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Aiming to investigate whether genetic risk factors (GRFs) for fracture and bone mineral density (BMD) identified from people of European descent can help improve the prediction of osteoporotic fracture (OF) risk and BMD in Chinese populations, we built assessment models for femoral neck (FN)-fracture prediction and BMD value prediction using 700 elderly Chinese Han subjects and 1,620 unrelated Chinese Han subjects, respectively. 17 fracture-associated genes and 82 FN-BMD associated genes identified in people of European descent were used to build a logistic regression model with clinical risk factors (CRFs) for FN-fracture prediction in Chinese. Meanwhile 107 BMD-associated genes from people of European descent were used to build a multiple linear regression model with CRFs for BMD prediction in Chinese. A Lasso algorithm was employed for informative SNP selection to construct the genetic risk score (GRS) with ten-fold cross-validation. The results showed that, adding fracture GRF and FN-BMD GRF to the model with CRFs, the area under the receiver operating characteristic curve (AUC) decrease from 0.653 to 0.587 and 0.588, respectively, for FN fracture prediction. 62.3% and 61.8% of the risk variation were explained by the Model with CRFs and fracture GRF and by the Model with CRFs and FN-BMD GRF, respectively, as compared to 65.5% in the Model with CRFs only. The net reclassification improvement (NRI) index in the reclassification analysis is 0.56% (P = 0.57) and 1.13% (P = 0.29), respectively. There is no significant difference either between the performance of the model with CRFs and that of the model with both CRFs and GRF for BMD prediction. We concluded that, in the current study, GRF of fracture identified in people of European descent does not contributes to improve the fracture prediction in Chinese; and GRF of BMD from people of European descent cannot help improve the accuracy of the fracture prediction in Chinese perhaps partially because GRF of BMD from people of European descent may not contribute to BMD prediction in Chinese. This study highlights the limited utility of the current genetics studies largely focused on people of European descent for disease or risk factor prediction in other ethnic groups, and calls for more and larger scale studies focused on other ethnic groups.

Unlabelled: Osteoporosis is mainly characterized by low bone mineral density (BMD), and can be attributed to excessive bone resorption by osteoclasts. Migration of circulating monocytes from blood to bone is important for subsequent osteoclast differentiation and bone resorption. Identification of those genes and pathways related to osteoclastogenesis and BMD will contribute to a better understanding of the pathophysiological mechanisms of osteoporosis. In this study, we applied the LC-nano-ESI-MS(E) (Liquid Chromatograph-nano-Electrospray Ionization-Mass Spectrometry) for quantitative proteomic profiling in 33 female Caucasians with discordant BMD levels, with 16 high vs. 17 low BMD subjects. Protein quantitation was accomplished by label-free measurement of total ion currents collected from MS(E) data. Comparison of protein expression in high vs. low BMD subjects showed that ITGA2B (p=0.0063) and GSN (p=0.019) were up-regulated in the high BMD group. Additionally, our protein-RNA integrative analysis showed that RHOA (p=0.00062) differentially expressed between high vs. low BMD groups. Network analysis based on multiple tools revealed two pathways: "regulation of actin cytoskeleton" (p=1.13E-5, FDR=3.34E-4) and "leukocyte transendothelial migration" (p=2.76E-4, FDR=4.71E-3) that are functionally relevant to osteoporosis. Consistently, ITGA2B, GSN and RHOA played crucial roles in these two pathways respectively. All together, our study strongly supported the contribution of the genes ITGA2B, GSN and RHOA and the two pathways to osteoporosis risk., Biological Significance: Mass spectrometry based quantitative proteomics study integrated with network analysis identified novel genes and pathways related to osteoporosis. The results were further verified in multiple level studies including protein-RNA integrative analysis and genome wide association studies., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Runs of homozygosity (ROHs), in which both parental alleles are identical, have been proposed to have recessive effects on multiple human complex diseases. Osteoporosis is a common complex disease characterized by low bone mineral density (BMD), which is highly heritable. And recessive loci that contribute to BMD variations have been identified. In this study, we performed a genome-wide ROHs association study using our SNP array data from three GWAS samples including 4,900 subjects from Caucasian and Chinese populations. Significant results were further subjected to replication in 3,747 additional subjects. ROHs associated with BMD were also tested for associations with osteoporotic fractures in a GWAS fracture sample. Combining results from all the samples, we identified 697 autosomal regions with ROHs. Among these, we detected genome-wide significant associations between BMD and 6 ROHs, including ROH1q31.3, 1p31.1, 3q26.1, 11q12.1, 21q22.1 and 15q22.3 (combined P = 6.29 × 10(-5)-3.17 × 10(-8)). Especially, ROH1p31.1 was found to be associated with increased risk of osteoporotic hip fractures (odds ratio [OR] = 3.71, P = 0.032). To investigate the functional relevance of the identified ROHs, we performed cis-expression quantitative trait locus (eQTL) analysis in lymphoblast cell lines. Three ROHs, including ROH1p31.1, 11q12.1, and 15q22.3, were found to be significantly associated with mRNA expression levels of their nearby genes (PeQTL < 0.05). In summary, our findings reveal that ROHs could play as recessive-acting determinants contributing to the pathogenesis of osteoporosis. Further molecular and functional studies are needed to explore and clarify the potential mechanism., (© 2015 American Society for Bone and Mineral Research.)

