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Acta Agronomica Sinica ›› 2026, Vol. 52 ›› Issue (10): 3006-3022.doi: 10.3724/SP.J.1006.2026.64032

• CROP GENETICS & BREEDING·GERMPLASM RESOURCES·MOLECULAR GENETICS • Previous Articles     Next Articles

Analysis of genotype-by-environment interaction for potato yield and flavonoid content

Lei Peng1(), Li Li2, Yuan Jian-Long1, Xia Lu-Lu1, Zhou Xiao-Cheng1, Zhang Feng1,*()   

  1. 1 Agronomy College, Gansu Agricultural University / State Key Laboratory of Arid Land Crop Science, Lanzhou 730070, Gansu, China
    2 Shandan Bailie School of Gansu Province, Zhangye 734100, Gansu, China
  • Received:2026-03-13 Accepted:2026-06-11 Online:2026-10-12 Published:2026-06-22
  • Contact: Zhang Feng, E-mail: zhangf@gsau.edu.cn
  • Supported by:
    Major Science and Technology Project of Gansu Province(21ZD11NA009);Industrial Support Program for Higher Education Institutions of Gansu Province(2023CYZC-44);Scientific Research Innovation Platform Project for Higher Education Institutions of Gansu Province(2024CXPT-01)

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Abstract:

To achieve coordinated improvement in yield and nutritional quality, the GGE (genotype+genotype×environment interaction) biplot method was employed to systematically dissect the genetic differences, environmental adaptability, and trait stability of potato yield and flavonoid content across different ecological regions. The core objective was to screen elite genotypes with high yield, high flavonoid content, and stability adapted to specific ecological zones, thereby providing theoretical support for breeding nutritionally enhanced potato varieties. A total of 130 potato varieties (lines) were tested in a two-year (2021-2022), 3-location trial conducted in three ecological regions of Gansu province: Weiyuan county, Anding district, and Yongchang county. Plot yield and contents of catechin, rutin, nicotiflorin, quercetin, and kaempferol were measured after harvest. Joint analysis of variance and GGE biplot were used to analyze the genotype×environment interaction for yield and quality traits. The ANOVA showed that, except for plot yield which had no significant year effect, the effects of genotype, environment, and genotype×environment interaction were highly significant (P < 0.01) for all traits. Plot yield, catechin content, and rutin content were mainly influenced by genotype, whereas nicotiflorin, quercetin and kaempferol contents were predominantly affected by genotype×environment interaction. GGE analysis for plot yield indicated that G122 had the highest yield at Anding (2021, 2022), Weiyuan (2022), and Yongchang (2022). G27 had the highest yield at Yongchang in 2021, G36 had the highest yield at Weiyuan in 2021. C18, G122, C6, G102, G42, G49, C3, and G9 were high-yielding and stable. The discriminative power of test locations ranked as Yongchang > Anding > Weiyuan, with the Anding being the most representative. GGE analysis of flavonoid content revealed that G79 had the highest catechin content across all three locations, the highest kaempferol content at Anding and Weiyuan, and the highest nicotiflorin content at Yongchang. C16 had the highest rutin and nicotiflorin contents at Anding and Weiyuan. G37, G4, and G46 had the highest rutin, quercetin, and kaempferol contents, respectively, at Yongchang. G32 had the highest quercetin content at Anding and Weiyuan. G98, G63, G1, G129, and G102 had high and stable catechin content. G37, G113, G49, C11, and G93 had high and stable rutin content. G37, G122, G13, and G21 had high and stable nicotiflorin content. G46, G71, G124, and G79 had high and stable quercetin content. G9, C5, and G27 had high and stable kaempferol content. The discriminative power of test locations ranked as Yongchang > Weiyuan > Anding, with the Yongchang being the most representative for flavonoid traits. Using the GGE model combining plot yield and flavonoid content, elite lines (G122, G102, G49, and G93) were identified as high-yielding, high in flavonoid content, and stable. Considering both discriminatory power and representativeness, Anding is recommended as an ideal environment for yield evaluation, while Yongchang is more suitable for screening varieties (lines) for both yield and flavonoid content under specific environments.

