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Evaluating the Correlations Between Osteoporosis and Lifestyle-Related Factors Using Transcriptome-Wide Association Study

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Abstract

Osteoporosis (OP) is a multi-factorial bone disease influenced by genetic factors, age, and lifestyles. The aim of this study is to evaluate the genetic correlations between OP and multiple lifestyle-related factors, and explore the genes underlying the detected genetic correlations. Linkage disequilibrium score regression (LDSC) analysis was applied to evaluate the genetic correlations of total body bone mineral density (TB-BMD) of different ages (including 15–30 years, 30–45 years, 45–60 years, and over 60 years) with four common lifestyle/environment-related factors (including serum 25-hydroxyvitamin D, cigarette smoking, alcohol dependence, and caffeine metabolites). Transcriptome-wide association studies (TWAS) of TB-BMD (30–45 years) and smoking were conducted in peripheral blood (PB), whole blood (WB), and adipose tissues. The identified candidate genes were also subjected to gene set enrichment analysis (GSEA). Genetic correlation was only observed between TB-BMD (30–45 years) and cigarette smoking status (P = 0.01, LD score = 0.11 ± 0.04). No significant genetic correlation was detected for other lifestyle/environmental factors, including serum 25-hydroxyvitamin D, alcohol dependence, and caffeine metabolites for TB-BMD within all of the four age groups. TWAS identified 85 genes in PB and 163 genes in WB for TB-BMD, as well as 123 genes in PB and 257 genes in WB for smoking. Multiple common candidate genes shared by both TB-BMD and smoking were detected, such as MAP1LC3B (PTB-BMD-PB = 1.00 × 10–3, Psmoking-PB = 9.62 × 10–3, PTB-BMD-WB = 2.99 × 10–2) and SLC23A3 (PTB-BMD-WB = 1.48 × 10–2, Psmoking-WB = 8.76 × 10–3). GSEA detected one GO terms for TB-BMD (cytosol) in WB, one GO term for smoking (mitochondrion) in PB, and one pathway (oocyte meiosis) for smoking in WB.

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Abbreviations

OP:

Osteoporosis

LDSC:

Linkage disequilibrium score regression

TB-BMD:

Total body bone mineral density

TWAS:

Transcriptome-wide association studies

PB:

Peripheral blood

WB:

Whole blood

GSEA:

Gene set enrichment analysis

BMD:

Bone mineral density

BMI:

Body mass index

eQTL:

Expression quantitative trait loci

mQTL:

Methylation quantitative trait loci

GWAS:

Genome-wide association studies

GO:

Gene ontology

DXA:

Dual-energy X-ray absorptiometry

MAF:

Minor allele frequency

HRC:

Haplotype Reference Consortium

FUSION:

Functional Summary-based Imputation

DAVID:

The Database for Annotation, Visualization and Integrated Discovery

RE:

Arginine-glutamic acid

RERE:

Encodes a member of the atrophin family of RE dipeptide repeat-containing proteins

ROS:

Reactive oxygen species

cGMP:

The cyclic nucleotide cyclic guanosine 3′, 5′monophosphate

References

  1. Aaseth J, Boivin G, Andersen O (2012) Osteoporosis and trace elements—an overview. J Trace Elem Med Biol 26(2):149–152

    CAS  PubMed  Google Scholar 

  2. Medina-Gomez C, Kemp JP, Trajanoska K, Luan J, Chesi A, Ahluwalia TS, Mook-Kanamori DO, Ham A, Hartwig FP, Evans DS 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(1):88–102

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Niu T, Liu N, Yu X, Zhao M, Choi HJ, Leo PJ, Brown MA, Zhang L, Pei YF, Shen H et al (2016) Identification of IDUA and WNT16 phosphorylation-related non-synonymous polymorphisms for bone mineral density in meta-analyses of genome-wide association studies. J Bone Miner Res 31(2):358–368

    CAS  PubMed  Google Scholar 

  4. Pei YF, Xie ZG, Wang XY, Hu WZ, Li LB, Ran S, Lin Y, Hai R, Shen H, Tian Q et al. (2016) Association of 3q13.32 variants with hip trochanter and intertrochanter bone mineral density identified by a genome-wide association study. Osteoporosis Int 27(11), 3343–3354.

