Advertisement

Assessing the Genetic Correlations Between Blood Plasma Proteins and Osteoporosis: A Polygenic Risk Score Analysis

  • Xiao Liang
  • Yanan Du
  • Yan Wen
  • Li Liu
  • Ping Li
  • Yan Zhao
  • Miao Ding
  • Bolun Cheng
  • Shiqiang Cheng
  • Mei Ma
  • Lu Zhang
  • Hui Shen
  • Qing Tian
  • Xiong Guo
  • Feng Zhang
  • Hong-Wen Deng
Original Research

Abstract

Osteoporosis is a common metabolic bone disease. The impact of global blood plasma proteins on the risk of osteoporosis remains elusive now. We performed a large-scale polygenic risk score (PRS) analysis to evaluate the potential effects of blood plasma proteins on the development of osteoporosis in 2286 Caucasians, including 558 males and 1728 females. Bone mineral density (BMD) and bone areas at ulna & radius, hip, and spine were measured using Hologic 4500W DXA. BMD/bone areas values were adjusted for age, sex, height, and weight as covariates. Genome-wide SNP genotyping of 2286 Caucasian subjects was performed using Affymetrix Human SNP Array 6.0. The 267 blood plasma proteins-associated SNP loci and their genetic effects were obtained from recently published genome-wide association study (GWAS) using a highly multiplexed aptamer-based affinity proteomics platform. The polygenetic risk score (PRS) of study subjects for each blood plasma protein was calculated from the genotypes data of the 2286 Caucasian subjects by PLINK software. Pearson correlation analysis of individual PRS values and BMD/bone area value was performed using R. Additionally, gender-specific analysis also was performed by Pearson correlation analysis. 267 blood plasma proteins were analyzed in this study. For BMD, we observed association signals between 41 proteins and BMD, mainly including whole body total BMD versus Factor H (p value = 9.00 × 10−3), whole body total BMD versus BGH3 (p value = 1.40 × 10−2), spine total BMD versus IGF-I (p value = 2.15 × 10−2), and spine total BMD versus SAP (p value = 3.90 × 10−2). As for bone areas, association evidence was observed between 45 blood plasma proteins and bone areas, such as ferritin versus spine area (p value = 1.90 × 10−2), C4 versus hip area (p value = 1.25 × 10−2), and hemoglobin versus right ulna and radius area (p value = 2.70 × 10−2). Our study results suggest the modest impact of blood plasma proteins on the variations of BMD/bone areas, and identify several candidate blood plasma proteins for osteoporosis.

Keywords

Genome-wide association study Blood plasma proteins Osteoporosis Polygenic risk score analysis 

Notes

Acknowledgements

This study is supported by the National Natural Scientific Foundation of China (81472925, 81673112), the Key projects of international cooperation among governments in scientific and technological innovation (2016YFE0119100), the Technology Research and Development Program of in Shaanxi Province of China (2013KJXX-51), and the Fundamental Research Funds for the Central Universities. QT, HS, and HWD were partially supported by grants from the National Institutes of Health [R01AR057049, R01AR059781, P20 GM109036, R01MH107354, R01MH104680, R01GM109068, AR069055, and U19 AG055373], the Edward G. Schlieder Endowment fund, and the Tsai and Kung endowment fund to Tulane University.

Compliance with Ethical Standards

Conflict of interest

Xiao Liang, Yanan Du, Yan Wen, Li Liu, Ping Li, Yan Zhao, Miao Ding, Bolun Cheng, Shiqiang Cheng, Mei Ma, Lu Zhang, Hui Shen, Qing Tian, Xiong Guo, Feng Zhang, and Hong-Wen Deng declare that they have no conflicts of interest.

Human and Animal Rights

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.

Informed Consent

“Informed consent was obtained from all individual participants included in the study.”