Key words: yield, flavonoid, GGE biplot, genotype-environment interaction, multi-years and sites, pilot evaluation

Table 1

Tested materials"

编号 品种(系) 编号 品种(系) 编号 品种(系)
Code Variety (line) Code Variety (line) Code Variety (line)
G1 CIP 381381.13 G63 CIP 394614.117 G128 CIP 391919.3
G4 CIP 392617.54 G64 CIP 394881.8 G129 CIP 391930.1
G5 CIP 392634.52 G65 CIP 395186.6 G131 CIP 394906.6
G8 CIP 393227.66 G67 CIP 395195.7 A1 215
G9 CIP 393228.67 G68 CIP 395196.4 A2 1867
G10 CIP 393371.164 G70 CIP 395432.51 A3 2137
G11 CIP 391004.18 G71 CIP 395434.1 A4 Range
G12 CIP 392657.171 G72 CIP 395436.8 A5 SC04
G13 CIP 393280.64 G74 CIP 396311.1 A6 SC05
G14 CIP 391047.64 G77 CIP 397014.2 A7 Russet Burbank
G15 CIP 391058.175 G79 CIP 397029.21 A8 Atlantic
G16 CIP 393085.5 G81 CIP 397039.51 A9 Shepody
G17 CIP 398192.213 G82 CIP 397044.25 E1 H0902
G21 CIP 398180.289 G84 CIP 397065.2 E2 H0913
G22 CIP 398180.292 G85 CIP 397067.2 E3 H0916
G23 CIP 398180.612 G86 CIP 397069.5 E4 H0931
G25 CIP 398203.509 G87 CIP 397073.15 E5 H0933
G27 CIP 398208.33 G88 CIP 397078.12 E6 H0938
G29 CIP 300054.29 G91 CIP 397098.12 E7 H0941
G30 CIP 301024.14 G92 CIP 397099.6 E8 H0951
G31 CIP 301029.18 G93 CIP 397100.9 E9 H0952
G32 CIP 301040.63 G94 CIP 397196.3 E10 H0953
G33 CIP 300046.22 G96 CIP 397197.9 E11 荷兰4 Helan 4
G35 CIP 300054.29 G98 CIP 388611.22 E12 Innovator
G36 CIP 300056.33 G99 CIP 388615.22 C1 北方002 Beifang 002
G37 CIP 300063.4 G100 CIP 389468.3 C2 北方106 Beifang 106
G39 CIP 300072.1 G102 CIP 391180.6 C3 定薯4号 Dingshu 4
G42 CIP 300101.11 G104 CIP 391724.1 C4 东农310 Dongnong 310
G43 CIP 379706.27 G105 CIP 392032.2 C5 甘农薯7号 Gannongshu 7
G44 CIP 385499.11 G106 CIP 392740.4 C6 甘农薯9号 Gannongshu 9
G45 CIP 385561.124 G107 CIP 392745.7 C7 丽薯13号 Lishu 13
G46 CIP 388676.1 G108 CIP 392759.1 C8 龙薯12号 Longshu 12
G48 CIP 390478.9 G110 CIP 393615.6 C9 龙薯4号Longshu 4
G49 CIP 391207.2 G112 CIP 397030.31 C10 陇薯10号 Longshu 10
G50 CIP 391382.18 G113 CIP 397035.26 C11 陇薯16号 Longshu 16
G51 CIP 392781.1 G114 CIP 302428.20 C12 陇薯7号 Longshu 7
G52 CIP 392797.22 G115 CIP 302476.108 C13 闽薯4号 Minshu 4
G53 CIP 392822.3 G116 CIP 302499.30 C14 青薯10号 Qingshu 10
G54 CIP 392973.48 G118 CIP 304350.100 C15 天薯12号 Tianshu 12
G56 CIP 394034.65 G121 CIP 304371.67 C16 云薯901 Yunshu 901
G57 CIP 394034.7 G122 CIP 304383.41 C17 中薯18号 Zhongshu 18
G59 CIP 394600.52 G124 CIP 304387.39 C18 中薯22号 Zhongshu 22
G61 CIP 394613.139 G125 CIP 304405.47
G62 CIP 394613.32 G127 CIP 397077.16

