Mapping Expression Quantitative Trait Loci with Next Generation Sequencing in SLE

使用下一代测序在 SLE 中绘制表达数量性状位点

• 批准号: 9015408
• 负责人: Min Chen
• 金额: $ 12.02万
• 依托单位: UNIVERSITY OF TEXAS DALLAS
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-02-01 至 2017-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9015408
• 关键词: Accounting; Affect; Alleles; American; Autoimmune Diseases; Autoimmunity; Award; Biological; Biomedical Research; Blood; Body part; Brain; Chromosome Mapping; Chronic; Clinical; Complex; Computational algorithm; Computing Methodologies; Country; Coupled; DNA; DNA Sequence; Data; Data Analyses; Detection; Diagnosis; Disease; Distant; Event; Gene Expression; Gene Expression Regulation; Genes; Genetic; Genetic Polymorphism; Genetic Predisposition to Disease; Genetic Transcription; Genetic Variation; Genomics; Genotype; Goals; Health; Hemorrhage; Hereditary Disease; Heterogeneity; Human; Immune; Immunology; Individual; Inflammation; Joints; Kidney; Lead; Maps; Mentors; Methods; MicroRNAs; Modeling; Molecular; Molecular Genetics; Nuclear Antigens; Other Genetics; Pathogenesis; Pathway interactions; Patients; Pattern; Pharmaceutical Preparations; Phenotype; Play; Predisposition; Prevention; Process; Prognostic Marker; Protein Isoforms; Quantitative Trait Loci; RNA Splicing; Reading; Regulation; Regulatory Element; Reporting; Research; Research Personnel; Research Project Grants; Resolution; Role; Sample Size; Sampling; Site; Skin; Statistical Algorithm; Statistical Methods; Statistical Models; Survival Rate; Symptoms; Systemic Lupus Erythematosus; Systems Biology; Technology; Training; Transcript; Untranslated RNA; Validation; Variant; aggressive therapy; analytical tool; base; biomedical scientist; career; case control; data management; design; flexibility; genetic disorder diagnosis; genetic predictors; genetic profiling; genetic variant; genome sequencing; genome wide association study; improved; insight; mRNA Expression; next generation sequencing; novel; patient population; prevent; rare variant; reference genome; research and development; response; rheumatologist; therapy development; tool; transcriptome sequencing; treatment response; user friendly software

DESCRIPTION (provided by applicant): Systemic lupus erythematosus (SLE) is an autoimmune disease that leads to chronic inflammation and may affect any part of the body, especially the skin, joints, kidneys, brain, and blood. It is estimated that over 1.5 million Americans have SLE and over 16,000 new cases of SLE are reported annually across the country, calling for development of therapies to prevent or manage long-term manifestations of the disease. While the survival rate has been greatly enhanced due to advances in research on the mechanisms of the disease and aggressive therapy, there is no cure for SLE. Among autoimmune disorders, SLE is one of the most difficult to understand and treat, with great heterogeneities in the pathogenesis, manifestations and responses to therapy. Genetic predisposition is a major factor of SLE, and genetic variation is perhaps an important component toward the heterogeneities. Recent studies have suggested that expression quantitative trait loci (eQTL) mapping, an effective tool for the discovery of genetic footprints o transcription variations, may increase the chance of detecting polymorphisms related to susceptibilities and therapeutic responses in SLE patients. The goal of this project is to develop statistical and computational approaches to eQTL mapping with next generation sequencing data, to improve the detection of important genetic variants, provide great insights into gene regulation and lead to a deeper understanding of the genetics of SLE. To understand the molecular mechanisms and genetic factors of SLE, my primary mentor, Dr. Wakeland, and his collaborators have been generating RNA sequencing data, coupled with targeted DNA sequencing data, for samples from hundreds of SLE patients and controls. These large-scale and multi-dimensional sequencing data, with unprecedented resolution and accuracy, provide us great opportunities to generate significant scientific findings, while posing great challenges for data management, data analysis and results interpretation. We have three specific aims: (1) Aim 1: To develop and implement computational and statistical algorithms to build a pipeline for processing RNA and DNA sequencing data. We will build a pipeline to perform quality check for raw sequencing reads, align reads to the reference genome, call SNPs for DNA-seq samples, and identify splicing events, reconstruct isoforms and quantify isoform and overall gene expressions for RNA-seq samples. (2) Aim 2: To develop statistical models to map isoform-specific eQTLs targeting common alleles. We will first map gene eQTLs in immune-related genetic regions using conventional statistical methods. We will then develop a novel statistical model to identify SNPs that influence the regulation of isoform expression. (3) Aim 3: To develop statistical approaches to map multiple loci for improved detection of rare and weak-effect variants. We will develop multi-loci methods that can use prior biological information to group SNPs into annotated genetic regions. First, we seek to identify local eQTL intervals located within and near a gene region. Second, we will develop methods to first aggregate SNPs in the same functional sets like pathways or networks and then identify gene sets that may regulate the gene expression. This multi-loci strategy may identify distant genetic regulators. The proposed research is for a K25 Mentored Quantitative Research Development Award that will prepare the candidate for a successful career in quantitative biomedical research. The primary career goal is to become an independent investigator and an expert in developing statistical and computational methodologies for high-throughput genetic data to improve the understanding of genetic profiles, to discover genetic diagnosis and prognosis markers, as well as to promote prevention and treatment for SLE and other genetic disorders.

