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作物学报 ›› 2020, Vol. 46 ›› Issue (3): 354-364.doi: 10.3724/SP.J.1006.2020.94089

• 作物遗传育种·种质资源·分子遗传学 • 上一篇    下一篇

马铃薯产量组分的基因型与环境互作及稳定性

叶夕苗1,程鑫1,安聪聪1,袁剑龙1,余斌1,文国宏2,李高峰2,程李香1,王玉萍1,张峰1,*()   

  1. 1. 甘肃农业大学 / 甘肃省干旱生境作物学国家重点实验室培育基地 / 甘肃省遗传改良与种质创新重点实验室, 甘肃兰州 730070
    2. 甘肃省农业科学院 / 马铃薯研究所, 甘肃兰州 730070
  • 收稿日期:2019-06-21 接受日期:2019-09-26 出版日期:2020-03-12 发布日期:2019-10-11
  • 通讯作者: 张峰 E-mail:zhangf@gsau.edu.cn
  • 作者简介:E-mail: yeximiaogs@163.com
  • 基金资助:
    本研究由国家重点研发计划项目(2017YFD0101905);国家自然科学基金项目(31471433);甘肃省高等学校协同创新团队项目(2018C-17);甘肃省科技重大专项计划项目资助(17ZD2NA016)

Genotype × environment interaction and stability of yield components for potato lines

Xi-Miao YE1,Xin CHENG1,Cong-Cong AN1,Jian-Long YUAN1,Bin YU1,Guo-Hong WEN2,Gao-Feng LI2,Li-Xiang CHENG1,Yu-Ping WANG1,Feng ZHANG1,*()   

  1. 1. Gansu Agricultural University / Gansu Provincial Key Laboratory of Aridland Crop Science / Gansu Key Laboratory of Crop Improvement & Germplasm Enhancement, Lanzhou 730070, Gansu, China;
    2. Gansu Acadamy of Agricultural Sciences / Potato Insititute, Lanzhou 730070, Gansu, China
  • Received:2019-06-21 Accepted:2019-09-26 Online:2020-03-12 Published:2019-10-11
  • Contact: Feng ZHANG E-mail:zhangf@gsau.edu.cn
  • Supported by:
    This study was supported by the National Key R&D Program of China(2017YFD0101905);the National Natural Science Foundation of China(31471433);Gansu High Educational Scientific Special Project(2018C-17);Gansu Province Science and Technology Major Special Projects(17ZD2NA016)

摘要:

本研究主要探究基因型和基因型与环境互作(genotype + genotypes and environment interactions, GGE)双标图在马铃薯育种中的应用。综合评价马铃薯品系产量性状在不同环境中的丰产性、稳定性和适应性, 筛选出适应不同生态环境的产量性状优良品系。同时评价各试点的区分力和代表性, 为试点的选择提供依据。2015年和2016年在甘肃安定区鲁家沟镇、安定区内官镇、渭源县五竹镇3个试点种植国际马铃薯中心引进的101份高代品系和对照青薯9号。收获后记录小区产量、小区大薯产量、小区小薯产量、单株产量、单株大薯产量、单株小薯产量、单株结薯数、单株大薯数、单株小薯数; 采用联合方差和GGE双标图对产量性状进行基因型与环境互作分析。方差分析表明, 除小区小薯产量在基因型与环境互作效应中无显著差异外, 其他产量组分在基因型效应、环境效应和互作效应中均呈现极显著差异(P<0.01)。小区产量、小区大薯产量、小区小薯产量、单株产量、单株大薯产量、单株结薯数环境效应平方和占总方差平方和最大; 单株小薯产量、单株大薯数和单株小薯数的基因型与环境互作效应平方和占总方差平方和最大。GGE分析结果表明, 适应性最强的品系在鲁家沟试点是G86; 在五竹镇试点是G65; 在内官镇试点是G86。参试品系中丰产品系有G86、G116、G124; 稳产品系有G124、G125、G10; 高产稳产品系有G86、G116、G124、青薯9号。单株大薯数高的品系有G45、G86、G67, 稳定性好的品系有G67、G116、G51, 对照青薯9号的单株大薯产量不稳定。综合鉴别力和代表性的强弱, 依次为鲁家沟镇2016年、鲁家沟镇2015年、五竹镇2015年、五竹镇2016年、内官镇2015年、内官镇2016年。GGE模型能够直观地展现多年多点品系试验结果, 并客观评价参试品系的丰产性、稳定性和适应性, 同时可以对试点的代表性和区分力进行评价。以GGE模型综合评价, 高产稳产品系有G116、G124、G125、G122、青薯9号; 高产不稳定的品系有G86、G10、G121、G106、G107、G72。最理想的生态区试点是鲁家沟镇, 对品种的鉴别力最强的试点是五竹镇。