Table 2

Basic information of locations climate"

试点
Location		 海拔
Altitude (m)		 年降水量
Annual precipitation (mm)		 年平均温度
Annual averaged temperature (℃)		 灌溉模式
Irrigation method		 年日照时数
Annual sunshine hours (h)
2021年渭源21-WY 2460 518.06 6.28 无Rain-fed 1824.58
2022年渭源22-WY 402.76 7.94 无Rain-fed 1906.83
2021年安定21-AD 1920 419.53 7.35 无Rain-fed 1659.52
2022年安定22-AD 295.67 7.41 无Rain-fed 1697.14
2021年永昌21-YC 1954 334.88 8.17 漫灌Flood irrigation 1897.07
2022年永昌22-YC 210.27 8.07 漫灌Flood irrigation 1881.65

Table 3

Statistical description of yield per plot (kg 1.98 m-2)"

性状
Trait		 环境
Environment		 最大值
Max.		 最小值
Min.		 平均值
Mean		 标准差
SD		 变异系数
CV (%)
小区产量
Yield per plot		 2021年安定21-AD 6.71 0.13 2.73 1.49 54.6
2021年渭源21-WY 8.98 1.19 3.70 1.64 44.3
2021年永昌21-YC 10.87 0.40 4.13 2.32 56.2
2022年安定22-AD 10.22 0.31 3.02 1.91 63.3
2022年渭源22-WY 7.69 0.59 2.81 1.41 50.2
2022年永昌22-YC 13.56 0.45 4.74 2.86 60.3

Fig. 1

Significant analysis of yield per plot AD, WY, and YC stand for the Anding, Weiyuan, and Yongchang sites, respectively. The values in the figure (2.45, 2.79, 3.54, 2.49, 3.87, and 4.08) indicate the median yield per plot under each environment, and unit is kg 1.98 m-2. Different lowercase letters indicate significant differences at the 0.05 level. Multiple comparisons were conducted using Duncan’s new multiple range test."

Table 4

Analysis of variance of yield"

产量性状
Yield trait		 变异来源
Source of variation		 平方和
Sum of square		 自由度
df		 均方
Mean square		 F检验
F test		 P-value 遗传力
H2 (%)
小区产量
Yield per plot		 基因型Genotype (G) 5248.926 129 40.689 23.52 < 0.001 66.8
年份Year (Y) 0.555 1 0.555 0.32 0.571
环境Environment (E) 996.504 2 498.252 288.02 < 0.001
年份×环境Y×E 247.416 2 123.708 71.51 < 0.001
基因型×年份G×Y 810.767 129 6.285 3.63 < 0.001
基因型×环境G×E 1657.553 258 6.425 3.71 < 0.001
基因型×年份×环境G×Y×E 1603.025 258 6.213 3.59 < 0.001
残差Residual 2698.898 1560 1.730
总变异Total variation 13,263.445 2339

Fig. 2

Adaptability analysis of yield per plot in varieties (lines) Green fonts indicate varieties (lines), and blue fonts indicate environments."

Fig. 3

Stability analysis of yield per plot in varieties (lines) Green fonts indicate varieties (lines), and blue fonts indicate environments. “○” indicates average environment; the axis with arrow indicates average environment axis."

Fig. 4

Locations analysis based on the yield per plot Green fonts indicate varieties (lines), and blue fonts indicate environments. “○” indicates average environment; the axis with arrow indicates average environment axis."

Fig. 5

Distribution of flavonoid content in potato AD, WY, and YC stand for the Anding, Weiyuan, and Yongchang locations, respectively."

