Testing for genetic interactions in complex disease with distance correlation

TL;DR

This paper introduces a novel application of distance correlation to detect genetic interactions (epistasis) in complex diseases, offering a faster and potentially more powerful alternative to existing methods, demonstrated through simulations and schizophrenia data analysis.

Contribution

The paper proposes using distance correlation for epistasis detection, deriving its asymptotic distribution for practical application, and demonstrating its effectiveness over existing methods.

Findings

Method shows satisfactory calibration of significance.

Comparable or better power than existing methods.

Biologically meaningful insights in schizophrenia data.

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.