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: 8607899
• 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/8607899
• 关键词: Accounting; Affect; Alleles; American; Autoimmune Diseases; Autoimmune Process; 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; Functional RNA; Gene Expression; Gene Expression Regulation; Genes; Genetic; Genetic Polymorphism; Genetic Predisposition to Disease; Genetic Transcription; Genetic Variation; Genome; Genomics; Genotype; Goals; Hemorrhage; Hereditary Disease; Heterogeneity; Human; Immune; Immunology; Individual; Inflammation; Joints; Kidney; Lead; Maps; Mentors; Methods; MicroRNAs; Modeling; Molecular; Molecular Genetics; Nuclear; 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; Validation; Variant; aggressive therapy; analytical tool; base; biomedical scientist; career; case control; data management; design; flexibility; genetic profiling; genetic variant; genome sequencing; genome wide association study; improved; insight; mRNA Expression; next generation sequencing; novel; prevent; public health relevance; 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）：全身性红斑狼疮（SLE）是一种自身免疫性疾病，会导致慢性炎症，可能会影响身体的任何部分，尤其是皮肤，关节，肾脏，大脑和血液。据估计，全国每年有超过150万美国人患有SLE，每年有16,000例新的SLE病例，呼吁开发预防或管理该疾病的长期表现的疗法。尽管由于疾病机制和侵略性疗法的研究进展，生存率大大提高了，但无法治愈SLE。在自身免疫性疾病中，SLE是最难理解和治疗的一种，在发病机理，表现和对治疗的反应中具有很大的异质性。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，表达定量性状基因座（EQTL）映射是发现遗传足迹O转录变化的有效工具，可能会增加检测与SLE患者的敏感性和治疗反应相关的多态性的机会。该项目的目的是使用下一代测序数据开发统计和计算方法来映射EQTL映射，以改善对重要遗传变异的检测，为基因调节提供了很好的见解，并使人们对SLE的遗传学有了更深入的了解。为了了解SLE的分子机制和遗传因素，我的主要导师Wakeland博士及其合作者一直在生成RNA测序数据，以及来自数百名SLE患者和对照组的样品的靶向DNA测序数据。这些具有前所未有的分辨率和准确性的大规模和多维测序数据为我们提供了产生重要的科学发现的绝佳机会，同时为数据管理，数据分析和结果解释构成了巨大挑战。我们有三个特定的目标：（1）目标1：开发和实施计算和统计算法，以构建处理RNA和DNA测序数据的管道。我们将建立一条管道，以执行原始测序读取，与参考基因组的读数，将SNP对准DNA-Seq样品，并识别剪接事件，重建同工型和量化同工型和RNA-SEQ样品的总体基因表达式。 （2）目标2：开发统计模型以绘制针对共同等位基因的同工型特异性EQTL。我们将使用常规统计方法首先在免疫相关遗传区域绘制基因eqTL。然后，我们将开发一个新型的统计模型，以识别影响同工型表达调节的SNP。 （3）目标3：开发统计方法来绘制多个基因座，以改善对稀有和弱效应变体的检测。我们将开发多LOCI方法，可以使用先前的生物学信息将SNP分组为带注释的遗传区域。首先，我们试图确定位于基因区域内部和附近的局部EQTL间隔。其次，我们将开发方法以在途径或网络（例如途径或网络）中首先汇总SNP，然后识别可能调节基因表达的基因集。这种多层策略可以识别遥远的遗传调节因子。拟议的研究是为K25指导的定量研究开发奖，该奖项将使候选人为成功的定量生物医学研究事业做好准备。主要的职业目标是成为一名独立研究者，也是为高通量遗传数据开发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，以及促进对SLE和其他遗传疾病的预防和治疗。