关键词: 产量组分, GGE双标图, 多年多点, 试点评价

Abstract:

This study mainly focused on the application of GGE (genotype + genotypes and environment interactions) biplot in potato breeding, to evaluate the productivity, stability and adaptability of yield traits of potato lines in different environments comprehensively, and screen out the excellent lines adapted to different mage-environments. The representativeness and discriminating ability of each test-environment were also evaluated, providing a basis for the selection of test-environment. A total of 101 advanced lines from International Potato Center (CIP) and potato variety Qingshu 9 were planted in Neiguan Town, Lujiagou Town and Wuzhu Town of Gansu province in 2015 and 2016 to measure the plot yield, plot yield of large-sized tubers, plot yield of small-sized tubers, yield per plant, large-sized tuber yield per plant, small-sized tuber yield per plant, tuber number per plant, large-sized tuber number per plant and small-sized tuber number per plant. The genotype and environment interactions were analyzed by the combined analysis of variance and GGE biplot. Except the plot yield of small-sized tubers had no significant difference in genotype and environment interactions effect, all the other yield components had significant differences (P < 0.01) in genotype effect, environmental effect and genotype and environment interaction effect. The square sum of environmental effect on the plot yield, plot yield of large-sized tubers, plot yield of small-sized tubers, yield per plant, large-sized tuber yield per plant, tuber number per plant, and the square sum of genotype and environment interaction effect on the plot yield of small-sized tubers, the large-sized tuber number per plant, and the small-sized tuber number per plant were worth the largest in the square sum of total variance. The most adaptable lines in Lujiagou Town were G86, in Wuzhu Town G65, in Neiguan Town G86. The high-yield lines were G86, G116, and G124; the stable-yield lines were G124, G125, and G10; the high-yielding and stable lines were G86, G116, G124, and Qingshu 9. The lines with more large-sized tuber number per plant were G45, G86, and G67, and the lines with good stability were G67, G116, and G51. The variety Qingshu 9 did not have stable large-sized tuber yield per plant. According to the comprehensive discrimination and representativeness, the order of test-environments were Lujiagou Town in 2016, Lujiagou Town in 2015, Wuzhu Town in 2015, Wuzhu Town in 2016, Neiguan Town in 2015, and Neiguan Town in 2016. GGE model can intuitively display the results in the genotype-location-year framework, and objectively evaluate the productivity, stability and adaptability of tested lines, as well as the representativeness and discriminating ability of test-environment. According to the comprehensive evaluation of GGE model, the high-yielding and stable lines were G116, G124, G125, G122, and Qingshu 9, and the high-yielding and unstable lines were G86, G10, G121, G106, G107, and G72. The most ideal mage-environment is Lujiagou Town, and Wuzhu Town is the test-environment with the strongest discriminating ability for varieties identification.

Key words: yield component, GGE biplot, multi-years and sites, pilot evaluation

表1

102份引进国际马铃薯中心高代品系"

