GWAS in MrOS and SOF

点击翻译按钮获取中文摘要

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.