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基因组选择研究进展及其在林木中的发展趋势

杜庆章 战鹏宇 李鹏 李先义 廖晨翰 郭诗曼 张德强

杜庆章, 战鹏宇, 李鹏, 李先义, 廖晨翰, 郭诗曼, 张德强. 基因组选择研究进展及其在林木中的发展趋势[J]. 北京林业大学学报, 2020, 42(11): 1-8. doi: 10.12171/j.1000-1522.20200152
引用本文: 杜庆章, 战鹏宇, 李鹏, 李先义, 廖晨翰, 郭诗曼, 张德强. 基因组选择研究进展及其在林木中的发展趋势[J]. 北京林业大学学报, 2020, 42(11): 1-8. doi: 10.12171/j.1000-1522.20200152
Du Qingzhang, Zhan Pengyu, Li Peng, Li Xianyi, Liao Chenhan, Guo Shiman, Zhang Deqiang. Advances in genomic selection and its development trend in forest[J]. Journal of Beijing Forestry University, 2020, 42(11): 1-8. doi: 10.12171/j.1000-1522.20200152
Citation: Du Qingzhang, Zhan Pengyu, Li Peng, Li Xianyi, Liao Chenhan, Guo Shiman, Zhang Deqiang. Advances in genomic selection and its development trend in forest[J]. Journal of Beijing Forestry University, 2020, 42(11): 1-8. doi: 10.12171/j.1000-1522.20200152

基因组选择研究进展及其在林木中的发展趋势

doi: 10.12171/j.1000-1522.20200152
基金项目: 北京市科技新星计划课题(Z181100006218024),中央高校基本科研业务费(2015ZCQ-SW-01),北京林业大学“校级大学生创新创业训练计划”(X201910022071)
详细信息
    作者简介:

    杜庆章,博士,副教授。主要研究方向:林木分子育种。Email:Qingzhangdu@bjfu.edu.cn 地址:100083 北京市海淀区清华东路35号北京林业大学

    责任作者:

    张德强,博士,教授。主要研究方向:林木分子育种。Email:Deqiangzhang@bjfu.edu.cn 地址:同上

  • 中图分类号: S722.3+3

Advances in genomic selection and its development trend in forest

  • 摘要: 随着新一代基因组测序技术的快速发展,基因组选择技术在促进优良基因型精准高效选育方面展现了前所未有的应用前景。近年来,基因组选择在动植物数量性状遗传育种领域的研究进展引起了广泛关注,关于其在林木改良驯化中的应用 也逐渐被报道。本文通过综述基因组选择的基本概况、主要模型方法及其在动植物中的研究进展,进一步探讨了基因组选择在林木育种研究中的现状和发展趋势,强调了对预测模型优化与机器学习等新兴技术的引入与方法创新,提出了联合利用全基因组关联分析与基因组编辑技术的优化育种方案,为加快林木优良品种的精准选育提供了新思路。

     

  • 表  1  常用的基因组选择模型

    Table  1.   Common models for genomic selection

    模型
    Model
    基本原理
    Basic principle
    标记效应
    Marker effect
    方差假设
    Variance assumption
    QTL效应
    Effect of
    QTLs
    估计准确度
    Estimation accuracy
    参考文献
    Reference
    最小二乘法
    Least square method
    多元线性回归方程
    Multiple linear regression equation
    固定
    Fixed
    高估
    Overvalued
    最低
    Minimum
    [25-26]
    最佳线性无偏预测法
    Best linear unbiased prediction (RR-BLUP)
    预测变量的均匀收缩为零允许标记有不均匀效果
    Uniform shrinkage of the predictive variable is zero, allowing the marker to have an uneven effect
    随机
    Random
    相等
    Equal
    低估
    Underestimated
    较高
    Higher
    [3, 25, 27-28]
    基因组最佳线性无偏估计
    Genomic best linear unbiased prediction (GBLUP)
    矩阵G代替传统亲缘关系矩阵A
    Matrix G replaces the traditional relationship matrix A
    随机
    Random
    相等
    Equal
    偏高估
    Partial overvalued
    较高
    Higher
    [25-27, 29]
    贝叶斯A
    Bayes A
    效应方差的先验和后验均为逆卡方分布,小标记收缩效果为零
    Prior and posterior effects of variance are inverse chi-square distribution, and the shrinkage effect of small markers is zero
    随机
    Random
    所有SNP均有效应
    All SNPs have an effect
    更准确估计
    More accurate estimation
    最高,但低于贝叶斯
    Max., but lower than Bayes B
    [3, 22, 28,30]
    贝叶斯B
    Bayes B
    效应方差的先验和后验均为逆卡方分布,收缩和变量选择方法
    Prior and posterior effects of variance are inverse chi-square distribution, contraction and variable selection methods
    随机
    Random
    大多数SNP无效应
    Most SNPs have no effect
    更准确估计
    More accurate estimation
    最高,高于贝叶斯
    Max., higher than Bayes B
    [3, 22, 26, 30]
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出版历程
  • 收稿日期:  2020-05-19
  • 修回日期:  2020-06-23
  • 网络出版日期:  2020-11-17
  • 刊出日期:  2020-12-14

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