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An Ultrahigh-Dimensional Mapping Model of High-Order Epistatic Networks for Complex Traits
Current Genomics ( IF 2.6 ) Pub Date : 2018-06-01 , DOI: 10.2174/1389202919666171218162210
Kirk Gosik 1 , Lidan Sun 1 , Vernon M Chinchilli 1 , Rongling Wu 1
Affiliation  

Background: Genetic interactions involving more than two loci have been thought to affect quantitatively inherited traits and diseases more pervasively than previously appreciated. However, the detection of such high-order interactions to chart a complete portrait of genetic architecture has not been well explored. Methods: We present an ultrahigh-dimensional model to systematically characterize genetic main effects and interaction effects of various orders among all possible markers in a genetic mapping or association study. The model was built on the extension of a variable selection procedure, called iFORM, derived from forward selection. The model shows its unique power to estimate the magnitudes and signs of high-order epistatic effects, in addition to those of main effects and pairwise epistatic effects. Results: The statistical properties of the model were tested and validated through simulation studies. By analyzing a real data for shoot growth in a mapping population of woody plant, mei (Prunus mume), we demonstrated the usefulness and utility of the model in practical genetic studies. The model has identified important high-order interactions that contribute to shoot growth for mei. Conclusion: The model provides a tool to precisely construct genotype-phenotype maps for quantitative traits by identifying any possible high-order epistasis which is often ignored in the current genetic literature.

中文翻译:

复杂特征高阶上位网络的超高维映射模型

背景:涉及两个以上基因座的遗传相互作用被认为比以前认为的更普遍地影响数量遗传性状和疾病。然而,检测这种高阶相互作用以绘制完整的遗传结构图还没有得到很好的探索。方法:我们提出了一个超高维模型,以系统地表征遗传作图或关联研究中所有可能标记之间的各种顺序的遗传主效应和相互作用效应。该模型建立在一个变量选择过程的扩展之上,称为 iFORM,源自前向选择。除了主效应和成对上位效应之外,该模型还显示了其独特的能力来估计高阶上位效应的大小和符号。结果:通过模拟研究对模型的统计特性进行了测试和验证。通过分析木本植物 mei (Prunus mume) 作图种群中枝条生长的真实数据,我们证明了该模型在实际遗传研究中的有用性和实用性。该模型已经确定了重要的高阶相互作用,这些相互作用有助于 mei 的枝条生长。结论:该模型通过识别当前遗传文献中经常被忽略的任何可能的高阶上位性,为精确构建数量性状的基因型-表型图提供了一种工具。该模型已经确定了重要的高阶相互作用,这些相互作用有助于 mei 的枝条生长。结论:该模型通过识别当前遗传文献中经常被忽略的任何可能的高阶上位性,为精确构建数量性状的基因型-表型图提供了一种工具。该模型已经确定了重要的高阶相互作用,这些相互作用有助于 mei 的枝条生长。结论:该模型通过识别当前遗传文献中经常被忽略的任何可能的高阶上位性,为精确构建数量性状的基因型-表型图提供了一种工具。
更新日期:2018-06-01
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