| Osteoporosis is the most common and serious skeletal disorder among the elderly. Osteoporosis is characterized by increased bone fragility and low bone mineral density (BMD), especially in postmenopausal women with estrogen levels decline, with a strong genetic component; heritability is estimated to be as high as 85%. Serious consequences of osteoporotic fracture, brings a heavy burden to the family, society and medical systems. Osteoporosis as a complex disease caused by multiple minor genes and the environment interaction, and heredity, gender, age, sport, diet, smoking, drinking, hormone, and health status and other related factors. Human genetic disease gene prediction is a hot issue in bioinformatics. In recent years, the whole genome-wide association analysis is widely used in identifying candidate genes of complex disease. A number of SNPs (single nucleotide polymorphisms)-based GWASs and meta-analysis have identified some genes/loci associated with BMD. However, such conventional GWASs focus on disease-associated SNPs at genome-wide significance level (P< 5×10-8), which only explain less than 6% of the risk of osteoporosis. Missing genetic mechanism may be due to other genome, such as joint between genetic effect, etc. Now, scientists have turned their attention to extract the information exceeding GWAS results. New methods have been brought through the analysis of these data to predict causal genes in complex diseases and to interpret the genetic basis of the human disease and molecular basis. Using network analysis to find candidate genes in disease is a new method which could improve the ability of disease gene detection via detecting gene cluster and to explain the disease produces in the process of biological process.Circulating Monocytes were extracted from peripheral venous blood from Caucasian sample include 77 subjects chosen from a 2069 Caucasians sample with extremely low (n=38) and high (n=39) BMD. Network analysis method was used to find co-expression gene subnetworks/modules associated with BMD. The key nodes in the subnetwork are candidate genes for follow-up studies. Results from the gene network 10 gene expression modules were detected, and pink and black modules were confirmed significantly correlated with BMD. Based on GO (Gene Ontology) analysis and the functional annotation of pink and black module genes, we found some genes in black modules in an important pathway in the protein biosynthesis process. For further analysis, we found the principal component gene and the hub genes in black module are involved in important biological processes. We found RPL13A, RPL0 and SNORNA32A in black module are label genes in the process from bone marrow mesenchymal stem cells to the relevant osteogenetic differentiation process, and these genes common in protein biosynthesis pathway. At the same time, module black is significantly associated with spine BMD in the results of GEFOS-2 GWAS data set. The findings suggest that module black maybe play an important role in bone metabolism and bone loss.in women.The study used a bivariate GWAS to explore potential pleiotropic SNPs/genes in the current 2069 US Caucasians at genome-wide level. Our study indicated that DYNC2H1 gene may contribute to the genetic mechanisms of both alcohol drinking and spine BMD in male Caucasians. Moreover, our study suggested potential pleiotropic roles of OPRM1, IL1RN in females and GRIK4 gene in males underlying variation of both alcohol drinking and BMD. |
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