This SCOR proposes a novel inter-disciplinary genomic convergence approach that integrates results from Genetic Epidemiology, Functional Genomics, and Proteomics in Projects 1-3 to identify female osteoporosis genes and their functions. In addition, future molecular and functional studies aimed at confirming the genes to identified and thus pinpointing specific causal mutations are exemplified in Stage 2 of Project 2 of this SCOR by using two significant genes (RUNX1 and HOC) we identified in our earlier genetic epidemiology and genomics/functional genomics studies. The approach and each component project involve heavily data management and analyses. It is necessary to set up a Biostatistics and Bioinformatics Core to serve all the three projects efficiently and economically. The Core will essentially serve as a resource and foster exchange and collaboration for all individual projects in the SCOR. The Biostatistics and Bioinformatics Core has, but are not limited to, the following specific aims: To collaborate with project investigators on the experimental design issues and the design of questionnaires and forms for efficient data acquisition, entry, tracking, retrieval and transfer, etc. To implement (and develop if necessary) efficient and high quality data entry/management database system for all individual projects and to build quality control measures into those systems. To work with project investigators on an ongoing basis to ensure that the requirements of each protocol are satisfied for data acquisition. To monitor the emergence of and evaluate new methods and programs for data management and analyses. To work with project investigators on choosing and correct usage of the available updated and most appropriate software for analyses. To oversee and conduct the statistical analyses of all data generated from this SCOR project. To work with project investigators on interpretation of the analysis results and summarizing the results for publications. To work with project investigators on custom software design and development tailored to the special need of this SCOR project should the need arise. To provide biostatistics and bioinformatics support for all types of gene mapping, genomics and functional genomics platforms to be used in this SCOR. * To develop new statistical methods and computational algorithms should the need arise for individual projects and the whole SCOR.

National Institute of Health (NIH)
National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)
Specialized Center (P50)
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Special Emphasis Panel (ZRG1-HOP-U (40))
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Tulane University
New Orleans
United States
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Liu, Hui-Min; He, Jing-Yang; Zhang, Qiang et al. (2018) Improved detection of genetic loci in estimated glomerular filtration rate and type 2 diabetes using a pleiotropic cFDR method. Mol Genet Genomics 293:225-235
Lin, Xu; Peng, Cheng; Greenbaum, Jonathan et al. (2018) Identifying potentially common genes between dyslipidemia and osteoporosis using novel analytical approaches. Mol Genet Genomics 293:711-723
Xiao, X; Roohani, D; Wu, Q (2018) Genetic profiling of decreased bone mineral density in an independent sample of Caucasian women. Osteoporos Int 29:1807-1814
Ran, Shu; Zhang, Lei; Liu, Lu et al. (2017) Gene-based genome-wide association study identified 19p13.3 for lean body mass. Sci Rep 7:45025
Pei, Yu-Fang; Ren, Hai-Gang; Liu, Lu et al. (2017) Genomic variants at 20p11 associated with body fat mass in the European population. Obesity (Silver Spring) 25:757-764
Zhu, W; Shen, H; Zhang, J-G et al. (2017) Cytosolic proteome profiling of monocytes for male osteoporosis. Osteoporos Int 28:1035-1046
He, Hao; Lin, Dongdong; Zhang, Jigang et al. (2017) Comparison of statistical methods for subnetwork detection in the integration of gene expression and protein interaction network. BMC Bioinformatics 18:149
Zhang, Mingzhi; Zhao, Lan-Juan; Zhou, Yu et al. (2017) SNP rs11185644 of RXRA gene is identified for dose-response variability to vitamin D3 supplementation: a randomized clinical trial. Sci Rep 7:40593
Dong, Shan-Shan; Hu, Wei-Xin; Yang, Tie-Lin et al. (2017) SNP-SNP interactions between WNT4 and WNT5A were associated with obesity related traits in Han Chinese Population. Sci Rep 7:43939
Yao, Shi; Guo, Yan; Dong, Shan-Shan et al. (2017) Regulatory element-based prediction identifies new susceptibility regulatory variants for osteoporosis. Hum Genet 136:963-974

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