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KDSNP: A kernel-based approach to detecting high-order SNP interactions

机译：KDSNP：基于内核的方法来检测高阶SNP交互

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摘要

Despite the accumulation of quantitative trait loci (QTL) data in many complex human diseases, most of current approaches that have attempted to relate genotype to phenotype have achieved limited success, and genetic factors of many common diseases are yet remained to be elucidated. One of the reasons that makes this problem complex is the existence of single nucleotide polymorphism (SNP) interaction, or epistasis. Due to excessive amount of computation for searching the combinatorial space, existing approaches cannot fully incorporate high-order SNP interactions into their models, but limit themselves to detecting only lower-order SNP interactions. We present an empirical approach based on ridge regression with polynomial kernels and model selection technique for determining the true degree of epistasis among SNPs. Computer experiments in simulated data show the ability of the proposed method to correctly predict the number of interacting SNPs provided that the number of samples is large enough relative to the number of SNPs. For cases in which the number of the available samples is limited, we propose to perform sliding window approach to ensure sufficiently large sample/SNP ratio in each window. In computational experiments using heterogeneous stock mice data, our approach has successfully detected subregions that harbor known causal SNPs. Our analysis further suggests the existence of additional candidate causal SNPs interacting to each other in the neighborhood of the known causal gene. Software is available from https://github.com/HirotoSaigo/KDSNP.
机译：尽管在许多复杂的人类疾病中积累了定量特征基因座（QTL）数据，但大多数试图将基因型与表型相关的目前的方法取得了有限的成功，并且仍有许多常见疾病的遗传因素仍有待阐明。使这个问题复杂的原因之一是存在单一核苷酸多态性（SNP）相互作用或超越。由于搜索组合空间的计算量过多，现有方法不能完全将高阶SNP交互纳入其模型，但仅限于检测仅检测低阶SNP交互。我们介绍了一种基于多项式核和模型选择技术的脊回归的经验方法，用于确定SNP中的全新学位。模拟数据中的计算机实验显示所提出的方法正确预测相互作用的SNP的数量的能力，条件是相对于SNP的数量足够大。对于可用样本的数量有限的情况，我们建议执行滑动窗口方法，以确保每个窗口中足够大的样本/ SNP比率。在使用异构储蓄小鼠数据的计算实验中，我们的方法已成功地检测到港口已知因果SNP的次区域。我们的分析进一步表明存在在已知因果基因附近相互作用的额外候选因果SNP。软件可从https://github.com/hirotosaigo/kdsnp获得。

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