    CAS  PubMed  Google Scholar 

  5. Baccaro LF, Conde DM, Costa-Paiva L, Pinto-Neto AM (2015) The epidemiology and management of postmenopausal osteoporosis: a viewpoint from Brazil. Clin Interv Aging 10:583–591

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Bierut LJ, Agrawal A, Bucholz KK, Doheny KF, Laurie C, Pugh E, Fisher S, Fox L, Howells W, Bertelsen S et al (2010) A genome-wide association study of alcohol dependence. Proc Natl Acad Sci USA 107(11):5082–5087

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Karohl C, Su S, Kumari M, Tangpricha V, Veledar E, Vaccarino V, Raggi P (2010) Heritability and seasonal variability of vitamin D concentrations in male twins. Am J Clin Nutr 92(6):1393–1398

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Jiang X, O'Reilly PF, Aschard H, Hsu YH, Richards JB, Dupuis J, Ingelsson E, Karasik D, Pilz S, Berry D et al (2018) Genome-wide association study in 79,366 European-ancestry individuals informs the genetic architecture of 25-hydroxyvitamin D levels. Nat Commun 9:12

    Google Scholar 

  9. Bulik-Sullivan B, Finucane HK, Anttila V, Gusev A, Day FR, Loh P-R, ReproGen Consortium, Psychiatric Genomics Consortium, Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Case Control Consortium, Duncan L et al. (2015) An atlas of genetic correlations across human diseases and traits. Nat Genet 47(11):1236–1241

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Bulik-Sullivan BK, Loh PR, Finucane HK, Ripke S, Yang J, Schizophrenia Working Group of the Psychiatric Genomics Consortium, Patterson N, Daly MJ, Price AL, Neale BM (2015) LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet 47(3):291–295

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Liang X, Wu C, Zhao H, Liu L, Du Y, Li P, Wen Y, Zhao Y, Ding M, Cheng B et al (2018) Assessing the genetic correlations between early growth parameters and bone mineral density: a polygenic risk score analysis. Bone 116:301–306

    PubMed  PubMed Central  Google Scholar 

  12. Farh KKH, Marson A, Zhu J, Kleinewietfeld M, Housley WJ, Beik S, Shoresh N, Whitton H, Ryan RJH, Shishkin AA et al (2015) Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518(7539):337–343

    CAS  PubMed  Google Scholar 

  13. Nicolae DL, Gamazon E, Zhang W, Duan S, Dolan ME, Cox NJ (2010) Trait-associated snps are more likely to be eQTLs: annotation to enhance discovery from GWAS. PLoS Genet 6(4):e1000888

    PubMed  PubMed Central  Google Scholar 

  14. Zhu Z, Zhang F, Hu H, Bakshi A, Robinson MR, Powell JE, Montgomery GW, Goddard ME, Wray NR, Visscher PM et al (2016) Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet 48(5):481–487

    CAS  PubMed  Google Scholar 

  15. Gusev A, Ko A, Shi H, Bhatia G, Chung W, Penninx BW, Jansen R, de Geus EJ, Boomsma DI, Wright FA et al (2016) Integrative approaches for large-scale transcriptome-wide association studies. Nat Genet 48(3):245–252

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Gusev A, Mancuso N, Won H, Kousi M, Finucane HK, Reshef Y, Song L, Safi A, McCarroll S, Neale BM et al (2018) Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights. Nat Genet 50(4):538–548

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Went M, Kinnersley B, Sud A, Johnson DC, Weinhold N, Forsti A, van Duin M, Orlando G, Mitchell JS, Kuiper R et al (2019) Transcriptome-wide association study of multiple myeloma identifies candidate susceptibility genes. Hum Genomics 13(1):37

    PubMed  PubMed Central  Google Scholar 

  18. Cornelis MC, Kacprowski T, Menni C, Gustafsson S, Pivin E, Adamski J, Artati A, Eap CB, Ehret G, Friedrich N et al (2016) Genome-wide association study of caffeine metabolites provides new insights to caffeine metabolism and dietary caffeine-consumption behavior. Hum Mol Genet 25(24):5472–5482

    CAS  PubMed  Google Scholar 

  19. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38:904

    CAS  PubMed  Google Scholar 

  20. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, Maller J, Sklar P, de Bakker PIW, Daly MJ et al (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81(3):559–575

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Fuchsberger C, Abecasis GR, Hinds DA (2015) minimac2: faster genotype imputation. Bioinformatics 31(5):782–784

    CAS  PubMed  Google Scholar 

  22. Howie B, Fuchsberger C, Stephens M, Marchini J, Abecasis GR (2012) Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat Genet 44(8):955–959

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Shen GS, Li Y, Zhao GY, Bin Zhou H, Xie ZG, Xu W, Chen HN, Dong QR, Xu YJ (2015) Cigarette smoking and risk of hip fracture in women: a meta-analysis of prospective cohort studies. Injury 46(7):1333–1340

    PubMed  Google Scholar 

  24. Bergman BC, Perreault L, Hunerdosse D, Kerege A, Playdon M, Samek AM, Eckel RH (2012) Novel and reversible mechanisms of smoking-induced insulin resistance in humans. Diabetes 61(12):3156–3166

