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Exome-wide screening identifies novel rare risk variants for bone mineral density

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Abstract

Summary

Bone mineral density (BMD) is an independent risk factor of osteoporosis-related fractures. We performed gene-based burden tests to assess the association between rare variants and BMD, and identified several BMD candidate genes.

Purpose

BMD is highly heritable and a major predictor of osteoporotic fractures, but its genetic basis remains unclear. We aimed to identify rare risk variants contributing to BMD.

Methods

Utilizing the newly released UK Biobank 200,643 exome dataset, we conducted a gene-based exome-wide association study in males and females, respectively. First, 100,639 males and 117,338 females with BMD values were included in the polygenic risk scores (PRS) analysis. Among individuals with lower 30% PRS, cases were individuals with top 10% BMD, and individuals with bottom 10% BMD were the controls. Considering the effects of vitamin D (VD), individuals with the highest 30% VD concentration were selected for VD-BMD analysis. After quality control, 741 males and 697 females were included in the BMD analysis, and 717 males and 708 females were included in the VD-BMD analysis. The variants were annotated by ANNOVAR software, then BMD and VD-BMD qualified variants were imported into the SKAT R-package to perform gene-based burden tests, respectively.

Results

The gene-based burden test of the exonic variants identified genome-wide candidate associations in ANKRD18A (P = 1.60 × 10−5, PBonferroni adjust = 2.11 × 10−3), C22orf31 (P = 3.49 × 10−4, PBonferroni adjust = 3.17 × 10−2), and SPATC1L (P = 1.09 × 10−5, PBonferroni adjust = 8.80 × 10−3). For VD-BMD analysis, three genes were associated with BMD, such as NIPAL1 (P = 1.06 × 10−3, PBonferroni adjust = 3.91 × 10−2).

Conclusions

Our study suggested that rare variants contribute to BMD, providing new sights for broadening the genetic structure of BMD.

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References

  1. Kanis JA (1997) Diagnosis of osteoporosis. Osteoporos Int 7(3):108–116. https://doi.org/10.1007/BF03194355

    Article  Google Scholar 

  2. Hernlund E, Svedbom A, Ivergård M et al (2013) Osteoporosis in the European Union: medical management, epidemiology and economic burden. Arch Osteoporos 8(1):136. https://doi.org/10.1007/s11657-013-0136-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Svedbom A, Hernlund E, Ivergård M et al (2013) Osteoporosis in the European Union: a compendium of country-specific reports. Arch Osteoporos 8(1):137. https://doi.org/10.1007/s11657-013-0137-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Richards JB, Zheng H-F, Spector TD (2012) Genetics of osteoporosis from genome-wide association studies: advances and challenges. Nat Rev Genet 13(8):576–588. https://doi.org/10.1038/nrg3228

    Article  CAS  PubMed  Google Scholar 

  5. Kanis JA, Johnell O, Oden A, Johansson H, McCloskey E (2008) FRAX and the assessment of fracture probability in men and women from the UK. Osteoporos Int 19(4):385–397. https://doi.org/10.1007/s00198-007-0543-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Bischoff-Ferrari HA, Dietrich T, Orav EJ, Dawson-Hughes B (2004) Positive association between 25-hydroxy vitamin d levels and bone mineral density: a population-based study of younger and older adults. Am J Med 116(9):634–639. https://doi.org/10.1016/j.amjmed.2003.12.029

    Article  CAS  PubMed  Google Scholar 

  7. van Schoor NM, Visser M, Pluijm SMF, Kuchuk N, Smit JH, Lips P (2008) Vitamin D deficiency as a risk factor for osteoporotic fractures. Bone 42(2):260–266. https://doi.org/10.1016/j.bone.2007.11.002

    Article  CAS  PubMed  Google Scholar 

  8. Reid IR, Bolland MJ, Grey A (2014) Effects of vitamin D supplements on bone mineral density: a systematic review and meta-analysis. Lancet 383(9912):146–155. https://doi.org/10.1016/S0140-6736(13)61647-5

    Article  CAS  PubMed  Google Scholar 

  9. Arden NK, Baker J, Hogg C, Baan K, Spector TD (1996) The heritability of bone mineral density, ultrasound of the calcaneus and hip axis length: a study of postmenopausal twins. J Bone Miner Res 11(4):530–534. https://doi.org/10.1002/jbmr.5650110414

