Skip to main content

Advertisement

Log in

Association of 3q13.32 variants with hip trochanter and intertrochanter bone mineral density identified by a genome-wide association study

  • Original Article
  • Published:
Osteoporosis International Aims and scope Submit manuscript

Abstract

Summary

We performed a GWAS of trochanter and intertrochanter bone mineral density (BMD) in the Framingham Heart Study and replicated in three independent studies. Our results identified one novel locus around the associated variations at chromosomal region 3q13.32 and replicated two loci at chromosomal regions 3p21 and 8q24. Our findings provide useful insights that enhance our understanding of bone development, osteoporosis, and fracture pathogenesis.

Introduction

Hip trochanter (TRO) and intertrochanter (INT) subregions have important clinical relevance to subtrochanteric and intertrochanteric fractures but have rarely been studied by genome-wide association studies (GWASs).

Methods

Aiming to identify genomic loci associated with BMD variation at TRO and INT regions, we performed a GWAS utilizing the Framingham Heart Study (FHS, N = 6,912) as discovery sample and utilized the Women’s Health Initiative (WHI) African-American subsample (N = 845), WHI Hispanic subsample (N = 446), and Omaha osteoporosis study (N = 971), for replication.

Results

Combining the evidence from both the discovery and the replication samples, we identified one novel locus around the associated variations at chromosomal region 3q13.32 (rs1949542, discovery p = 6.16 × 10−8, replication p = 2.86 × 10−4 for INT-BMD; discovery p = 1.35 × 10−7, replication p = 4.16 × 10−4 for TRO-BMD, closest gene RP11-384F7.1). We also replicated two loci at chromosomal regions 3p21 (rs148725943, discovery p = 6.61 × 10−7, replication p = 5.22 × 10−4 for TRO-BMD, closest gene CTNNB1) and 8q24 (rs7839059, discovery p = 2.28 × 10−7, replication p = 1.55 × 10−3 for TRO-BMD, closest gene TNFRSF11B) that were reported previously. We demonstrated that the effects at both 3q13.32 and 3p21 were specific to the TRO, but not to the femoral neck and spine. In contrast, the effect at 8q24 was common to all the sites.

Conclusion

Our findings provide useful insights that enhance our understanding of bone development, osteoporosis, and fracture pathogenesis.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Cummings SR, Melton LJ (2002) Epidemiology and outcomes of osteoporotic fractures. Lancet 359:1761–1767

    Article  PubMed  Google Scholar 

  2. Reginster JY, Burlet N (2006) Osteoporosis: a still increasing prevalence. Bone 38:S4–9

    Article  PubMed  Google Scholar 

  3. Peacock M, Turner CH, Econs MJ, Foroud T (2002) Genetics of osteoporosis. Endocr Rev 23:303–326

    Article  CAS  PubMed  Google Scholar 

  4. Richards JB, Rivadeneira F, Inouye M et al (2008) Bone mineral density, osteoporosis, and osteoporotic fractures: a genome-wide association study. Lancet 371:1505–1512

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Styrkarsdottir U, Halldorsson BV, Gretarsdottir S et al (2008) Multiple genetic loci for bone mineral density and fractures. N Engl J Med 358:2355–2365

    Article  CAS  PubMed  Google Scholar 

  6. Duncan EL, Danoy P, Kemp JP 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Estrada K, Styrkarsdottir U, Evangelou E, 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(5):491–501

  8. Guo Y, Tan LJ, Lei SF et al (2010) Genome-wide association study identifies ALDH7A1 as a novel susceptibility gene for osteoporosis. PLoS Genet 6:e1000806

    Article  PubMed  PubMed Central  Google Scholar 

  9. Koller DL, Ichikawa S, Lai D et al (2010) Genome-wide association study of bone mineral density in premenopausal European-American women and replication in African-American women. J Clin Endocrinol Metab 95:1802–1809

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Rivadeneira F, Styrkarsdottir U, Estrada K et al (2009) Twenty bone-mineral-density loci identified by large-scale meta-analysis of genome-wide association studies. Nat Genet 41:1199–1206

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Xiong DH, Liu XG, Guo YF et al (2009) Genome-wide association and follow-up replication studies identified ADAMTS18 and TGFBR3 as bone mass candidate genes in different ethnic groups. Am J Hum Genet 84:388–398

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Zhang L, Choi HJ, Estrada K et al (2014) Multistage genome-wide association meta-analyses identified two new loci for bone mineral density. Hum Mol Genet 23:1923–1933

    Article  CAS  PubMed  Google Scholar 

  13. Zheng HF, Forgetta V, Hsu YH et al (2015) Whole-genome sequencing identifies EN1 as a determinant of bone density and fracture. Nature 526:112–117

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Davis TR, Sher JL, Horsman A, Simpson M, Porter BB, Checketts RG (1990) Intertrochanteric femoral fractures. Mechanical failure after internal fixation. J Bone Joint Surg Br 72:26–31

    CAS  PubMed  Google Scholar 

  15. Parker MJ, Dutta BK, Sivaji C, Pryor GA (1997) Subtrochanteric fractures of the femur. Injury 28:91–95

    Article  CAS  PubMed  Google Scholar 

  16. Cupples LA, Arruda HT, Benjamin EJ et al (2007) The Framingham Heart Study 100K SNP genome-wide association study resource: overview of 17 phenotype working group reports. BMC Med Genet 8(Suppl 1):S1

