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Functional Enrichment Analysis Identifying Regulatory Information Associated with Human Fracture

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Abstract

Dozens of loci associated with fracture have been identified by genome-wide association studies (GWASs). However, most of these variants are located in the noncoding regions including introns, long terminal repeats, and intergenic regions. Although combining regulation information helps to identify the causal SNPs and interpret the involvement of these variants in the etiology of human fracture, regulation information which was truly associated with fracture was unknown. A novel functional enrichment method GARFIELD (GWAS Analysis of Regulatory of Functional Information Enrichment with LD correction) was applied to identify fracture-associated regulation information, including transcript factor binding sites, expression quantitative trait loci (eQTLs), chromatin states, enhancer, promoter, dyadic, super enhancer and Epigenome marks. Fracture SNPs were significantly enriched in exon (Bonferroni correction, p value < 7.14 × 10–3) at two GWAS p value thresholds through GARFIELD. High level of fold-enrichment was observed in super enhancer of monocyte and the enhancer of chondrocyte (Bonferroni correction, p value < 4.45 × 10–3). eQTLs of 44 tissues/cells and 10 transcription factors (TFs) were identified to be associated with human fracture. These results provide new insight into the etiology of human fracture, which might increase the identification of the causal SNPs through the fine-mapping study combined with functional annotation, as well as polygenic risk score.

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All data used in this study were available online.

References

  1. Michaëlsson K, Melhus H, Ferm H, Ahlbom A, Pedersen NL (2005) Genetic liability to fractures in the elderly. Arch Intern Med 165:1825–1830

    Article  PubMed  Google Scholar 

  2. Welter D, MacArthur J, Morales J, Burdett T, Hall P, Junkins H (2013) The NHGRI GWAS catalog, a curated resource of SNP-trait associations. Nucleic Acids Res 42:D1001–D1006

    Article  PubMed  PubMed Central  Google Scholar 

  3. Maurano MT, Humbert R, Rynes E, Thurman RE, Haugen E, Wang H (2012) Systematic localization of common disease-associated variation in regulatory DNA. Science 337:1190–1195

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Boyle AP, Hong EL, Hariharan M, Cheng Y, Schaub MA, Kasowski M (2012) Annotation of functional variation in personal genomes using RegulomeDB. Genome Res 22:1790–1797

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Bryois J, Garrett ME, Song L, Safi A, Giusti-Rodriguez P, Johnson GD (2018) Evaluation of chromatin accessibility in prefrontal cortex of individuals with schizophrenia. Nat Commun 9:3121–3121

    Article  PubMed  PubMed Central  Google Scholar 

  6. Di Rienzo A, Kichaev G, Yang W-Y, Lindstrom S, Hormozdiari F, Eskin E (2014) Integrating functional data to prioritize causal variants in statistical fine-mapping studies. PLoS Genet 10:e1004722

    Article  Google Scholar 

  7. Ripatti S, Shi J, Park J-H, Duan J, Berndt ST, Moy W (2016) Winner’s curse correction and variable thresholding improve performance of polygenic risk modeling based on genome-wide association study summary-level data. PLoS Genet 12:e1006493

    Article  Google Scholar 

  8. Abecasis GR, Auton A, Brooks LD, DePristo MA, Durbin RM, Handsaker RE (2012) An integrated map of genetic variation from 1,092 human genomes. Nature 491:56–65

    Article  PubMed  Google Scholar 

  9. Shen H, Li J, Zhang J, Xu C, Jiang Y, Wu Z (2013) Comprehensive characterization of human genome variation by high coverage whole-genome sequencing of forty four Caucasians. PLoS ONE 8:e59494

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Trynka G, Westra HJ, Slowikowski K, Hu X, Xu H, Stranger BE (2015) Disentangling the effects of colocalizing genomic annotations to functionally prioritize non-coding variants within complex-trait loci. Am J Hum Genet 97:139–152

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Iotchkova V, Ritchie GRS, Geihs M, Morganella S, Min JL, Walter K (2019) GARFIELD classifies disease-relevant genomic features through integration of functional annotations with association signals. Nat Genet 51:343–353

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Morris JA, Kemp JP, Youlten SE, Laurent L, Logan JG, Chai RC (2019) An atlas of genetic influences on osteoporosis in humans and mice. Nat Genet 51:258–266

    Article  CAS  PubMed  Google Scholar 

  13. GTEx Consortium, Aguet F, Brown AA, Castel SE, Davis JR, He Y (2017) Genetic effects on gene expression across human tissues. Nature 550:204

    Article  PubMed Central  Google Scholar 

  14. Satterlee JS, Chadwick LH, Tyson FL, McAllister K, Beaver J, Birnbaum L (2019) The NIH common fund/roadmap epigenomics program: successes of a comprehensive consortium. Sci Adv 5:eaaw6507

    Article  PubMed  PubMed Central  Google Scholar 

  15. Hnisz D, Abraham Brian J, Lee Tong I, Lau A, Saint-André V, Sigova Alla A (2013) Super-enhancers in the control of cell identity and disease. Cell 155:934–947

    Article  CAS  PubMed  Google Scholar 

  16. Manabe N, Kawaguchi H, Chikuda H, Miyaura C, Inada M, Nagai R (2001) Connection between B lymphocyte and osteoclast differentiation pathways. J Immunol (Baltimore, Md. 1950) 167:2625–2631

    Article  CAS  Google Scholar 

  17. Geissmann F, Manz MG, Jung S, Sieweke MH, Merad M, Ley K (2010) Development of monocytes, macrophages, and dendritic cells. Science 327:656–661

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Deng FY, Lei SF, Zhang Y, Zhang YL, Zheng YP, Zhang LS (2011) Peripheral blood monocyte-expressed ANXA2 gene is involved in pathogenesis of osteoporosis in humans. Mol Cell Proteomics 10(M111):011700

