Identifying Gene Variants That Are Pathogenic In Osteoporosis Using An Omics Data And Bioinformatics Approach
Main Article Content
Introduction. The biological cause of osteoporosis, a metabolic bone disease, is osteoclastic bone resorption that is not offset by osteoblastic bone synthesis. Fractures become more likely as a result of the bones being brittle and weak. Common genetic variants that indicate hereditary susceptibility factors to osteoporosis in the general population, as well as mutations affecting specific genes that cause uncommon monogenic causes of osteoporosis, are the two main types of osteoporosis. Bone defects can now be caused by numerous additional genes. In this study, we aimed to identify variants of this pathogen across continents using genome-based and bioinformatics methodologies. Methods. We integrated osteoporosis-associated variants into this study using various bioinformatics-based techniques by using GWAS data from the National Human Genome Research Institute (NHGRI). Results. We found that the variant rs3742909 is likely to cause osteoporosis. SMOC1 gene expression in whole blood tissue also appears to be affected by this variant. We found that this genomic variant requires additional research to validate functional and clinical studies in patients with osteoporosis. Conclusions. We suggest that better understanding of disease susceptibility, including osteoporosis, can be achieved through the merging of genome-based databases and bioinformatics. Our goal is to validate the findings of this study both in vitro and in vivo during the preclinical stage.
Keywords:
Bioinformatics
Genetic factors
Genomic variants
Single nucleotide polymorphisms
Osteoporosis
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2. Tobias JH, Karasik D. Editorial: Recent Advances in the Genetics of Osteoporosis. Front Endocrinol [Internet]. 2021 [cited 2024 Sep 25];12.
3. Pagliari D, Ciro Tamburrelli F, Zirio G, Newton EE, Cianci R. The role of “bone immunological niche” for a new pathogenetic paradigm of osteoporosis. Anal Cell Pathol (Amst). 2015;2015:434389.
4. Lupsa BC, Insogna K. Bone Health and Osteoporosis. Endocrinol Metab Clin North Am. 2015;44:517–30.
5. Salari N, Ghasemi H, Mohammadi L, Behzadi M hasan, Rabieenia E, Shohaimi S, et al. The global prevalence of osteoporosis in the world: a comprehensive systematic review and meta-analysis. J Orthop Surg Res. 2021;16:609.
6. Vaisi-Raygani A, Mohammadi M, Jalali R, Ghobadi A, Salari N. The prevalence of obesity in older adults in Iran: a systematic review and meta-analysis. BMC Geriatr. 2019;19:371.
7. Cheraghi P, Cheraghi Z, Bozorgmehr S. The Prevalence and risk factors of osteoporosis among the elderly in Hamadan province: A cross sectional study. Medical Journal of the Islamic Republic of Iran. 2018;32:111.
8. Irani AD, Poorolajal J, Khalilian A, Esmailnasab N, Cheraghi Z. Prevalence of osteoporosis in Iran: A meta-analysis. J Res Med Sci. 2013;18:759–66.
9. Boudin E, Fijalkowski I, Hendrickx G, Van Hul W. Genetic control of bone mass. Mol Cell Endocrinol. 2016;432:3–13.
10. Policastro LJ, Saggi SJ, Goldfarb DS, Weiss JP. Personalized Intervention in Monogenic Stone Formers. J Urol. 2018;199:623–32.
11. Zhai N, Lu Y, Wang Y, Zhang S, Peng C, Zhang S, et al. Splice receptor-site mutation c.697-2A>G of the COL1A1 gene in a Chinese family with osteogenesis imperfecta. Intractable Rare Dis Res. 2019;8:150–3.
12. Xu XJ, Lv F, Liu Y, Wang JY, Song YW, Asan null, et al. A cryptic balanced translocation involving COL1A2 gene disruption cause a rare type of osteogenesis imperfecta. Clin Chim Acta. 2016;460:33–9.
13. Li H, Yin C, Li J, Huang Q, Huai Y, Chu X, et al. MiR-12200-5p Targets Multiple Members of Wnt Signaling Pathway toInhibit Osteoblast Differentiation and Bone Formation. EMIDDT. 2023;23:1254–64.
