Literature DB >> 30306195

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

Xiao Liang1, Yanan Du1, Yan Wen1, Li Liu1, Ping Li1, Yan Zhao1, Miao Ding1, Bolun Cheng1, Shiqiang Cheng1, Mei Ma1, Lu Zhang1, Hui Shen2, Qing Tian2, Xiong Guo1, Feng Zhang3, Hong-Wen Deng4.   

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.

Entities:  

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

Mesh:

Substances:

Year:  2018        PMID: 30306195      PMCID: PMC6368453          DOI: 10.1007/s00223-018-0483-4

Source DB:  PubMed          Journal:  Calcif Tissue Int        ISSN: 0171-967X            Impact factor:   4.333


  42 in total

1.  Bone mineral density, its predictors, and outcomes in peritoneal dialysis patients.

Authors:  Alicja E Grzegorzewska; Monika Młot-Michalska
Journal:  Adv Perit Dial       Date:  2011

2.  Targeted disruption of TGFBI in mice reveals its role in regulating bone mass and bone size through periosteal bone formation.

Authors:  Hongrun Yu; Jon E Wergedal; Yongliang Zhao; Subburaman Mohan
Journal:  Calcif Tissue Int       Date:  2012-05-27       Impact factor: 4.333

3.  Large-scale genome-wide linkage analysis for loci linked to BMD at different skeletal sites in extreme selected sibships.

Authors:  Yi-Hsiang Hsu; Xin Xu; Henry A Terwedow; Tianhua Niu; Xuimei Hong; Di Wu; Lihua Wang; Joseph D Brain; Mary L Bouxsein; Steve R Cummings; Cliff J Rosen; Xiping Xu
Journal:  J Bone Miner Res       Date:  2007-02       Impact factor: 6.741

4.  Serum ferritin levels are positively associated with bone mineral density in elderly Korean men: the 2008-2010 Korea National Health and Nutrition Examination Surveys.

Authors:  Kyung Shik Lee; Ji Su Jang; Dong Ryul Lee; Yang Hyun Kim; Ga Eun Nam; Byoung-Duck Han; Kyung Do Han; Kyung Hwan Cho; Seon Mee Kim; Youn Seon Choi; Do Hoon Kim
Journal:  J Bone Miner Metab       Date:  2013-12-14       Impact factor: 2.626

5.  RGD-CAP ((beta)ig-h3) is expressed in precartilage condensation and in prehypertrophic chondrocytes during cartilage development.

Authors:  S Ohno; T Doi; S Tsutsumi; Y Okada; K Yoneno; Y Kato; K Tanne
Journal:  Biochim Biophys Acta       Date:  2002-08-15

6.  The bone mineral density in acquired growth hormone deficiency correlates with circulating levels of insulin-like growth factor I.

Authors:  A G Johansson; P Burman; K Westermark; S Ljunghall
Journal:  J Intern Med       Date:  1992-11       Impact factor: 8.989

7.  Expression patterns of betaig-h3 in chondrocyte differentiation during endochondral ossification.

Authors:  Min-Su Han; Jung-Eun Kim; Hong-In Shin; In-San Kim
Journal:  Exp Mol Med       Date:  2008-08-31       Impact factor: 8.718

8.  Second-generation PLINK: rising to the challenge of larger and richer datasets.

Authors:  Christopher C Chang; Carson C Chow; Laurent Cam Tellier; Shashaank Vattikuti; Shaun M Purcell; James J Lee
Journal:  Gigascience       Date:  2015-02-25       Impact factor: 6.524

9.  Analysis of association of MEF2C, SOST and JAG1 genes with bone mineral density in Mexican-Mestizo postmenopausal women.

Authors:  Rafael Velázquez-Cruz; Rogelio F Jiménez-Ortega; Alma Y Parra-Torres; Manuel Castillejos-López; Nelly Patiño; Manuel Quiterio; Teresa Villarreal-Molina; Jorge Salmerón
Journal:  BMC Musculoskelet Disord       Date:  2014-11-28       Impact factor: 2.362

10.  Connecting genetic risk to disease end points through the human blood plasma proteome.

Authors:  Karsten Suhre; Matthias Arnold; Aditya Mukund Bhagwat; Richard J Cotton; Rudolf Engelke; Johannes Raffler; Hina Sarwath; Gaurav Thareja; Annika Wahl; Robert Kirk DeLisle; Larry Gold; Marija Pezer; Gordan Lauc; Mohammed A El-Din Selim; Dennis O Mook-Kanamori; Eman K Al-Dous; Yasmin A Mohamoud; Joel Malek; Konstantin Strauch; Harald Grallert; Annette Peters; Gabi Kastenmüller; Christian Gieger; Johannes Graumann
Journal:  Nat Commun       Date:  2017-02-27       Impact factor: 14.919

View more
  6 in total

1.  A genome-wide scan for pleiotropy between bone mineral density and nonbone phenotypes.

Authors:  Maria A Christou; Georgios Ntritsos; Georgios Markozannes; Fotis Koskeridis; Spyros N Nikas; David Karasik; Douglas P Kiel; Evangelos Evangelou; Evangelia E Ntzani
Journal:  Bone Res       Date:  2020-07-01       Impact factor: 13.567

2.  A genetic correlation scan identifies blood proteins associated with bone mineral density.

Authors:  Jiawen Xu; Shaoyun Zhang; Haibo Si; Yi Zeng; Yuangang Wu; Yuan Liu; Mingyang Li; Limin Wu; Bin Shen
Journal:  BMC Musculoskelet Disord       Date:  2022-06-03       Impact factor: 2.562

3.  [Study on adsorption of microRNA-124 by long chain non-coding RNA MALAT1 regulates osteogenic differentiation of mesenchymal stem cells].

Authors:  Yang Zhang; Hai Guo; Li Ma; Jinyu Zhu; Anyun Guo; Yong He
Journal:  Zhongguo Xiu Fu Chong Jian Wai Ke Za Zhi       Date:  2020-02-15

4.  A genome-wide scan for pleiotropy between bone mineral density and nonbone phenotypes.

Authors:  Maria A Christou; Georgios Ntritsos; Georgios Markozannes; Fotis Koskeridis; Spyros N Nikas; David Karasik; Douglas P Kiel; Evangelos Evangelou; Evangelia E Ntzani
Journal:  Bone Res       Date:  2020-07-01       Impact factor: 13.567

5.  Pathway and network analysis of genes related to osteoporosis.

Authors:  Lin Guo; Jia Han; Hao Guo; Dongmei Lv; Yun Wang
Journal:  Mol Med Rep       Date:  2019-06-06       Impact factor: 2.952

6.  Association Between Hemoglobin Levels and Osteoporosis in Chinese Patients with Type 2 Diabetes Mellitus: A Cross-Sectional Study.

Authors:  Tingting Ye; Liujin Lu; Liuqing Guo; Min Liang
Journal:  Diabetes Metab Syndr Obes       Date:  2022-09-14       Impact factor: 3.249

  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.