    Article  CAS  PubMed  Google Scholar 

  10. Slemenda CW, Turner CH, Peacock M, Christian JC, Sorbel J, Hui SL, Johnston CC (1996) The genetics of proximal femur geometry, distribution of bone mass and bone mineral density. Osteoporos Int 6(2):178–182. https://doi.org/10.1007/BF01623944

    Article  CAS  PubMed  Google Scholar 

  11. Zheng H-F, Forgetta V, Hsu Y-H et al (2015) Whole-genome sequencing identifies EN1 as a determinant of bone density and fracture. Nature 526(7571):112–117. https://doi.org/10.1038/nature14878

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Kemp JP, Morris JA, Medina-Gomez C et al (2017) Identification of 153 new loci associated with heel bone mineral density and functional involvement of GPC6 in osteoporosis. Nat Genet 49(10):1468–1475. https://doi.org/10.1038/ng.3949

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Morris JA, Kemp JP, Youlten SE et al (2019) An atlas of genetic influences on osteoporosis in humans and mice. Nat Genet 51(2):258–266. https://doi.org/10.1038/s41588-018-0302-x

    Article  CAS  PubMed  Google Scholar 

  14. Bodmer W, Bonilla C (2008) Common and rare variants in multifactorial susceptibility to common diseases. Nat Genet 40(6):695–701. https://doi.org/10.1038/ng.f.136

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Cohen J, Pertsemlidis A, Kotowski IK, Graham R, Garcia CK, Hobbs HH (2005) Low LDL cholesterol in individuals of African descent resulting from frequent nonsense mutations in PCSK9. Nat Genet 37(2):161–165. https://doi.org/10.1038/ng1509

    Article  CAS  PubMed  Google Scholar 

  16. Stein EA, Mellis S, Yancopoulos GD et al (2012) Effect of a monoclonal antibody to PCSK9 on LDL cholesterol. N Engl J Med 366(12):1108–1118. https://doi.org/10.1056/NEJMoa1105803

    Article  CAS  PubMed  Google Scholar 

  17. Kiezun A, Garimella K, Do R et al (2012) Exome sequencing and the genetic basis of complex traits. Nat Genet 44(6):623–630. https://doi.org/10.1038/ng.2303

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Sazonovs A, Barrett JC (2018) Rare-variant studies to complement genome-wide association studies. Annu Rev Genomics Hum Genet 19(1):97–112. https://doi.org/10.1146/annurev-genom-083117-021641

    Article  CAS  PubMed  Google Scholar 

  19. Bis JC, Jian X, Kunkle BW et al (2020) Whole exome sequencing study identifies novel rare and common Alzheimer’s-associated variants involved in immune response and transcriptional regulation. Mol Psychiatry 25(8):1859–1875. https://doi.org/10.1038/s41380-018-0112-7

    Article  CAS  PubMed  Google Scholar 

  20. Styrkarsdottir U, Thorleifsson G, Sulem P et al (2013) Nonsense mutation in the LGR4 gene is associated with several human diseases and other traits. Nature 497(7450):517–520. https://doi.org/10.1038/nature12124

    Article  CAS  PubMed  Google Scholar 

  21. Euesden J, Lewis CM, O'Reilly PF (2015) PRSice: Polygenic Risk Score software. Bioinformatics 31(9):1466–1468. https://doi.org/10.1093/bioinformatics/btu848

    Article  CAS  PubMed  Google Scholar 

  22. Lu T, Zhou S, Wu H, Forgetta V, Greenwood CMT, Richards JB (2021) Individuals with common diseases but with a low polygenic risk score could be prioritized for rare variant screening. Genet Med 23(3):508–515. https://doi.org/10.1038/s41436-020-01007-7

    Article  CAS  PubMed  Google Scholar 

  23. Zhou D, Yu D, Scharf JM et al (2021) Contextualizing genetic risk score for disease screening and rare variant discovery. Nat Commun 12(1):4418. https://doi.org/10.1038/s41467-021-24387-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Cheng S, Cheng B, Liu L et al (2022) Exome-wide screening identifies novel rare risk variants for major depression disorder. Mol Psychiatry 27(7):3069–3074. https://doi.org/10.1038/s41380-022-01536-4

