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作物杂志,2023, 第5期: 43–48 doi: 10.16035/j.issn.1001-7283.2023.05.007

• 遗传育种·种质资源·生物技术 • 上一篇    下一篇

利用玉米F1群体进行玉米全株鲜重的全基因组预测分析

杨宗莹1(), 肖贵2(), 张红伟3()   

  1. 1吉林农业大学农学院，130118，吉林长春
    2定西市农业科学研究院，743000，甘肃定西
    3中国农业科学院作物科学研究所，100193，北京
  • 收稿日期:2022-04-06 修回日期:2022-07-27 出版日期:2023-10-15 发布日期:2023-10-16
  • 通讯作者: 张红伟，研究方向为玉米分子育种，E-mail：zhanghongwei@caas.cn
  • 作者简介:杨宗莹，研究方向为玉米遗传育种，E-mail：yangzongying111@yeah.net;|肖贵为共同第一作者，研究方向为优良牧草新品种选育及示范推广，E-mail：294301232@qq.com
  • 基金资助:
    中国农业科学院创新工程(CAAS-ZDRW202004)

Whole-Genome Predictive Analysis of Fresh Weight per Plant Using the Maize F1 Population

Yang Zongying1(), Xiao Gui2(), Zhang Hongwei3()   

  1. 1College of Agronomy, Jilin Agricultural University, Changchun 130118, Jilin, China
    2Dingxi Academy of Agricultural Sciences, Dingxi 743000, Gansu, China
    3Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
  • Received:2022-04-06 Revised:2022-07-27 Online:2023-10-15 Published:2023-10-16

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摘要：

基因组预测是一种可以提高育种效率的分子育种技术。利用中北410（SN915，YH-1）和北农368（60271，2193）的亲本为父本与120个玉米自交系组配4个杂交群体。利用10k的SNP芯片对亲本进行基因分型，在3个环境下对4个群体的全株鲜重进行评价。结果表明，从环境来看，甘肃种植的全株鲜重最高;从群体来看，群体2的鲜重最高;4个群体的变异系数为0.27~0.33，表明4个测交群体的表型变异较大。通过实施一对一（利用其中一个群体为训练群体分别预测其他群体）以及多对一（利用3个群体及第4个群体的一半作为训练群体预测第4个群体的另一半）的预测方案，表明杂交群体间（一对一）的基因组预测准确性低于群体内（多对一）的基因组预测准确性，亲缘关系近的群体间预测效果更好。通过在训练群体中加入与预测群体有亲缘关系的材料可以改进基因组预测的效果。

关键词: 玉米, 基因组预测, 训练群体, 生物量

Abstract:

Genomic prediction is a molecular technique that can improve the efficiency of molecular breeding. In this study, 120 maize inbred lines were crossed to the parents of Zhongbei 410 (SN915, YH-1) and Beinong 368 (60271, 2193) to construct four testcross populations. The 10k SNP chip was used to genotype the parental lines, and the whole-plant fresh weight of the four populations were evaluated in three environments. The results showed that the fresh weight was highest in Gansu environment, and population 2 had the highest whole plant fresh weight. The coefficients of variances of the four populations ranged from 0.27 to 0.33, indicating the phenotypic variations of the four populations were great. By implementing the one-to-one (one population was used as the training population to predict other populations) and multiple-to-one (three populations and a half of the forth population were used as the training population to predict the other half of the forth population) prediction schemes, we found that the prediction accuracy of one-to-one genome prediction was lower than that of the multiple-to-one prediction, and closely related groups predicted better. The accuracy of genomic prediction could be improved by adding the relatives of the prediction population in the training population and optimizing the relationship between the training population and the predicted population.

Key words: Maize, Genomic prediction, Training population, Biomass

图1

不同环境中材料及不同群体全株鲜重汇总

表1

4个群体鲜重基本统计数据汇总

群体Population 群体大小Population size 平均值Mean (kg) 标准差SD 最小值Minimum (kg) 最大值Maximum (kg) 变异系数CV
P1 324 1.18 0.38 0.35 2.38 0.32
P2 346 1.36 0.37 0.49 2.64 0.27
P3 342 1.13 0.37 0.41 2.05 0.33
P4 343 1.26 0.36 0.55 2.76 0.29

图2

3个环境中全株鲜重相关性分析 “**”和“***”分别表示0.01和0.001水平差异极显著

图3

测序标记及遗传相似性分析 (a) 测序标记的分布;(b) 所有亲本材料进化树分析，1-120为母本材料，121、122、123和124分别为中北410的亲本（SN915，YH-1）和北农368父母本（60271，2193）;(c) 所有标记的4个群体的主成分分析，蓝色、红色、绿色和黄色分别为P1、P2、P3和P4;(d) 利用热图分析4个群体的亲缘关系，从左到右分别是P1、P2、P3和P4

图4

利用GBLUP和BayesB模型对不同群体全株鲜重的全基因组预测 (a) 以1个群体作为训练群体预测其他群体，(b) 以随机1个群体的随机一半材料加剩余3个群体作为训练群体预测剩余半个群体

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