Bioinformatics and Population Genetics to Identify Rheumatoid Arthritis Genes

描述（由申请人提供）：项目摘要。该提案描述了Soumya Raychaudhuri在统计遗传学方面的五年职业发展计划。Raychaudhuri博士是布里格姆妇女医院（BWH）的流变学研究员。他将把他在生物信息学方面的背景与布罗德研究所的统计遗传学资源以及BWH的免疫学和临床优势相结合。Raychaudhuri博士将由Broad医学和人口遗传学项目计算生物学实验室的助理兼主任Mark Daly指导; Daly博士是自身免疫疾病统计遗传学方面公认的专家，具有良好的指导记录。Raychaudhuri博士将与大卫阿尔特什基、彼得格雷格森、丹所罗门和罗伯特普兰格密切合作，并从他们那里获得关于完成拟议项目的一般职业建议和具体科学指导。他将与Lars Klareskog和Paul de Bakker合作，帮助对类风湿性关节炎（RA）患者进行基因分型和分析。该研究计划将强调RA的遗传学，目标是识别和复制增加疾病风险的新基因座。RA基因的检测一直很困难，因为它是一种多基因疾病，可能涉及许多基因的复杂相互作用的适度影响。迄今为止，遗传研究已将PTPN 22、TNFAIP 3和STAT 4确定为主要组织相容性复合体之外的可复制基因座。我们假设一些易感等位基因是常见的变异。因此，为了识别未识别的位点，我们将努力集中在RA的三个全基因组关联扫描（GWAS）的联合分析上。然而，每项单独的研究可能太小，无法检测必要的影响。我们进一步假设，相关基因可能在共同的途径或共享的生物过程。功能基因组学数据（基因表达，蛋白质相互作用，科学文本）的计算分析可能能够阐明关系。检测功能相关的基因变异与疾病的关联可能会增加遗传学方法的力量。我们建议：（1）开发和应用新方法来联合收割机小RA GWAS以增加功率，以及（2）开发和应用新的生物信息学方法来将功能基因组学数据整合到统计遗传分析中。本案无关本文提出的研究旨在利用群体遗传学数据，利用生物信息学方法寻找类风湿关节炎的致病基因。这些基因的鉴定对于（1）RA的快速鉴定和诊断，（2）识别疾病发病机制中的关键途径，和（3）定义未来的药物靶点具有意义。

DESCRIPTION (provided by applicant): Project Summary. This proposal describes a five year career development plan for Soumya Raychaudhuri in statistical genetics. Dr. Raychaudhuri is a Rheumatology fellow at the Brigham and Women's Hospital (BWH). He will integrate his background in bioinformatics with the statistical genetics resources of the Broad Institute and the immunological and clinical strengths of BWH. Dr. Raychaudhuri will be mentored by Mark Daly, an associate and director for the Computational Biology lab of the Medical and Population Genetics Program at Broad; Dr. Daly is a recognized expert in the statistical genetics of auto-immune disease with a strong mentoring track record. Dr. Raychaudhuri will work closely with David Altshuler, Peter Gregerson, Dan Solomon, and Robert Plenge, and receive from them general career advice and specific scientific guidance on the completion of the proposed project. He will work with Lars Klareskog and Paul de Bakker in collaboration to help genotype and analyze cohorts of rheumatoid arthritis (RA) patients. The research program will emphasize the genetics of RA, with a goal of identifying and replicating new loci that confer increased disease risk. Detection of RA genes has been difficult since it is a polygenic disease that probably involves modest effects from many genes with complex interactions. Genetic studies have thus far identified PTPN22, TNFAIP3, and STAT4 as replicable loci outside the Major Histocompatibility Complex. We hypothesize that some susceptibility alleles are common variants. Therefore, to identify unrecognized loci, we focus our efforts on the combined analysis of three genome-wide association scans (GWAS) in RA. Each individual study may be too small to detect necessary effects, however. We further hypothesize that involved genes may be in common pathways or share biological processes. Computational analysis of functional genomics data (gene expression, protein interaction, scientific text) may be able to elucidate relationships. Testing functionally related gene variants in concert for disease association may increase the power of genetics approaches. We propose: (1) developing and applying new methods to combine small RA GWAS to increase power, and (2) developing and applying novel bioinformatics approaches to integrate functional genomics data into statistical genetic analysis. Relevance. The research proposed here aspires to find disease genes in RA using bioinformatics approaches with population genetics data. The identification of these genes has implications for (1) the rapid identification and diagnosis of RA, (2) recognizing critical pathways in the pathogenesis of the disease, and (3) defining future pharmaceutical targets.