Genome-wide Association Study to Identify Genetic Components of Knee OA: The OAI

描述(由申请人提供)：OAI骨关节炎(OA)是最常见的关节炎原因。大约有2100万美国人被医生诊断为骨性关节炎，还有更多的人患有未诊断的疾病。膝关节骨性关节炎对老年人造成的慢性残疾与心血管疾病一样多，而且比其他任何疾病都要严重。识别膝骨性关节炎的高危个体对于减轻这种疾病的负担至关重要，而且越来越多的数据表明，遗传因素在决定各种膝骨性关节炎表型的人群变异性方面发挥着重要作用。因此，该项目的目标是通过对骨关节炎倡议(OAI)的所有4800名参与者进行单独的基因分型，确定增加中老年男性和女性膝骨性关节炎易感性的基因改变。OAI旨在确定影像、生化和遗传标记作为OA疾病发展和进展(既往疾病恶化)替代终点的有效性。通过OAI内的一系列仔细描述的表型，OAI提供了系统地检查各种OA结构、临床和功能表型和特征的遗传基础的机会。通过采用全基因组关联研究而不是候选基因方法，基因的不可知性选择提供了识别关联的机会，而没有先入为主的假设，这可能为OA的病理生理学提供新的见解。为了展示这一新资源在探索导致骨性关节炎风险的遗传因素方面的力量，我们将对放射学膝关节骨性关节炎的遗传关联进行分析。我们建议的一个重要特征是，在约翰斯顿县骨关节炎项目中纳入‘in silo’复制，然后在NIH资助的多中心骨关节炎研究(MOST)中对与放射性膝骨性关节炎最相关的SNPs进行基因分型，MOST是一个独立的队列，具有类似的研究设计和标准化的临床表型定义，允许评估有效性和可比性。最后，我们将利用作为关节炎研究活动的一部分进行的第二次骨关节病遗传学研究来确定与放射性骨性关节炎相关的SNPs是否会增加膝关节置换的风险。该项目的结果应该为疾病途径和机制提供有价值的见解，从而有助于确定筛查、预防和治疗骨性关节炎的新靶点。此外，通过在骨关节炎倡议(一种公共使用的数据集)内进行这项GWA，这项研究成为一种全国性的资源，以促进来自不同科学领域的研究人员利用GWA开始探索OAI中捕获的各种肌肉骨骼表型的假定因果遗传变异。将全球气象组织的数据纳入审调处的数据集，将使审调处作为一种科学资源在未来几年获得增量价值。本项目是为了响应美国复苏和再投资法案(ARRA)研究和研究基础设施‘大机遇’RC2计划(RFA-OD-09-004)而提交的。拟议的工作符合ARRA的目标，加快了科学的节奏，并带来了大量的就业机会保留和创造。

DESCRIPTION (provided by applicant): The OAI Osteoarthritis (OA) is the most common cause of arthritis. Approximately 21 million Americans have physician diagnosed OA and many more have undiagnosed disease. Knee OA is responsible for as much chronic disability in the elderly as cardiovascular disease and more disability than any other medical condition. Identifying individuals at risk for knee OA is critical for reducing the burden of this disease and there are increasing data suggesting that genetic factors play an important role in determining population variability in a variety of OA phenotypes. Thus, the goal of this project is to identify genetic alterations that increase susceptibility to knee OA among middle-aged and older men and women by individually genotyping all 4800 participants in the Osteoarthritis Initiative (OAI). The OAI was designed to determine the validity of imaging, biochemical, and genetic markers as surrogate endpoints for OA disease development and progression (worsening of established disease). With the spectrum of carefully characterized phenotypes within the OAI, the OAI affords the opportunity to systematically examine the genetic underpinnings of a variety of OA structural, clinical and functional phenotypes and traits. By employing a genome-wide association study rather than a candidate gene approach, the agnostic selection of genes offers opportunities to identify associations without preconceived hypotheses that could offer new insights into the pathophysiology of OA. As a demonstration of the power of this new resource to explore genetic factors contributing to risk for OA, we will perform analyses of the genetic associations of radiographic knee OA. An important feature of our proposal is the inclusion of an 'in silico' replication in the Johnston County Osteoarthritis Project followed by genotyping of the SNPs most strongly associated with radiographic knee OA within the NIH-funded Multicenter Osteoarthritis Study (MOST), an independent cohort with a similar study design and standardized clinical phenotype definitions allowing for assessment of validity and comparability. Finally, we will take advantage of a second GWAS conducted as part of Arthritis Research Campaign Osteoarthitic Genetics to determine whether the SNPs reliably associated with radiographic OA contribute to risk for knee replacement. The results from this project should provide valuable insights into disease pathways and mechanisms, thus helping in the identification of novel targets for screening, prevention, and treatment of OA. In addition, by performing this GWAS within the Osteoarthritis Initiative, a public use dataset, this study becomes a national resource to facilitate investigators from diverse scientific fields to utilize the GWAS to begin to explore putative causal genetic variants for a wide range of musculoskeletal phenotypes captured in the OAI. The inclusion of the GWAS data to the OAI dataset will lend incremental value to the OAI as a scientific resource for years to come. This project is submitted in response to the American Recovery and Reinvestment Act (ARRA) Research and Research Infrastructure 'Grand Opportunities' RC2 program (RFA-OD-09-004). The proposed work is in line with the goals of the ARRA, accelerating the tempo of science and leading to significant job retention and creation.