Fig. 6

Mean flavonoid content in potato and significance analysis AD, WY, and YC indicate the Anding, Weiyuan, and Yongchang locations, respectively. The values in the figure are presented as mean ± standard error. Different uppercase letters indicate significant differences in the same component across different experimental sites (P < 0.05), while different lowercase letters indicate significant differences among different components at the same experimental location (P < 0.05). Multiple comparisons were conducted using Duncan’s new multiple range test."

Table 5

ANOVA analysis of flavonoid content in potato"

类黄酮
Flavonoid		 变异来源
Source of variation		 平方和
Sum of square		 自由度
df		 均方
Mean square		 F检验
F test		 P-value 遗传力
H2 (%)
儿茶素
(+)-Catechin		 基因型Genotype (G) 678,154.867 128 5298.085 58.101 < 0.001 82.2
环境Environment (E) 7777.558 2 3888.779 42.646 < 0.001
基因型×环境G×E 239,741.196 253 947.594 10.392 < 0.001
残差Residual 70,031.562 768 91.187
总变异Total variation 996,091.985 1151
芦丁
Rutin		 基因型Genotype (G) 270,237.273 128 2111.229 63.317 < 0.001 65.8
环境Environment (E) 27,651.806 2 13,825.903 414.648 < 0.001
基因型×环境G×E 183,944.222 254 724.190 21.719 < 0.001
残差Residual 25,674.664 770 33.344
总变异Total variation 505,062.501 1154
烟花苷
Nicotiflorin		 基因型Genotype (G) 95,291.403 128 744.464 157.680 < 0.001 28.4
环境Environment (E) 24,097.042 2 12,048.521 2551.915 < 0.001
基因型×环境G×E 135,118.727 255 529.877 112.230 < 0.001
残差Residual 3644.894 772 4.721
总变异Total variation 257,411.814 1157
槲皮素
Quercetin		 基因型Genotype (G) 231.739 128 1.810 17.926 < 0.001 36.7
环境Environment (E) 4.571 2 2.286 22.631 < 0.001
基因型×环境G×E 288.000 244 1.180 11.687 < 0.001
残差Residual 75.748 750 0.101
总变异Total variation 600.827 1124
山奈酚
Kaempferol		 基因型Genotype (G) 6.125 128 0.048 13.546 < 0.001 -7.8
环境Environment (E) 0.275 2 0.137 38.889 < 0.001
基因型×环境G×E 13.133 251 0.052 14.812 < 0.001
残差Residual 2.685 760 0.004
总变异Total variation 22.176 1141

Fig. 7

Adaptability analysis of flavonoid content in varieties (lines) A: (+)-Catechin; B: Rutin; C: Nicotiflorin; D: Quercetin; E: Kaempferol. Green fonts indicate varieties (lines), and blue fonts indicate environments."

Fig. 8

Stability analysis of flavonoid content in varieties (lines) A: (+)-Catechin; B: Rutin; C: Nicotiflorin; D: Quercetin; E: Kaempferol. Green fonts indicate varieties (lines), and blue fonts indicate environments. “○” indicates average environment; the axis with arrow indicates average environment axis."

Fig. 9

Locations analysis based on the content of flavonoid A: (+)-Catechin; B: Rutin; C: Nicotiflorin; D: Quercetin; E: Kaempferol. Green fonts indicate varieties (lines), and blue fonts indicate environments. “○” indicates average environment; the axis with arrow indicates average environment axis."

Fig. 10

Correlation analysis of yield per plot and flavonoid content in potato * and ** mean significant correlation at the 0.05 and 0.01 probability levels, respectively."

