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dc.contributor.authorAntoniades, Athosen
dc.contributor.authorMatthews, P. M.en
dc.contributor.authorPattichis, Constantinos S.en
dc.contributor.authorGalwey, N. W.en
dc.creatorAntoniades, Athosen
dc.creatorMatthews, P. M.en
dc.creatorPattichis, Constantinos S.en
dc.creatorGalwey, N. W.en
dc.description.abstractComplex diseases may be caused by interactions or combined effects between multiple genetic and environmental factors. One of the main limitations of testing for interaction between genetic loci in large whole genome studies is the high computational cost of performing such analyses. In this study a new methodology for interaction testing (commonly referred to in genetics as the epistatic effect) between two single nucleotide polymorphisms (SNPs) and a categorical phenotype is presented. It is shown that it provides reasonable approximations with a significantly shorter run time. The proposed measure based on the Pearson's chi-square additive property is compared to fitting a logistic regression model on a randomly selected subset of 218 SNP loci from a study that included 550,000 SNPs). For each possible pair of SNPs a chi-square test for the epistatic effect on case-control status is estimated by fitting a logistic regression model, and compared to the results of the proposed method. Results indicate strong agreement (Pearson's correlation r>0.95) while the proposed method is found to be 20 times faster. This provides a significant pragmatic advantage for the proposed method since the number of tests for epistasis can now be increased by a factor of 20 while the computational cost remains the same.en
dc.sourceConference proceedings : ...Annual International Conference of the IEEE Engineering in Medicine and Biology Society.IEEE Engineering in Medicine and Biology Society.Conferenceen
dc.subjectsingle nucleotide polymorphismen
dc.subjectComputational Biologyen
dc.subjectLogistic Modelsen
dc.subjectstatistical modelen
dc.subjectChi-Square Distributionen
dc.subjectchi square distributionen
dc.subjectPolymorphism, Single Nucleotideen
dc.subjectEpistasis, Geneticen
dc.titleA computationally fast measure of epistasis for 2 SNPs and a categorical phenotype.en
dc.description.endingpage6197 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied SciencesΤμήμα Πληροφορικής / Department of Computer Science
dc.source.abbreviationConf Proc IEEE Eng Med Biol Socen
dc.contributor.orcidPattichis, Constantinos S. [0000-0003-1271-8151]

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