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Pathway analysis of genome-wide association study for bone mineral density

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Abstract

The aim of this study was to identify the candidate causal single nucleotide polymorphisms (SNPs) and candidate causal mechanisms that contribute to bone mineral density (BMD) and to generate a SNP to gene to pathway hypothesis using an analytical pathway-based approach. We used hip BMD GWAS data of the genotypes of 301,019 SNPs in 5,715 Europeans. ICSNPathway (identify candidate causal SNPs and pathways) analysis was applied to the BMD GWAS dataset. The first stage involved the pre-selection of candidate causal SNPs by linkage disequilibrium analysis and the functional SNP annotation of the most significant SNPs found. The second stage involved the annotation of biological mechanisms for the pre-selected candidate causal SNPs using improved-gene set enrichment analysis. ICSNPathway analysis identified seven candidate SNPs, eight candidate pathways, and seven hypothetical biological mechanisms. Eight pathways are as follows; gamma-hexachlorocyclohexane degradation (nominal p-value < 0.001, false discovery rate (FDR) <0.001), regulation of the smoothened signaling pathway (nominal p-value < 0.001, FDR = 0.016), TACI and BCMA stimulation of B cell immune response (nominal p-value < 0.001, FDR = 0.021), endonuclease activity (nominal p-value = 0.001, FDR = 0,026), regulation of defense response to virus (nominal p-value = 0.001, FDR = 0.028), serine_type_endopeptidase_inhibitor_activity (nominal p-value = 0.001, FDR = 0.044), endoribonuclease activity (nominal p-value = 0.002, FDR = 0.045), and myeloid leukocyte differentiation (nominal p-value = 0.001, FDR = 0.050). The most significant causal pathway was gamma-hexachlorocyclohexane degradation. CYP3A5, PON2, PON3, CMBL, PON1, ALPL, CYP3A43, CYP3A7, ACP6, ACPP, and ALPI (p < 0.05) are involved in the pathway of gamma-hexachlorocyclohexane degradation. Further examination of the gene contents revealed that DBR1, DICER1, EXO1, FEN1, POP1, POP4, RPP30, and RPP38 were involved in 2 of the 8 pathways (p < 0.05). By applying ICSNPathway analysis to BMD GWAS data, we identified seven candidate SNPs and eight pathways involving gamma-hexachlorocyclohexane degradation, which may contribute to low BMD.

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References

  1. Nguyen TV, Blangero J, Eisman JA (2000) Genetic epidemiological approaches to the search for osteoporosis genes. J Bone Miner Res 15:392–401

    Article  PubMed  CAS  Google Scholar 

  2. Lee YH, Rho YH, Choi SJ, Ji JD, Song GG (2006) Meta-analysis of genome-wide linkage studies for bone mineral density. J Hum Genet 51:480–486

    Article  PubMed  Google Scholar 

  3. Lee YH, Woo JH, Choi SJ, Ji JD, Song GG (2010) Associations between osteoprotegerin polymorphisms and bone mineral density: a meta-analysis. Mol Biol Rep 37:227–234

    Article  PubMed  CAS  Google Scholar 

  4. Lee YH, Woo JH, Choi SJ, Ji JD, Song GG (2009) Association between the A1330 V polymorphism of the low-density lipoprotein receptor-related protein 5 gene and bone mineral density: a meta-analysis. Rheumatol Int 29:539–544

    Article  PubMed  CAS  Google Scholar 

  5. Ralston SH, Uitterlinden AG (2010) Genetics of osteoporosis. Endocr Rev 31:629–662

    Article  PubMed  CAS  Google Scholar 

  6. Harley JB, Alarcon-Riquelme ME, Criswell LA, Jacob CO, Kimberly RP, Moser KL, Tsao BP, Vyse TJ, Langefeld CD, Nath SK, Guthridge JM, Cobb BL, Mirel DB, Marion MC, Williams AH, Divers J, Wang W, Frank SG, Namjou B, Gabriel SB, Lee AT, Gregersen PK, Behrens TW, Taylor KE, Fernando M, Zidovetzki R, Gaffney PM, Edberg JC, Rioux JD, Ojwang JO, James JA, Merrill JT, Gilkeson GS, Seldin MF, Yin H, Baechler EC, Li QZ, Wakeland EK, Bruner GR, Kaufman KM, Kelly JA (2008) Genome-wide association scan in women with systemic lupus erythematosus identifies susceptibility variants in ITGAM, PXK, KIAA1542 and other loci. Nat Genet 40:204–210

