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).
翻译:理解上下文(遗传相互作用)可能揭示出常见疾病的基因组学基础,包括因其社会经济负担沉重而引起最大兴趣的疾病,如精神分裂症。 远程关联是一种关联性衡量标准,它描述随机变量之间的一般统计独立性,而不仅仅是线性变量。 我们在此提出远程关联,作为从单核分裂多形态(SNPs)的病例控制数据中检测上下文的新工具。 这种方法将在理论上(数学统计数据,在高维统计推断的背景下)和从应用的角度(模拟和真实数据集)开发。