Abstract
Aiming to uncover a shared genetic basis of abdominal obesity and osteoporosis, we performed a bivariate GWAS meta-analysis of femoral neck BMD (FNK-BMD) and trunk fat mass adjusted by trunk lean mass (TFMadj) in 11,496 subjects from 6 samples, followed by in silico replication in the large-scale UK Biobank (UKB) cohort. A series of functional investigations were conducted on the identified variants. Bivariate GWAS meta-analysis identified two novel pleiotropic loci 12q15 (lead SNP rs73134637, p = 3.45 × 10–7) and 10p14 (lead SNP rs2892347, p = 2.63 × 10–7) that were suggestively associated and that were replicated in the analyses of related traits in the UKB sample (osteoporosis p = 0.06 and 0.02, BMI p = 0.03 and 4.61 × 10–3, N up to 499,520). Cis-eQTL analysis demonstrated that allele C at rs73134637 was positively associated with IFNG expression in whole blood (N = 369, p = 0.04), and allele A at rs11254759 (10p14, p = 9.49 × 10–7) was negatively associated with PRKCQ expression in visceral adipose tissue (N = 313, p = 0.04) and in lymphocytes (N = 117, p = 0.03). As a proof-of-principle experiment, the function of rs11254759, which is 235 kb 5′-upstream from PRKCQ gene, was investigated by the dual-luciferase reporter assay, which clearly showed that the haplotype carrying rs11254759 regulated PRKCQ expression by upregulating PRKCQ promoter activity (p = 4.60 × 10–7) in an allelic specific manner. Mouse model analysis showed that heterozygous PRKCQ deficient mice presented decreased fat mass compared to wild-type control mice (p = 3.30 × 10–3). Mendelian randomization analysis demonstrated that both FNK-BMD and TFMadj were causally associated with fracture risk (p = 1.26 × 10–23 and 1.18 × 10–11). Our findings may provide useful insights into the genetic association between osteoporosis and abdominal obesity.
Similar content being viewed by others
References
Aaseth J, Boivin G, Andersen O (2012) Osteoporosis and trace elements—an overview. J Trace Elem Med Biol 26:149–152. https://doi.org/10.1016/j.jtemb.2012.03.017
Benner C, Spencer CC, Havulinna AS, Salomaa V, Ripatti S, Pirinen M (2016) FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinformatics 32:1493–1501. https://doi.org/10.1093/bioinformatics/btw018
Booth A, Magnuson A, Foster M (2014) Detrimental and protective fat: body fat distribution and its relation to metabolic disease. Horm Mol Biol Clin Investig 17:13–27. https://doi.org/10.1515/hmbci-2014-0009
Bu F-X, Zhao L-J, Pei Y-F, Recker R, Deng H-W (2009) Powerful bivariate genome-wide association analyses and follow-up replication studies identified several pleitropic genes for both obesity and femoral neck geometry. ASBMR 31st Annual Meeting FR0001–FR0464. J Bone Miner Res 24:S94-S149. https://doi.org/10.1002/jbmr.5650241302
Canela-Xandri O, Rawlik K, Tenesa A (2018) An atlas of genetic associations in UK Biobank. Nat Genet 50:1593–1599. https://doi.org/10.1038/s41588-018-0248-z
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 4:7. https://doi.org/10.1186/s13742-015-0047-8
Compston J (2013) Obesity and bone. Curr Osteoporos Rep 11:30–35. https://doi.org/10.1007/s11914-012-0127-y
Deng HW et al (2000) Determination of bone mineral density of the hip and spine in human pedigrees by genetic and life-style factors. Genet Epidemiol 19:160–177. https://doi.org/10.1002/1098-2272(200009)19:2%3c160:AID-GEPI4%3e3.0.CO;2-H
Dickinson ME et al (2016) High-throughput discovery of novel developmental phenotypes. Nature 537:508–514. https://doi.org/10.1038/nature19356
Duncan EL et al (2011) Genome-wide association study using extreme truncate selection identifies novel genes affecting bone mineral density and fracture risk. PLoS Genet 7:e1001372. https://doi.org/10.1371/journal.pgen.1001372
ENCODE Project Consortium (2011) A user's guide to the encyclopedia of DNA elements (ENCODE). PLoS Biol 9:e1001046. https://doi.org/10.1371/journal.pbio.1001046
Ensrud KE et al (1997) Body size and hip fracture risk in older women: a prospective study. Study of Osteoporotic Fractures Research Group. Am J Med 103:274–280. https://doi.org/10.1016/s0002-9343(97)00025-9
Estrada K et al (2012) Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture. Nat Genet 44:491–501. https://doi.org/10.1038/ng.2249
Fall T, Ingelsson E (2014) Genome-wide association studies of obesity and metabolic syndrome. Mol Cell Endocrinol 382:740–757. https://doi.org/10.1016/j.mce.2012.08.018
Genomes Project C et al (2010) A map of human genome variation from population-scale sequencing. Nature 467:1061–1073. https://doi.org/10.1038/nature09534
Gimble JM, Robinson CE, Wu X, Kelly KA (1996) The function of adipocytes in the bone marrow stroma: an update. Bone 19:421–428
Gomez-Ambrosi J, Rodriguez A, Catalan V, Fruhbeck G (2008) The bone-adipose axis in obesity and weight loss. Obes Surg 18:1134–1143. https://doi.org/10.1007/s11695-008-9548-1
