Journal of Neural Transmission

, Volume 122, Issue 1, pp 145–153 | Cite as

A model to investigate SNPs’ interaction in GWAS studies

  • Enrico Cocchi
  • Antonio Drago
  • Chiara Fabbri
  • Alessandro Serretti
Psychiatry and Preclinical Psychiatric Studies - Original Article


Genome-wide association studies (GWAS) are able to identify the role of individual SNPs in influencing a phenotype. Nevertheless, such analysis is unable to explain the biological complexity of several diseases. We elaborated an algorithm that starting from genes in molecular pathways implicated in a phenotype is able to identify SNP–SNP interaction’s role in association with the phenotype. The algorithm is based on three steps. Firstly, it identifies the biological pathways (gene ontology) in which the genes under analysis play a role (GeneMANIA). Secondly, it identifies the group of SNPs that best fits the phenotype (and covariates) under analysis, not considering individual SNP regression coefficients but fitting the regression for the group itself. Finally, it operates an analysis of SNP interactions for each possible couple of SNPs within the group. The sensitivity and specificity of our algorithm was validated in simulated datasets (HapGen and Simulate Phenotypes programs). The impact on efficiency deriving from changes in the number of SNPs/patients under analysis, linkage disequilibrium and minor allele frequency thresholds was analyzed. Our algorithm showed a strong stability throughout all analysis operated, resulting in an overall sensitivity of 81.67 % and a specificity of 98.35 %. We elaborated a stable algorithm that may detect SNPs interactions, especially those effects that pass undetected in classical GWAS. This method may contribute to face the two relevant limitations of GWAS: lack of biological informative power and amount of time needed for the analysis.


Genetic association analysis SNPs interaction GWAS Biological pathways Method analysis 


Conflict of interest

Dr. Serretti is or has been a consultant/speaker for: Abbott, Astra Zeneca, Clinical Data, Boheringer, Bristol Myers Squibb, Eli Lilly, GlaxoSmithKline, Janssen, Lundbeck, Pfizer, Sanofi, and Servier. The other authors declare that they have no conflicts of interest.

Supplementary material

702_2014_1341_MOESM1_ESM.doc (33 kb)
Supplementary material 1 (DOC 33 kb)
702_2014_1341_MOESM2_ESM.doc (31 kb)
Supplementary material 2 (DOC 31 kb)


