Bone mineral density (BMD) and lean body mass (LBM) not only have a considerable heritability each, but also are genetically correlated. However, common genetic determinants shared by both traits are largely unknown. In the present study, we performed a bivariate genome-wide association study (GWAS) meta-analysis of hip BMD and trunk lean mass (TLM) in 11,335 subjects from 6 samples, and performed replication in estimated heel BMD and TLM in 215,234 UK Biobank (UKB) participants. We identified 2 loci that nearly attained the genome-wide significance (GWS, p < 5.0 × 10−8) level in the discovery GWAS meta-analysis and that were successfully replicated in the UKB sample: 11p15.2 (lead SNP rs12800228, discovery p = 2.88 × 10−7, replication p = 1.95 × 10−4) and 18q21.32 (rs489693, discovery p = 1.67 × 10−7, replication p = 1.17 × 10−3). The above 2 pleiotropic loci may play a pleiotropic role for hip BMD and TLM development. So our findings provide useful insights that further enhance our understanding of genetic interplay between BMD and LBM.
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Notelovitz M. Osteoporosis: screening, prevention, and management. Fertil Steril 1993;59:707–25.
Peacock M, Turner CH, Econs MJ, Foroud T. Genetics of osteoporosis. Endocr Rev 2002;23:303–26.
Cummings SR, Melton LJ. Epidemiology and outcomes of osteoporotic fractures. Lancet 2002;359:1761–7.
Xia WB, He SL, Xu L, Liu AM, Jiang Y, Li M, et al. Rapidly increasing rates of hip fracture in Beijing, China. J Bone Min Res 2012;27:125–9.
Burge R, Dawson-Hughes B, Solomon DH, Wong JB, King A, Tosteson A. Incidence and economic burden of osteoporosis-related fractures in the United States, 2005–2025. J Bone Min Res 2007;22:465–75.
Arden NK, Spector TD. Genetic influences on muscle strength, lean body mass, and bone mineral density: a twin study. J Bone Miner Res: Off J Am Soc Bone Miner Res 1997;12:2076–81.
Pei YF, Hu WZ, Yang XL, Wei XT, Feng GJ, Zhang H, et al. Two functional variants at 6p21.1 were associated with lean mass. Skelet Muscle 2019;9:28.
Jensky NE, Criqui MH, Wright CM, Wassel CL, Alcaraz JE, Allison MA. The association between abdominal body composition and vascular calcification. Obes (Silver Spring) 2011;19:2418–24.
Wassel CL, Laughlin GA, Saad SD, Araneta MR, Wooten W, Barrett-Connor E, et al. Associations of abdominal muscle area with 4-year change in coronary artery calcium differ by ethnicity among post-menopausal women. Ethn Dis 2015;25:435–42.
Hicks GE, Fritz JM, Delitto A, McGill SM. Preliminary development of a clinical prediction rule for determining which patients with low back pain will respond to a stabilization exercise program. Arch Phys Med Rehabil 2005;86:1753–62.
Abraham KA, Feingold H, Fuller DD, Jenkins M, Mateika JH, Fregosi RF. Respiratory-related activation of human abdominal muscles during exercise. J Physiol 2002;541(Pt 2):653–63.
Yamamoto J, Bergstrom J, Davis A, Wing D, Schousboe JT, Nichols JF, et al. Trunk lean mass and its association with 4 different measures of thoracic kyphosis in older community dwelling persons. PLoS ONE 2017;12:e0174710.
Cianferotti L, Brandi ML. Muscle-bone interactions: basic and clinical aspects. Endocrine 2014;45:165–77.
Brotto M, Bonewald L. Bone and muscle: Interactions beyond mechanical. Bone 2015;80:109–14.
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 Miner Res: Off J Am Soc Bone Miner Res 2019;34:1086–94.
Kemp JP, Morris JA, Medina-Gomez C, Forgetta V, Warrington NM, Youlten SE, et al. Identification of 153 new loci associated with heel bone mineral density and functional involvement of GPC6 in osteoporosis. Nat Genet 2017;49:1468–75.
