  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). On the methodological side, we
highlight the derivation of the explicit asymptotic distribution of the test
statistic. We show that this is the only way to obtain enough computational
speed for the method to be used in practice, in a scenario where the resampling
techniques found in the literature are impractical. Our simulations show
satisfactory calibration of significance, as well as comparable or better power
than existing methodology. We conclude with the application of our technique to
a schizophrenia genetics dataset, obtaining biologically sound insights.
