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作物学报 ›› 2021, Vol. 47 ›› Issue (6): 1100-1108.doi: 10.3724/SP.J.1006.2021.04138

• 作物遗传育种·种质资源·分子遗传学 • 上一篇    下一篇

花生巢式群体的脂肪含量遗传分析

黄冰艳1(), 孙子淇1, 刘华1, 房元瑾1, 石磊1, 苗利娟1, 张毛宁1, 张忠信1, 徐静1, 张梦圆2, 董文召1, 张新友1,*()   

  1. 1河南省农业科学院河南省作物分子育种研究院/郑州大学研究生研究培训基地/农业农村部黄淮海油料作物重点实验室/河南省油料作物遗传改良重点实验室, 河南郑州 450002
    2河南科技大学农学院, 河南洛阳 471023
  • 收稿日期:2020-06-24 接受日期:2020-12-01 出版日期:2021-06-12 网络出版日期:2020-12-30
  • 通讯作者: 张新友
  • 作者简介:E-mail:huangbingyan@aliyun.com
  • 基金资助:
    国家现代农业产业技术体系建设专项(CARS-13);河南省现代农业产业技术体系建设专项(2016-05);河南省重大科技专项(201300111000)

Genetic analysis of fat content based on nested populations in peanut (Arachis hypogaea L.)

HUANG Bing-Yan1(), SUN Zi-Qi1, LIU Hua1, FANG Yuan-Jin1, SHI Lei1, MIAO Li-Juan1, ZHANG Mao-Ning1, ZHANG Zhong-Xin1, XU Jing1, ZHANG Meng-Yuan2, DONG Wen-Zhao1, ZHANG Xin-You1,*()   

  1. 1Henan Academy of Crops Molecular Breeding, Henan Academy of Agricultural Sciences/Graduate R & T Base of Zhengzhou University/Key Laboratory of Oil Crops in Huang-Huai-Hai Plans, Ministry of Agriculture and Rural Affairs/Henan Provincial Key Laboratory for Oil Crops Improvement, Zhengzhou 450002, Henan, China
    2College of Agriculture, Henan University of Science and Technology, Luoyang 471023, Henan, China
  • Received:2020-06-24 Accepted:2020-12-01 Published:2021-06-12 Published online:2020-12-30
  • Contact: ZHANG Xin-You
  • Supported by:
    The China Agricultural Research System(CARS-13);The Henan Agricultural Research System(2016-05);The Key Scientific and Technological Project of Henan Province(201300111000)

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摘要：

巢式群体可以利用多个亲本解析复杂性状的遗传机制。本研究利用1个共同亲本与6个基础亲本所配置巢式组合F2:3家系的种子脂肪含量数据, 分析了花生脂肪含量的遗传模型, 旨在探明不同的基础亲本组合中脂肪含量性状的遗传差异, 为制定脂肪含量遗传改良的亲本选配和后代选择策略提供依据。6个组合的共同亲本为高脂肪含量的普通型大果品种豫花15号, 其他6个基础亲本为不同脂肪含量和不同植物学类型的品种。结果表明, 在不同杂交组合中脂肪含量的遗传模式有所不同, 6个组合分别符合无主基因模型、1对主基因加性显性模型和2对主基因等显性模型3种遗传模式。各种遗传效应的估计值也各不相同, 主基因遗传力从32%到80%, 说明不同杂交组合中, 控制脂肪含量的基因位点差异及其重组和分离方式不同。高脂肪含量双亲杂交后代的高脂肪含量个体较多, 但主基因遗传力较低, 不宜在早代实施表型选择; 双亲脂肪含量差异较大的后代脂肪含量变异幅度更大, 能够选择到不同脂肪含量的类型。本研究也表明, 巢式组合具有较丰富的脂肪含量变异类型, 揭示出脂肪含量性状遗传的复杂性和多基因调控的特点, 为较全面地了解脂肪含量的遗传提供了基础。该巢式群体也将有助于进一步开展脂肪含量的QTL定位研究。

关键词: 花生(Arachis hypogaea L.), 巢式群体, 脂肪含量, F2:3家系, 遗传模型

Abstract:

Nested populations can be used to dissect the heredity of complex traits. The genetic models of fat content of F2:3 families in nested combinations with one common parent and six founder parents were analyzed, aiming to detect the genetic differences among the founders and to provide bases for breeding strategy for fat content improvement in peanut kernels. The common parent was Yuhua 15, an irregular-type variety with high fat content, and the other six founder parents were different botanical varieties with different fat contents. The results showed that the genetic model of fat content traits was different in different combinations. Six crosses were in accordance with three genetic patterns, including none major gene model, one major gene model with additive and dominant effect, and two major genes model with equal additive and dominant effect. The estimated values of various genetic effects were also different. The heritability of the main genes ranged from 32% to 80%, indicating that the gene loci controlling the fat content and their segregation patterns were different in different F2:3 populations. There were more individuals with high fat content in the offspring from combinations with both parents of high fat content. However, the heritability was low and phenotypic selections for fat content were not suggested in the early generations in such combinations. The offspring from combinations with parents of significantly different fat content had a larger variation range in fat content, and phenotypes with variable fat content were available. In this study, the large variances in the nested populations demonstrated the genetic complexity of fat content and the characteristics of multi major gene regulation. These results provide a comprehensive base for understanding the genetics and regulation of fat content, and the nested populations will be helpful in further QTL detection of fat content in peanut.

