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Authors: Sidney K. Chu 1 ; Samuel Guanglin Xu 2 ; Feng Xu 3 and Nelson L. S. Tang 1

Affiliations: 1 The Chinese University of Hong Kong, China ; 2 Shanghai American School (Pudong), China ; 3 The University of Hong Kong, China

ISBN: 978-989-758-170-0

Keyword(s): Genome Wide Association Study, SNP-SNP Interaction, Genetic Susceptibility, Statistical Modelling.

Related Ontology Subjects/Areas/Topics: Algorithms and Software Tools ; Bioinformatics ; Biomedical Engineering ; Biostatistics and Stochastic Models

Abstract: In the recent years of the GWAS era, large-scale genotyping of million polymorphisms (SNPs) among thousands of patients have identified new disease predisposition loci. However, these conventional GWAS statistical models only analyse SNPs singularly and cannot detect significant SNP-SNP (gene-gene) interaction. Studies of interacting genetic variants (SNPs) are useful to elucidate a disease’s underlying biological pathway. Therefore, a powerful and efficient statistical model to detect SNP-SNP interaction is urgently needed. We hypothesize that among all the exhaustive model patterns of interaction (>100), only limited patterns are plausible based on the principle of protein-protein interaction (in the context of GWAS data analysis). The production of proteins by the process of translation of DNA predicts that gene-gene interaction resulting in a phenotype should only occur in classical genetic epistasis models, such as dominant-dominant, and recessive-recessive models. We developed a statistical analysis model, IAC (Interaction Analysis by Chi-Square), to examine such interactions. We then exhausted different population and statistical parameters, upon a total of 532 simulated case-control experiments to study the effects of these parameters on statistical power and type I error of using an interaction vs. singular SNP analysis. Our method has also detected potential pairwise interactions associated with Parkinson's disease that were previously undetected in conventional methods. We showed that the detection of SNP-SNP interaction is actually feasible using typical sample sizes found in common GWAS studies. This approach may be applied in complimentarily with other models in two-stage association tests to efficiently detect candidate SNPs for further study. (More)

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Paper citation in several formats:
Chu, S.; Xu, S.; Xu, F. and Tang, N. (2016). Gene-gene Interaction Analysis by IAC (Interaction Analysis by Chi-Square) - A Novel Biological Constraint-based Interaction Analysis Framework.In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2016) ISBN 978-989-758-170-0, pages 142-150. DOI: 10.5220/0005654601420150

@conference{bioinformatics16,
author={Sidney K. Chu. and Samuel Guanglin Xu. and Feng Xu. and Nelson L. S. Tang.},
title={Gene-gene Interaction Analysis by IAC (Interaction Analysis by Chi-Square) - A Novel Biological Constraint-based Interaction Analysis Framework},
booktitle={Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2016)},
year={2016},
pages={142-150},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005654601420150},
isbn={978-989-758-170-0},
}

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2016)
TI - Gene-gene Interaction Analysis by IAC (Interaction Analysis by Chi-Square) - A Novel Biological Constraint-based Interaction Analysis Framework
SN - 978-989-758-170-0
AU - Chu, S.
AU - Xu, S.
AU - Xu, F.
AU - Tang, N.
PY - 2016
SP - 142
EP - 150
DO - 10.5220/0005654601420150

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