In recent years there has been intense research to find genetic factors that influence common complex traits. The approach that is commonly followed to discover those associations between genetic factors and complex traits such as diseases is to perform a Genome-Wide Association Study (GWAS). It has been pointed out that there is no single marker for disease risk and no single protective marker but, rather, a collection of markers that confer a graded risk of disease. As an example of this, it has been suggested that many genes with small effects rather than few genes with strong effects contribute to the development of asthma. For human height the heritability explained with SNPs discovered with GWAS is about 5%. However, a recent study showed that it is possible to explain around 45% of the phenotypic variance for height with GWAS data. The problem is that the individual effects of the interacting SNPs are too small to be detected with common statistical methods. This shows that there is a need for powerful methods that are able to consider interactions between SNPs with low marginal effects. In this document we describe a wide range of methods that have been proposed to detect interactions between SNPs in association studies data. We will give examples of statistical methods (explaining also how to deal with the multiple testing problem), search methods (deterministic and stochastic) and machine learning methods.