Bioinformatics and Population Genetics to Identify Rheumatoid Arthritis Genes

项目摘要。该提案描述了#年Soumya Raychaudhuri的五年职业发展计划。 统计遗传学。雷乔杜里博士是布里格姆妇女医院的风湿病研究员 (BWH)。他将把他在生物信息学方面的背景与博德的统计遗传学资源结合起来 研究所和BWH的免疫学和临床优势。 Raychaudhuri博士将由计算中心的助理和主任Mark Daly指导 医学和人口遗传学项目的生物实验室；Daly博士是公认的 自身免疫性疾病的统计遗传学，有很强的指导记录。雷乔杜里博士将会工作 与大卫·阿尔特舒勒、彼得·格雷格森、丹·所罗门和罗伯特·普伦格密切合作，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。他会的 与Lars Klareskog和Paul de Bakker合作，帮助分型和分析队列 类风湿关节炎(RA)患者。 该研究计划将强调类风湿性关节炎的遗传学，目标是识别和复制新的 增加患病风险的基因座。RA基因的检测一直很困难，因为它是一个多基因 这种疾病可能涉及许多具有复杂相互作用的基因的适度影响。遗传学研究 到目前为止，我已经确定PTPN22、TNFAIP3和STAT4是主要组织亲和性之外的可复制基因座 复杂。我们假设一些易感等位基因是常见的变异。因此，要确定 未识别的基因座，我们集中精力对三个全基因组关联扫描进行联合分析 (Gwas)在RA中。然而，每项单独的研究可能太小，无法检测到必要的影响。我们进一步 假设涉及的基因可能在共同的路径上或共享生物过程。计算型 对功能基因组数据(基因表达、蛋白质相互作用、科学文本)的分析或许能够 阐明关系。联合检测与疾病相关的功能相关基因变异可能 增加遗传学方法的力量。我们建议：(1)开发和应用新的方法来 结合小型RA WGA以增加功率，以及(2)开发和应用新型生物信息学 将功能基因组学数据整合到统计遗传分析中的方法。 关联性。本文提出的研究立志于利用生物信息学发现类风湿关节炎的致病基因 使用群体遗传学数据的方法。这些基因的识别对于(1)快速 识别和诊断类风湿性关节炎，(2)识别疾病发病机制中的关键途径，以及 (3)明确未来的药物靶点。

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 WGAS 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.