Bioinformatics and Population Genetics to Identify Rheumatoid Arthritis Genes

描述(申请人提供)：项目摘要。这份提案描述了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)的风湿病研究员。他将把他在生物信息学方面的背景与博德研究所的统计遗传学资源以及BWH的免疫学和临床优势结合起来。雷乔杜里博士将由布罗德大学医学和人口遗传学项目计算生物学实验室副主任马克·戴利指导；戴利博士是一位在自身免疫性疾病的统计遗传学方面公认的专家，有良好的指导记录。雷乔杜里博士将与大卫·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的科学指导。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。该研究计划将强调类风湿性关节炎的遗传学，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，可能涉及许多具有复杂相互作用的基因的适度影响。到目前为止，遗传学研究已经确定PTPN22、TNFAIP3和STAT4是主要组织相容性复合体外的可复制基因座。我们假设一些易感等位基因是常见的变异。因此，为了确定未识别的基因座，我们集中精力对RA的三个全基因组关联扫描(GWAS)进行联合分析。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。对功能基因组数据(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两者之间的关系。联合检测与疾病相关的功能相关基因变异可能会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RA GWAs以提高能力，以及(2)开发和应用新的生物信息学方法来将功能基因组数据整合到统计遗传分析中。关联性。本研究的目的是利用生物信息学方法，结合人群遗传学数据，寻找类风湿关节炎的致病基因。这些基因的识别对于(1)RA的快速识别和诊断，(2)认识疾病发病机制中的关键途径，以及(3)确定未来的药物靶点具有重要意义。

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.