Imperial College London

Professor Paul M. Matthews

Faculty of MedicineDepartment of Brain Sciences

Edmond and Lily Safra Chair, Head of Department



+44 (0)20 7594 2855p.matthews




Ms Siobhan Dillon +44 (0)20 7594 2855




E502Burlington DanesHammersmith Campus






BibTex format

author = {Antoniades, A and Matthews, PM and Pattichis, CS and Galwey, NW},
doi = {10.1109/IEMBS.2010.5627741},
pages = {6194--6197},
title = {A computationally fast measure of epistasis for 2 SNPs and a categorical phenotype},
url = {},
year = {2010}

RIS format (EndNote, RefMan)

AB - Complex 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. © 2010 IEEE.
AU - Antoniades,A
AU - Matthews,PM
AU - Pattichis,CS
AU - Galwey,NW
DO - 10.1109/IEMBS.2010.5627741
EP - 6197
PY - 2010///
SP - 6194
TI - A computationally fast measure of epistasis for 2 SNPs and a categorical phenotype
UR -
ER -