References

  1. 1.
    Aaseth J, Boivin G, Andersen O (2012) Osteoporosis and trace elements—an overview. J Trace Elements Med Biol 26:149–152CrossRefGoogle Scholar
  2. 2.
    Yang T-L, Guo Y, Li J, Zhang L, Shen H, Li SM, Li SK, Tian Q, Liu Y-J, Papasian CJ, Deng H-W (2013) Gene–gene interaction between RBMS3 and ZNF516 influences bone mineral density. J Bone Miner Res 28:828–837CrossRefPubMedCentralGoogle Scholar
  3. 3.
    Velázquezcruz R, Jiménezortega RF, Parratorres AY, Castillejoslópez M, Patiño N, Quiterio M, Villarrealmolina T, Salmerón J (2014) Analysis of association of MEF2C, SOST and JAG1 genes with bone mineral density in Mexican-Mestizo postmenopausal women. BMC Musculoskel Disord 15:400CrossRefGoogle Scholar
  4. 4.
    Sassi R, Sahli H, Souissi C, El Mahmoudi H, Zouari B, ElGaaied ABA, Sellami S, Ferrari SL (2014) Association of LRP5 genotypes with osteoporosis in Tunisian post-menopausal women. BMC Musculoskel Disord 15:144–144CrossRefGoogle Scholar
  5. 5.
    Deng FY, Lei SF, Zhang Y, Zhang YL, Zheng YP, Zhang LS, Pan R, Wang L, Tian Q, Shen H, Zhao M, Lundberg YW, Liu YZ, Papasian CJ, Deng HW (2011) Peripheral blood monocyte-expressed ANXA2 gene is involved in pathogenesis of osteoporosis in humans. Mol Cell Proteomics 10:M111.011700CrossRefPubMedCentralGoogle Scholar
  6. 6.
    Chupeerach C, Harnroongroj T, Phonrat B, Tungtrongchitr A, Schweigert FJ, Tungtrongchitr R, Preutthipan S (2011) Decreased retinol transport proteins in Thai post-menopausal women with osteoporosis. Southeast Asian J Trop Med Public Health 42:1515–1520PubMedPubMedCentralGoogle Scholar
  7. 7.
    Qundos U, Drobin K, Mattsson C, Hong MG, Sjoberg R, Forsstrom B, Solomon D, Uhlen M, Nilsson P, Michaelsson K, Schwenk JM (2016) Affinity proteomics discovers decreased levels of AMFR in plasma from Osteoporosis patients. Proteomics Clin Appl 10:681–690CrossRefPubMedCentralGoogle Scholar
  8. 8.
    Wu Q, Zhong ZM, Pan Y, Zeng JH, Zheng S, Zhu SY, Chen JT (2015) Advanced oxidation protein products as a novel marker of oxidative stress in postmenopausal osteoporosis. Med Sci Monit Int Med J Exp Clin Res 21:2428–2432Google Scholar
  9. 9.
    Horne BD, Anderson JL, Carlquist JF, Muhlestein JB, Renlund DG, Bair TL, Pearson RR, Camp NJ (2005) Generating genetic risk scores from intermediate phenotypes for use in association studies of clinically significant endpoints. Ann Hum Genet 69:176–186CrossRefPubMedCentralGoogle Scholar
  10. 10.
    Kawai VK, Levinson RT, Adefurin A, Kurnik D, Collier SP, Conway D, Stein CM (2017) A genetic risk score that includes common type 2 diabetes risk variants is associated with gestational diabetes. Clin Endocrinol 87:149–155CrossRefGoogle Scholar
  11. 11.
    Belsky DW, Moffitt TE, Sugden K, Williams B, Houts R, Mccarthy J, Caspi A (2013) Development and evaluation of a genetic risk score for obesity. Biodemogr Soc Biol 59:85–100CrossRefGoogle Scholar
  12. 12.
    Ripatti S, Tikkanen E, Orho-Melander M, Havulinna AS, Silander K, Sharma A, Guiducci C, Perola M, Jula A, Sinisalo J, Lokki ML, Nieminen MS, Melander O, Salomaa V, Peltonen L, Kathiresan S (2010) A multilocus genetic risk score for coronary heart disease: case-control and prospective cohort analyses. Lancet 376:1393–1400CrossRefPubMedCentralGoogle Scholar
