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作物学报 ›› 2020, Vol. 46 ›› Issue (3): 365-384.doi: 10.3724/SP.J.1006.2020.91044

• 作物遗传育种·种质资源·分子遗传学 • 上一篇    下一篇

小麦重要产量性状的主基因+多基因混合遗传分析

解松峰1,2,*,吉万全1,*(),张耀元1,*,张俊杰1,胡卫国3,李俊4,王长有1,张宏1,陈春环1   

  1. 1. 西北农林科技大学农学院 / 旱区作物逆境生物学国家重点实验室 / 国家小麦改良中心农学院杨凌分中心, 陕西杨凌 712100
    2. 农业部富硒产品开发与质量控制重点实验室 / 富硒食品开发国家地方联合工程实验室 / 中国富硒产业研究院/安康市富硒产品研发中心, 陕西安康 725000
    3. 河南省农业科学院小麦研究中心, 河南郑州 450002
    4. 四川省农业科学院作物研究所, 四川成都610066
  • 收稿日期:2019-07-01 接受日期:2019-09-26 出版日期:2020-03-12 网络出版日期:2019-10-11
  • 通讯作者: 解松峰,吉万全,张耀元
  • 作者简介:解松峰, E-mail: xiesongfengboheng@163.com
  • 基金资助:
    本研究由国家重点研发计划项目(2016YFD0102004);农业部作物基因资源与种质创制陕西科学观测实验站, 农业部富硒产品开发与质量控制重点实验室(试运行), 陕西省农业科技创新转化项目(NYKJ-2015-037);陕西省创新能力支撑计划项目(2018TD-021);陕西省创新能力支撑计划项目(2018PT-31);富硒食品开发国家地方联合工程实验室(陕西)项目资助

Genetic effects of important yield traits analysed by mixture model of major gene plus polygene in wheat

Song-Feng XIE1,2,*,Wan-Quan JI1,*(),Yao-Yuan ZHANG1,*,Jun-Jie ZHANG1,Wei-Guo HU3,Jun LI4,Chang-You WANG1,Hong ZHANG1,Chun-Huan CHEN1   

  1. 1. College of Agronomy, Northwest A&F University / State Key Laboratory of Crop Stress Biology in Arid Areas / Yangling Sub-centre, National Wheat Improvement Centre, Yangling 712100, Shaanxi, China;
    2. Key Laboratory of Se-enriched Products Development and Quality Control, Ministry of Agriculture and Rural Affairs, National-Local Joint Engineering Laboratory of Se-enriched Food Development, Ankang R&D Center for Se-enriched Prducts, Ankang 725000, Shaanxi, China;
    3. Wheat Research Centre, Henan Academy of Agricultural Sciences, Zhengzhou 450002, Henan, China;
    4. Crop Research Institute, Sichuan Academy of Agricultural Sciences, Chengdu 610066, Sichuan, China
  • Received:2019-07-01 Accepted:2019-09-26 Published:2020-03-12 Published online:2019-10-11
  • Contact: Song-Feng XIE,Wan-Quan JI,Yao-Yuan ZHANG
  • Supported by:
    The study was supported by the Key Research and Development Program of China(2016YFD0102004);the Shaanxi Resrarch Station of Crop Gene Resources & Germplasm Enhancement, Ministry of Agriculture, Key Laboratory of Se-enriched Products Development and Quality Control, Ministry of Agriculture and Rural Affairs, Shaanxi Provincial Agricultural Science and Technology Innovation and Transformation Project(NYKJ-2015-037);the Innovation Capability Support Program Project in Shaanxi Province(2018TD-021);the Innovation Capability Support Program Project in Shaanxi Province(2018PT-31);the National-Local Joint Engineering Laboratory of Se-enriched Food Development (Shaanxi).

