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EpiReSIM:一种不考虑边缘效应的基于欠定方程组的上位模型重抽样方法。

EpiReSIM: A Resampling Method of Epistatic Model without Marginal Effects Using Under-Determined System of Equations.

机构信息

School of Computer Science, Qufu Normal University, Rizhao 276826, China.

Science and Technology Innovation Service Institution of Rizhao, Rizhao 276827, China.

出版信息

Genes (Basel). 2022 Dec 4;13(12):2286. doi: 10.3390/genes13122286.

DOI:10.3390/genes13122286
PMID:36553553
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9777644/
Abstract

Simulation experiments are essential to evaluate epistasis detection methods, which is the main way to prove their effectiveness and move toward practical applications. However, due to the lack of effective simulators, especially for simulating models without marginal effects (eNME models), epistasis detection methods can hardly verify their effectiveness through simulation experiments. In this study, we propose a resampling simulation method (EpiReSIM) for generating the eNME model. First, EpiReSIM provides two strategies for solving eNME models. One is to calculate eNME models using prevalence constraints, and another is by joint constraints of prevalence and heritability. We transform the computation of the model into the problem of solving the under-determined system of equations. Introducing the complete orthogonal decomposition method and Newton's method, EpiReSIM calculates the solution of the underdetermined system of equations to obtain the eNME model, especially the solution of the high-order model, which is the highlight of EpiReSIM. Second, based on the computed eNME model, EpiReSIM generates simulation data by a resampling method. Experimental results show that EpiReSIM has advantages in preserving the biological properties of minor allele frequencies and calculating high-order models, and it is a convenient and effective alternative method for current simulation software.

摘要

模拟实验对于评估上位性检测方法至关重要,这是证明其有效性并推动实际应用的主要途径。然而,由于缺乏有效的模拟器,特别是对于没有边缘效应的模型(eNME 模型)的模拟,上位性检测方法很难通过模拟实验验证其有效性。在本研究中,我们提出了一种用于生成 eNME 模型的重采样模拟方法(EpiReSIM)。首先,EpiReSIM 提供了两种解决 eNME 模型的策略。一种是使用患病率约束来计算 eNME 模型,另一种是通过患病率和遗传性的联合约束来计算。我们将模型的计算转化为求解欠定方程组的问题。通过引入完全正交分解方法和牛顿法,EpiReSIM 计算得到了欠定方程组的解,从而获得了 eNME 模型,特别是高阶模型的解,这是 EpiReSIM 的亮点之一。其次,基于计算得到的 eNME 模型,EpiReSIM 通过重采样方法生成模拟数据。实验结果表明,EpiReSIM 在保留小等位基因频率的生物学特性和计算高阶模型方面具有优势,是当前模拟软件的一种方便有效的替代方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b818/9777644/949692a4e69f/genes-13-02286-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b818/9777644/287ee1f22cc7/genes-13-02286-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b818/9777644/9b3279326d72/genes-13-02286-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b818/9777644/949692a4e69f/genes-13-02286-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b818/9777644/287ee1f22cc7/genes-13-02286-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b818/9777644/9b3279326d72/genes-13-02286-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b818/9777644/949692a4e69f/genes-13-02286-g003.jpg

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EpiMOGA: An Epistasis Detection Method Based on a Multi-Objective Genetic Algorithm.EpiMOGA:一种基于多目标遗传算法的上位性检测方法。
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