Gonnelli S, Caffarelli C, Nuti R (2014) Obesity and fracture risk. Clin Cases Miner Bone Metab 11:9–14. https://doi.org/10.11138/ccmbm/2014.11.1.009
GTEx Consortium (2013) The Genotype-Tissue Expression (GTEx) project. Nat Genet 45:580–585. https://doi.org/10.1038/ng.2653
Guo Y et al (2011) The fat mass and obesity associated gene, FTO, is also associated with osteoporosis phenotypes. PLoS ONE 6:e27312. https://doi.org/10.1371/journal.pone.0027312
Haslam DW, James WP (2005) Obesity Lancet 366:1197–1209. https://doi.org/10.1016/S0140-6736(05)67483-1
Hsu YH et al (2006) Relation of body composition, fat mass, and serum lipids to osteoporotic fractures and bone mineral density in Chinese men and women. Am J Clin Nutr 83:146–154. https://doi.org/10.1093/ajcn/83.1.146
Hu Y et al (2018) Identification of novel potentially pleiotropic variants associated with osteoporosis and obesity using the cFDR method. J Clin Endocrinol Metab 103:125–138. https://doi.org/10.1210/jc.2017-01531
Kemp JP et al (2017) Identification of 153 new loci associated with heel bone mineral density and functional involvement of GPC6 in osteoporosis. Nat Genet 49:1468–1475. https://doi.org/10.1038/ng.3949
Kim SK (2018) 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 13:e0200785. https://doi.org/10.1371/journal.pone.0200785
Konstantopoulos S (2006) Fixed and mixed effects models in meta-analysis. IZA Discussion Paper No. 2198. https://ssrn.com/abstract=919993
Liu YZ et al (2009) Powerful bivariate genome-wide association analyses suggest the SOX6 gene influencing both obesity and osteoporosis phenotypes in males. PLoS ONE 4:e6827. https://doi.org/10.1371/journal.pone.0006827
Locke AE et al (2015) Genetic studies of body mass index yield new insights for obesity biology. Nature 518:197–206. https://doi.org/10.1038/nature14177
Looker AC, Frenk SM (2015) Percentage of adults aged 65 and over with osteoporosis or low bone mass at the femur neck or lumbar spine: United States, 2005–2010. Division of Health and Nutrition Examination Surveys. November 2015. https://www.cdc.gov/nchs/data/hestat/osteoporsis/osteoporosis2005_2010.htm
Maes HH, Neale MC, Eaves LJ (1997) Genetic and environmental factors in relative body weight and human adiposity. Behav Genet 27:325–351
Manabe N et al (2001) Connection between B lymphocyte and osteoclast differentiation pathways. J Immunol 167:2625–2631
Medina-Gomez C et al (2018) Life-course genome-wide association study meta-analysis of total body BMD and assessment of age-specific effects. Am J Hum Genet 102:88–102. https://doi.org/10.1016/j.ajhg.2017.12.005
Morris JA et al (2019) An atlas of genetic influences on osteoporosis in humans and mice. Nat Genet 51:258–266. https://doi.org/10.1038/s41588-018-0302-x
Mullin BH et al (2018) Expression quantitative trait locus study of bone mineral density gwas variants in human osteoclasts. J Bone Miner Res 33:1044–1051. https://doi.org/10.1002/jbmr.3412
Notelovitz M (1993) Osteoporosis: screening, prevention, and management. Fertil Steril 59:707–725
O'Rourke RW, White AE, Metcalf MD, Winters BR, Diggs BS, Zhu X, Marks DL (2012) Systemic inflammation and insulin sensitivity in obese IFN-gamma knockout mice. Metab Clin Exp 61:1152–1161. https://doi.org/10.1016/j.metabol.2012.01.018
Pei YF et al (2014) Meta-analysis of genome-wide association data identifies novel susceptibility loci for obesity. Hum Mol Genet 23:820–830. https://doi.org/10.1093/hmg/ddt464
Pischon T et al (2008) General and abdominal adiposity and risk of death in Europe. N Engl J Med 359:2105–2120. https://doi.org/10.1056/NEJMoa0801891
Pruim RJ et al (2010) LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics 26:2336–2337. https://doi.org/10.1093/bioinformatics/btq419
Purcell S et al (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:559–575. https://doi.org/10.1086/519795
Rexrode KM et al (1998) Abdominal adiposity and coronary heart disease in women. JAMA 280:1843–1848
Rivadeneira F et al (2009) Twenty bone-mineral-density loci identified by large-scale meta-analysis of genome-wide association studies. Nat Genet 41:1199–1206. https://doi.org/10.1038/ng.446
Tan LJ et al (2015) Bivariate Genome-Wide Association Study Implicates ATP6V1G1 as a Novel Pleiotropic Locus Underlying Osteoporosis and Age at Menarche. J Clin Endocrinol Metab 100:E1457–1466. https://doi.org/10.1210/jc.2015-2095
The Women's Health Initiative Study Group (1998) Design of the Women's Health Initiative clinical trial and observational study. The Women's Health Initiative Study Group. Control Clin Trials 19:61–109
Ward LD, Kellis M (2012) HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res 40:D930–934. https://doi.org/10.1093/nar/gkr917
Zaitlen N, Kraft P, Patterson N, Pasaniuc B, Bhatia G, Pollack S, Price AL (2013) Using extended genealogy to estimate components of heritability for 23 quantitative and dichotomous traits. PLoS Genet 9:e1003520. https://doi.org/10.1371/journal.pgen.1003520