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Styrkarsdottir U, Halldorsson BV, Gretarsdottir S, Gudbjartsson DF, Walters GB, Ingvarsson T, Jonsdottir T, Saemundsdottir J, Center JR, Nguyen TV et al (2008) Multiple genetic loci for bone mineral density and fractures. N Engl J Med 358(22):2355–2365

    CAS  PubMed  Google Scholar 

  26. Pacifici R (1996) Estrogen, cytokines, and pathogenesis of postmenopausal osteoporosis. J Bone Miner Res 11(8):1043–1051

    CAS  PubMed  Google Scholar 

  27. Astles PA, Moore AJ, Preziosi RF (2006) A comparison of methods to estimate cross-environment genetic correlations. J Evol Biol 19(1):114–122

    CAS  PubMed  Google Scholar 

  28. Qian GF, Yuan LS, Chen M, Ye D, Chen GP, Zhang Z, Li CJ, Vijayan V, Xiao Y (2019) PPWD1 is associated with the occurrence of postmenopausal osteoporosis as determined by weighted gene coexpression network analysis. Mol Med Rep 20(4):3202–3214

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Davis TL, Walker JR, Ouyang H, MacKenzie F, Butler-Cole C, Newman EM, Eisenmesser EZ, Dhe-Paganon S (2008) The crystal structure of human WD40 repeat-containing peptidylprolyl isomerase (PPWD1). FEBS J 275(9):2283–2295

    CAS  PubMed  Google Scholar 

  30. Montalvo-Ortiz JL, Cheng Z, Kranzler HR, Zhang H, Gelernter J (2019) Genomewide study of epigenetic biomarkers of opioid dependence in European-American women. Sci Rep 9(1):4660

    PubMed  PubMed Central  Google Scholar 

  31. Li HYG, Kung WCA, Huang QY (2011) Bone mineral density is linked to 1p36 and 7p15-13 in a southern Chinese population. J Bone Miner Metab 29(1):80–87

    PubMed  Google Scholar 

  32. Cohen KS, Cheng S, Larson MG, Cupples LA, McCabe EL, Wang YA, Ngwa JS, Martin RP, Klein RJ, Hashmi B et al (2013) Circulating CD34(+) progenitor cell frequency is associated with clinical and genetic factors. Blood 121(8):E50–E56

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Pirro M, Leli C, Fabbriciani G, Manfredelli MR, Callarelli L, Bagaglia F, Scarponi AM, Mannarino E (2010) Association between circulating osteoprogenitor cell numbers and bone mineral density in postmenopausal osteoporosis. Osteoporosis Int 21(2):297–306

    CAS  Google Scholar 

  34. Shen G, Ren H, Shang Q, Qiu T, Yu X, Zhang Z, Huang J, Zhao W, Zhang Y, Liang D et al (2018) Autophagy as a target for glucocorticoid-induced osteoporosis therapy. Cell Mol Life Sci 75(15):2683–2693

    CAS  PubMed  Google Scholar 

  35. Yang Y, Zheng X, Li B, Jiang S, Jiang L (2014) Increased activity of osteocyte autophagy in ovariectomized rats and its correlation with oxidative stress status and bone loss. Biochem Biophys Res Commun 451(1):86–92

    CAS  PubMed  Google Scholar 

  36. Luo D, Ren H, Li T, Lian K, Lin D (2016) Rapamycin reduces severity of senile osteoporosis by activating osteocyte autophagy. Osteoporosis Int 27(3):1093–1101

    CAS  Google Scholar 

  37. Chen ZH, Lam HC, Jin Y, Kim HP, Cao J, Lee SJ, Ifedigbo E, Parameswaran H, Ryter SW, Choi AM (2010) Autophagy protein microtubule-associated protein 1 light chain-3B (LC3B) activates extrinsic apoptosis during cigarette smoke-induced emphysema. Proc Natl Acad Sci USA 107(44):18880–18885

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Miao Q, Xu Y, Zhang H, Xu P, Ye J (2019) Cigarette smoke induces ROS mediated autophagy impairment in human corneal epithelial cells. Environ Pollut 245:389–397

    CAS  PubMed  Google Scholar 

  39. Guaragnella N, Coyne LP, Chen XJ, Giannattasio S (2018) Mitochondria-cytosol-nucleus crosstalk: learning from Saccharomyces cerevisiae. FEMS Yeast Res 18(8):10000

