Searching for genetic interactions in complex disease by using distance correlation

Understanding epistasis (genetic interaction) may shed some light on the genomic basis of common diseases, including disorders of maximum interest due to their high socioeconomic burden, like schizophrenia. Distance correlation is an association measure that characterises general statistical independence between random variables, not only the linear one. Here, we propose distance correlation as a novel tool for the detection of epistasis from case-control data of single nucleotide polymorphisms (SNPs). This approach will be developed both theoretically (mathematical statistics, in a context of high-dimensional statistical inference) and from an applied point of view (simulations and real datasets).