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）是关节炎最常见的原因。大约2100万美国人被医生诊断为OA，还有更多的人患有未确诊的疾病。膝关节炎与心血管疾病一样是老年人慢性残疾的罪魁祸首，比其他任何疾病都要多。识别有膝关节炎风险的个体对于减轻这种疾病的负担至关重要，越来越多的数据表明，遗传因素在决定各种膝关节炎表型的人群变异性方面起着重要作用。因此，该项目的目标是通过对所有4800名骨关节炎倡议（OAI）参与者进行个体基因分型，确定中老年男性和女性中增加膝关节OA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（既定疾病恶化）的替代终点的有效性。通过在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，基因的不可知论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。为了证明这种新资源在探索导致OA风险的遗传因素方面的力量，我们将对膝关节OA的x线摄影遗传关联进行分析。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“计算机”复制，然后在美国国立卫生研究院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，这是一个独立的队列，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相关的snp是否与膝关节置换术的风险可靠相关。该项目的结果将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OA的新靶点。此外，通过在骨关节炎倡议（一个公共使用的数据集）中执行该GWAS，该研究成为一个国家资源，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假定因果遗传变异。将GWAS数据纳入OAI数据集将在未来几年为OAI作为一种科学资源增加价值。该项目是根据美国复苏和再投资法案（ARRA）研究和研究基础设施“重大机遇”RC2计划（RFA-OD-09-004）提交的。拟议的工作符合ARRA的目标，加快科学的节奏，并导致大量的工作保留和创造。

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.