    Article  PubMed  CAS  Google Scholar 

  7. Manolio TA (2010) Genomewide association studies and assessment of the risk of disease. N Engl J Med 363:166–176

    Article  PubMed  CAS  Google Scholar 

  8. Johnson AD, O’Donnell CJ (2009) An open access database of genome-wide association results. BMC Med Genet 10:6

    Article  PubMed  Google Scholar 

  9. Hom G, Graham RR, Modrek B, Taylor KE, Ortmann W, Garnier S, Lee AT, Chung SA, Ferreira RC, Pant PV, Ballinger DG, Kosoy R, Demirci FY, Kamboh MI, Kao AH, Tian C, Gunnarsson I, Bengtsson AA, Rantapaa-Dahlqvist S, Petri M, Manzi S, Seldin MF, Ronnblom L, Syvanen AC, Criswell LA, Gregersen PK, Behrens TW (2008) Association of systemic lupus erythematosus with C8orf13-BLK and ITGAM-ITGAX. N Engl J Med 358:900–909

    Article  PubMed  CAS  Google Scholar 

  10. Schadt EE (2009) Molecular networks as sensors and drivers of common human diseases. Nature 461:218–223

    Article  PubMed  CAS  Google Scholar 

  11. Wang K, Li M, Hakonarson H (2010) Analysing biological pathways in genome-wide association studies. Nat Rev Genet 11:843–854

    Article  PubMed  CAS  Google Scholar 

  12. Zhang K, Chang S, Cui S, Guo L, Zhang L, Wang J (2011) ICSNPathway: identify candidate causal SNPs and pathways from genome-wide association study by one analytical framework. Nucl Acids Res 39:W437–443

    Article  PubMed  CAS  Google Scholar 

  13. Styrkarsdottir U, Halldorsson BV, Gretarsdottir S, Gudbjartsson DF, Walters GB, Ingvarsson T, Jonsdottir T, Saemundsdottir J, Center JR, Nguyen TV, Bagger Y, Gulcher JR, Eisman JA, Christiansen C, Sigurdsson G, Kong A, Thorsteinsdottir U, Stefansson K (2008) Multiple genetic loci for bone mineral density and fractures. N Engl J Med 358:2355–2365

    Article  PubMed  CAS  Google Scholar 

  14. Reiner A, Yekutieli D, Benjamini Y (2003) Identifying differentially expressed genes using false discovery rate controlling procedures. Bioinformatics 19:368–375

    Article  PubMed  CAS  Google Scholar 

  15. Kanehisa M, Goto S, Furumichi M, Tanabe M, Hirakawa M (2010) KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucl Acids Res 38:D355–360

    Article  PubMed  CAS  Google Scholar 

  16. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G (2000) Gene ontology: tool for the unification of biology. The gene ontology consortium. Nat Genet 25:25–29

    Article  PubMed  CAS  Google Scholar 

  17. Johnson AD, Handsaker RE, Pulit SL, Nizzari MM, O’Donnell CJ, de Bakker PI (2008) SNAP: a web-based tool for identification and annotation of proxy SNPs using HapMap. Bioinformatics 24:2938–2939

    Article  PubMed  CAS  Google Scholar 

  18. Lei SF, Jiang H, Deng FY, Deng HW (2007) Searching for genes underlying susceptibility to osteoporotic fracture: current progress and future prospect. Osteoporos Int 18:1157–1175

    Article  PubMed  Google Scholar 

  19. Huss JW 3rd, Orozco C, Goodale J, Wu C, Batalov S, Vickers TJ, Valafar F, Su AI (2008) A gene wiki for community annotation of gene function. PLoS Biol 6:e175

    Article  PubMed  Google Scholar 

  20. Hong MG, Pawitan Y, Magnusson PK, Prince JA (2009) Strategies and issues in the detection of pathway enrichment in genome-wide association studies. Hum Genet 126:289–301

    Article  PubMed  CAS  Google Scholar 

  21. Eleftherohorinou H, Hoggart CJ, Wright VJ, Levin M, Coin LJ (2011) Pathway-driven gene stability selection of two rheumatoid arthritis GWAS identifies and validates new susceptibility genes in receptor mediated signalling pathways. Hum Mol Genet 20:3494–3506

    Article  PubMed  CAS  Google Scholar 

  22. Elbers CC, van Eijk KR, Franke L, Mulder F, van der Schouw YT, Wijmenga C, Onland-Moret NC (2009) Using genome-wide pathway analysis to unravel the etiology of complex diseases. Genet Epidemiol 33:419–431

    Article  PubMed  Google Scholar 

  23. Jia P, Wang L, Meltzer HY, Zhao Z (2011) Pathway-based analysis of GWAS datasets: effective but caution required. Int J Neuropsychopharmacol 14:567–572

    Article  PubMed  CAS  Google Scholar 

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Acknowledgments

The authors gratefully acknowledge the efforts of those involved in the generation of BMD GWAS data.

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Correspondence to Young Ho Lee.

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Lee, Y.H., Choi, S.J., Ji, J.D. et al. Pathway analysis of genome-wide association study for bone mineral density. Mol Biol Rep 39, 8099–8106 (2012). https://doi.org/10.1007/s11033-012-1657-1

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