    Article  PubMed  PubMed Central  Google Scholar 

  40. Suhm T, Kaimal JM, Dawitz H, Peselj C, Masser AE, Hanzen S, Ambrozic M, Smialowska A, Bjorck ML, Brzezinski P et al (2018) Mitochondrial translation efficiency controls cytoplasmic protein homeostasis. Cell Metab 27(6):1309.e1306–1322.e1306

    Google Scholar 

  41. Zhu W, Shen H, Zhang JG, Zhang L, Zeng Y, Huang HL, Zhao YC, He H, Zhou Y, Wu KH et al (2017) Cytosolic proteome profiling of monocytes for male osteoporosis. Osteoporosis Int 28(3):1035–1046

    CAS  Google Scholar 

  42. Lane RK, Hilsabeck T, Rea SL (2015) The role of mitochondrial dysfunction in age-related diseases. Biochim Biophys Acta 1847(11):1387–1400

    CAS  PubMed  Google Scholar 

  43. Zhang M, Su YQ, Sugiura K, Xia G, Eppig JJ (2010) Granulosa cell ligand NPPC and its receptor NPR2 maintain meiotic arrest in mouse oocytes. Science 330(6002):366–369

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Friebe A, Sandner P, Seifert R (2015) From bedside to bench—meeting report of the 7th International Conference on cGMP “cGMP: generators, effectors and therapeutic implications” in Trier, Germany, from June 19th to 21st 2015. Naunyn Schmiedebergs Arch Pharmacol 388(12), 1237–1246.

    CAS  PubMed  Google Scholar 

  45. Liou SF, Hsu JH, Chu HC, Lin HH, Chen IJ, Yeh JL (2015) KMUP-1 promotes osteoblast differentiation through cAMP and cGMP pathways and signaling of BMP-2/Smad1/5/8 and Wnt/beta-catenin. J Cell Physiol 230(9):2038–2048

    CAS  PubMed  Google Scholar 

  46. Chen XD, Xiao P, Lei SF, Liu YZ, Guo YF, Deng FY, Tan LJ, Zhu XZ, Chen FR, Recker RR et al (2010) Gene expression profiling in monocytes and SNP association suggest the importance of the STAT1 gene for osteoporosis in both Chinese and Caucasians. J Bone Miner Res 25(2):339–355

    CAS  PubMed  Google Scholar 

  47. Mohiti-Ardekani J, Soleymani-Salehabadi H, Owlia MB, Mohiti A (2014) Relationships between serum adipocyte hormones (adiponectin, leptin, resistin), bone mineral density and bone metabolic markers in osteoporosis patients. J Bone Miner Metab 32(4):400–404

    CAS  PubMed  Google Scholar 

  48. Kalra R, Singh SP, Savage SM, Finch GL, Sopori ML (2000) Effects of cigarette smoke on immune response: chronic exposure to cigarette smoke impairs antigen-mediated signaling in T cells and depletes IP3-Sensitive Ca2+ stores. J Pharmacol Exp Ther 293(1):166–171

    CAS  PubMed  Google Scholar 

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Acknowledgements

This study was supported by the National Natural Scientific Foundation of China (81472925, 81673112, 81703177), and the Key projects of international cooperation among governments in scientific and technological innovation (2016YFE0119100), and the Fundamental Research Funds for the Central Universities.

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Author notes

  1. Yanan Du, Ping Li, and Yan Wen contributed equally to this work.

Authors and Affiliations

  1. Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi’an Jiaotong University, No. 76 Yan Ta West Road, Xi’an, 710061, People’s Republic of China

    Yanan Du, Ping Li, Yan Wen, Xiao Liang, Li Liu, Bolun Cheng, Miao Ding, Yan Zhao, Mei Ma, Lu Zhang, Shiqiang Cheng, Xiong Guo & Feng Zhang

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  1. Yanan Du
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  2. Ping Li
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  5. Li Liu
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Correspondence to Yan Wen or Feng Zhang.

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Conflict of interest

Yanan Du, Ping Li, Yan Wen, Xiao Liang, Li Liu, Bolun Cheng, Miao Ding, Yan Zhao, Mei Ma, Lu Zhang, Shiqiang Cheng, Xiong Guo, and Feng Zhang declare that they have no competing interests.

Human and Animal Rights

This study was conducted on published available data or biological material. All procedures performed in this study were in accordance with the ethical standards of the Human Ethics Committee of Xi’an Jiaotong University.

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All researches were approved by a Medical Ethical Committee, and the written informed consent was provided by each participant or their parents [2].

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Du, Y., Li, P., Wen, Y. et al. Evaluating the Correlations Between Osteoporosis and Lifestyle-Related Factors Using Transcriptome-Wide Association Study. Calcif Tissue Int 106, 256–263 (2020). https://doi.org/10.1007/s00223-019-00640-y

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