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捕捉与大麦三个农艺性状相关的成对上位性效应。

Capturing pair-wise epistatic effects associated with three agronomic traits in barley.

作者信息

Xu Yi, Wu Yajun, Wu Jixiang

机构信息

Department of Agronomy, Horticulture, and Plant Science, South Dakota State University, Box 2140C, Brookings, SD, 57007, USA.

Department of Biology and Microbiology, South Dakota State University, Brookings, SD, 57007, USA.

出版信息

Genetica. 2018 Apr;146(2):161-170. doi: 10.1007/s10709-018-0008-0. Epub 2018 Jan 18.

Abstract

Genetic association mapping has been widely applied to determine genetic markers favorably associated with a trait of interest and provide information for marker-assisted selection. Many association mapping studies commonly focus on main effects due to intolerable computing intensity. This study aims to select several sets of DNA markers with potential epistasis to maximize genetic variations of some key agronomic traits in barley. By doing so, we integrated a MDR (multifactor dimensionality reduction) method with a forward variable selection approach. This integrated approach was used to determine single nucleotide polymorphism pairs with epistasis effects associated with three agronomic traits: heading date, plant height, and grain yield in barley from the barley Coordinated Agricultural Project. Our results showed that four, seven, and five SNP pairs accounted for 51.06, 45.66 and 40.42% for heading date, plant height, and grain yield, respectively with epistasis being considered, while corresponding contributions to these three traits were 45.32, 31.39, 31.31%, respectively without epistasis being included. The results suggested that epistasis model was more effective than non-epistasis model in this study and can be more preferred for other applications.

摘要

遗传关联作图已被广泛应用于确定与感兴趣的性状有利相关的遗传标记,并为标记辅助选择提供信息。由于计算强度难以承受,许多关联作图研究通常集中于主效应。本研究旨在选择几组具有潜在上位性的DNA标记,以最大化大麦某些关键农艺性状的遗传变异。通过这样做,我们将多因素降维(MDR)方法与前向变量选择方法相结合。这种综合方法用于确定与大麦协调农业项目中的三个农艺性状(抽穗期、株高和籽粒产量)相关的具有上位性效应的单核苷酸多态性对。我们的结果表明,在考虑上位性的情况下,四对、七对和五对单核苷酸多态性分别占抽穗期、株高和籽粒产量的51.06%、45.66%和40.42%,而在不考虑上位性的情况下,对这三个性状的相应贡献分别为45.32%、31.39%和31.31%。结果表明,在本研究中,上位性模型比非上位性模型更有效,并且在其他应用中可能更受青睐。

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