  1. 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. doi: 10.1038/75556 Google Scholar
  2. Botta V, Louppe G, Geurts P, Wehenkel L (2014) Exploiting SNP Correlations within Random Forest for Genome-Wide Association Studies. PLoS One 9:e93379. doi: 10.1371/journal.pone.0093379 PubMedCentralPubMedCrossRefGoogle Scholar
  3. Cacabelos R, Martinez-Bouza R, Carril JC, Fernandez-Novoa L, Lombardi V, Carrera I, Corzo L, McKay A (2012) Genomics and pharmacogenomics of brain disorders. Curr Pharm Biotechnol 13:674–725PubMedCrossRefGoogle Scholar
  4. Crow TJ (2011) The missing genes: what happened to the heritability of psychiatric disorders? Mol Psychiatry 16:362–364. doi: 10.1038/mp.2010.92 PubMedCrossRefGoogle Scholar
  5. Daly MJ, Altshuler D (2005) Partners in crime. Nat genet 37:337–338. doi: 10.1038/ng0405-337 PubMedCrossRefGoogle Scholar
  6. Fabbri C, Di Girolamo G, Serretti A (2013) Pharmacogenetics of antidepressant drugs: an update after almost 20 years of research. Am J Med Genet B Neuropsychiatr Genet 162:487–520. doi: 10.1002/ajmg.b.32184 CrossRefGoogle Scholar
  7. Gejman PV, Sanders AR, Duan J (2010) The role of genetics in the etiology of schizophrenia. Psychiatr Clin North Am 33(1):35–66PubMedCentralPubMedCrossRefGoogle Scholar
  8. Gerke J, Lorenz K, Cohen B (2009) Genetic interactions between transcription factors cause natural variation in yeast. Science 323:498–501. doi: 10.1126/science.1166426 PubMedCrossRefGoogle Scholar
  9. Goldstein DB (2009) Common genetic variation and human traits. N Engl J Med 360:1696–1698. doi: 10.1056/NEJMp0806284 PubMedCrossRefGoogle Scholar
  10. Hemani G, Shakhbazov K, Westra HJ, Esko T, Henders AK, McRae AF, Yang J, Gibson G, Martin NG, Metspalu A, Franke L, Montgomery GW, Visscher PM, Powell JE (2014) Detection and replication of epistasis influencing transcription in humans. Nature 508:249–253. doi: 10.1038/nature13005 PubMedCentralPubMedCrossRefGoogle Scholar
  11. Hung J-H, Yang T-H, Hu Z, Weng Z, DeLisi C (2012) Gene set enrichment analysis: performance evaluation and usage guidelines. Brief Bioinform 13:281–291. doi: 10.1093/bib/bbr049 PubMedCentralPubMedCrossRefGoogle Scholar
  12. International HapMap Consortium, Frazer KA, Ballinger DG, Cox DR, Hinds DA, Stuve LL, Gibbs RA, Belmont JW, Boudreau A, Hardenbol P, Leal SM, Pasternak S, Wheeler DA, Willis TD, Yu F, Yang H, Zeng C, Gao Y, Hu H, Hu W, Li C, Lin W, Liu S, Pan H, Tang X, Wang J, Wang W, Yu J, Zhang B, Zhang Q, Zhao H, Zhao H, Zhou J, Gabriel SB, Barry R, Blumenstiel B, Camargo A, Defelice M, Faggart M, Goyette M, Gupta S, Moore J, Nguyen H, Onofrio RC, Parkin M, Roy J, Stahl E, Winchester E, Ziaugra L, Altshuler D, Shen Y, Yao Z, Huang W, Chu X, He Y, Jin L, Liu Y, Shen Y, Sun W, Wang H, Wang Y, Wang Y, Xiong X, Xu L, Waye MMY, Tsui SKW, Xue H, Wong JT-F, Galver LM, Fan J-B, Gunderson K, Murray SS, Oliphant AR, Chee MS, Montpetit A, Chagnon F, Ferretti V, Leboeuf M, Olivier J-F, Phillips MS, Roumy S, Sallée C, Verner A, Hudson TJ, Kwok P-Y, Cai D, Koboldt DC, Miller RD, Pawlikowska L, Taillon-Miller P, Xiao M, Tsui L-C, Mak W, Song YQ, Tam PKH, Nakamura Y, Kawaguchi T, Kitamoto T, Morizono T, Nagashima A, Ohnishi Y, Sekine A, Tanaka T, Tsunoda T, Deloukas P, Bird CP, Delgado M, Dermitzakis ET, Gwilliam R, Hunt S, Morrison J, Powell D, Stranger BE, Whittaker P, Bentley DR, Daly MJ, de Bakker PIW, Barrett J, Chretien YR, Maller J, McCarroll S, Patterson N, Pe’er I, Price A, Purcell S, Richter DJ, Sabeti P, Saxena R, Schaffner SF, Sham PC, Varilly P, Altshuler D, Stein LD, Krishnan L, Smith AV, Tello-Ruiz MK, Thorisson GA, Chakravarti A, Chen PE, Cutler DJ, Kashuk CS, Lin S, Abecasis GR, Guan W, Li Y, Munro HM, Qin ZS, Thomas DJ, McVean G, Auton A, Bottolo L, Cardin N, Eyheramendy S, Freeman C, Marchini J, Myers S, Spencer C, Stephens M, Donnelly P, Cardon LR, Clarke G, Evans DM, Morris AP, Weir BS, Tsunoda T, Mullikin JC, Sherry ST, Feolo M, Skol A, Zhang H, Zeng C, Zhao H, Matsuda I, Fukushima Y, Macer DR, Suda E, Rotimi CN, Adebamowo CA, Ajayi I, Aniagwu T, Marshall PA, Nkwodimmah C, Royal CDM, Leppert MF, Dixon M, Peiffer A, Qiu R, Kent A, Kato K, Niikawa N, Adewole IF, Knoppers BM, Foster MW, Clayton EW, Watkin J, Gibbs RA, Belmont JW, Muzny D, Nazareth L, Sodergren E, Weinstock GM, Wheeler DA, Yakub I, Gabriel SB, Onofrio RC, Richter DJ, Ziaugra L, Birren BW, Daly MJ, Altshuler D, Wilson RK, Fulton LL, Rogers J, Burton J, Carter NP, Clee CM, Griffiths M, Jones MC, McLay K, Plumb RW, Ross MT, Sims SK, Willey DL, Chen Z, Han H, Kang L, Godbout M, Wallenburg JC, L’Archevêque P, Bellemare G, Saeki K, Wang H, An D, Fu H, Li Q, Wang Z, Wang R, Holden AL, Brooks LD, McEwen JE, Guyer MS, Wang VO, Peterson JL, Shi M, Spiegel J, Sung LM, Zacharia LF, Collins FS, Kennedy K, Jamieson R, Stewart J (2007) A second generation human haplotype map of over 3.1 million SNPs. Nature 449:851–61. doi: 10.1038/nature06258