描述（由申请人提供）：系统性红斑狼疮（SLE）是一种自身免疫性疾病，可导致慢性炎症，并可能影响身体的任何部位，特别是皮肤、关节、肾脏、大脑和血液。据估计，超过150万美国人患有SLE，全国每年报告超过16，000例SLE新病例，需要开发治疗方法以预防或管理该疾病的长期表现。虽然由于疾病机制和积极治疗的研究进展，生存率已大大提高，但SLE仍无法治愈。在自身免疫性疾病中，SLE是最难理解和治疗的疾病之一，其发病机制、临床表现和治疗反应具有很大的异质性。遗传易感性是SLE发病的主要因素，遗传变异可能是导致其异质性的重要因素。最近的研究表明，表达数量性状基因座（eQTL）定位，一个有效的工具，发现转录变异的遗传足迹，可能会增加检测的机会，多态性与SLE患者的易感性和治疗反应。该项目的目标是利用下一代测序数据开发eQTL定位的统计和计算方法，以改善重要遗传变异的检测，提供对基因调控的深刻见解，并加深对SLE遗传学的理解。为了了解SLE的分子机制和遗传因素，我的主要导师Wakeland博士和他的合作者一直在为数百名SLE患者和对照组的样本生成RNA测序数据，以及靶向DNA测序数据。这些大规模和多维的测序数据具有前所未有的分辨率和准确性，为我们提供了产生重大科学发现的巨大机会，同时也为数据管理，数据分析和结果解释带来了巨大挑战。我们有三个具体目标：（1）目标1：开发和实施计算和统计算法，以建立处理RNA和DNA测序数据的管道。我们将建立一个管道来执行原始测序读数的质量检查，将读数与参考基因组对齐，调用DNA-seq样本的SNP，并识别剪接事件，重建亚型并量化RNA-seq样本的亚型和整体基因表达。(2)目的2：建立针对常见等位基因的同工型特异eQTL定位的统计模型。我们将首先使用传统的统计方法在免疫相关的遗传区域定位基因eQTL。然后，我们将开发一种新的统计模型，以确定单核苷酸多态性，影响异构体表达的调节。(3)目的3：开发统计方法来定位多个基因座，以改善罕见和弱效应变异的检测。我们将开发多位点方法，可以使用先前的生物信息将SNP分组到注释的遗传区域中。首先，我们试图确定本地eQTL的间隔内和附近的基因区域。其次，我们将开发方法，首先在相同的功能集（如通路或网络）中聚合SNP，然后识别可能调节基因表达的基因集。这种多位点策略可以识别远距离遗传调节因子。拟议的研究是一个K25指导定量研究发展奖，将准备候选人在定量生物医学研究的成功职业生涯。主要的职业目标是成为一名独立的研究者和开发高通量遗传数据的统计和计算方法的专家，以提高对遗传特征的理解，发现遗传诊断和预后标志物，以及促进SLE和其他遗传疾病的预防和治疗。