Abstract
Osteoporosis and obesity are two severe complex diseases threatening public health worldwide. Both diseases are under strong genetic determinants as well as genetically correlated. Aiming to identify pleiotropic genes underlying obesity and osteoporosis, we performed a bivariate genome-wide association (GWA) meta-analysis of hip bone mineral density (BMD) and total body fat mass (TBFM) in 12,981 participants from seven samples, and followed by in silico replication in the UK biobank (UKB) cohort sample (N = 217,822). Combining the results from discovery meta-analysis and replication sample, we identified one novel locus, 17q21.31 (lead SNP rs12150327, NC_000017.11:g.44956910G > A, discovery bivariate P = 4.83 × 10−9, replication P = 5.75 × 10−5) at the genome-wide significance level (ɑ = 5.0 × 10−8), which may have pleiotropic effects to both hip BMD and TBFM. Functional annotations highlighted several candidate genes, including KIF18B, C1QL1, and PRPF19 that may exert pleiotropic effects to the development of both body mass and bone mass. Our findings can improve our understanding of the etiology of osteoporosis and obesity, as well as shed light on potential new therapies.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 12 print issues and online access
$259.00 per year
only $21.58 per issue
Buy this article
- Purchase on SpringerLink
- Instant access to the full article PDF.
USD 39.95
Prices may be subject to local taxes which are calculated during checkout



Similar content being viewed by others
Data availability
The GWAS summary statistics were deposited in the GWAS catalog: (ftp://ftp-private.ebi.ac.uk/SummaryStatsUploads/XintongWei_prePMID/); The 1KG phase 3 reference panel can be download at: (ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/). Online tools: Qtlizer: (http://genehopper.de/qtlizer); HaploReg v4.1: (http://pubs.broadinstitute.org/mammals/haploreg/haploreg.php); STRING: (https://string-db.org/); Gene Atlas: (http://geneatlas.roslin.ed.ac.uk/); PathCards: (https://pathcards.genecards.org/).
References
Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C, et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2014;384:766–81.
Venniyoor A. The most important questions in cancer research and clinical oncology-Question 2-5. Obesity-related cancers: more questions than answers. Chin J Cancer. 2017;36:18.
Flegal KM, Carroll MD, Ogden CL, Johnson CL. Prevalence and trends in obesity among US adults, 1999-2000. JAMA. 2002;288:1723–7.
Wolf AM, Colditz GA. Current estimates of the economic cost of obesity in the United States. Obes Res. 1998;6:97–106.
Elder SJ, Roberts SB, McCrory MA, Das SK, Fuss PJ, Pittas AG, et al. Effect of body composition methodology on heritability estimation of body fatness. Open Nutr J. 2012;6:48–58.
Ramirez-Salazar EG, Carrillo-Patino S, Hidalgo-Bravo A, Rivera-Paredez B, Quiterio M, Ramirez-Palacios P, et al. Serum miRNAs miR-140-3p and miR-23b-3p as potential biomarkers for osteoporosis and osteoporotic fracture in postmenopausal Mexican-Mestizo women. Gene. 2018;679:19–27.
Lampropoulos CE, Papaioannou I, D’Cruz DP. Osteoporosis-a risk factor for cardiovascular disease? Nat Rev Rheumatol. 2012;8:587–98.
Peacock M, Turner CH, Econs MJ, Foroud T. Genetics of osteoporosis. Endocr Rev. 2002;23:303–26.
Kokabu S, Lowery JW, Jimi E. Cell fate and differentiation of bone marrow mesenchymal stem cells. Stem Cells Int. 2016;2016:3753581.
Magni P, Dozio E, Galliera E, Ruscica M, Corsi MM. Molecular aspects of adipokine-bone interactions. Curr Mol Med. 2010;10:522–32.
Ho-Pham LT, Nguyen UD, Nguyen TV. Association between lean mass, fat mass, and bone mineral density: a meta-analysis. J Clin Endocrinol Metab. 2014;99:30–8.
