Application of multi-locus analytical methods to identify interacting loci in case-control studies

Abstract

To identify interacting loci in genetic epidemiological studies the application of multi-locus methods of analysis is warranted. Several more advanced classification methods have been developed in the past years, including multiple logistic regression, sum statistics, logic regression, and the multifactor dimensionality reduction method. The objective of our study was to apply these four multi-locus methods to simulated case-control datasets that included a variety of underlying statistical two-locus interaction models, in order to compare the methods and evaluate their strengths and weaknesses. The results showed that the ability to identify the interacting loci was generally good for the sum statistic method, the logic regression and MDR. The performance of the logistic regression was more dependent on the underlying model and multiple comparison adjustment procedure. However, identification of the interacting loci in a model with two two-locus interactions of common disease alleles with relatively small effects was impaired in all methods. Several practical and methodological issues that can be considered in the application of these methods, and that may warrant further research, are identified and discussed.