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: 8842931
• 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/8842931
• 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; Genome; 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 Sequences; 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; Therapeutic; 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; research and development; response; rheumatologist; therapy development; tool; transcriptome sequencing; 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）是一种导致慢性炎症的自身免疫性疾病，可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，全国每年报告的SLE新病例超过16,000例，这要求开发治疗方法来预防或控制疾病的长期表现。虽然由于对疾病机制的研究和积极治疗的进展，生存率大大提高，但目前还没有治愈SLE的方法。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、表现和治疗反应具有很大的异质性。遗传易感性是SLE的主要因素，遗传变异可能是异质性的重要组成部分。最近的研究表明，表达数量性状位点（eQTL）定位是发现转录变异遗传足迹的有效工具，可能会增加检测SLE患者易感性和治疗反应相关多态性的机会。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，以提高对重要遗传变异的检测，为基因调控提供深刻的见解，并进一步了解SLE的遗传学。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、多维度的测序数据，以前所未有的分辨率和精度，为我们提供了产生重大科学发现的巨大机会，同时也给数据管理、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进行原始测序reads的质量检查，将reads与参考基因组比对，为DNA-seq样品调用snp，鉴定剪接事件，重建同种异构体并量化RNA-seq样品的同种异构体和总体基因表达。(2)目标2：建立统计模型，绘制针对常见等位基因的等位基因特异性等位基因图谱。我们将首先使用传统的统计方法绘制免疫相关遗传区域的基因eqtl。然后，我们将开发一种新的统计模型来识别影响同种异构体表达调节的snp。(3)目标3：开发统计方法来绘制多个基因座，以改进对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。首先，我们试图确定位于基因区域内和附近的局部eQTL区间。其次，我们将开发方法，首先在相同的功能集（如通路或网络）中聚集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端遗传调控因子。拟议的研究是为了获得K25指导定量研究发展奖，这将为定量生物医学研究的成功职业生涯做好准备。主要职业目标是成为一名独立研究者和高通量遗传数据统计和计算方法开发专家，以提高对遗传谱的理解，发现遗传诊断和预后标记，以及促进SLE和其他遗传疾病的预防和治疗。