Bioinformatics and Population Genetics to Identify Rheumatoid Arthritis Genes

描述（由申请人提供）：项目概述。本文描述了苏米亚·雷乔杜里在统计遗传学方面的五年职业发展计划。Raychaudhuri博士是布莱根妇女医院（BWH）的风湿病学研究员。他将把他在生物信息学方面的背景与Broad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。Raychaudhuri博士将由马克·戴利（Mark Daly）指导，戴利是布罗德大学医学和人口遗传学项目计算生物学实验室的副主任和主任；戴利博士是公认的自身免疫疾病统计遗传学专家，拥有强大的指导记录。Raychaudhuri博士将与David Altshuler、Peter Gregerson、Dan Solomon和Robert Plenge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，帮助类风湿关节炎（RA）患者进行基因分型和分析。该研究项目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位点。由于RA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，因此检测RA基因一直很困难。迄今为止，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位点。我们假设一些易感等位基因是常见的变异。因此，为了确定未被识别的位点，我们将重点放在RA的三个全基因组关联扫描（GWAS）的联合分析上。然而，每个单独的研究可能太小，无法检测到必要的影响。我们进一步假设所涉及的基因可能在共同的途径或共享的生物过程。功能基因组数据的计算分析（基因表达，蛋白质相互作用，科学文本）可能能够阐明关系。检测与疾病相关的功能相关的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组合小型RA GWAS以提高功率；(2)开发和应用新的生物信息学方法将功能基因组学数据整合到统计遗传分析中。的相关性。本文提出的研究旨在利用生物信息学方法与群体遗传学数据寻找RA的疾病基因。这些基因的鉴定具有以下意义：(1)快速识别和诊断RA，(2)识别疾病发病机制的关键途径，以及(3)确定未来的药物靶点。

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.