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.

翻译：理解上位性（遗传相互作用）可能有助于揭示常见疾病的基因组基础，包括因高社会经济负担而备受关注的精神分裂症等疾病。距离相关是一种关联度量，可表征随机变量之间的一般统计独立性，而不仅仅是线性关系。本文提出将距离相关作为从病例-对照单核苷酸多态性数据中检测上位性的新工具。在方法论层面，我们重点推导了检验统计量的显式渐近分布，证明这是在该方法中文献中重采样技术不实用时，获得足够计算速度以使其实际应用的唯一途径。模拟实验表明，该方法在显著性校准方面令人满意，且其统计功效与现有方法相当或更优。最后，我们将该技术应用于精神分裂症遗传学数据集，获得了具有生物学意义的见解。