Detecting gene–gene interactions that underlie human diseases

Abstract

Following the identification of several disease-associated polymorphisms by whole genome association analysis, interest is now focussing on the detection of effects that, due to their interaction with other genetic (or environmental) factors, may not be identified by using standard single-locus tests. In addition to increasing power to detect association, there is also a hope detecting interactions between loci will allow us to elucidate the biological and biochemical pathways underpinning disease. Here I provide a critical survey of the current methodological approaches (and related software packages) used to detect interactions between genetic loci that contribute to human genetic disease. I also discuss the difficulties in determining the biologcal relevance of statistical interactions.The search for genetic factors that influence common, complex traits, and the characterisation of the effects of those factors is both a goal and a challenge for modern geneticists. In the last couple of years, the field has been revolutionised by the success of genome-wide association (GWA) studies 1 2 3 4 5 . Most such studies have used a singlelocus analysis strategy, whereby each variant is tested individually for association with some phenotype. However, an oft-cited reason for the lack of success in genetic studies of complex disease 6 7 is the existence of interactions between loci. If a genetic factor operates primarily through a complex mechanism involving multiple other genes, and possibly environmental factors, the fear is that the effect will be missed if one examines it in isolation, without allowing for its potential interactions with these other (unknown) factors. For this reason, several methods and software packages 8 9 10 11 12 13 14 15 have been developed to consider statistical interactions between loci, when analysing data from genetic association studies. Although, in some cases, the motivation for such analyses is to increase the power to detect effects 16 , in other cases the motivation has been to detect statistical interactions between loci that are informtive about the biological and biochemical pathways underpinning the disease 7 . We return to this complex issue of biological interpretation of statistical interaction later in the article.The purpose of this Review article is to provide a survey of the current methodological approaches and related software packages that are currently used to detect interactions between genetic loci that contribute to human genetic disease. Although the focus is on human genetics, many of the concepts and approaches are strongly related to methods used in animal and plant genetics. I begin by describing what is meant by (statistical) interaction, and setting up definitions and notation for following sections. I then explain how one may test for interaction between two (or more) known genetic factors, and how one may address the slightly different question of testing for association with a single factor, while at the same time allowing for interaction with othe...

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