  13. 13.
    Suhre K, Arnold M (2017) Connecting genetic risk to disease end points through the human blood plasma proteome. Nat Commun 8:14357CrossRefPubMedCentralGoogle Scholar
  14. 14.
    Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ (2015) Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience 4:7CrossRefPubMedCentralGoogle Scholar
  15. 15.
    Zheng HF, Forgetta V, Hsu YH, Estrada K, Rosello-Diez A, Leo PJ, Dahia CL, Park-Min KH, Tobias JH, Kooperberg C, Kleinman A, Styrkarsdottir U, Liu CT, Uggla C, Evans DS, Nielson CM, Walter K, Pettersson-Kymmer U, McCarthy S, Eriksson J, Kwan T, Jhamai M, Trajanoska K, Memari Y, Min J, Huang J, Danecek P, Wilmot B, Li R, Chou WC, Mokry LE, Moayyeri A, Claussnitzer M, Cheng CH, Cheung W, Medina-Gomez C, Ge B, Chen SH, Choi K, Oei L, Fraser J, Kraaij R, Hibbs MA, Gregson CL, Paquette D, Hofman A, Wibom C, Tranah GJ, Marshall M, Gardiner BB, Cremin K, Auer P, Hsu L, Ring S, Tung JY, Thorleifsson G, Enneman AW, van Schoor NM, de Groot LC, van der Velde N, Melin B, Kemp JP, Christiansen C, Sayers A, Zhou Y, Calderari S, van Rooij J, Carlson C, Peters U, Berlivet S, Dostie J, Uitterlinden AG, Williams SR, Farber C, Grinberg D, LaCroix AZ, Haessler J, Chasman DI, Giulianini F, Rose LM, Ridker PM, Eisman JA, Nguyen TV, Center JR, Nogues X, Garcia-Giralt N, Launer LL, Gudnason V, Mellstrom D, Vandenput L, Amin N, van Duijn CM, Karlsson MK, Ljunggren O, Svensson O, Hallmans G, Rousseau F, Giroux S, Bussiere J, Arp PP et al (2015) Whole-genome sequencing identifies EN1 as a determinant of bone density and fracture. Nature 526:112–117CrossRefPubMedCentralGoogle Scholar
  16. 16.
    Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, Maller J, Sklar P, de Bakker PIW, Daly MJ, Sham PC (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:559–575CrossRefPubMedCentralGoogle Scholar
  17. 17.
    Reinboth B, Thomas J, Hanssen E, Gibson MA (2006) Beta ig-h3 interacts directly with biglycan and decorin, promotes collagen VI aggregation, and participates in ternary complexing with these macromolecules. J Biol Chem 281:7816–7824CrossRefPubMedCentralGoogle Scholar
  18. 18.
    Ohno S, Doi T, Tsutsumi S, Okada Y, Yoneno K, Kato Y, Tanne K (2002) RGD-CAP ((beta)ig-h3) is expressed in precartilage condensation and in prehypertrophic chondrocytes during cartilage development. Biochim et Biophys Acta 1572:114–122CrossRefGoogle Scholar
  19. 19.
    Han MS, Kim JE, Shin HI, Kim IS (2008) Expression patterns of betaig-h3 in chondrocyte differentiation during endochondral ossification. Exp Mol Med 40:453–460CrossRefPubMedCentralGoogle Scholar
  20. 20.
    Ohno S, Doi T, Fujimoto K, Ijuin C, Tanaka N, Tanimoto K, Honda K, Nakahara M, Kato Y, Tanne K (2002) RGD-CAP (betaig-h3) exerts a negative regulatory function on mineralization in the human periodontal ligament. J Dent Res 81:822–825CrossRefPubMedCentralGoogle Scholar
  21. 21.
    Yu H, Wergedal JE, Zhao Y, Mohan S (2012) Targeted disruption of TGFBI in mice reveals its role in regulating bone mass and bone size through periosteal bone formation. Calcif Tissue Int 91:81–87CrossRefPubMedCentralGoogle Scholar