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摘要：

以单株产量等为代表的重要性状是选育小麦高产良种的主攻目标性状, 分析小麦重要产量性状的数量遗传特性, 为深入研究其遗传机制提供依据。本研究选用品冬34为母本(P1)和BARRAN为父本(P2)配置杂交组合, 在2年4个环境下应用主基因+多基因混合遗传模型方法对该组合单世代(P1、P2、RIL7:8、RIL8:9)单株产量、千粒重、株高、穗下节间长、旗叶上节间长和分蘖数进行遗传及相关分析。结果表明, 除千粒重和分蘖数外, 其余性状间均显著或极显著相关, 穗下节间长与旗叶上节间长平均相关系数达0.91 (P≤0.001)。最优遗传模型对于单株产量是4对加性上位性主基因+多基因遗传模型, 其主基因加性效应值分别为3.78、2.89、-6.18和0.15, 多基因遗传率为86.23%; 对于千粒重是2对互补作用主基因+加性效应多基因混合遗传模型, 多基因加性效应值是22.37, 主基因遗传率为66.96%, 多基因遗传率为28.25%; 对于株高是2对累积作用主基因+加性作用多基因混合遗传模型, 控制株高的第1对主基因加性效应值是5.15, 加性×加性上位性互作效应值为-9.66, 多基因加性效应值为-9.31, 主基因遗传率为58.57%, 多基因遗传率为39.71%; 对于穗下节间长和旗叶上节间长均是加性-上位性多基因遗传模型, 其主基因遗传率分别为97.65%和99.14%; 对于分蘖数是加性-上位性多基因混合遗传模型, 主基因遗传率为78.89%, 多基因遗传率为19.87%。这些性状在多个环境下主要受主基因+多基因混合遗传控制。在选育优良品系的过程中, 要兼顾适应生态环境条件的重要表现, 进一步为筛选与目标性状紧密连锁标记及推进分子标记辅助选择提供理论依据。

关键词: 小麦, 产量性状, 相关分析, 主基因+多基因, 遗传效应

Abstract:

The important traits represented by yield per plant are the main target traits in high-yield breeding of wheat, and analysing then the quantitative genetic characteristics will provide a basis for further study on the genetic mechanism. In this study, we selected the winter Pingdong 34 as the female parent (P1) and the Barran as the male parent (P2) to configure the hybrid combination, and applied the main gene + polygene mixed genetic model method to analyse the length and number of tillers in the single generation (P1, P2) RIL7:8, RIL8:9 at four environments in two years genetic model and correlation among yield per plant, 1000-grain weight, plant height, internode length, internode length and number of tillers. There were significant or extremely significant condation among all the traits, except for the 1000-grain weight and number of tillers. The average correlation coefficient between the internodes and the upper leaves of the flag was 0.91 (P ≤ 0.001). The optimal genetic model for yield per plant was four pairs of additive epigenetic gene + polygenic genetic model with the main gene additive effect values of 3.78, 2.89, -6.18, and 0.15, respectively, and the multigene heritability of 86.23%. The optimal genetic model of 1000-grain weight of a mixed genetic model with two pairs of complementary main genes + additive effects. The multi-gene additive effect value was 22.37, the main gene heritability was 66.96%, and the multi-gene heritability was 28.25%. The optimal genetic model of plant height was a mixed genetic model of two pairs of cumulative main genes + additive multigenes. The additive value of the first pair of main genes controlling plant height was 5.15. Additive × additive epistatic interaction value was -9.66, the multigene additive effect value was -9.31, the major gene heritability was 58.57%, and the polygene heritability was 39.71%. The optimal genetic model of the internode length below spike above the flag leaf was the additive-superordinate multi-gene genetic model, and the main gene heritability was 97.65% and 99.14%, respectively. The optimal genetic model of the number of tillers was an additive-superordinate multi-gene mixed genetic model with a genetic rate of 78.89% for primary genes and 19.87% for multiple genes. These traits were mainly dominated by the combination of major gene + polygene in multiple environments. In the process of breeding excellent strains, it is necessary to take into account the important performances adapting to the ecological environment conditions, and further provide a theoretical basis for screening closely linked to target traits markers and promoting molecular marker-assisted selection.

Key words: wheat, yied traits, conrelation analysis, major gene plus polygen, genetic effects

表1

重组自交系及其亲本穗部性状最佳线性无偏预测描述性分析"