    Article  CAS  PubMed  Google Scholar 

  25. Bycroft C, Freeman C, Petkova D et al (2018) The UK Biobank resource with deep phenotyping and genomic data. Nature 562(7726):203–209. https://doi.org/10.1038/s41586-018-0579-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Szustakowski JD, Balasubramanian S, Kvikstad E et al (2021) Advancing human genetics research and drug discovery through exome sequencing of the UK Biobank. Nat Genet 53(7):942–948. https://doi.org/10.1038/s41588-021-00885-0

    Article  CAS  PubMed  Google Scholar 

  27. Krasheninina O, Hwang Y-C, Bai X, Zalcman A, Maxwell E, Reid JG, Salerno WJ (2020) Open-source mapping and variant calling for large-scale NGS data from original base-quality scores. bioRxiv:2020–2012. https://doi.org/10.1101/2020.12.15.356360

  28. Nagelkerke NJD (1991) A note on a general definition of the coefficient of determination. Biometrika 78:691–692

    Article  Google Scholar 

  29. Sun YV, Sung YJ, Tintle N, Ziegler A (2011) Identification of genetic association of multiple rare variants using collapsing methods. Genet Epidemiol 35(S1):S101–S106. https://doi.org/10.1002/gepi.20658

    Article  PubMed  PubMed Central  Google Scholar 

  30. Trajanoska K, Morris JA, Oei L et al (2018) Assessment of the genetic and clinical determinants of fracture risk: genome wide association and mendelian randomisation study. BMJ 362:k3225. https://doi.org/10.1136/bmj.k3225

    Article  PubMed  PubMed Central  Google Scholar 

  31. He X, Fuller CK, Song Y, Meng Q, Zhang B, Yang X, Li H (2013) Sherlock: Detecting Gene-disease associations by matching patterns of expression QTL and GWAS. Am J Hum Genet 92(5):667–680. https://doi.org/10.1016/j.ajhg.2013.03.022

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Kichaev G, Bhatia G, Loh P-R et al (2019) Leveraging polygenic functional enrichment to improve GWAS power. Am J Hum Genet 104(1):65–75. https://doi.org/10.1016/j.ajhg.2018.11.008

    Article  CAS  PubMed  Google Scholar 

  33. Du Z, Weinhold N, Song GC et al (2020) A meta-analysis of genome-wide association studies of multiple myeloma among men and women of African ancestry. Blood Adv 4(1):181–190. https://doi.org/10.1182/bloodadvances.2019000491

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Pei Y-F, Liu Y-Z, Yang X-L, Zhang H, Feng G-J, Wei X-T, Zhang L (2020) The genetic architecture of appendicular lean mass characterized by association analysis in the UK Biobank study. Commun Biol 3(1):608. https://doi.org/10.1038/s42003-020-01334-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Sakaue S, Kanai M, Tanigawa Y et al (2021) A cross-population atlas of genetic associations for 220 human phenotypes. Nat Genet 53(10):1415–1424. https://doi.org/10.1038/s41588-021-00931-x

    Article  CAS  PubMed  Google Scholar 

  36. He B, Shi J, Wang X, Jiang H, Zhu H-J (2020) Genome-wide pQTL analysis of protein expression regulatory networks in the human liver. BMC Biol 18(1):97. https://doi.org/10.1186/s12915-020-00830-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Fry RC, Svensson JP, Valiathan C, Wang E et al (2008) Genomic predictors of interindividual differences in response to DNA damaging agents. Genes Dev 22(19):2621–2626. https://doi.org/10.1101/gad.1688508

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Miyakoshi N, Hongo M, Mizutani Y, Shimada Y (2013) Prevalence of sarcopenia in Japanese women with osteopenia and osteoporosis. J Bone Miner Metab 31(5):556–561. https://doi.org/10.1007/s00774-013-0443-z

    Article  PubMed  Google Scholar 

  39. Goytain A, Hines RM, El-Husseini A, Quamme GA (2007) NIPA1(SPG6), the basis for autosomal dominant form of hereditary spastic paraplegia, encodes a functional Mg2+ transporter. J Biol Chem 282(11):8060–8068. https://doi.org/10.1074/jbc M610314200

