Computational Characterization of Osteoporosis Associated SNPs and Genes Identified by Genome-Wide Association Studies

Qin, Longjuan; Liu, Yuyong; Wang, Ya; Wu, Guiju; Chen, Jie; Ye, Weiyuan; Yang, Jiangke; Huang, Qingyang

doi:10.1371/journal.pone.0150070

Cited by 37 publications

(31 citation statements)

References 40 publications

(44 reference statements)

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“…222 SNPs linked to osteoporosis GWAS-associated lead SNPs had also been identified by using LD information in the Caucasians population via HapMap website [9]. Moreover, we obtained 107 known osteoporosis GWAS-associated genes which showed significant connectivity among proteins.…”

Section: Resultsmentioning

confidence: 99%

“…At the same time, the osteoporosis GWAS-associated genes have also been analyzed with Protein-Protein Interaction (PPI) network analysis tool in the study of the osteoporosis GWASassociated SNPs associated by the online PPI tool named String. Combining with GO and pathway analysis, we found that the hub proteins associated and the Wnt signaling pathway were related to the mesenchymal stem cell differentiation and hormone signaling that was related to the metabolism of osteoporosis [9]. Finally, it was found that the osteoporosis GWAS-associated SNPs in special region of genes had longrange interaction signal with other locus by analyzing the long-range interaction of osteoporosis associated SNPs on GWAS3D [10].…”

Section: Introductionmentioning

confidence: 88%

“…We identified the suspected risky SNPs associated with osteoporosis by algorithm based on the analysis of osteoporosis GWAS-associated SNPs with the method mentioned above [9]. It is assumed that the SNPs that are similar to the osteoporosis GWAS-associated SNPs are possible risky SNPs associated with osteoporosis.…”

Section: Methodsmentioning

confidence: 99%

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Identifying the Risky SNP of Osteoporosis with ID3-PEP Decision Tree Algorithm

Yang

Jiang

et al. 2017

Complexity

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In the past 20 years, much progress has been made on the genetic analysis of osteoporosis. A number of genes and SNPs associated with osteoporosis have been found through GWAS method. In this paper, we intend to identify the suspected risky SNPs of osteoporosis with computational methods based on the known osteoporosis GWAS-associated SNPs. The process includes two steps. Firstly, we decided whether the genes associated with the suspected risky SNPs are associated with osteoporosis by using random walk algorithm on the PPI network of osteoporosis GWAS-associated genes and the genes associated with the suspected risky SNPs. In order to solve the overfitting problem in ID3 decision tree algorithm, we then classified the SNPs with positive results based on their features of position and function through a simplified classification decision tree which was constructed by ID3 decision tree algorithm with PEP (Pessimistic-Error Pruning). We verified the accuracy of the identification framework with the data set of GWAS-associated SNPs, and the result shows that this method is feasible. It provides a more convenient way to identify the suspected risky SNPs associated with osteoporosis.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 88%

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Identifying the Risky SNP of Osteoporosis with ID3-PEP Decision Tree Algorithm

Yang

Jiang

et al. 2017

Complexity

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“…Полногеномные исследования выявили дополнительные полиморфные локусы, ассоциированные с МПК, качеством кости и/или риском переломов. Влияние этих локусов может реализоваться через связывание транскрипционных факторов, микроРНК, активность сигнального пути Wnt и других внутриклеточных путей передачи сигнала, дифференцировку стволовых мезенхимальных клеток и остеоцитов [22,23].…”

