Osteoporosis is the most prevalent metabolic bone disease responsible for a major public health problem. Osteoporosis is mainly characterized by low bone mineral density (BMD). In general, women have lower BMD and higher risk of osteoporosis than men. Most BMD variation is determined by genetic factors with heritability greater than 60%. However, the specific genes involved are largely unknown. Our studies and the studies of others have demonstrated that some osteoporosis risk genes/genomic regions are gender specific. The GOAL of this SCOR is primarily to identify osteoporosis risk genes and their functional aspects in females and, secondarily, to assess the female specificity of these identified genes/functions in male samples. In addition, we will also perform in-depth molecular and cellular functional studies for specific mechanisms and confirmation of the risk genes identified, by studying two novel genes we discovered recently. This SCOR will pioneer a comprehensive and novel approach in bone genetics by investigating osteoporosis at the genome-, transcriptome-, and proteome-wide levels simultaneously. We will use the samples largely recruited or archived for targeted recruitment and adopt state-of-the-art technologies proved successful in our recent pilot studies. This qenomic convergence approach will pinpoint and consolidate the most significant genes identified in each of the individual projects. The genes identified will be subject to replication studies within and across populations by ourselves and our collaborators. All the genes identified in the genomic convergence approach will be subject to in-depth functional studies for confirmation and functional mechanisms as exemplified in Stage 2 of Project 2 of this SCOR. This SCOR is composed of three projects, all aimed at identifying osteoporosis risk genes but from different genomic approaches. Project 1 is to perform a whole genome association scan using dense SNPs to identify those genes/regions that are associated with risk of osteoporosis. Project 2 is to perform a DMA microarray study to scan >40,000 known human genes and ESTs to identify those mRNAs and corresponding genes associated with osteoporosis. Project 3 is to perform proteomics studies to identify those proteins (and corresponding genes) associated with osteoporosis. The SCOR has three cores: A) Administrative Core;B) Clinical Core;and C) Biostatistics and Bioinformatics Core. Each core serves all the three projects. For example, the Clinical Core recruits samples that are shared by Projects 2 and 3;provides support for clinical related issues (such as choice of important medical and environmental factors for co-variate analyses) and for human subject research issues in Project 1. Identifying genes and their functions for human BMD variation, especially for women, is important for 1) gaining insights into the fundamental molecular mechanisms underlying risk of osteoporosis, 2) discovering new pathways and targets for therapeutic cures;3) identifying genetically susceptible individuals, so that future preventions and interventions can be targeted to and based on individuals'specific genotypes.

Agency
National Institute of Health (NIH)
Institute
National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)
Type
Specialized Center (P50)
Project #
5P50AR055081-06
Application #
8144279
Study Section
Special Emphasis Panel (ZRG1-HOP-U (40))
Program Officer
Sharrock, William J
Project Start
2007-09-07
Project End
2013-07-31
Budget Start
2011-08-01
Budget End
2013-07-31
Support Year
6
Fiscal Year
2011
Total Cost
$1,183,598
Indirect Cost
Name
Tulane University
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
053785812
City
New Orleans
State
LA
Country
United States
Zip Code
70118
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
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
Greenbaum, Jonathan; Deng, Hong-Wen (2017) A Statistical Approach to Fine Mapping for the Identification of Potential Causal Variants Related to Bone Mineral Density. J Bone Miner Res 32:1651-1658
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; Sun, Dianjianyi; Zeng, Yong et al. (2017) A Systems Genetics Approach Identified GPD1L and its Molecular Mechanism for Obesity in Human Adipose Tissue. Sci Rep 7:1799
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
Liu, Hui-Min; He, Jing-Yang; Zhang, Qiang et al. (2017) Improved detection of genetic loci in estimated glomerular filtration rate and type 2 diabetes using a pleiotropic cFDR method. Mol Genet Genomics :
Chen, Yuan-Cheng; Greenbaum, Jonathan; Shen, Hui et al. (2017) Association Between Gut Microbiota and Bone Health: Potential Mechanisms and Prospective. J Clin Endocrinol Metab 102:3635-3646
Greenbaum, Jonathan; Wu, Kehao; Zhang, Lan et al. (2017) Increased detection of genetic loci associated with risk predictors of osteoporotic fracture using a pleiotropic cFDR method. Bone 99:62-68

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