Key words: peanut (Arachis hypogaea L.), nested populations, fat content, F2:3 family, inheritance model

表1

巢式杂交群体信息"

群体代号
Population code		 杂交组合
Cross combination		 F2:3家系个数
Number of F2:3 family
AC 远杂9102×豫花15号 Yuanza 9102×Yuhua 15 472
BC 中花6号×豫花15号 Zhonghua 6×Yuhua 15 392
CE 豫花15号×粤油20 Yuhua 15×Yueyou 20 286
CF 豫花15号×四粒红 Yuhua 15×Silihong 395
CG 豫花15号×伏花生 Yuhua 15×Fuhuasheng 256
CH 豫花15号×NC94022 Yuhua 15×NC94022 269

图1

F2的脂肪含量群体分布"

表2

脂肪含量遗传模式分析"

组合
Cross combination		 模型
Models		 最大似然值
Maximum likelihood value		 AIC值
AIC value
AC 1对主基因加性显性 1MG-AD -944.2088 1896.418
无主基因 0MG -946.4774 1896.955
1对主基因负向完全显性 1MG-NCD -944.7637 1897.527
2对主基因等加性 2MG-EA -946.4752 1898.950
BC 2对主基因等显性 2MG-EAD -729.4342 1464.868
1对主基因加性显性 1MG-AD -728.9680 1465.936
无主基因 0MG -731.3869 1466.774
1对主基因完全显性 1MG-EAD -729.4457 1466.891
CE 2对主基因等显性 2MG-EAD -521.0105 1048.021
1对主基因加性显性 1MG-AD -520.7663 1049.533
2对主基因完全显性 2MG-CD -521.1385 1050.277
1对主基因完全显性 1MG-EAD -521.1419 1050.284
CF 无主基因 0MG -719.3989 1442.798
2对主基因等显性 2MG-EAD -718.5605 1443.121
2对主基因等加性 2MG-EA -719.3983 1444.797
1对主基因加性 1MG-A -719.4056 1444.811
CG 2对主基因等显性 2MG-EAD -547.5336 1101.067
1对主基因加性显性 MG-AD -548.7668 1105.534
2对主基因完全显性 2MG-CD -548.9751 1105.950
1对主基因完全显性 1MG-EAD -548.9754 1105.951
CH 无主基因 0MG -565.0724 1134.145
2对主基因等显性 2MG-EAD -564.9840 1135.968
2对主基因等加性 2MG-EA -565.0737 1136.147
1对主基因加性 1MG-A -565.0762 1136.152

表3

遗传模式的适合性测验"