Zhang L, Bonham AJ, Li J, Pei YF, Chen J, Papasian CJ, Deng HW (2009a) Family-based bivariate association tests for quantitative traits. PLoS ONE 4:e8133. https://doi.org/10.1371/journal.pone.0008133
Zhang L et al (2014a) Multistage genome-wide association meta-analyses identified two new loci for bone mineral density. Hum Mol Genet 23:1923–1933. https://doi.org/10.1093/hmg/ddt575
Zhang L, Li J, Pei YF, Liu Y, Deng HW (2009b) Tests of association for quantitative traits in nuclear families using principal components to correct for population stratification. Ann Hum Genet 73:601–613. https://doi.org/10.1111/j.1469-1809.2009.00539.x
Zhang L, Pei YF, Fu X, Lin Y, Wang YP, Deng HW (2014b) FISH: fast and accurate diploid genotype imputation via segmental hidden Markov model. Bioinformatics 30:1876–1883. https://doi.org/10.1093/bioinformatics/btu143
Zhang L, Pei YF, Li J, Papasian CJ, Deng HW (2009c) Univariate/multivariate genome-wide association scans using data from families and unrelated samples. PLoS ONE 4:e6502. https://doi.org/10.1371/journal.pone.0006502
Zhao LJ, Liu YJ, Liu PY, Hamilton J, Recker RR, Deng HW (2007) Relationship of obesity with osteoporosis. J Clin Endocrinol Metab 92:1640–1646. https://doi.org/10.1210/jc.2006-0572
Zheng HF et al (2015) Whole-genome sequencing identifies EN1 as a determinant of bone density and fracture. Nature 526:112–117. https://doi.org/10.1038/nature14878
Zhu Z et al (2018) Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat Commun 9:224. https://doi.org/10.1038/s41467-017-02317-2
Acknowledgements
We appreciate all the volunteers who participated in this study. We are grateful to the UK Biobank for releasing large-scale summary association results for replication. We are grateful to Loula M Burton at the Tulane University for editing the manuscript. Lei Zhang and Yu-Fang Pei are partially supported by the national natural science foundation of China (31571291, 31771417 and 31501026) and a project of the priority academic program development (PAPD) of Jiangsu higher education institutions. Rong Hai is partially supported by the Inner Mongolia Autonomous Region Medical Health Science & Technology Research Program (201702180). Hui Shen and Hong-Wen Deng are 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 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 (https://www.ncbi.nlm.nih.gov/sites/entrez?db=gap) through dbGaP accession phs000342.v14.p10. The WHI (Women’s Health Initiative) program is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, and the US 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 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 https://www.ncbi.nlm.nih.gov/sites/entrez?db=gap through dbGaP accession phs000200.v10.p3.
Funding
Lei Zhang and Yu-Fang Pei are partially supported by the national natural science foundation of China (31571291, 31771417 and 31501026) and a project of the priority academic program development (PAPD) of Jiangsu higher education institutions. Rong Hai is partially supported by the Inner Mongolia Autonomous region medical health science & technology research program (201702180). Hui Shen and Hong-Wen Deng are 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.
Author information
Authors and Affiliations
Contributions
LZ designed the study. LZ and HWD collected the data. YFP and LZ analyzed the data. LL, XLY, HZ and RH performed the literature search. YFP, LL, LZ interpreted the data. LZ and LL generated the figures. LL drafted the early version of the manuscript. LZ, HWD, XTW, GJF, HS, QT, ZJZ, JL and HPP revised the manuscript. LZ, HWD and YFP supervised the study. All authors were involved in writing the paper and had final approval of the submitted and published versions.
Corresponding authors
Ethics declarations
Conflict of interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
Ethics approval
All samples were approved by the respective institutional ethics review boards.
Consent to participate
Informed consent was obtained from all individual participants included in the study.
Consent for publication
All participants signed informed consent regarding publishing their data.
Availability of data and material
The GWAS summary statistics will be publicly available.
Code availability
All computer software are publicly available.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Hong-Wen Deng, Yu-Fang Pei and Lei Zhang have jointly supervised this study.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Liu, L., Yang, XL., Zhang, H. et al. Two novel pleiotropic loci associated with osteoporosis and abdominal obesity. Hum Genet 139, 1023–1035 (2020). https://doi.org/10.1007/s00439-020-02155-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00439-020-02155-1