14. Manduchi E, Orzechowski PR, Ritchie MD, Moore JH. Exploration of a diversity of computational and statistical measures of association for genome-wide genetic studies. BioData Min. 2019;12:14.
15. Zhang X, Deng HW, Shen H, Ehrlich M. Prioritization of Osteoporosis-Associated Genome-wide Association Study (GWAS) Single-Nucleotide Polymorphisms (SNPs) Using Epigenomics and Transcriptomics. JBMR Plus. 2021;5:e10481.
16. Ding R, Wang Q, Gong L, Zhang T, Zou X, Xiong K, et al. scQTLbase: an integrated human single-cell eQTL database. Nucleic Acids Res. 2024;52:D1010–7.
17. Yang F, Wang J, GTEx Consortium, Pierce BL, Chen LS. Identifying cis-mediators for trans-eQTLs across many human tissues using genomic mediation analysis. Genome Res. 2017;27:1859–71.
18. Jeong R, Bulyk ML. Chromatin accessibility variation provides insights into missing regulation underlying immune-mediated diseases. bioRxiv. 2024;2024.04.12.589213.
19. Zhong Y, De T, Alarcon C, Park CS, Lec B, Perera MA. Discovery of novel hepatocyte eQTLs in African Americans. Gignoux CR, editor. PLoS Genet. 2020;16:e1008662.
20. Yudhani RD, Pakha DN, Suyatmi S, Irham LM. Identifying pathogenic variants related to systemic lupus erythematosus by integrating genomic databases and a bioinformatic approach. Genomics Inform. 2023;21:e37.
21. Irham LM, Chou WH, Calkins MJ, Adikusuma W, Hsieh SL, Chang WC. Genetic variants that influence SARS-CoV-2 receptor TMPRSS2 expression among population cohorts from multiple continents. Biochemical and Biophysical Research Communications. 2020;529:263–9.
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24. Humolungo DTWS, Anjani R, Irham LM, Sulistyani N, Ma’ruf M, Amukti DP, et al. Identification of pathogenic gene variants in carpal tunnel syndrome using bioinformatics approaches. E3S Web Conf. 2024;501:01022.
25. Vieira GM, Gellen LPA, da Veiga Borges Leal DF, Pastana LF, Vinagre LWMS, Aquino VT, et al. Correlation between Genomic Variants and Worldwide Epidemiology of Prostate Cancer. Genes. 2022;13:1039.
26. Muhammad Irham L, Chou WH, Wang YS, Adikusuma W, Sung-Ching Wong H, Aryani Perwitasari D, et al. Evaluation for the Genetic Association between Store-Operated Calcium Influx Pathway (STIM1 and ORAI1) and Human Hepatocellular Carcinoma in Patients with Chronic Hepatitis B Infection. Biology. 2020;9:388.
27. Mäkitie O, Zillikens MC. Early-Onset Osteoporosis. Calcif Tissue Int. 2022;110:546–61.
28. Rawung R, Bagy RG. Osteoporosis: Diagnosis and Management. e-CliniC. 2021;9:360–9.
29. Teerlink CC, Jurynec MJ, Hernandez R, Stevens J, Hughes DC, Brunker CP, et al. A role for the MEGF6 gene in predisposition to osteoporosis. Annals of Human Genetics. 2021;85:58–72.
30. Abdillah Imron Nasution. HUBUNGAN MOLEKULER OSTEOPOROSIS, INFLAMASI, SISTEM IMUNOLOGI DAN AGING [Internet]. Unpublished; 2015 [cited 2024 Sep 25]. Available from: http://rgdoi.net/10.13140/RG.2.1.4389.6160
31. Zhivodernikov IV, Kirichenko TV, Markina YV, Postnov AY, Markin AM. Molecular and Cellular Mechanisms of Osteoporosis. International Journal of Molecular Sciences. 2023;24:15772.
32. Takahata Y, Hagino H, Kimura A, Urushizaki M, Kobayashi S, Wakamori K, et al. Smoc1 and Smoc2 regulate bone formation as downstream molecules of Runx2. Commun Biol. 2021;4:1–11.