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.
Zillikens MC, Demissie S, Hsu YH, Yerges-Armstrong LM, Chou WC, Stolk L, et al. Large meta-analysis of genome-wide association studies identifies five loci for lean body mass. Nat Commun 2017;8:80.
Liu XG, Tan LJ, Lei SF, Liu YJ, Shen H, Wang L, et al. Genome-wide association and replication studies identified TRHR as an important gene for lean body mass. Am J Hum Genet 2009;84:418–23.
Medina-Gomez C, Kemp JP, Dimou NL, Kreiner E, Chesi A, Zemel BS, et al. Bivariate genome-wide association meta-analysis of pediatric musculoskeletal traits reveals pleiotropic effects at the SREBF1/TOM1L2 locus. Nat Commun 2017;8:121.
Sun L, Tan LJ, Lei SF, Chen XD, Li X, Pan R, et al. Bivariate genome-wide association analyses of femoral neck bone geometry and appendicular lean mass. PLoS ONE 2011;6:e27325.
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.
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, Bonham AJ, Li J, Pei YF, Chen J, Papasian CJ, et al. Family-based bivariate association tests for quantitative traits. PLoS ONE 2009;4:e8133.
Zheng HF, Forgetta V, Hsu YH, Estrada K, Rosello-Diez A, Leo PJ, et al. Whole-genome sequencing identifies EN1 as a determinant of bone density and fracture. Nature 2015;526:112–7.
Michailidou K, Beesley J, Lindstrom S, Canisius S, Dennis J, Lush MJ, et al. Genome-wide association analysis of more than 120,000 individuals identifies 15 new susceptibility loci for breast cancer. Nat Genet 2015;47:373–80.
Stuart PE, Nair RP, Tsoi LC, Tejasvi T, Das S, Kang HM, et al. Genome-wide association analysis of psoriatic arthritis and cutaneous psoriasis reveals differences in their genetic architecture. Am J Hum Genet 2015;97:816–36.
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.
Konstantopoulos S “Fixed and Mixed Effects Models in Meta-Analysis.”. IZA Discussion Paper 2198. 2006.
Manichaikul A, Mychaleckyj JC, Rich SS, Daly K, Sale M, Chen WM. Robust relationship inference in genome-wide association studies. Bioinformatics 2010;26:2867–73.
Ward LD, Kellis M. HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res 2012;40(Database issue):D930–4.
International HapMap Consortium. A haplotype map of the human genome. Nature 2005;437:1299–320.
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.
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.
Chang D, Nalls MA, Hallgrimsdottir IB, Hunkapiller J, van der Brug M, Cai F, et al. A meta-analysis of genome-wide association studies identifies 17 new Parkinson’s disease risk loci. Nat Genet 2017;49:1511–6.
Local A, Huang H, Albuquerque CP, Singh N, Lee AY, Wang W, et al. Identification of H3K4me1-associated proteins at mammalian enhancers. Nat Genet 2018;50:73–82.
Yang TL, Guo Y, Liu YJ, Shen H, Liu YZ, Lei SF, et al. Genetic variants in the SOX6 gene are associated with bone mineral density in both Caucasian and Chinese populations. Osteoporos Int 2012;23:781–7.
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.
Dy P, Smits P, Silvester A, Penzo-Mendez A, Dumitriu B, Han Y, et al. Synovial joint morphogenesis requires the chondrogenic action of Sox5 and Sox6 in growth plate and articular cartilage. Dev Biol 2010;341:346–59.
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.
Loos RJ, Lindgren CM, Li S, Wheeler E, Zhao JH, Prokopenko I, et al. Common variants near MC4R are associated with fat mass, weight and risk of obesity. Nat Genet 2008;40:768–75.
Fernandez-Rhodes L, Demerath EW, Cousminer DL, Tao R, Dreyfus JG, Esko T, et al. Association of adiposity genetic variants with menarche timing in 92,105 women of European descent. Am J Epidemiol 2013;178:451–60.
