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首页> 外文会议>Bioinformatics Research and Development; Lecture Notes in Bioinformatics; 4414 >Comparing Logic Regression Based Methods for Identifying SNP Interactions
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Comparing Logic Regression Based Methods for Identifying SNP Interactions

机译：比较基于逻辑回归的SNP相互作用识别方法

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In single-nucleotide polymorphism (SNP) association studies interactions are often of main interest. Logic regression is a regression methodology that can identify complex Boolean interactions of binary variables. It has been applied successfully to SNP data but only identifies a single best model, while usually there is a number of models that are almost as good. Extensions of logic regression that consider several plausible models are Monte Carlo logic regression (MCLR) and a full Bayesian version of logic regression (FBLR) proposed in this paper. FBLR allows the incorporation of biological knowledge such as known pathways. We compare the performance in identifying SNP interactions associated with the case-control status of the three logic regression based methods and stepwise logistic regression in a simulation study and in a study of breast cancer.
机译：在单核苷酸多态性（SNP）关联研究中，相互作用通常是主要关注的问题。逻辑回归是一种可以识别二进制变量的复杂布尔交互作用的回归方法。它已成功应用于SNP数据，但仅确定了一个最佳模型，而通常存在许多几乎相同的模型。考虑几种可行模型的逻辑回归扩展包括蒙特卡罗逻辑回归（MCLR）和本文提出的贝叶斯完整版本的逻辑回归（FBLR）。 FBLR允许纳入生物学知识，例如已知途径。在模拟研究和乳腺癌研究中，我们比较了在确定与三种基于逻辑回归的方法的病例对照状态和逐步逻辑回归相关的SNP相互作用时的性能。

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