Statistical models to investigate long-distance QTL transcription regulation

项目总结/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个 人类基因组的突变。但是将这些结果转化为医学治疗需要一个 精确了解突变如何导致疾病的机制。目前， 单核苷酸多态性（SNPs）的调节作用，在很大程度上， 局部或顺式表达的数量性状基因座（eQTL）在少数人体组织。但 并非所有疾病或复杂表型都由顺式eQTL介导。很少有长途电话，或者 尽管trans-eQTL在人类组织中起着重要的作用， 在一些复杂的表型中起重要作用。选择性剪接也被证明可以调节 然而，对调节选择性剪接的SNP知之甚少。的 拟议的K99/R 00研究旨在设计统计方法， 转录本网络，以确定调节基因和mRNA亚型转录的SNP。 无论是在本地还是在长距离，并验证这些ndings，为 目的是提供对复杂表型和疾病机制的深入了解。 我们建议利用我们目前在艾德鉴定的人类中的顺式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基因网络。我们将使用 贝叶斯统计模型，用于计算特定网络相对于 基因在网络中联合，与相关的eQTL提供信息，是否调控 基因是其他网络基因的上游或下游。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ND接近最佳的解决方案 解决此类问题。我们将使用这些网络来鉴定trans-eQTL，并量化 在一个特定的过程中，使用贝叶斯统计检验，我们开发了每个trans-eQTL的效果， 实验室随后，我们建议利用新型RNA测序技术的机会 和非参数统计模型来鉴定每个转录基因的转录物同种型， 同时，通过扩展稀疏因子分析模型，对个体特异性转录水平进行分析。 这将使我们能够鉴定调控特定转录本异构体转录的QTL （tQTL）通过选择性剪接事件通过扩展我们用于eQTL鉴定的方法。 我们将使用我们为eQTL开发的方法来构建转录异构体网络 （Transcript Networks）。最后，我们将使用转录本网络来鉴定和量化tQTL， 在局部和长遗传距离上调节转录物同种型的个体特异性水平， 与eQTL一样。我们将公开我们所有的方法和结果。

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.