Integration of multiple profiling data and construction of functional gene networks may provide additional insights into the molecular mechanisms of complex diseases. Osteoporosis is a worldwide public health problem, but the complex gene-gene interactions, post-transcriptional modifications and regulation of functional networks are still unclear. To gain a comprehensive understanding of osteoporosis etiology, transcriptome gene expression microarray, epigenomic miRNA microarray and methylome sequencing were performed simultaneously in 5 high hip BMD (Bone Mineral Density) subjects and 5 low hip BMD subjects. SPIA (Signaling Pathway Impact Analysis) and PCST (Prize Collecting Steiner Tree) algorithm were used to perform pathway-enrichment analysis and construct the interaction networks. Through integrating the transcriptomic and epigenomic data, firstly we identified 3 genes (FAM50A, ZNF473 and TMEM55B) and one miRNA (hsa-mir-4291) which showed the consistent association evidence from both gene expression and methylation data; secondly in network analysis we identified an interaction network module with 12 genes and 11 miRNAs including AKT1, STAT3, STAT5A, FLT3, hsa-mir-141 and hsa-mir-34a which have been associated with BMD in previous studies. This module revealed the crosstalk among miRNAs, mRNAs and DNA methylation and showed four potential regulatory patterns of gene expression to influence the BMD status. In conclusion, the integration of multiple layers of omics can yield in-depth results than analysis of individual omics data respectively. Integrative analysis from transcriptomics and epigenomic data improves our ability to identify causal genetic factors, and more importantly uncover functional regulation pattern of multi-omics for osteoporosis etiology.

Background: Osteoporosis is caused by excessive bone resorption (by osteoclasts) over bone formation (by osteoblasts). Monocytes are important to osteoporosis by serving as progenitors of osteoclasts and produce cytokines for osteoclastogenesis., Aim: To identify osteoporosis-related genes, we performed microarray analyses of monocytes using Affymetrix 1.0 ST arrays in 42 (including 16 pre- and 26 postmenopausal) high hip BMD (bone mineral density) vs. 31 (including 15 pre- and 16 postmenopausal) low hip BMD Caucasian female subjects. Here, high vs. low BMD is defined as belonging to top vs. bottom 30% of BMD values in population., Method: Differential gene expression analysis in high vs. low BMD subjects was conducted in the total cohort as well as pre- and post-menopausal subjects. Focusing on the top differentially expressed genes identified in the total, the pre- and the postmenopausal subjects (with a p <5E-03), we performed replication of the findings in 3 independent datasets of microarray analyses of monocytes (total N = 125)., Results: We identified (in the 73 subjects) and successfully replicated in all the 3 independent datasets 2 genes, DAXX and PLK3. Interestingly, both genes are apoptosis induction genes and both down-regulated in the low BMD subjects. Moreover, using the top 200 genes identified in the meta-analysis across all of the 4 microarray datasets, GO term enrichment analysis identified a number of terms related to induction of apoptosis, for which the majority of component genes are also down-regulated in the low BMD subjects. Overall, our result may suggest that there might be a decreased apoptosis activity of monocytes in the low BMD subjects., Conclusion: Our study for the first time suggested a decreased apoptosis rate (hence an increased survival) of monocytes, an important osteoclastogenic cell, as a novel mechanism for osteoporosis.

Context: To date, few systems genetics studies in the bone field have been performed. We designed our study from a systems-level perspective by integrating genome-wide association studies (GWASs), human protein-protein interaction (PPI) network, and gene expression to identify gene modules contributing to osteoporosis risk., Methods: First we searched for modules significantly enriched with bone mineral density (BMD)-associated genes in human PPI network by using 2 large meta-analysis GWAS datasets through a dense module search algorithm. One included 7 individual GWAS samples (Meta7). The other was from the Genetic Factors for Osteoporosis Consortium (GEFOS2). One was assigned as a discovery dataset and the other as an evaluation dataset, and vice versa., Results: In total, 42 modules and 129 modules were identified significantly in both Meta7 and GEFOS2 datasets for femoral neck and spine BMD, respectively. There were 3340 modules identified for hip BMD only in Meta7. As candidate modules, they were assessed for the biological relevance to BMD by gene set enrichment analysis in 2 expression profiles generated from circulating monocytes in subjects with low versus high BMD values. Interestingly, there were 2 modules significantly enriched in monocytes from the low BMD group in both gene expression datasets (nominal P value <.05). Two modules had 16 nonredundant genes. Functional enrichment analysis revealed that both modules were enriched for genes involved in Wnt receptor signaling and osteoblast differentiation., Conclusion: We highlighted 2 modules and novel genes playing important roles in the regulation of bone mass, providing important clues for therapeutic approaches for osteoporosis.

