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）是关节炎的最常见原因。大约有2100万美国人被医生诊断为OA，更多的人患有未诊断的疾病。膝关节OA是老年人慢性残疾的原因，与心血管疾病一样多，比任何其他医疗条件都多。识别膝关节OA风险个体对于减少这种疾病的负担至关重要，越来越多的数据表明遗传因素在确定各种OA表型的群体变异性方面发挥重要作用。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，来确定增加中老年男性和女性对膝关节OA易感性的遗传改变。OAI旨在确定影像学、生化和遗传标志物作为OA疾病发展和进展（已确立疾病恶化）替代终点的有效性。通过OAI中仔细表征的表型谱，OAI提供了系统检查各种OA结构、临床和功能表型和性状的遗传基础的机会。通过采用全基因组关联研究而不是候选基因方法，基因的不可知选择提供了机会，以确定协会没有先入为主的假设，可以提供新的见解OA的病理生理学。为了证明这一新资源在探索OA风险遗传因素方面的作用，我们将对放射学膝关节OA的遗传相关性进行分析。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“计算机模拟”复制，然后在NIH资助的多中心骨关节炎研究（MOST）中对与放射学膝关节OA最密切相关的SNP进行基因分型，MOST是一个独立的队列，具有相似的研究设计和标准化的临床表型定义，可以评估有效性和可比性。最后，我们将利用作为关节炎研究运动骨关节炎遗传学的一部分进行的第二次GWAS来确定与放射学OA可靠相关的SNP是否有助于膝关节置换术的风险。该项目的结果应该为疾病途径和机制提供有价值的见解，从而有助于识别用于筛查，预防和治疗OA的新靶点。此外，通过在骨关节炎倡议（一个公共使用数据集）中执行该GWAS，该研究成为一项国家资源，以促进来自不同科学领域的研究人员利用GWAS开始探索OAI中捕获的广泛肌肉骨骼表型的推定因果遗传变异。将GWAS数据纳入OAI数据集将在未来几年内为OAI作为科学资源提供增量价值。该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“大机会”RC 2计划（RFA-OD-09-004）而提交的。拟议的工作符合ARRA的目标，加快了科学的克里思，并导致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.