  22. 22.
    Ahn SH, Lee S, Kim H, Lee SH, Kim BJ, Koh JM (2016) Higher serum ferritin level and lower femur neck strength in women at the stage of bone loss (≥ 45 years of age): the Fourth Korea National Health and Nutrition Examination Survey (KNHANES IV). Endocr Res 41:334–342CrossRefPubMedCentralGoogle Scholar
  23. 23.
    Sarrai M, Duroseau H, D’Augustine J, Moktan S, Bellevue R (2007) Bone mass density in adults with sickle cell disease. Br J Haematol 136:666–672CrossRefPubMedCentralGoogle Scholar
  24. 24.
    Chon SJ, Choi YR, Roh YH, Yun BH, Cho S, Choi YS, Lee BS, Seo SK (2014) Association between levels of serum ferritin and bone mineral density in Korean premenopausal and postmenopausal women: KNHANES 2008–2010. PloS ONE 9:e114972CrossRefPubMedCentralGoogle Scholar
  25. 25.
    Lee KS, Jang JS, Lee DR, Kim YH, Nam GE, Han BD, Do Han K, Cho KH, Kim SM, Choi YS, Kim DH (2014) Serum ferritin levels are positively associated with bone mineral density in elderly Korean men: the 2008–2010 Korea National Health and Nutrition Examination Surveys. J Bone Miner Metab 32:683–690CrossRefPubMedCentralGoogle Scholar
  26. 26.
    Heidari B, Hosseini R, Javadian Y, Bijani A, Sateri MH, Nouroddini HG (2015) Factors affecting bone mineral density in postmenopausal women. Arch Osteoporos 10:1–7CrossRefGoogle Scholar
  27. 27.
    Janssen JA, Burger H, Stolk RP, Grobbee DE, de Jong FH, Lamberts SW, Pols HA (1998) Gender-specific relationship between serum free and total IGF-I and bone mineral density in elderly men and women. Eur J Endocrinol 138:627–632CrossRefPubMedCentralGoogle Scholar
  28. 28.
    Langlois JA, Rosen CJ, Visser M, Hannan MT, Harris T, Wilson PW, Kiel DP (1998) Association between insulin-like growth factor I and bone mineral density in older women and men: the Framingham Heart Study. J Clin Endocrinol Metab 83:4257–4262PubMedPubMedCentralGoogle Scholar
  29. 29.
    Johansson AG, Burman P, Westermark K, Ljunghall S (1992) The bone mineral density in acquired growth hormone deficiency correlates with circulating levels of insulin-like growth factor I. J Intern Med 232:447–452CrossRefPubMedCentralGoogle Scholar
  30. 30.
    Munoz-Torres M, Mezquita-Raya P, Lopez-Rodriguez F, Torres-Vela E, de Dios Luna J, Escobar-Jimenez F (2001) The contribution of IGF-I to skeletal integrity in postmenopausal women. Clin Endocrinol 55:759–766CrossRefGoogle Scholar
  31. 31.
    Romagnoli E, Minisola S, Carnevale V, Scarda A, Rosso R, Scarnecchia L, Pacitti MT, Mazzuoli G (1993) Effect of estrogen deficiency on IGF-I plasma levels: relationship with bone mineral density in perimenopausal women. Calcif Tissue Int 53:1–6CrossRefPubMedCentralGoogle Scholar
  32. 32.
    Oh YH, Moon JH, Cho B (2017) Association between hemoglobin level and bone mineral density in korean adults. J Bone Metab 24:161–173CrossRefPubMedCentralGoogle Scholar
  33. 33.
    Korkmaz U, Korkmaz N, Yazici S, Erkan M, Baki AE, Yazici M, Ozhan H, Ataoglu S (2012) Anemia as a risk factor for low bone mineral density in postmenopausal Turkish women. Eur J Intern Med 23:154–158CrossRefPubMedCentralGoogle Scholar
  34. 34.