Malhotra AK, Correll CU, Chowdhury NI, Muller DJ, Gregersen PK, Lee AT, et al. Association between common variants near the melanocortin 4 receptor gene and severe antipsychotic drug-induced weight gain. Arch Gen Psychiatry 2012;69:904–12.
Pei YF, Zhang L, Liu Y, Li J, Shen H, Liu YZ, et al. Meta-analysis of genome-wide association data identifies novel susceptibility loci for obesity. Hum Mol Genet 2014;23:820–30.
Hsu YH, Zillikens MC, Wilson SG, Farber CR, Demissie S, Soranzo N, et al. An integration of genome-wide association study and gene expression profiling to prioritize the discovery of novel susceptibility Loci for osteoporosis-related traits. PLoS Genet 2010;6:e1000977.
Jackson HE, Ono Y, Wang X, Elworthy S, Cunliffe VT, Ingham PW. The role of Sox6 in zebrafish muscle fiber type specification. Skelet Muscle 2015;5:2.
Braun TP, Orwoll B, Zhu X, Levasseur PR, Szumowski M, Nguyen ML, et al. Regulation of lean mass, bone mass, and exercise tolerance by the central melanocortin system. PLoS ONE 2012;7:e42183.
Consortium GT. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 2015;348:648–60.
Urano T, Shiraki M, Sasaki N, Ouchi Y, Inoue S. Large-scale analysis reveals a functional single-nucleotide polymorphism in the 5′-flanking region of PRDM16 gene associated with lean body mass. Aging cell 2014;13:739–43.
Comuzzie AG, Cole SA, Laston SL, Voruganti VS, Haack K, Gibbs RA, et al. Novel genetic loci identified for the pathophysiology of childhood obesity in the Hispanic population. PloS ONE 2012;7:e51954.
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.
Zhu X, Feng T, Tayo BO, Liang J, Young JH, Franceschini N, et al. Meta-analysis of correlated traits via summary statistics from GWASs with an application in hypertension. Am J Hum Genet 2015;96:21–36.
Zhou X, Stephens M. Efficient multivariate linear mixed model algorithms for genome-wide association studies. Nat Methods 2014;11:407–9.
Zhang H, Liu CT, Wang X. An association test for multiple traits based on the generalized Kendall’s Tau. J Am Stat Assoc 2010;105:473–81.
Liu YJ, Zhang L, Pei Y, Papasian CJ, Deng HW. On genome-wide association studies and their meta-analyses: lessons learned from osteoporosis studies. J Clin Endocrinol Metab 2013;98:E1278–82.
Hirschfeld HP, Kinsella R, Duque G. Osteosarcopenia: where bone, muscle, and fat collide. Osteoporos Int 2017;28:2781–90.
We appreciate all the volunteers who participated into this study. This analysis of the UK Biobank sample was conducted using the UK Biobank resource under application number 41542. Y.F.P. and L.Z. are partially supported by the funding from national natural science foundation of China (31501026, 31771417 and 31571291), a project funded by the Priority Academic Program Development (PAPD) of Jiangsu higher education institutions. H.W.D. is partially supported by the National Institutes of Health (R01AR059781, P20GM109036, R01MH107354, R01MH104680, R01GM109068, R01AR069055, U19AG055373, R01DK115679), the Edward G. Schlieder Endowment and the Drs. W. C. Tsai and P. T. Kung Professorship in Biostatistics from Tulane University. 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 at http://www.ncbi.nlm.nih.gov/sites/entrez?db=gap 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 at http://www.ncbi.nlm.nih.gov/sites/entrez?db=gap through dbGaP accession phs000200.v10.p3. The authors state that there is no conflict of interest.
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Feng, G., Wei, X., Zhang, H. et al. Identification of pleiotropic loci underlying hip bone mineral density and trunk lean mass. J Hum Genet (2020). https://doi.org/10.1038/s10038-020-00835-4