品系编号
Line number
CIP编号
CIP entry
品系编号
Line number
CIP编号
CIP entry
品系编号
Line number
CIP编号
CIP entry
G1 CIP 381381.13 G45 CIP 385561.124 G92 CIP 397099.6
G3 CIP 391583.25 G46 CIP 388676.1 G93 CIP 397100.9
G4 CIP 392617.54 G48 CIP 390478.9 G94 CIP 397196.3
G5 CIP 392634.52 G49 CIP 391207.2 G95 CIP 397196.8
G8 CIP 393227.66 G50 CIP 391382.18 G96 CIP 397197.9
G9 CIP 393228.67 G51 CIP 392781.1 G98 CIP 388611.22
G10 CIP 393371.164 G52 CIP 392797.22 (青薯9号Qingshu 9) G99 CIP 388615.22
G11 CIP 391004.18 G53 CIP 392822.3 G100 CIP 389468.3
G12 CIP 392657.171 G54 CIP 392973.48 G101 CIP 390637.1
G13 CIP 393280.64 G56 CIP 394034.65 G102 CIP 391180.6
G14 CIP 391047.34 G57 CIP 394034.7 G104 CIP 391724.1
G15 CIP 391058.175 G58 CIP 394579.36 G105 CIP 392032.2
G16 CIP 393085.5 G59 CIP 394600.52 G106 CIP 392740.4
G17 CIP 398192.213 G61 CIP 394613.139 G107 CIP 392745.7
G18 CIP 398098.119 G62 CIP 394613.32 G108 CIP 392759.1
G19 CIP 398098.203 G63 CIP 394614.117 G109 CIP 393613.2
G21 CIP 398180.289 G64 CIP 394881.8 G110 CIP 393615.6
G22 CIP 398180.292 G65 CIP 395186.6 G112 CIP 397030.31
G23 CIP 398180.612 G67 CIP 395195.7 G113 CIP 397035.26
G25 CIP 398203.509 G68 CIP 395196.4 G114 CIP 302428.20
G27 CIP 398208.33 G70 CIP 395432.51 G115 CIP 302476.108
G30 CIP 301024.14 G71 CIP 395434.1 G116 CIP 302499.30
G31 CIP 301029.18 G72 CIP 395436.8 G118 CIP 304350.100
G32 CIP 301040.63 G74 CIP 396311.1 G119 CIP 304350.118
G33 CIP 300046.22 G77 CIP 397014.2 G120 CIP 304350.95
G35 CIP 300054.29 G79 CIP 397029.21 G121 CIP 304371.67
G36 CIP 300056.33 G81 CIP 397039.51 G122 CIP 304383.41
G37 CIP 300063.4 G82 CIP 397044.25 G123 CIP 304383.80
G39 CIP 300072.1 G84 CIP 397065.2 G124 CIP 304387.39
G40 CIP 300093.14 G85 CIP 397067.2 G125 CIP 304405.47
G41 CIP 300099.22 G86 CIP 397069.5 G127 CIP 397077.16
G42 CIP 300101.11 G87 CIP 397073.15 G128 CIP 391919.3
G43 CIP 379706.27 G88 CIP 397078.12 G129 CIP 391930.1
G44 CIP 385499.11 G91 CIP 397098.12 G131 CIP 394906.6

表2

试点环境"

试点
Location
试点编号
Location code
海拔
Altitude
(m)
年降水量
Annual precipitation (mm)
年日照时数
Annual sunshine
(h)
年平均温度
Mean annual
temperature (℃)
无霜期
Frostless period (d)
五竹镇 Wuzhuzhen WZ 2450 540 2462 3.5 145
内官镇 Neiguanzhen NG 2080 390 2050 6.2 141
鲁家沟镇 Lujiagouzhen LJG 1898 220 2780 6.3 160

表3

马铃薯产量性状方差分析"

性状
Trait
变异来源
Source of variation
自由度
df
平方和
Sum of square
均方
Mean squares
F检验
F-test
显著性
Significance
小区产量
Plot yield
基因型G 101 6142.832 60.8201 26.97 < 0.001
环境E 5 12098.734 1228.566 276.57 < 0.001
互作G × E 505 11907.641 23.579 5.31 < 0.001
残差Residual 1224 5437.244 4.442
总变异Total 1835 35586.452
小区大薯产量
Plot yield of large-sized tuber
基因型G 101 933.319 9.2408 8.29 < 0.001
环境E 5 7671.285 1534.257 20.39 < 0.001
互作G × E 505 7585.699 15.021 1.64 < 0.001
残差Residual 1224 11207.720 9.157
总变异Total 1835 27398.024
小区小薯产量
Plot yield of small-sized tuber
基因型G 101 1323.103 13.100 1.71 < 0.001
环境E 5 2806.794 561.359 73.31 < 0.001
互作G × E 505 1867.107 3.697 0.48 1
残差Residual 1224 9372.213 7.657
总变异Total 1835 15369.218
单株产量
Yield of tuber per plant
基因型G 101 119.526 1.1834 5.30 < 0.001
环境E 5 198.688 39.7376 177.82 < 0.001
互作G × E 505 174.808 0.3462 1.55 < 0.001
残差Residual 1224 273.529 0.2235
总变异Total 1835 766.550
单株大薯产量
Yield of large-sized tubers per plant
基因型G 101 119.390 1.1821 5.38 < 0.001
环境E 5 183.742 36.7485 167.16 < 0.001
互作G × E 505 169.916 0.3365 1.53 < 0.001
残差Residual 1224 269.091 0.2198
总变异Total 1835 742.139
单株小薯产量
Yield of small-sized tubers per plant
基因型G 101 2.882 0.0285 5.79 < 0.001
环境E 5 0.352 0.0704 14.28 < 0.001
互作G × E 505 5.948 0.0118 2.39 < 0.001
残差Residual 1224 6.029 0.0049
总变异Total 1835 15.211
单株结薯数
Number of tuber per plant
基因型G 101 2978.502 29.490 4.32 < 0.001
环境E 5 9208.894 1841.779 270.03 < 0.001
互作G × E 505 7497.026 14.846 2.18 < 0.001
残差Residual 1224 8348.500 6.821
总变异Total 1835 28032.922
单株大薯数
Number of large-sized tubers per plant
基因型G 101 475.476 4.708 5.54 < 0.001
环境E 5 2510.065 502.014 21.21 < 0.001
互作G × E 505 4349.834 8.614 1.92 < 0.001
残差Residual 1224 5487.667 4.483
总变异Total 1835 12822.982
单株小薯数
Number of small-sized tubers per plant
基因型G 101 2434.490 24.104 5.51 < 0.001
环境E 5 270.209 54.042 12.35 < 0.001
互作G × E 505 4347.791 8.609 1.97 < 0.001
残差Residual 1224 5354.333 4.374
总变异Total 1835 12406.824