Section: обсуждение основного результата исследованияunclassified

Associations of polymorphisms in the gene promoters of cytokines and matrix metalloproteinases with bone mineral density in postmenopausal type 2 diabetic women

et al. 2018

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Background. Osteoporosis and type 2 diabetes are common comorbidities in postmenopausal women. An important role in the bone remodeling over the menopausal transition can be played by cytokines and matrix metalloproteinases (MMPs). It was shown that allelic variants in polymorphic positions of the genes of cytokines and MMPs affect the expression of these molecules under normal and pathological conditions. Aims. To examine associations between polymorphisms in the gene promoters of cytokines (TNFA, IL1B, IL4, IL6, IL10, VEGFA) and MMPs (MMP2, MMP3, MMP9) with bone mineral density (BMD) in postmenopausal women with type 2 diabetes. Materials and methods. We studied 197 Caucasian diabetic women, from 50 to70 years of age. An examination of BMD in the spine, proximal femur and forearm was performed by DEXA. Thirteen polymorphisms in the promoters of TNFA: -238 A/G (rs361525), -308 A/G(rs1800629) and -863 C/A (rs1800630), IL1B: -31 C/T (rs1143627), IL4: -590 C/T (rs2243250), IL6: -174 C/G (rs1800795), IL10: -592 C/A (rs1800872) and -1082 A/G (rs1800896), VEGFA: -2578 C/A (rs699947) and +936 C/T (rs3025039), MMP2: -1306 C/T (rs243865), MMP3: -1171 5A/6A (rs3025058) and MMP9: -1562 C/T (rs3918242), were investigated. Results. Seventy-three women had normal BMD, in 90 ones we revealed osteopenia, and 34 women had osteoporosis. Age, BMI and smoking were strongest predictors of BMD in multivariate regression analysis (p0.0001, p=0.003 and p=0.01, respectively). In the additive model, C allele and CC genotype in MMP9 -1562 position were associated with low BMD (OR 2.16, p=0.0007 and OR 2.02, p=0.0008, respectively). Association of the polymorphism with BMD remained significant after adjustment for clinical risk factors (p0.001). Twelve combinations of genotypes, associated positively with low BMD, were revealed by bioinformatic analysis (all p0.005). The СС genotype in position -1562 of MMP9, CC genotype in position -863 of TNFA, GG genotype in position -308 of TNFA, and AA genotype in position -1082 of IL10 were the most prevalent variants in these combinations. Conclusions. Variability in the gene promoters of cytokines and MMPs may confer individual susceptibility to osteoporosis in postmenopausal type 2 diabetic women.

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“…Multiple GWASs have shown that CCDC170 is one of the genes most strongly linked to BMD across populations[13–15]. Recent study have found that multiple SNPs in CCDC170 are associated with BMD, including rs6929137, rs371804, rs3734805, rs6932260, rs6904261, rs6932603, rs9383935, rs9383589, rs3734806 and rs3757322[16]. The functions of most of these SNPs in vivo and in vitro remain unverified.…”

Section: Introductionmentioning

confidence: 99%

Risk variant in miR-544a binding site in CCDC170 is associated with osteoporosis

Liu

Wang

Tan

et al. 2019

Preprint

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MicroRNAs (miRNAs) play essential roles in regulating bone formation and homeostasis. Genomic variations within miRNA target sites may therefore be important sources of genetic differences in osteoporosis risk. To investigate this possibility, we searched for miRNA recognition sites within genes using the TargetScan, miRNASNP and miRbase databases. In this study, we showed that miR-544a differentially regulated the allele variants of rs6932603 in the CCDC170 3 ʹ untranslated (3ʹ-UTR). We also showed that miR-544a suppressed osteogenesis and promoted osteoclastogenesis by regulating the expressions of Runx2, Osterix, Alp, Col1a1, Osteopontin (OPN) ,Osteocalcin (OCN) and Osteoprotegerin (OPG). Our results suggest that allele-specific regulation of CCDC170 by miR-544a explains the observed disease risk, and provides a potential therapeutic target for osteoporosis therapy.

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Computational Characterization of Osteoporosis Associated SNPs and Genes Identified by Genome-Wide Association Studies

Cited by 37 publications

References 40 publications

Identifying the Risky SNP of Osteoporosis with ID3-PEP Decision Tree Algorithm

Identifying the Risky SNP of Osteoporosis with ID3-PEP Decision Tree Algorithm

Associations of polymorphisms in the gene promoters of cytokines and matrix metalloproteinases with bone mineral density in postmenopausal type 2 diabetic women

Risk variant in miR-544a binding site in CCDC170 is associated with osteoporosis

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