Testing for genetic interactions in complex disease with distance correlation
Abstract
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.
Cite
@article{arxiv.2012.05285,
title = {Testing for genetic interactions in complex disease with distance correlation},
author = {Fernando Castro-Prado and Javier Costas and Dominic Edelmann and Wenceslao González-Manteiga and David R. Penas},
journal= {arXiv preprint arXiv:2012.05285},
year = {2023}
}
Comments
15 pages with 3 figures, plus a 10-page supplement. This document supersedes a much older version of the manuscript, in which we used other theoretical and computational approaches. Simulations, real data analyses and the writing of the paper have also been improved