环境Environ. 亲本Parent 重组自交系群体 RIL
品东34
Pindong 34		 Warran
MY11847		 最小值
Min.		 最大值
Max.		 平均数
Average		 标准差
SD		 变异系数CV (%) 遗传力 Heritability 偏度系数
Skew.		 峰度系数
Kurt.
单株产量GYP (g)
E1 36.45 5.53 1.18 89.83 24.32 14.41 0.59 0.86 1.02** 1.55**
E2 18.66 6.24 2.03 65.55 23.31 9.93 0.43 0.47 0.66** 1.11**
E3 28.50 15.89 2.85 71.32 25.56 11.03 0.43 0.77 0.66** 0.82**
E4 28.56 24.38 1.10 47.20 16.97 8.00 0.47 0.75 0.48** 0.23**
Average 28.04 13.01 1.79 68.48 22.54 10.84 0.48 0.71 0.705** 0.9275**
千粒重TGW (g)
E1 55.85 26.00 23.25 66.50 45.12 7.43 0.16 0.33 0.09 -0.29
E2 58.40 24.65 24.18 66.17 45.01 7.09 0.16 0.37 -0.08 -0.26
E3 57.08 24.15 18.90 59.70 38.30 7.37 0.19 0.76 -0.10 -0.20
E4 71.20 68.15 11.88 71.20 47.89 8.27 0.17 0.63 -0.25 0.35**
Average 60.63 35.74 19.55 65.89 44.08 7.54 0.17 0.52 -0.08 -0.10
株高 PH (cm)
E1 88.00 86.00 30.00 158.00 96.51 19.09 0.20 0.91 -0.21 -0.07
E2 86.50 90.50 50.80 161.80 108.26 22.05 0.20 0.92 -0.18 -0.39
E3 85.75 99.33 39.25 149.25 102.88 18.74 0.18 0.94 -0.43 0.02
E4 94.67 102.00 33.67 155.00 104.76 20.89 0.20 0.92 -0.35 -0.24
Average 88.73 94.46 38.43 156.01 103.10 20.19 0.20 0.92 -0.29 -0.17
分蘖数NTP
E1 28.00 16.00 3.00 79.00 20.48 7.33 0.36 0.57 1.94** 2.54**
E2 15.75 18.25 6.50 54.00 22.02 5.63 0.26 0.42 1.09** 3.30**
E3 23.00 35.50 9.00 59.00 28.60 7.43 0.26 0.90 0.58** 0.56**
E4 10.00 17.00 3.33 21.00 9.76 2.55 0.26 0.44 0.96** 2.10**
Average 19.19 21.69 5.46 53.25 20.22 5.74 0.28 0.60 1.14** 4.63**
穗下节间长SIL (cm)
E1 36.00 34.00 16.00 66.00 40.40 9.12 0.23 0.89 0.00 -0.17
E2 33.25 33.75 18.60 66.00 40.45 8.54 0.21 0.88 -0.11 -0.20
E3 31.45 32.70 14.23 54.10 35.31 6.42 0.18 0.86 -0.32 0.20*
E4 32.83 33.50 16.83 62.50 39.13 8.27 0.21 0.91 -0.23 -0.24
Average 33.38 33.49 16.42 62.15 38.82 8.09 0.21 0.88 -0.16 -0.10
旗叶上节间长FIL (cm)
E1 13.00 21.00 3.00 46.00 20.19 6.94 0.34 0.87 0.27** 0.18*
E2 11.75 20.00 3.80 42.20 20.16 6.35 0.32 0.89 0.15* 0.14**
E3 9.50 14.65 0.00 30.00 15.39 4.31 0.28 0.59 0.01 0.51**
E4 11.00 17.50 2.50 35.00 18.40 5.58 0.30 0.89 0.06 -0.02
Average 11.31 18.29 2.33 38.30 18.53 5.80 0.31 0.81 0.12* 0.21*

图1

RIL 群体重要产量性状的次数分布（柱形）、拟混合分布（实线）与成分（虚线）分布"

图2

A?B?C?D分别表示小麦重组自交系群体在2015-2016年陕西杨凌(E1: 2016SY)?2016-2017年陕西杨凌(E2: 2017SY)?2016-2017河南原阳(E3: 2017HY)?2016-2017四川广元(E4: 2017SG) 4个环境下重要产量性状的频率分布?相关性和拟合曲线 上三角形面板(对角线的右上方)显示了2个变量属性之间的相关系数以及显著性水平, 相关系数越大字号越大(星号越多表明越显著); 下三角形面板(对角线的左下方)为2个变量属性的散点图,显示的是具有拟合线的双变量散点图?对角线是各个变量的直方图, 每个显着性水平由一个符号表示, 对应关系依次为: 无符号 P < 0.1, “*” P < 0.05, “**”P < 0.01, “***”P < 0.001, “****”P < 0.0001?"

Table 2

Akaike information criterion(AIC) and Maximum likelihood values(MLV) of the genetic models from 'Pindong 34' and 'Barran'"

Table3

Fitness trsts of selected models in ‘Pindong 34’ and ‘Barran’"

Table 4

Estimates of 1st order genetic parameters of important yield traits for population from Pindong 34 $\times$ Barran"

Table 5

Estimates of 2nd order genetic parameters of important yeild traits for population from Pindong 34 $\times$ Barran"

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