Lu Y, Day FR, Gustafsson S, Buchkovich ML, Na J, Bataille V, et al. New loci for body fat percentage reveal link between adiposity and cardiometabolic disease risk. Nat Commun. 2016;7:10495.
Yengo L, Sidorenko J, Kemper KE, Zheng Z, Wood AR, Weedon MN, et al. Meta-analysis of genome-wide association studies for height and body mass index in approximately 700000 individuals of European ancestry. Hum Mol Genet. 2018;27:3641–9.
Morris JA, Kemp JP, Youlten SE, Laurent L, Logan JG, Chai RC, et al. An atlas of genetic influences on osteoporosis in humans and mice. Nat Genet. 2019;51:258–66.
Liu YZ, Pei YF, Liu JF, Yang F, Guo Y, Zhang L, et al. Powerful bivariate genome-wide association analyses suggest the SOX6 gene influencing both obesity and osteoporosis phenotypes in males. PLoS ONE. 2009;4:e6827.
Tryka KA, Hao L, Sturcke A, Jin Y, Wang ZY, Ziyabari L, et al. NCBI’s database of genotypes and phenotypes: dbGaP. Nucleic Acids Res. 2014;42:D975–9.
Zhang L, Choi HJ, Estrada K, Leo PJ, Li J, Pei YF, et al. Multistage genome-wide association meta-analyses identified two new loci for bone mineral density. Hum Mol Genet. 2014;23:1923–33.
Pei YF, Hu WZ, Yan MW, Li CW, Liu L, Yang XL, et al. Joint study of two genome-wide association meta-analyses identified 20p12.1 and 20q13.33 for bone mineral density. Bone. 2018;110:378–85.
Mahmood SS, Levy D, Vasan RS, Wang TJ. The Framingham Heart Study and the epidemiology of cardiovascular disease: a historical perspective. Lancet. 2014;383:999–1008.
Design of the Women’s Health Initiative clinical trial and observational study. The Women’s Health Initiative Study Group. Control Clin Trials. 1998;19:61–109.
Estrada K, Styrkarsdottir U, Evangelou E, Hsu YH, Duncan EL, Ntzani EE, et al. Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture. Nat Genet. 2012;44:491–501.
Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. 2006;38:904–9.
Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81:559–75.
Genomes Project C, Abecasis GR, Auton A, Brooks LD, DePristo MA, Durbin RM, et al. An integrated map of genetic variation from 1,092 human genomes. Nature. 2012;491:56–65.
Zhang L, Pei YF, Fu X, Lin Y, Wang YP, Deng HW. FISH: fast and accurate diploid genotype imputation via segmental hidden Markov model. Bioinformatics. 2014;30:1876–83.
Zhang L, Li J, Pei YF, Liu Y, Deng HW. Tests of association for quantitative traits in nuclear families using principal components to correct for population stratification. Ann Hum Genet. 2009;73:601–13.
Zhang L, Pei YF, Li J, Papasian CJ, Deng HW. Univariate/multivariate genome-wide association scans using data from families and unrelated samples. PLoS ONE. 2009;4:e6502.
Konstantopoulos S. Fixed and mixed effects models in meta-analysis. IZA Discussion Paper No. 2198. 2006.
Tin A, Marten J, Halperin Kuhns VL, Li Y, Wuttke M, Kirsten H, et al. Target genes, variants, tissues and transcriptional pathways influencing human serum urate levels. Nat Genet. 2019;51:1459–74.
Dixon AL, Liang L, Moffatt MF, Chen W, Heath S, Wong KC, et al. A genome-wide association study of global gene expression. Nat Genet. 2007;39:1202–7.
Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12:e1001779.
Graafmans WC, Van Lingen A, Ooms ME, Bezemer PD, Lips P. Ultrasound measurements in the calcaneus: precision and its relation with bone mineral density of the heel, hip, and lumbar spine. Bone. 1996;19:97–100.