    Article  PubMed  PubMed Central  Google Scholar 

  17. 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

    Article  Google Scholar 

  18. Purcell S, Neale B, Todd-Brown K et al (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:559–575

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Zhang L, Li J, Pei YF, Liu Y, Deng HW (2009) Tests of association for quantitative traits in nuclear families using principal components to correct for population stratification. Ann Hum Genet 73:601–613

    Article  PubMed  PubMed Central  Google Scholar 

  20. Abecasis GR, Altshuler D, Auton A, Brooks LD, Durbin RM, Gibbs RA, Hurles ME, McVean GA (2010) A map of human genome variation from population-scale sequencing. Nature 467:1061–1073

    Article  PubMed  Google Scholar 

  21. Zhang L, Pei YF, Fu X, Lin Y, Wang YP, Deng HW (2014) FISH: fast and accurate diploid genotype imputation via segmental hidden Markov model. Bioinformatics 30:1876–1883

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Li Y, Willer CJ, Ding J, Scheet P, Abecasis GR (2010) MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet Epidemiol 34:816–834

    Article  PubMed  PubMed Central  Google Scholar 

  23. Willer CJ, Li Y, Abecasis GR (2010) METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26:2190–2191

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Mumpower JL (1991) Risk, ambiguity, insurance, and the winner’s curse. Risk Anal 11:519–522

    Article  CAS  PubMed  Google Scholar 

  25. Li J, Ji L (2005) Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix. Heredity 95:221–227

    Article  CAS  PubMed  Google Scholar 

  26. 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

    Article  CAS  PubMed  Google Scholar 

  27. Kundaje A, Meuleman W, Ernst J et al (2015) Integrative analysis of 111 reference human epigenomes. Nature 518:317–330

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Consortium EP (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 489:57–74

    Article  Google Scholar 

  29. Lonsdale J, Thomas J, Salvatore M, Phillips R (2013) The Genotype-Tissue Expression (GTEx) project. Nat Genet 45:580–585

    Article  CAS  Google Scholar 

  30. Westra HJ, Peters MJ, Esko T et al (2013) Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat Genet 45:1238–1243

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Paternoster L, Lorentzon M, Lehtimaki T et al (2013) Genetic determinants of trabecular and cortical volumetric bone mineral densities and bone microstructure. PLoS Genet 9:e1003247

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Suhre K, Shin SY, Petersen AK et al (2011) Human metabolic individuality in biomedical and pharmaceutical research. Nature 477:54–60

    Article  CAS  PubMed  Google Scholar 

  33. Cui W, Cuartas E, Ke J, Zhang Q, Einarsson HB, Sedgwick JD, Li J, Vignery A (2007) CD200 and its receptor, CD200R, modulate bone mass via the differentiation of osteoclasts. Proc Natl Acad Sci U S A 104:14436–14441

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Sunaga N, Kohno T, Kolligs FT, Fearon ER, Saito R, Yokota J (2001) Constitutive activation of the Wnt signaling pathway by CTNNB1 (beta-catenin) mutations in a subset of human lung adenocarcinoma. Genes Chromosomes Cancer 30:316–321

    Article  CAS  PubMed  Google Scholar 

  35. Yu Y, Wu J, Wang Y, Zhao T, Ma B, Liu Y, Fang W, Zhu WG, Zhang H (2012) Kindlin 2 forms a transcriptional complex with beta-catenin and TCF4 to enhance Wnt signalling. EMBO Rep 13:750–758

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Richards JB, Zheng HF, Spector TD (2012) Genetics of osteoporosis from genome-wide association studies: advances and challenges. Nat Rev Genet 13:576–588

    Article  CAS  PubMed  Google Scholar 

  37. Pei YF, Zhang L, Papasian CJ, Wang YP, Deng HW (2014) On individual genome-wide association studies and their meta-analysis. Hum Genet 133:265–279

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

This study was partially supported by the National Natural Science Foundation of China (31571291 and 31100902 to L.Z., 31501026 to Y.F.P., 81460223 to R.H.), the NIH (P50AR055081, R01AG026564, R01AR050496, RC2DE020756, R01AR057049, and R03TW008221 to H.W.D.), the Natural Science Foundation of Jiangsu Province of China (BK20150323 to Y.F.P.), the Franklin D. Dickson/Missouri Endowment and the Edward G. Schlieder Endowment (to H.W.D.), the Startup Funding Project of Soochow University (Q413900214 to L.Z. and Q413900114 to Y.F.P.), and a Project of The Priority Academic Program Development of Jiangsu Higher Education Institutions. Computing service was partially provided by the University of Shanghai for science and technology computing cluster.

The FHS 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 FHS and does not necessarily reflect the opinions or views of the FHS, 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 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 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.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to H.-W. Deng or L. Zhang.

Ethics declarations

Conflicts of interest

None

Additional information

Hong-Wen Deng and Lei Zhang jointly supervised this study.

Yu-Fang Pei and Zong-Gang Xie contributed equally to this work.

Electronic supplementary material

Below is the link to the electronic supplementary material.

ESM 1

(XLSX 49 kb)

ESM 2

(XLSX 12 kb)

ESM 3

(XLSX 9 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pei, YF., Xie, ZG., Wang, XY. et al. Association of 3q13.32 variants with hip trochanter and intertrochanter bone mineral density identified by a genome-wide association study. Osteoporos Int 27, 3343–3354 (2016). https://doi.org/10.1007/s00198-016-3663-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00198-016-3663-y

Keywords

Navigation