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首页> 外文期刊>Genomics >TrioMDR: Detecting SNP interactions in trio families with model-based multifactor dimensionality reduction
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TrioMDR: Detecting SNP interactions in trio families with model-based multifactor dimensionality reduction

机译：TRIOMDR：检测Trio家族的SNP交互，具有基于模型的多因素维数减少

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摘要

Single nucleotide polymorphism (SNP) interactions can explain the missing heritability of common complex diseases. Many interaction detection methods have been proposed in genome-wide association studies, and they can be divided into two types: population-based and family-based. Compared with population-based methods, family-based methods are robust vs. population stratification. Several family-based methods have been proposed, among which Multifactor Dimensionality Reduction (MDR)-based methods are popular and powerful. However, current MDR-based methods suffer from heavy computational burden. Furthermore, they do not allow for main effect adjustment. In this work we develop a two-stage model-based MDR approach (TrioMDR) to detect multi-locus interaction in trio families (i.e., two parents and one affected child). TrioMDR combines the MDR framework with logistic regression models to check interactions, so TrioMDR can adjust main effects. In addition, unlike consuming permutation procedures used in traditional MDR-based methods, TrioMDR utilizes a simple semi-parameter P-values correction procedure to control type I error rate, this procedure only uses a few permutations to achieve the significance of a multi-locus model and significantly speeds up TrioMDR. We performed extensive experiments on simulated data to compare the type I error and power of TrioMDR under different scenarios. The results demonstrate that TrioMDR is fast and more powerful in general than some recently proposed methods for interaction detection in trios. The R codes of TrioMDR are available at: https://github.com/TrioMDR/TrioMDR.
机译：单核苷酸多态性（SNP）相互作用可以解释普遍复杂疾病的遗传性。在基因组 - 宽协会研究中提出了许多相互作用的检测方法，它们可以分为两种类型：基于人群和基于家庭的。与基于人口的方法相比，基于家庭的方法是鲁棒与人口分层。已经提出了几种基于家庭的方法，其中包括基于多重吸引力的维度（MDR）的方法是流行且强大的方法。然而，基于MDR的方法遭受了重大计算负担。此外，它们不允许进行主要效果调整。在这项工作中，我们开发了一种基于两阶段的模型的MDR方法（Triomdr），以检测Trio家族中的多基因座互动（即，两个父母和一个受影响的孩子）。 Triomdr将MDR框架与Logistic回归模型结合起来检查交互，因此Triomdr可以调整主要效果。此外，与传统的MDR的方法中使用的消耗置换过程不同，Triomdr利用简单的半参数P值校正过程来控制I错误率，仅使用一些序列来实现多基因座的重要性模型和显着加速Triomdr。我们对模拟数据进行了广泛的实验，以比较不同场景下的I误差和Triomdr的权力。结果表明，Triomdr通常比三个最近提出的TRIOS相互作用检测方法更快，更强大。 Triomdr的R代码可用于：https://github.com/triomdr/triomdr。

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