Ward LD, Kellis M. HaploReg v4: systematic mining of putative causal variants, cell types, regulators and target genes for human complex traits and disease. Nucleic Acids Res. 2016;44:D877–81.
Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, et al. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2015;43:D447–52.
Styrkarsdottir U, Halldorsson BV, Gretarsdottir S, Gudbjartsson DF, Walters GB, Ingvarsson T, et al. Multiple genetic loci for bone mineral density and fractures. N Engl J Med. 2008;358:2355–65.
Kichaev G, Bhatia G, Loh PR, Gazal S, Burch K, Freund MK, et al. Leveraging polygenic functional enrichment to improve GWAS power. Am J Hum Genet. 2019;104:65–75.
Mucientes A, Herranz E, Moro E, Lajas C, Candelas G, Fernandez-Gutierrez B, et al. Differential expression of HOX genes in mesenchymal stem cells from osteoarthritic patients is independent of their promoter methylation. Cells. 2018;7:244.
Watts KL, Delaney C, Humphries RK, Bernstein ID, Kiem HP. Combination of HOXB4 and Delta-1 ligand improves expansion of cord blood cells. Blood. 2010;116:5859–66.
Lappalainen T, Sammeth M, Friedlander MR, t Hoen PA, Monlong J, Rivas MA, et al. Transcriptome and genome sequencing uncovers functional variation in humans. Nature. 2013;501:506–11.
Westra HJ, Peters MJ, Esko T, Yaghootkar H, Schurmann C, Kettunen J, et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat Genet. 2013;45:1238–43.
Kawashima T, Hirose K, Satoh T, Kaneko A, Ikeda Y, Kaziro Y, et al. MgcRacGAP is involved in the control of growth and differentiation of hematopoietic cells. Blood. 2000;96:2116–24.
Cho SY, Shin ES, Park PJ, Shin DW, Chang HK, Kim D, et al. Identification of mouse Prp19p as a lipid droplet-associated protein and its possible involvement in the biogenesis of lipid droplets. J Biol Chem. 2007;282:2456–65.
Jun G, Ibrahim-Verbaas CA, Vronskaya M, Lambert JC, Chung J, Naj AC, et al. A novel Alzheimer disease locus located near the gene encoding tau protein. Mol Psychiatry. 2016;21:108–17.
Michailidou K, Lindstrom S, Dennis J, Beesley J, Hui S, Kar S, et al. Association analysis identifies 65 new breast cancer risk loci. Nature 2017;551:92–4.
International Multiple Sclerosis Genetics Consortium, Wellcome Trust Case Control Consortium, Sawcer S, Hellenthal G, Pirinen M, Spencer CC, et al. Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis. Nature. 2011;476:214–9.
Kim SK. Identification of 613 new loci associated with heel bone mineral density and a polygenic risk score for bone mineral density, osteoporosis and fracture. PLoS ONE. 2018;13:e0200785.
Schumaker VN, Zavodszky P, Poon PH. Activation of the first component of complement. Annu Rev Immunol. 1987;5:21–42.
Teo BH, Bobryshev YV, Teh BK, Wong SH, Lu J. Complement C1q production by osteoclasts and its regulation of osteoclast development. Biochem J. 2012;447:229–37.
Wei Z, Lei X, Petersen PS, Aja S, Wong GW. Targeted deletion of C1q/TNF-related protein 9 increases food intake, decreases insulin sensitivity, and promotes hepatic steatosis in mice. Am J Physiol Endocrinol Metab. 2014;306:E779–90.
Desert C, Baeza E, Aite M, Boutin M, Le Cam A, Montfort J, et al. Multi-tissue transcriptomic study reveals the main role of liver in the chicken adaptive response to a switch in dietary energy source through the transcriptional regulation of lipogenesis. BMC Genomics. 2018;19:187.