Low bone mineral density (BMD) is a risk factor for osteoporosis. Osteoporosis is more prevalent in females than in males. So far, the pathophysiological mechanisms underlying osteoporosis are unclear. Peripheral blood monocytes (PBMs) are precursors of bone-resorbing osteoclasts. This study aims to identify PBM-expressed proteins (genes) influencing hip BMD in humans. We utilized three independent study cohorts (N=34, 29, 40), including premenopausal Caucasians with discordant hip BMD. We studied PBM proteome-wide protein expression profiles in cohort 1 and identified 57 differentially expressed proteins (DEPs) between low vs. high BMD subjects. One protein gelsolin (GSN), after validation by Western blotting, was subject to follow-up. We compared GSN mRNA level in PBM between low vs. high BMD subjects in cohorts 2 and 3. We genotyped SNPs across GSN in 2286 unrelated Caucasians (cohort 4) and 1627 Chinese (cohort 5) and tested their association with hip BMD in females and males, respectively. We discovered and validated that GSN protein expression level in PBM was down-regulated 3.0-fold in low vs. high BMD subjects (P<0.05). Down-regulation of GSN in PBM in low BMD subjects was also observed at mRNA level in both cohort 2 and cohort 3. We identified that SNP rs767770 was significantly associated with hip BMD in female Caucasians (P=0.0003) only. Integrating analyses of the datasets at DNA, RNA, and protein levels from female Caucasians substantiated that GSN is highly significant for hip BMD (P=0.0001). We conclude that GSN is a significant gene influencing hip BMD in female Caucasians., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Aiming to identify novel genetic variants and to confirm previously identified genetic variants associated with bone mineral density (BMD), we conducted a three-stage genome-wide association (GWA) meta-analysis in 27 061 study subjects. Stage 1 meta-analyzed seven GWA samples and 11 140 subjects for BMDs at the lumbar spine, hip and femoral neck, followed by a Stage 2 in silico replication of 33 SNPs in 9258 subjects, and by a Stage 3 de novo validation of three SNPs in 6663 subjects. Combining evidence from all the stages, we have identified two novel loci that have not been reported previously at the genome-wide significance (GWS; 5.0 × 10(-8)) level: 14q24.2 (rs227425, P-value 3.98 × 10(-13), SMOC1) in the combined sample of males and females and 21q22.13 (rs170183, P-value 4.15 × 10(-9), CLDN14) in the female-specific sample. The two newly identified SNPs were also significant in the GEnetic Factors for OSteoporosis consortium (GEFOS, n = 32 960) summary results. We have also independently confirmed 13 previously reported loci at the GWS level: 1p36.12 (ZBTB40), 1p31.3 (GPR177), 4p16.3 (FGFRL1), 4q22.1 (MEPE), 5q14.3 (MEF2C), 6q25.1 (C6orf97, ESR1), 7q21.3 (FLJ42280, SHFM1), 7q31.31 (FAM3C, WNT16), 8q24.12 (TNFRSF11B), 11p15.3 (SOX6), 11q13.4 (LRP5), 13q14.11 (AKAP11) and 16q24 (FOXL1). Gene expression analysis in osteogenic cells implied potential functional association of the two candidate genes (SMOC1 and CLDN14) in bone metabolism. Our findings independently confirm previously identified biological pathways underlying bone metabolism and contribute to the discovery of novel pathways, thus providing valuable insights into the intervention and treatment of osteoporosis.

Background: Individuals’ peak bone mineral density (BMD) achieved and maintained at ages 20–40 years is the most powerful predictor of low bone mass and osteoporotic fractures later in life. The aim of this study was to identify metabolomic factors associated with peak BMD variation in US Caucasian women. Methods: A total of 136 women aged 20–40 years, including 65 subjects with low and 71 with high hip BMD, were enrolled. The serum metabolites were assessed using a liquid chromatography-mass spectrometry (LC-MS) method. The partial least-squares discriminant analysis (PLS-DA) method and logistic regression models were used, respectively, to examine the associations of metabolomic profiles and individual metabolites with BMD. Results: The low and high BMD groups could be differentiated by the detected serum metabolites using PLS-DA (Ppermutation = 0.008). A total of 14 metabolites, including seven amino acids and amino acid derivatives, five lipids (including three bile acids), and two organic acids, were significantly associated with the risk for low BMD. Most of these metabolites are novel in that they have never been linked with BMD in humans earlier. The prediction model including the newly identified metabolites significantly improved the classification of the groups with low and high BMD. The area under the receiver operating characteristic curve without and with metabolites were 0.88 (95% CI: 0.83–0.94) and 0.97 (95% CI: 0.94–0.99), respectively (P for the difference = 0.0004). Conclusion: Metabolomic profiling may improve the risk prediction of osteoporosis among Caucasian women. Our findings also suggest the potential importance of the metabolism of amino acids and bile acids in bone health. [ABSTRACT FROM AUTHOR]