[1] Dawson I K, Powell W, Hendre P, et al. The role of genetics in mainstreaming the production of new and orphan crops to diversify food systems and support human nutrition. New Phytol, 2019, 224: 37-54.
doi: 10.1111/nph.15895 pmid: 31063598
[2] Singh B, Goutam U, Kukreja S, et al. Potato biofortification: an effective way to fight global hidden hunger. Physiol Mol Biol Plants, 2021, 27: 2297-2313.
doi: 10.1007/s12298-021-01081-4
[3] Tatarowska B E, Plich J, Milczarek D, et al. Genotype by environment interaction (GEI) effect for potato tuber yield and their quality traits in organic multi-environment domains in Poland. Agriculture, 2024, 14: 1591.
doi: 10.3390/agriculture14091591
[4] Pour-Aboughadareh A, Khalili M, Poczai P, et al. Stability indices to deciphering the genotype-by-environment interaction (GEI) effect: an applicable review for use in plant breeding programs. Plants, 2022, 11: 414.
doi: 10.3390/plants11030414
[5] Bustos-Korts D, Romagosa I, Borràs-Gelonch G, et al. Genotype by environment interaction and adaptation. In: MeyersR,eds. Encyclopedia of Sustainability Science and Technology. New York: Springer, 2018. pp 1-44.
[6] Maulana H, Nafi’Ah H H, Solihin E, et al. Combined stability analysis to select stable and high yielding sweet potato genotypes in multi-environmental trials in West Java, Indonesia. Agric Nat Resour, 2022, 56: 761-772.
[7] Maulana H, Solihin E, Trimo L, et al. Genotype-by-environment interactions (GEIs) and evaluate superior sweet potato (Ipomoea batatas [L.] Lam) using combined analysis and GGE biplot. Heliyon, 2023, 9: e20203.
[8] 叶夕苗, 程鑫, 安聪聪, 等. 马铃薯产量组分的基因型与环境互作及稳定性. 作物学报, 2020, 46: 354-364.
doi: 10.3724/SP.J.1006.2020.94089
Ye X M, Cheng X, An C C, et al. Genotype×environment interaction and stability of yield components for potato lines. Acta Agron Sin, 2020, 46: 354-364 (in Chinese with English abstract).
doi: 10.3724/SP.J.1006.2020.94089
[9] Gauch H G Jr. Statistical analysis of yield trials by AMMI and GGE. Crop Sci, 2006, 46: 1488-1500.
doi: 10.2135/cropsci2005.07-0193
[10] Yan W K, Fetch J M, Frégeau-Reid J, et al. Genotype×location interaction patterns and testing strategies for oat in the Canadian prairies. Crop Sci, 2011, 51: 1903-1914.
doi: 10.2135/cropsci2010.09.0548
[11] Getahun B B, Tiruneh M A, Aliche E, et al. Genotype-by- environment interaction for quantitative trait loci affecting nitrogen use efficiency and associated traits in potato. Potato Res, 2022, 65: 777-807.
doi: 10.1007/s11540-022-09548-x
[12] Scavo A, Mauromicale G, Ierna A. Genotype×environment interactions of potato tuber quality characteristics by AMMI and GGE biplot analysis. Sci Hortic, 2023, 310: 111750.
doi: 10.1016/j.scienta.2022.111750
[13] Manrique K, Hermann M. Effect of G×E interaction on root yield and betacarotene content of selected sweet potato (Ipomoea batatas (L.) Lam.) varieties and breeding clones. In:CIP Program Report 1999-2000. Lima: International Potato Center, 2000. pp 281-287.
[14] Gurmu F, Hussein S, Laing M. Genotype-by-environment interaction and stability of sweetpotato genotypes for root dry matter, β-carotene and fresh root yield. Open Agric, 2017, 2: 473-485.