Sen R, Pezoa SA, Carpio Shull L, Hernandez-Lagunas L, Niswander LA, Artinger KB. Kat2a and Kat2b acetyltransferase activity regulates craniofacial cartilage and bone differentiation in zebrafish and mice. J Dev Biol. 2018;6:27.
Pei YF, Liu L, Liu TL, Yang XL, Zhang H, Wei XT, et al. Joint association analysis identified 18 new loci for bone mineral density. J Bone Min Res. 2019;34:1086–94.
Acknowledgements
We appreciate all the volunteers who participated into this study. YFP and LZ were partially supported by the funding from national natural science foundation of China (31771417 and 31571291). RH was supported by the Medical Health Research Program of Inner Mongolia Autonomous Region Health Commission (201702180). HWD and HS were partially supported by the National Institutes of Health (R01 AR069055, U19 AG055373, R01 MH104680, R01 AR059781, and P20 GM109036), the Franklin D. Dickson/Missouri Endowment and the Edward G. Schlieder Endowment. This study was benefited from a project funded by the Priority Academic Program Development (PAPD) of Jiangsu higher education institutions. The numerical calculations in this paper have been done on the supercomputing system of the National Supercomputing Center in Changsha. The funders had no role in study design, data collection and analysis, results interpretation, or preparation of the manuscript. The Framingham Heart Study is conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with Boston University (Contract No. N01-HC-25195). This manuscript was not prepared in collaboration with investigators of the Framingham Heart Study and does not necessarily reflect the opinions or views of the Framingham Heart Study, Boston University, or NHLBI. Funding for SHARe Affymetrix genotyping was provided by NHLBI Contract N02-HL-64278. SHARe Illumina genotyping was provided under an agreement between Illumina and Boston University. Funding support for the Framingham Whole Body and Regional Dual X-ray Absorptiometry (DXA) dataset was provided by NIH grants R01 AR/AG 41398. The datasets used for the analyses described in this manuscript were obtained from dbGaP through dbGaP accession phs000342.v14.p10. The WHI program is funded by the National Heart, Lung, and Blood Institute, National 20 Institutes of Health, U.S. Department of Health and Human Services through contracts N01WH22110, 24152, 32100-2, 32105-6, 32108-9, 32111-13, 32115, 32118-32119, 32122, 42107-26, 42129-32, and 44221. This manuscript was not prepared in collaboration with investigators of the WHI, has not been reviewed and/or approved by the Women’s Health Initiative (WHI), and does not necessarily reflect the opinions of the WHI investigators or the NHLBI. Funding for WHI SHARe genotyping was provided by NHLBI Contract N02-HL-64278. The datasets used for the analyses described in this manuscript were obtained from dbGaP through dbGaP accession phs000200.v10.p3. Funding support for the Genetic Determinants of Bone Fragility (the Indiana fragility study) was provided through the NIA Division of Geriatrics and Clinical Gerontology. Genetic Determinants of Bone Fragility is a genome-wide association studies funded as part of the NIA Division of Geriatrics and Clinical Gerontology. Support for the collection of datasets and samples were provided by the parent grant, Genetic Determinants of Bone Fragility (P01-AG018397). Funding support for the genotyping which was performed at the Johns Hopkins University Center for Inherited Diseases Research was provided by the NIH NIA. The datasets used for the analyses described in this manuscript were obtained from dbGaP through dbGaP accession phs000138.v2.p1.
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Rights and permissions
About this article
Cite this article
Wei, XT., Feng, GJ., Zhang, H. et al. Pleiotropic genomic variants at 17q21.31 associated with bone mineral density and body fat mass: a bivariate genome-wide association analysis. Eur J Hum Genet 29, 553–563 (2021). https://doi.org/10.1038/s41431-020-00727-3
Received:
Revised:
Accepted:
Published:
Version of record:
Issue date:
DOI: https://doi.org/10.1038/s41431-020-00727-3