    Laudisio A, Marzetti E, Pagano F, Bernabei R, Zuccala G (2009) Haemoglobin levels are associated with bone mineral density in the elderly: a population-based study. Clin Rheumatol 28:145–151CrossRefPubMedCentralGoogle Scholar
  35. 35.
    Grzegorzewska AE, Mlot-Michalska M (2011) Bone mineral density, its predictors, and outcomes in peritoneal dialysis patients. Advances in peritoneal dialysis. Conf Perit Dial 27:140–145Google Scholar
  36. 36.
    Cesari M, Pahor M, Lauretani F, Penninx BW, Bartali B, Russo R, Cherubini A, Woodman R, Bandinelli S, Guralnik JM, Ferrucci L (2005) Bone density and hemoglobin levels in older persons: results from the InCHIANTI study. Osteoporos Int 16:691–699CrossRefPubMedCentralGoogle Scholar
  37. 37.
    Kang KY, Hong YS, Park SH, Ju JH (2015) Increased serum alkaline phosphatase levels correlate with high disease activity and low bone mineral density in patients with axial spondyloarthritis. Semin Arthr Rheum 45:202–207CrossRefGoogle Scholar
  38. 38.
    Bergman A, Qureshi AR, Haarhaus M, Lindholm B, Barany P, Heimburger O, Stenvinkel P, Anderstam B (2017) Total and bone-specific alkaline phosphatase are associated with bone mineral density over time in end-stage renal disease patients starting dialysis. J Nephrol 30:255–262CrossRefPubMedCentralGoogle Scholar
  39. 39.
    Chen H, Li J, Wang Q (2018) Associations between bone-alkaline phosphatase and bone mineral density in adults with and without diabetes. Medicine 97:e0432CrossRefPubMedCentralGoogle Scholar
  40. 40.
    Zhang F, Xiao P, Yang F, Shen H, Xiong DH, Deng HY, Papasian CJ, Drees BM, Hamilton JJ, Recker RR, Deng HW (2008) A whole genome linkage scan for QTLs underlying peak bone mineral density. Osteoporos Int 19:303–310CrossRefPubMedCentralGoogle Scholar
  41. 41.
    Hsu YH, Xu X, Terwedow HA, Niu T, Hong X, Wu D, Wang L, Brain JD, Bouxsein ML, Cummings SR (2010) Large-scale genome-wide linkage analysis for loci linked to BMD at different skeletal sites in extreme selected sibships. J Bone Miner Res 22:184–194CrossRefGoogle Scholar
  42. 42.
    Xiao P, Shen H, Guo YF, Xiong DH, Liu YZ, Liu YJ, Zhao LJ, Long JR, Guo Y, Recker RR (2010) Genomic regions identified for BMD in a large sample including epistatic interactions and gender-specific effects. J Bone Miner Res 21:1536–1544CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Xiao Liang
    • 1
  • Yanan Du
    • 1
  • Yan Wen
    • 1
  • Li Liu
    • 1
  • Ping Li
    • 1
  • Yan Zhao
    • 1
  • Miao Ding
    • 1
  • Bolun Cheng
    • 1
  • Shiqiang Cheng
    • 1
  • Mei Ma
    • 1
  • Lu Zhang
    • 1
  • Hui Shen
    • 2
  • Qing Tian
    • 2
  • Xiong Guo
    • 1
  • Feng Zhang
    • 1
  • Hong-Wen Deng
    • 3
  1. 1.Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Collaborative Innovation Center of Endemic Diseases and Population Health Promotion in Sick Road Region, School of Public Health, Health Science CenterXi’an Jiaotong UniversityXi’anPeople’s Republic of China
  2. 2.Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, School of Public Health and Tropical MedicineTulane UniversityNew OrleansUSA
  3. 3.School of Basic Medical SciencesCentral South UniversityChangshaPeople’s Republic of China

Personalised recommendations