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首页> 外文期刊>Journal of the royal statistical society >Detecting epistatic selection with partially observed genotype data by using copula graphical models
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Detecting epistatic selection with partially observed genotype data by using copula graphical models

机译：通过使用copula图形模型，利用部分观察到的基因型数据检测上位选择

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In cross-breeding experiments it can be of interest to see whether there are any synergistic effects of certain genes. This could be by being particularly useful or detrimental to the individual. This type of effect involving multiple genes is called epistasis. Epistatic interactions can affect growth, fertility traits or even cause complete lethality. However, detecting epistasis in genomewide studies is challenging as multiple-testing approaches are underpowered. We develop a method for reconstructing an underlying network of genomic signatures of high dimensional epistatic selection from multilocus genotype data. The network captures the conditionally dependent short- and long-range linkage disequilibrium structure and thus reveals 'aberrant' marker-marker associations that are due to epistatic selection rather than gametic linkage. The network estimation relies on penalized Gaussian copula graphical models, which can account for a large number of markers p and a small number of individuals n. We demonstrate the efficiency of the proposed method on simulated data sets as well as on genotyping data in Arabidopsis thaliana and maize.
机译：在杂交实验中，观察某些基因是否有任何协同效应可能是令人感兴趣的。这可能对个人特别有用或有害。这种涉及多个基因的效应称为上位性。上位性相互作用可能会影响生长，生育特性，甚至造成完全的致死性。但是，由于多重测试方法的功能不足，因此在全基因组研究中检测上位性具有挑战性。我们开发了一种用于从多基因座基因型数据重建高维上位选择基因组签名的基础网络的方法。该网络捕获条件相关的短程和远程连锁不平衡结构，从而揭示由于上位选择而不是配子连锁而引起的“异常”标记-标记关联。网络估计依赖于惩罚的高斯copula图形模型，该模型可以解释大量的标记p和少量的个体n。我们在拟南芥和玉米的模拟数据集以及基因分型数据上证明了该方法的有效性。

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