组合
Cross
combination		 模型
Models		 U12 PU12 U22 PU22 U32 PU32 nW2 PnW2 Dn PDn
AC 1对主基因加性显性 1MG-AD 0.0539 0.8164 0.4283 0.5128 2.9528 0.0857 0.1785 0.3143 0.0467 0.2480
无主基因 0MG 0.4265 0.5137 1.0459 0.3064 2.4383 0.1184 0.2416 0.2045 0.0506 0.1718
1对主基因负向完全显性 1MG-NCD 0.1073 0.7432 0.5637 0.4528 3.0078 0.0829 0.1927 0.2835 0.0483 0.2129
2对主基因等加性 2MG-EA 0.4274 0.5132 1.0343 0.3091 2.3592 0.1245 0.2394 0.2074 0.0504 0.1755
BC 2对主基因等显性 2MG-EAD 0.0536 0.8169 0.0023 0.9615 1.1869 0.2760 0.0736 0.738 0.0334 0.7598
1对主基因加性显性 1MG-AD 0.0248 0.8749 0.0097 0.9216 1.0076 0.3155 0.0586 0.8246 0.0326 0.7864
无主基因 0MG 0.3584 0.5494 0.1334 0.7150 0.7358 0.3910 0.1308 0.4576 0.0391 0.5727
1对主基因完全显性 1MG-EAD 0.0516 0.8202 0.0021 0.9637 1.1286 0.2881 0.0688 0.7650 0.0334 0.7615
组合
Cross
combination		 模型
Models		 U12 PU12 U22 PU22 U32 PU32 nW2 PnW2 Dn PDn
CE 2对主基因等显性 2MG-EAD 0.0580 0.8097 0.0003 0.9863 0.7465 0.3876 0.0662 0.7798 0.0336 0.8915
1对主基因加性显性 1MG-AD 0.0241 0.8766 0.0019 0.9655 0.6001 0.4385 0.0419 0.9229 0.0271 0.9812
2对主基因完全显性 2MG-CD 0.0462 0.8298 0 0.9999 0.6924 0.4053 0.0521 0.8641 0.0303 0.9479
1对主基因完全显性 1MG-EAD 0.0465 0.8293 0 0.9994 0.6927 0.4052 0.0521 0.8638 0.0304 0.9474
CF 无主基因 0MG 0.2154 0.6426 0.0247 0.8752 1.3671 0.2423 0.0934 0.6303 0.0341 0.7341
2对主基因等显性 2MG-EAD 0.0791 0.7785 0.0018 0.9660 1.5871 0.2077 0.0770 0.7192 0.0354 0.6928
2对主基因等加性 2MG-EA 0.2156 0.6424 0.0271 0.8692 1.2986 0.2545 0.0918 0.6386 0.0338 0.7446
1对主基因加性 1MG-A 0.2160 0.6421 0.0272 0.8691 1.3008 0.2541 0.0919 0.6379 0.0338 0.7440
CG 2对主基因等显性 2MG-EAD 0 0.9982 0.0006 0.9809 0.0109 0.9168 0.0299 0.9766 0.0329 0.9352
1对主基因加性显性 1MG-AD 0.0051 0.9428 0.0186 0.8914 0.0720 0.7885 0.0430 0.9170 0.0284 0.9821
2对主基因完全显性 2MG-CD 0.0024 0.9609 0.0212 0.8841 0.1546 0.6942 0.0444 0.9094 0.0291 0.9772
1对主基因完全显性 1MG-EAD 0.0024 0.9608 0.0213 0.8841 0.1543 0.6945 0.0444 0.9094 0.0291 0.9778
CH 无主基因 0MG 0.0062 0.9374 0.0148 0.9033 0.6243 0.4295 0.1133 0.5313 0.0546 0.3845
2对主基因等显性 2MG-EAD 0.0009 0.9765 0.0263 0.8712 0.5813 0.4458 0.1173 0.5131 0.0549 0.3795
2对主基因等加性 2MG-EA 0.0061 0.9379 0.0128 0.9100 0.5685 0.4509 0.1122 0.5362 0.0542 0.3951
1对主基因加性 1MG-A 0.0061 0.9377 0.0128 0.9101 0.5694 0.4505 0.1122 0.5362 0.0542 0.3949

表4

脂肪含量遗传参数估计"

组合
Crosses		 模型
Models		 m da db ha hb Var.mg hmg
AC 1对主基因加性显性 1MG-AD 57.5201 1.1342 -2.3266 1.0357 31.9936
无主基因 0MG
1对主基因负向完全显性 1MG-NCD 57.2576 1.2970 0.9789 30.2396
2对主基因等加性 2MG-EA 56.9106 0.2372 0.0660 2.0382
BC 2对主基因等显性 2MG-EAD 55.5452 0.9807 1.1551 47.1385
1对主基因加性显性 1MG-AD 55.4744 1.0402 2.1944 0.8997 36.7188
无主基因 0MG
1对主基因完全显性 1MG-EAD 55.7181 1.2259 0.8773 35.8046
CE 2对主基因等显性 2MG-EAD 54.0134 1.0075 1.2100 52.9489
1对主基因加性显性 1MG-AD 53.9686 1.0950 2.1686 0.9448 41.3432
2对主基因完全显性 2MG-CD 54.1576 1.2797 0.1270 0.9596 41.9920
1对主基因完全显性 1MG-EAD 54.1894 1.2796 0.9498 41.5652
CF 无主基因 0MG
2对主基因等显性 2MG-EAD 53.6370 0.7674 0.7028 31.3534
2对主基因等加性 2MG-EA 54.0267 0.2282 0.0604 2.6958
1对主基因加性 1MG-A 54.0290 0.2888 0.0494 2.2052
组合
Crosses		 模型
Models		 m da db ha hb Var.mg hmg
CG 2对主基因等显性 2MG-EAD 51.6018 1.6437 3.6377 79.7548
1对主基因加性显性 1MG-AD 51.8983 2.3225 1.5772 2.9822 65.3833
2对主基因完全显性 2MG-CD 51.7690 2.1335 0.1047 2.7537 60.3730
1对主基因完全显性 1MG-EAD 51.7952 2.1334 2.7468 60.2228
CH 无主基因 0MG
2对主基因等显性 2MG-EAD 53.8575 0.7166 0.6290 16.0300
2对主基因等加性 2MG-EA 54.2152 0.3550 0.1469 3.7446
1对主基因加性 1MG-A 54.2168 0.4650 0.1280 3.2625
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