Bone Microarchitecture: The Framingham Osteoporosis Study

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This supplement ("Revision") to our currently funded grant entitled, "Bone Microarchitecture: The Framingham Osteoporosis Study," expands the scope of the genetics work of the parent grant. It will extend the genome- wide association study (GWAS) of the parent grant to the realm of potentially more functional, less common variants that will provide important data on the contribution of these variants to bone microarchitecture measured by high resolution peripheral quantitative computed tomography (HR-pQCT). Ideally, the best way to identify potentially functional genetic variants associated with bone microarchitecture is to have sequencing data from as much of the genome as possible. Although deep sequencing represents a powerful approach for the discovery of the complete spectrum of variants that cause diseases, the cost of obtaining and analyzing whole genome or even exome-sequences on a large human sample remains prohibitive. Therefore, this study will make use of exome chip data along with dense genome wide genotyping data in several cohorts with HR- pQCT phenotypes to impute less common and potentially functional variants across the whole genome. The imputation will make use of a growing reference panel of whole genome sequencing that has emerged from several international efforts. To accomplish our aim, we have assembled all the cohorts from around the world who currently have HR-pQCT phenotypes and DNA available. The approach to be taken in this project will involve the following steps: 1) Use existing GWAS genotyping and exome chip genotyping from the family- based Framingham Study to impute variants with minor allele frequency as low as 0.5%; 2) Obtain the same GWAS and exome chip genotyping in three other cohorts with the identical HR-pQCT-derived bone microarchitecture phenotypes; 3) Perform the whole genome imputation in those population-based cohorts using an integrated reference panel consisting of 10,000 publicly available whole genome sequences; 4) Meta-analyze results from cohort specific association analyses using the imputed genotypes; 5) Prioritize the most significant findings in the meta-analyses, using statistical significance levels as well as bioinformatic tools to predict functional potential (e.g. ENCODE, eQTL analysis); 6) Validate the accuracy of the imputed variants having the most significant association with bone microarchitecture by performing de-novo genotyping in the Framingham Study; 7) Replicate the most significant associations for variants with confirmed accurate genotype in five other HR-pQCT cohorts using de-novo genotyping. Our strategy of identifying both common and less common, potentially causal variants in protein-coding regions and non-coding regulatory regions represents a robust conceptual paradigm for the study of bone microarchitectural deterioration that characterizes the disease, osteoporosis.