[15] Sood S, Bhardwaj V, Kumar V, et al. BLUP and stability analysis of multi-environment trials of potato varieties in sub-tropical Indian conditions. Heliyon, 2020, 6: e05525.
[16] Shen N, Wang T F, Gan Q, et al. Plant flavonoids: classification, distribution, biosynthesis, and antioxidant activity. Food Chem, 2022, 383: 132531.
doi: 10.1016/j.foodchem.2022.132531
[17] Liu R H. Health-promoting components of fruits and vegetables in the diet. Adv Nutr, 2013, 4: 384S-392S.
doi: 10.3945/an.112.003517 pmid: 23674808
[18] Watkins B A, Mitchell A E, Shin A C, et al. Dietary flavonoid actions on senescence, aging, and applications for health. J Nutr Biochem, 2025, 139: 109862.
doi: 10.1016/j.jnutbio.2025.109862
[19] Martiniakova M, Babikova M, Mondockova V, et al. The role of macronutrients, micronutrients and flavonoid polyphenols in the prevention and treatment of osteoporosis. Nutrients, 2022, 14: 523.
doi: 10.3390/nu14030523
[20] 程军, 任一平, 张英, 等. 黄酮醇抑制丙烯酰胺在美拉德反应中的形成及与抗氧化间的相关性. 浙江大学学报(农业与生命科学版), 2013, 39: 178-184.
Cheng J, Ren Y P, Zhang Y, et al. Effect of flavonol on reduction of acrylamide in Maillard reaction and its correlation with antioxidant capacity. J Zhejiang Univ (Agric Life Sci), 2013, 39: 178-184 (in Chinese with English abstract).
[21] Ghorbani A. Mechanisms of antidiabetic effects of flavonoid rutin. Biomed Pharmacother, 2017, 96: 305-312.
doi: S0753-3322(17)34342-1 pmid: 29017142
[22] Agati G, Tattini M. Multiple functional roles of flavonoids in photoprotection. New Phytol, 2010, 186: 786-793.
doi: 10.1111/nph.2010.186.issue-4
[23] Wan X Z, Jia W, Wang Q, et al. Metabolomics strategy comprehensively unveils the effect of catechins intervention on the biomarkers of exposure to acrylamide and biomarkers of cardiometabolic risk. Environ Int, 2022, 169: 107517.
doi: 10.1016/j.envint.2022.107517
[24] Pozo-Bayón M Á, Monagas M, Bartolomé B, et al. Wine features related to safety and consumer health: an integrated perspective. Crit Rev Food Sci Nutr, 2012, 52: 31-54.
doi: 10.1080/10408398.2010.489398 pmid: 21991989
[25] Zhu S M, Fu R, Martin C, et al. Recent advances in biosynthesis, regulation, and metabolic engineering of flavonoids. Plant Physiol, 2025, 199: kiaf356.
[26] 张永成, 田丰. 马铃薯试验研究方法. 北京: 中国农业科学技术出版社, 2007. pp 117-118.
Zhang Y C, Tian F. Research Methods of Potato Experiment. Beijing: China Agricultural Science and Technology Press, 2007. pp 117-118 (in Chinese).
[27] Deußer H, Guignard C, Hoffmann L, et al. Polyphenol and glycoalkaloid contents in potato cultivars grown in Luxembourg. Food Chem, 2012, 135: 2814-2824.
doi: 10.1016/j.foodchem.2012.07.028 pmid: 22980877
[28] VSN International. Genstat Reference Manual (Release 20). Hemel Hempstead, UK: VSN International, 2019.
[29] 严威凯. 双标图分析在农作物品种多点试验中的应用. 作物学报, 2010, 36: 1805-1819.
doi: 10.3724/SP.J.1006.2010.01805
Yan W K. Optimal use of biplots in analysis of multi-location variety test data. Acta Agron Sin, 2010, 36: 1805-1819 (in Chinese with English abstract).
[30] 袁剑龙. 马铃薯重要农艺和品质性状全基因组关联分析及候选基因的功能验证. 甘肃农业大学博士学位论文, 甘肃兰州, 2024.