    Google Scholar 

  19. UK10K Consortium, Walter K, Min JL, Huang J, Crooks L, Memari Y (2015) The UK10K project identifies rare variants in health and disease. Nature 526:82–90

    Article  Google Scholar 

  20. Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR (2015) Genetic studies of body mass index yield new insights for obesity biology. Nature 518:197–206

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Finucane HK, Bulik-Sullivan B, Gusev A, Trynka G, Reshef Y, Loh PR (2015) Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat Genet 47:1228–1235

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Iwata T, Kawamoto T, Sasabe E, Miyazaki K, Fujimoto K, Noshiro M (2006) Effects of overexpression of basic helix-loop-helix transcription factor Dec1 on osteogenic and adipogenic differentiation of mesenchymal stem cells. Eur J Cell Biol 85:423–431

    Article  CAS  PubMed  Google Scholar 

  23. Chang HC, Kao CH, Chung SY, Chen WC, Aninda LP, Chen YH (2019) Bhlhe40 differentially regulates the function and number of peroxisomes and mitochondria in myogenic cells. Redox Biol 20:321–333

    Article  CAS  PubMed  Google Scholar 

  24. Xiao L, Naganawa T, Obugunde E, Gronowicz G, Ornitz DM, Coffin JD (2004) Stat1 controls postnatal bone formation by regulating fibroblast growth factor signaling in osteoblasts. J Biol Chem 279:27743–27752

    Article  CAS  PubMed  Google Scholar 

  25. Tajima K, Takaishi H, Takito J, Tohmonda T, Yoda M, Ota N (2010) Inhibition of STAT1 accelerates bone fracture healing. J Orthopaedic Res 28:937–941

    Article  CAS  Google Scholar 

  26. Hesslein DGT, Fretz JA, Xi Y, Nelson T, Zhou S, Lorenzo JA (2009) Ebf1-dependent control of the osteoblast and adipocyte lineages. Bone 44:537–546

    Article  CAS  PubMed  Google Scholar 

  27. Sun D, Zheng X, Chen Y, Jia C, Xu S, Lin C (2015) Enhancement of osteogenesis post-splenectomy does not attenuate bone loss in ovariectomized rats. J Orthop Res 33:1356–1363

    Article  PubMed  Google Scholar 

  28. Dhanwal DK (2011) Thyroid disorders and bone mineral metabolism. Indian J Endocrinol Metab 15:S107–S112

    Article  PubMed  PubMed Central  Google Scholar 

  29. Meng XH, Tan LJ, Xiao HM, Tang BS, Deng HW (2019) Examining the causal role of leptin in bone mineral density: a Mendelian randomization study. Bone 125:25–29

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Levy JR, Murray E, Manolagas S, Olefsky JM (1986) Demonstration of insulin receptors and modulation of alkaline phosphatase activity by insulin in rat osteoblastic cells. Endocrinology 119:1786–1792

    Article  CAS  PubMed  Google Scholar 

  31. Hickman J, McElduff A (1989) Insulin promotes growth of the cultured rat osteosarcoma cell line UMR-106-01: an osteoblast-like cell. Endocrinology 124:701–706

    Article  CAS  PubMed  Google Scholar 

  32. Fata JE, Kong YY, Li J, Sasaki T, Irie-Sasaki J, Moorehead RA (2000) The osteoclast differentiation factor osteoprotegerin-ligand is essential for mammary gland development. Cell 103:41–50

    Article  CAS  PubMed  Google Scholar 

  33. Zhu Z, Zhang F, Hu H, Bakshi A, Robinson MR, Powell JE (2016) Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet 48:481–487

    Article  CAS  PubMed  Google Scholar 

  34. Pavlides JM, Zhu Z, Gratten J, McRae AF, Wray NR, Yang J (2016) Predicting gene targets from integrative analyses of summary data from GWAS and eQTL studies for 28 human complex traits. Genome Med 8:84

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank all the study subjects for volunteering to participate in the study.

Funding

This study was partially supported by the Health Commission of Hunan Province (202205033867), Natural Science Foundation of Hunan Province (S2022JJMSXM3534), Natural Science Foundation of Changsha (kq2208475), and Changsha Science and Technology Bureau (kq1701016).

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Author notes

  1. Xiang-He Meng and Zhen Liu have contributed equally to this work.

Authors and Affiliations

  1. Hunan Provincial Key Laboratory of Regional Hereditary Birth Defects Prevention and Control, Changsha Hospital for Maternal & Child Health Care Affiliated to Hunan Normal University, Changsha, 410007, People’s Republic of China

    Xiang-He Meng, Ai-Min Deng & Zeng-Hui Mao

  2. Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, 410081, Hunan, People’s Republic of China

    Zhen Liu & Xiang-Ding Chen

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  1. Xiang-He Meng
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  2. Zhen Liu
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  4. Ai-Min Deng
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Contributions

XHM: calculation, manuscript writing. ZL, XDC: manuscript revision. AMD, ZHM: study conception and design, manuscript revision.

Corresponding authors

Correspondence to Xiang-He Meng, Ai-Min Deng or Zeng-Hui Mao.

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Xiang-He Meng, Zhen Liu, Xiang-Ding Chen, Ai-Min Deng, Zeng-Hui Mao have no conflict of interest to disclose.

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Meng, XH., Liu, Z., Chen, XD. et al. Functional Enrichment Analysis Identifying Regulatory Information Associated with Human Fracture. Calcif Tissue Int 113, 286–294 (2023). https://doi.org/10.1007/s00223-023-01108-w

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  • DOI: https://doi.org/10.1007/s00223-023-01108-w

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