GWAS in MrOS and SOF

描述(由申请人提供)：年龄相关疾病的遗传成分引起了人们的浓厚兴趣，全基因组关联研究(GWAS)是识别与疾病相关的染色体位点的重要工具。对于成功的全球遗传多样性分析来说，两个主要且相互关联的挑战一直是收集足够大的队列和开发准确和详细的表型数据。在这里，我们建议通过提供高密度基因分型和年龄相关肌肉骨骼疾病最重要的两项研究：骨质疏松性骨折研究(SOF)和男性骨质疏松性骨折研究(MROS)的详细表型数据，显著增强解决重要健康问题的遗传决定因素的能力。这些是以社区为基础的大型纵向观察研究，对老年女性和男性(可用于基因分型的组合队列-10,307)进行研究，拥有关于骨密度、骨结构、骨折、身体成分和骨关节炎的详细表型信息。这些队列使用非常相似的方法进行表型分型，从而允许对基因表型关联进行联合分析，并提供了一个检查性别对遗传学影响的机会。此外，可以获得大量关于协变量和环境影响的数据，这将使基因-环境分析成为可能。最后，除了肌肉骨骼表型外，SOF和MROS还拥有与许多额外的年龄相关疾病(例如睡眠、视力障碍、骨骼减少、身体表现、认知)相关的可用指标，这将有助于与其他队列合作研究中执行的荟萃分析的力量。具体地说，我们建议：1.对10,307名SOF和MROS参与者进行高密度(1M SNP)全基因组基因分型，从而有机会了解SNP与表型的关联，以及结构变异(例如拷贝数)和表型之间的关联。2.将关联的基因型和表型信息组合在一个数据集中，以便传输到DBGaP。这些数据将大大增加重大肌肉骨骼疾病遗传学的研究机会，并为其他与年龄相关的突出疾病的合作研究做出重要贡献。

DESCRIPTION (provided by applicant): The genetic components of age-related disorders are of intense interest, and genome wide association studies (GWAS) are an important tool in identifying chromosomal loci that are associated with disease. Two of the major and interlinked challenges for successful GWAS has been assembling cohorts of sufficient size and developing accurate and detailed phenotype data. Here we propose to significantly enhance the power to address the genetic determinates of important health issues by making available high density genotyping and detailed phenotyping data from two of the most important studies of age-related musculoskeletal disorders: The Study of Osteoporotic Fractures (SOF) and the Osteoporotic Fractures in Men Study (MrOS). These are large, community based longitudinal observational studies of older women and men (combined cohorts available for genotyping - 10,307) that have detailed phenotypic information for bone density, bone structure, fracture, body composition, and osteoarthritis. The cohorts use very similar methods for phenotyping and thus allow combined analyses of genotype phenotype associations, and provide an opportunity to examine the influence of sex on genetics. Moreover, extensive data is available on a host of covariates and environmental influences that will enable gene-environment analyses. Finally, in addition to the musculoskeletal phenotypes SOF and MrOS have available measures relevant to a host of additional age-related conditions (e.g. sleep, visual disturbance, sarcopenia, physical performance, cognition) that will contribute to the power of meta-analyses performed in collaborative studies with other cohorts. Specifically, we propose to: 1. Perform high density (1M SNP) genome wide genotyping in 10,307 SOF and MrOS participants, yielding opportunities to understand SNP-phenotype associations, as well as the associations between structural variation (e.g. copy number) and phenotype. 2. Assemble linked genotype and phenotype information in a data set for transfer to dbGaP. These data will significantly increase research opportunities in the genetics of major musculoskeletal disorders as well as contribute important cohorts to the collaborative study of other prominent age-related conditions.