Meta-analysis of GWAS data to identify novel rheumatoid arthritis risk loci

GWAS 数据的荟萃分析以确定新的类风湿性关节炎风险位点

• 批准号: 7844869
• 负责人: ROBERT M PLENGE
• 金额: $ 27.75万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-05-12 至 2011-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7844869
• 关键词: Alleles; American; Area; Autoimmune Diseases; Biological; Clinic; Collaborations; Communities; DNA; Data; Data Set; Development; Diagnosis; Disease; Disease Pathway; Epitopes; European; Fostering; Future; Gene Mutation; Genes; Genetic; Genome; Genomics; Genotype; Goals; HLA-DRB1; Heterogeneity; Human Genetics; Individual; Laboratories; Meta-Analysis; Methods; Modeling; Outcome; PTPN22 gene; Pathway interactions; Patient Care; Patients; Peptide antibodies; Population; Predisposition; PubMed; Publishing; Research; Research Personnel; Rheumatoid Arthritis; Risk; STAT4 gene; Sampling; Single Nucleotide Polymorphism; Statistical Models; Stratification; Susceptibility Gene; Sushi Domain; Sweden; Symptoms; Testing; Text; Translating; Variant; abstracting; analytical method; base; bone; case control; clinical care; cyclic citrullinated peptide; experience; genetic resource; genome wide association study; improved; insight; novel; public health relevance; sample collection; text searching; theories; treatment response

DESCRIPTION (provided by applicant): Genome-wide association studies (GWAS) have led to an increased understanding of the genetic basis of a common autoimmune disease, rheumatoid arthritis (RA). We recently conducted a meta- analysis of three published GWAS totaling ~15,000 case-control samples of European ancestry, and found evidence for 6 additional RA risk loci - bringing the total number of validated loci to 11. Statistical modeling of the GWAS meta-analysis demonstrated that (a) less than half the genetic burden of RA can be explained by all known risk loci and (b) dozens of additional common variants of modest effect size should be identified with more powerful GWAS. We propose the following Specific Aims to increase power of existing GWAS datasets in order to identify novel RA risk loci. Specific Aim 1: Conduct the largest GWAS performed to date to search for RA susceptibility loci - in total, more than 5,000 CCP+ RA cases and 17,000 controls. Maximizing the total number of case-control samples and increasing genomic coverage of common variants (through imputation) increase power in GWAS. Through existing collaborations, we have access to published GWA data on approximately 3,000 CCP+ cases and 12,000 controls; we also have access to unpublished GWA data on an additional 2,300 CCP+ cases and more than 5,000 controls. Our study will focus on RA patients of European ancestry seropositive for cyclic citrullinated peptide antibodies (CCP+ RA) to minimize heterogeneity across sample collections. Specific Aim 2: Search for shared biological pathways implicated by our GWAS meta- analysis using novel literature mining methods developed in our laboratory. We hypothesize that true RA risk genes share common biological pathways. We will test this hypothesis by assessing the degree of similarity between genes in regions implicated by GWAS, where we define similarity using a text-based approach based on PubMed abstracts. This approach can also help prioritize SNPs of intermediate significance for future replication. Specific Aim 3: Make all GWAS results available to the public to foster research by other investigators. Results from our meta-analysis will allow other groups to explore related areas of research: replicate best results in additional RA samples; explore commonality of autoimmune disease; create genetic prediction models of RA risk; and test RA risk alleles for outcome of treatment response. Completing these Aims will provide substantial progress towards our ultimate goal of a complete understanding of all RA genetic mutations - a necessary step before genetics can be translated to clinical care. PUBLIC HEALTH RELEVANCE: A long-term goal of understanding the genetic basis of rheumatoid arthritis is to improve care of patients with this common and debilitating disease. In theory, identifying specific pieces of DNA ("alleles") should aid in diagnosing a treatable condition either prior to onset of symptoms, or early in the course of disease before bone destruction occurs. In addition, genetics should provide insight into important steps of the disease pathway, allowing for the development of new therapies that target these pathways in at-risk individuals.

描述（由申请人提供）： 全基因组关联研究（GWAS）使人们对一种常见的自身免疫性疾病类风湿性关节炎（RA）的遗传基础有了更多的了解。我们最近对三个已发表的GWAS进行了Meta分析，总计约15，000个欧洲血统的病例对照样本，发现了6个额外的RA风险位点的证据-使验证位点的总数达到11个。GWAS荟萃分析的统计建模表明，（a）不到一半的RA遗传负担可以由所有已知的风险基因座解释，（B）几十个额外的常见变异的适度效应大小应确定更强大的GWAS。我们提出了以下具体目标，以增加现有GWAS数据集的功效，从而识别新的RA风险位点。具体目标1：进行迄今为止最大规模的GWAS以搜索RA易感基因座-总共有超过5，000例CCP+ RA病例和17，000例对照。最大限度地增加病例对照样本的总数和增加常见变异的基因组覆盖率（通过插补）增加了GWAS的功效。通过现有的合作，我们可以访问约3，000个CCP+病例和12，000个对照的已发表GWA数据;我们还可以访问另外2，300个CCP+病例和5，000多个对照的未发表GWA数据。我们的研究将重点关注环瓜氨酸肽抗体（CCP+ RA）血清阳性的欧洲血统RA患者，以尽量减少样本采集的异质性。具体目标二：使用我们实验室开发的新文献挖掘方法，搜索GWAS Meta分析所涉及的共享生物学途径。我们假设真正的RA风险基因共享共同的生物学途径。我们将通过评估GWAS所涉及的区域中基因之间的相似程度来测试这一假设，在GWAS中，我们使用基于PubMed摘要的基于文本的方法定义相似性。这种方法还可以帮助优先考虑对未来复制具有中等重要性的SNP。具体目标3：向公众提供GWAS的所有结果，以促进其他研究人员的研究。我们的荟萃分析结果将允许其他小组探索相关的研究领域：在其他RA样本中复制最佳结果;探索自身免疫性疾病的共性;创建RA风险的遗传预测模型;并测试RA风险等位基因的治疗反应结果。完成这些目标将为我们完全了解所有RA基因突变的最终目标提供实质性进展-这是遗传学转化为临床护理之前的必要步骤。 公共卫生关系： 了解类风湿性关节炎的遗传基础的长期目标是改善这种常见和衰弱疾病患者的护理。从理论上讲，识别特定的DNA片段（“等位基因”）应该有助于在症状发作之前或在骨破坏发生之前的疾病过程中早期诊断可治疗的疾病。此外，遗传学应该提供对疾病途径重要步骤的洞察，允许开发针对高危个体中这些途径的新疗法。