Low trauma fractures are a major public health problem, especially in the elderly. The most powerful measurable determinant of fracture risk is low bone mass, which is a complex trait with strong genetic determination. Identifying genes underlying variation in bone mass is a critical step towards unraveling functional mutations that cause low bone mass. Extensive regular association studies using unrelated random population samples and a limited few markers have generated controversies for prominent candidate genes studied. Regular linkage studies have been unable to resolve the controversies, largely due to the limited power with insufficient samples. The TDT, while powerful, is a robust approach that can test linkage of candidate genes (particularly those showing significant association) to bone mass. Establishing linkage and robust association is important in identifying genes for bone mass. Screening multiple markers is necessary for testing the significance of candidate genes systematically and exhaustively. We will screen dense (approximately 2kb apart) markers (mainly SNP) in/around 10 prominent candidate genes to test (individual markers and/or haplotypes for closely linked markers in strong linkage disequilibrium) for their association and/or linkage to bone mass variation. Eight hundred nuclear families each consisting of both parents and at least two healthy children will be genotyped. Three hundred ten such families have already been recruited. In addition, we will genotype one microsatellite marker inside or close to each of the 10 candidate genes in 2,886 subjects from 118 pedigrees (recruited already) as well as in the 800 nuclear families. We will use 1) powerful association tests (including those robust ones recently developed) with unrelated samples, 2) robust TDT with nuclear families, and 3) linkage tests in nuclear families and multigeneration pedigrees, to test the significance of these genes for bone mass variation. The study genes or their products have demonstrated biological functional importance in bone metabolism; however, whether or not they are associated with and linked to bone mass variation has not been established. Our proposed study is comprehensive and our statistical genetic analyses show that our study is of very high power and may identify a quantitative trait locus (QTL) with a heritability (h2) as low as 0.05 with a power of > 90%.

National Institute of Health (NIH)
National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)
Research Project (R01)
Project #
Application #
Study Section
Skeletal Biology Development and Disease Study Section (SBDD)
Program Officer
Mcgowan, Joan A
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Creighton University
Internal Medicine/Medicine
Schools of Medicine
United States
Zip Code
Xu, Chao; Fang, Jian; Shen, Hui et al. (2018) EPS-LASSO: test for high-dimensional regression under extreme phenotype sampling of continuous traits. Bioinformatics 34:1996-2003
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
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
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
Han, Ying-Ying; Zhao, Lan-Juan; Lin, Yong et al. (2017) Multiple analyses indicate the specific association of NR1I3, C6 and TNN with low hip BMD risk. J Genet Genomics 44:327-330
Peng, Cheng; Shen, Jie; Lin, Xu et al. (2017) Genetic sharing with coronary artery disease identifies potential novel loci for bone mineral density. Bone 103:70-77

Showing the most recent 10 out of 219 publications