Yuan J L. Genome-wide Association Studies and Functional Identification of Candidate Genes for Key Agronomic and Quality Traits in Potato (Solanum tuberosum L.). PhD Dissertation of Gansu Agricultural University, Lanzhou, Gansu, China, 2024 (in Chinese with English abstract).
[31] Zhou B Y, Yuan J L, Liang L J, et al. Genotype×environment interactions for potato yield and quality traits: identification of ideotypes adapted in different ecological regions of Northwest China. BMC Plant Biol, 2025, 25: 737.
doi: 10.1186/s12870-025-06741-1
[32] Tan W Y, Wong S P. On the probabilities of obtaining negative estimates of genetic variance components. Biometrical J, 1978, 20: 69-79.
doi: 10.1002/bimj.v20:1
[33] 单保山. 遗传力的概念及其发展I. 关于传统遗传力的几个问题. 河北农业大学学报, 1988, 11(1): 39-44.
Shan B S. The concept of heritability and its development I. Some views on heritability. J Hebei Agric Univ, 1988, 11(1): 39-44 (in Chinese with English abstract).
[34] 周丙月, 袁剑龙, 张玉梅, 等. 马铃薯品种(系)农艺性状的适应性和稳定性分析. 核农学报, 2023, 37: 274-289.
doi: 10.11869/j.issn.1000-8551.2023.02.0274
Zhou B Y, Yuan J L, Zhang Y M, et al. Analysis of genotype and environment interaction, adaptability and stability of agronomic traits of potato varieties (lines). J Nucl Agric Sci, 2023, 37: 274-289 (in Chinese with English abstract).
doi: 10.11869/j.issn.1000-8551.2023.02.0274
[35] Rommens C M, Richael C M, Yan H, et al. Engineered native pathways for high kaempferol and caffeoylquinate production in potato. Plant Biotechnol J, 2008, 6: 870-886.
doi: 10.1111/j.1467-7652.2008.00362.x pmid: 18662373
[36] Zhang Z, Tan W, Liu J T, et al. Metabolomic remodeling and genetic regulation in potato tubers during domestication. Mol Plant, 2026, 19: 328-345.
doi: 10.1016/j.molp.2025.12.005
[37] Petridis A, Döll S, Nichelmann L, et al. Arabidopsis thaliana G2-LIKE FLAVONOID REGULATOR and BRASSINOSTEROID ENHANCED EXPRESSION1 are low-temperature regulators of flavonoid accumulation. New Phytol, 2016, 211: 912-925.
doi: 10.1111/nph.2016.211.issue-3
[38] Liu Y H, Li Y M, Liu Z, et al. Integrated transcriptomic and metabolomic analysis revealed altitude-related regulatory mechanisms on flavonoid accumulation in potato tubers. Food Res Int, 2023, 170: 112997.
doi: 10.1016/j.foodres.2023.112997
[39] Kui L W, Micheletti D, Palmer J, et al. High temperature reduces apple fruit colour via modulation of the anthocyanin regulatory complex. Plant Cell Environ, 2011, 34: 1176-1190.
doi: 10.1111/pce.2011.34.issue-7
[40] Shi T C, Su Y, Lan Y B, et al. The molecular basis of flavonoid biosynthesis response to water, light, and temperature in grape berries. Front Plant Sci, 2024, 15: 1441893.
doi: 10.3389/fpls.2024.1441893
[41] 夏露露. 马铃薯酚酸类物质的全基因组关联分析及候选基因挖掘. 甘肃农业大学硕士学位论文, 甘肃兰州, 2023.
Xia L L. Genome-wide Association Study and Candidate Genes Mining of Phenolic Acids in Potato. MS Thesis of Gansu Agricultural University, Lanzhou, Gansu, China, 2023 (in Chinese with English abstract).
[42] Huot B, Yao J, Montgomery B L, et al. Growth-defense tradeoffs in plants: a balancing act to optimize fitness. Mol Plant, 2014, 7: 1267-1287.
doi: S1674-2052(14)60932-1 pmid: 24777989
[43] Dixon R A, Paiva N L. Stress-induced phenylpropanoid metabolism. Plant Cell, 1995, 7: 1085-1097.
doi: 10.2307/3870059
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