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

Abstract

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.

Keywords

Genetic association analysis SNPs interaction GWAS Biological pathways Method analysis 

Notes

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)

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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

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