Calcified Tissue International

, Volume 104, Issue 2, pp 171–181 | Cite as

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 ZhangEmail author
  • Hong-Wen DengEmail author
Original Research


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.


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



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.”


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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
    Email author
  • Hong-Wen Deng
    • 3
    Email author
  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

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