的是提供对复杂表型和疾病的机制的洞察。 我们建议利用我们目前发现的人类的cis-eQTL和基因表达数据 致力于在基因组规模上建立精确的、定向的基因网络。我们将使用以下技术构建这些网络 用于计算特定网络相对于每个网络的概率的贝叶斯统计模型 基因在网络中共同存在，相关的eQTL提供关于是否受调控的信息 基因位于其他网络基因的上游或下游。我们将使用马尔科夫链蒙特卡罗 和线性规划松弛方法，已被证明能找到次最优解 来解决这类问题。我们将使用这些网络来识别跨eQTL，并量化 利用我们开发的贝叶斯统计检验，对特定过程中的每个反式eQTL进行了评估 实验室。随后，我们提出利用新的rna测序技术的机会。 和非参数统计模型来识别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，对个体差异表达水平进行预测。 这将使我们能够识别调控特定转录异构体转录的qtl。 (TQTL)通过选择性剪接事件，通过扩展我们已有的eQTL鉴定方法。 我们将使用我们为eQTL开发的方法来构建转录异构体的网络 (文字记录网络)。最后，我们将使用转录网络来识别和量化tQTL 调节局部和远遗传距离的转录物异构体的个体特异性水平， 与eQTL一样。我们将把我们的所有方法和结果公之于众。

Project Summary/Abstract Thousands of genome-wide association studies link speci c 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, con ned 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 identi ed 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 tran- scription, both locally and over long distances, and to validate those ndings, 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 identi ed 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 nd near-optimal solutions to this type of problem. We will use these networks to identify trans-eQTLs, and quantify the e ect 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-speci c transcript levels by extending sparse factor analysis models. This will enable us to identify QTLs that regulate the transcription of speci c transcript isoforms (tQTLs) via alternative splicing events by extending the methods we have for eQTL identi cation. 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-speci c 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.