图1

产量性状适应性GGE分析 A: 小区产量; B: 小区大薯产量; C: 小区小薯产量; D: 单株产量; E: 单株大薯产量; F: 单株小薯产量。"

图2

丰产性和稳定性GGE分析 A: 小区产量; B: 小区大薯产量; C: 小区小薯产量; D: 单株产量; E: 单株大薯产量; F: 单株小薯产量。"

图3

品系综合评价、环境代表性和鉴别力 A: 小区产量; B: 小区大薯产量; C: 小区小薯产量; D: 单株产量; E: 单株大薯产量; F: 单株小薯产量。"

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[2] 郑希;吴建国;楼向阳;徐海明;石春海. 不同环境条件下稻米组氨酸和精氨酸的胚乳和母体植株QTL分析[J]. 作物学报, 2008, 34(03): 369 -375 .
[3] 柯丽萍;郑滔;吴学龙;何海燕;陈锦清. 甘蓝型油菜SLG基因片段的克隆及序列分析[J]. 作物学报, 2008, 34(05): 764 -769 .
[4] 郑永美;丁艳锋;王强盛;李刚华;王惠芝;王绍华. 起身肥对水稻分蘖和氮素吸收利用的影响[J]. 作物学报, 2008, 34(03): 513 -519 .
[5] 倪大虎;易成新;李莉;汪秀峰;张毅;赵开军;王春连;章琦;王文相;杨剑波. 分子标记辅助培育水稻抗白叶枯病和稻瘟病三基因聚合系[J]. 作物学报, 2008, 34(01): 100 -105 .
[6] 田志坚;易蓉;陈建荣;郭清泉;张学文. 苎麻纤维素合成酶基因cDNA的克隆及表达分析[J]. 作物学报, 2008, 34(01): 76 -83 .
[7] 戴小军;梁满中;陈良碧. 栽培稻种内核糖体基因的ITS序列比较研究[J]. 作物学报, 2007, 33(11): 1874 -1878 .
[8] 王春梅;冯祎高;庄丽芳;曹亚萍;亓增军;别同德;曹爱忠;陈佩度. 普通小麦近缘物种黑麦1R、簇毛麦1V及鹅观草1Rk#1染色体特异分子标记的筛选[J]. 作物学报, 2007, 33(11): 1741 -1747 .
[9] 邱崇力. 普通小麦与六倍体小黑麦杂交不亲和性的研究 Ⅰ.杂种的胚胎发育[J]. 作物学报, 1986, (01): 49 -56 .
[10] 柳武革;王丰;金素娟;朱小源;李金华;刘振荣;廖亦龙;朱满山;黄慧君;符福鸿;刘宜柏. 利用分子标记辅助选择聚合Pi-1和Pi-2基因改良两系不育系稻瘟病抗性[J]. 作物学报, 2008, 34(07): 1128 -1136 .