  13. Khatri P, Sirota M, Butte AJ (2012) Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput Biol 8:e1002375. doi: 10.1371/journal.pcbi.1002375 PubMedCentralPubMedCrossRefGoogle Scholar
  14. Lippert C, Listgarten J, Davidson RI, Baxter S, Poong H, Kadie CM, Heckerman D (2013) An exhaustive epistatic SNP association analysis on expanded Wellcome Trust data. Sci Rep 3. doi:  10.1038/srep01099
  15. Manolio TA (2010) Genomewide association studies and assessment of the risk of disease. N Engl J Med 363:166–176. doi: 10.1056/NEJMra0905980 PubMedCrossRefGoogle Scholar
  16. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, McCarthy MI, Ramos EM, Cardon LR, Chakravarti A, Cho JH, Guttmacher AE, Kong A, Kruglyak L, Mardis E, Rotimi CN, Slatkin M, Valle D, Whittemore AS, Boehnke M, Clark AG, Eichler EE, Gibson G, Haines JL, Mackay TFC, McCarroll SA, Visscher PM (2009) Finding the missing heritability of complex diseases. Nature 461:747–753. doi: 10.1038/nature08494 PubMedCentralPubMedCrossRefGoogle Scholar
  17. Marchini J, Donnelly P, Cardon LR (2005) Genome-wide strategies for detecting multiple loci that influence complex diseases. Nat Genet 37:413–417. doi: 10.1038/ng1537 PubMedCrossRefGoogle Scholar
  18. McClellan J, King M-C (2010) Genetic heterogeneity in human disease. Cell 141:210–217. doi: 10.1016/j.cell.2010.03.032 PubMedCrossRefGoogle Scholar
  19. Montpetit A, Chagnon F (2006) The Haplotype Map of the human genome: a revolution in the genetics of complex diseases. Med Sci (Paris) 22:1061–1067. doi: 10.1051/medsci/200622121061 CrossRefGoogle Scholar
  20. Nicodemus KK, Kolachana BS, Vakkalanka R, Straub RE, Giegling I, Egan MF, Rujescu D, Weinberger DR (2007) Evidence for statistical epistasis between catechol-O-methyltransferase (COMT) and polymorphisms in RGS4, G72 (DAOA), GRM3, and DISC1: influence on risk of schizophrenia. Hum Genet 120:889–906PubMedCrossRefGoogle Scholar
  21. Nicodemus KK, Callicott JH, Higier RG, Luna A, Nixon DC, Lipska BK, Vakkalanka R, Giegling I, Rujescu D, St Clair D, Muglia P, Shugart YY, Weinberger DR (2010) Evidence of statistical epistasis between DISC1, CIT and NDEL1 impacting risk for schizophrenia: biological validation with functional neuroimaging. Hum Genet 127:441–452PubMedCrossRefGoogle Scholar
  22. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, Maller J, Sklar P, de Bakker PIW, Daly MJ, Sham PC (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:559–575. doi: 10.1086/519795 PubMedCentralPubMedCrossRefGoogle Scholar
  23. R Core Team (2013) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
  24. Schwender H, Ruczinski I, Ickstadt K (2011) Testing SNPs and sets of SNPs for importance in association studies. Biostatistics 12:18–32. doi: 10.1093/biostatistics/kxq042 PubMedCentralPubMedCrossRefGoogle Scholar
  25. Visscher PM, Brown MA, McCarthy MI, Yang J (2012) Five years of GWAS discovery. Am J Hum Genet 90:7–24. doi: 10.1016/j.ajhg.2011.11.029 PubMedCentralPubMedCrossRefGoogle Scholar
  26. Wang K, Bucan M, Grant SFA, Schellenberg G, Hakonarson H (2010) Strategies for genetic studies of complex diseases. Cell 142:351–353. doi:  10.1016/j.cell.2010.07.025 (author reply 353–355)
  27. Warde-Farley D, Donaldson SL, Comes O, Zuberi K, Badrawi R, Chao P, Franz M, Grouios C, Kazi F, Lopes CT, Maitland A, Mostafavi S, Montojo J, Shao Q, Wright G, Bader GD, Morris Q (2010) The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res 38:W214–W220. doi: 10.1093/nar/gkq537 PubMedCentralPubMedCrossRefGoogle Scholar
  28. Wellcome Trust Case Control Consortium (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447:661–678. doi: 10.1038/nature05911 CrossRefGoogle Scholar
  29. Zhan Su (2010) SimulatePhenotypes: Simulates phenotypes for HAPGEN2 data setsGoogle Scholar

Copyright information

© Springer-Verlag Wien 2014

Authors and Affiliations

  • Enrico Cocchi
    • 1
  • Antonio Drago
    • 1
  • Chiara Fabbri
    • 1
  • Alessandro Serretti
    • 1
  1. 1.Department of Biomedical and Neuromotor Sciences, DIBINEMUniversity of BolognaBolognaItaly

Personalised recommendations