    Article  PubMed  Google Scholar 

  40. Zhang Y, Grant RA, Shivakumar MK et al (2021) Genome-wide association analysis across 16,956 patients identifies a novel genetic association between BMP6, NIPAL1, CNGA1 and Spondylosis. Spine 46(11):E625–E631

    Article  PubMed  Google Scholar 

  41. Nakayama A, Nakaoka H, Yamamoto K et al (2017) GWAS of clinically defined gout and subtypes identifies multiple susceptibility loci that include urate transporter genes. Ann Rheum Dis 76(5):869. https://doi.org/10.1136/annrheumdis-2016-209632

    Article  CAS  PubMed  Google Scholar 

  42. Yu G, Hsu W-L, Coghill AE et al (2019) Whole-exome sequencing of nasopharyngeal carcinoma families reveals novel variants potentially involved in nasopharyngeal carcinoma. Sci Rep 9(1):9916. https://doi.org/10.1038/s41598-019-46137-4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Yu W, Qiu Z, Gao N et al (2011) PAK1IP1, a ribosomal stress-induced nucleolar protein, regulates cell proliferation via the p53–MDM2 loop. Nucleic Acids Res 39(6):2234–2248. https://doi.org/10.1093/nar/gkq1117

    Article  CAS  PubMed  Google Scholar 

  44. 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(7):e0200785. https://doi.org/10.1371/journal.pone.0200785

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Warrington NM, Beaumont RN, Horikoshi M et al (2019) Maternal and fetal genetic effects on birth weight and their relevance to cardio-metabolic risk factors. Nat Genet 51(5):804–814. https://doi.org/10.1038/s41588-019-0403-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Horikoshi M, Beaumont RN, Day FR et al (2016) Genome-wide associations for birth weight and correlations with adult disease. Nature 538(7624):248–252. https://doi.org/10.1038/nature19806

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Funding

This work was supported by the National Natural Scientific Foundation of China (81922059); the Natural Science Basic Research Plan in Shaanxi Province of China (2021JCW-08).

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Author notes

  1. Dan He and Chuyu Pan contributed equally to this work as the first authors.

Authors and Affiliations

  1. Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi’an Jiaotong University, Xi’an, China

    D. He, C. Pan, Y. Zhao, W. Wei, X. Qin, Q. Cai, S. Shi, X. Chu, N. Zhang, Y. Jia, Y. Wen, B. Cheng, H. Liu & F. Zhang

  2. Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi’an Jiaotong University, Xi’an, China

    D. He, C. Pan, Y. Zhao, W. Wei, X. Qin, Q. Cai, S. Shi, X. Chu, N. Zhang, Y. Jia, Y. Wen, B. Cheng, H. Liu & F. Zhang

  3. Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi’an Jiaotong University, Xi’an, China

    D. He, C. Pan, Y. Zhao, W. Wei, X. Qin, Q. Cai, S. Shi, X. Chu, N. Zhang, Y. Jia, Y. Wen, B. Cheng, H. Liu & F. Zhang

  4. Department of Joint Surgery, HongHui Hospital, Xi’an Jiaotong University, Xi’an, China

    R. Feng & P. Xu

  5. School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, China

    F. Zhang

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  1. D. He
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Conflicts of Interest

Dan He, Chuyu Pan, Yijing Zhao, Wenming Wei, Xiaoyue Qin, Qingqing Cai, Sirong Shi, Xiaoge Chu, Na Zhang, Yumeng Jia, Yan Wen, Bolun Cheng, Huan Liu, Ruoyang Feng, Feng Zhang, Peng Xu declare that they have no conflict of interest.

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Supplementary file 1

Table S1. Basic characteristics of individuals included for PRS analysis and VD analysis. Supplementary figure 1. The distribution of non-benign coding variants of bone mineral density (BMD). Supplementary figure 2. The distribution of non-benign coding variants of vitamin D-bone mineral density (VD-BMD).

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He, D., Pan, C., Zhao, Y. et al. Exome-wide screening identifies novel rare risk variants for bone mineral density. Osteoporos Int 34, 965–975 (2023). https://doi.org/10.1007/s00198-023-06710-0

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