Welcome to Acta Agronomica Sinica,

Acta Agronomica Sinica ›› 2019, Vol. 45 ›› Issue (6): 856-871.doi: 10.3724/SP.J.1006.2019.83059

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

Epistatic and QTL × environment interaction effects for ear related traits in two maize (Zea mays) populations under eight watering environments

Xiao-Qiang ZHAO,Bin REN,Yun-Ling PENG(),Ming-Xia XU,Peng FANG,Ze-Long ZHUANG,Jin-Wen ZHANG,Wen-Jing ZENG,Qiao-Hong GAO,Yong-Fu DING,Fen-Qi CHEN   

  1. Gansu Provincial Key Laboratory of Aridland Crop Science / College of Agronomy, Gansu Agricultural University, Lanzhou 730070, Gansu, China
  • Received:2018-08-16 Accepted:2018-12-24 Online:2019-06-12 Published:2019-06-12
  • Contact: Xiao-Qiang ZHAO,Bin REN,Yun-Ling PENG E-mail:pengyunlingpyl@163.com
  • Supported by:
    This study was supported by the National Key R&D Project(2018YFD0100203-4);Chinese Academy of Sciences “Light of West China” Program(20180504);Lanzhou Sci & Technol Project (2018-1-103), the Key R&D Program of Gansu, China(18YF1NA071);the National Science Foundation of Gansu Province(18JR3RA189);the Key Science and Technology Projects in Gansu Province(17ZD2NA016)

Abstract:

Exploring genetic mechanisms of ear related traits in maize (Zea mays) under drought stress is important in maize molecular breeding for drought tolerance and high yield. Two F2:3 populations, namely 202 F2:3 families (LTPOP) and 218 F2:3 families (CTPOP) derived from the common male parent TS141 with drought-sensitive and larger ear and female parents Langhuang/Chang 7-2 with higher drought tolerance and small ear were used to investigate ear weight (EW), cob weight (CW), grain weight (GW), 100-kernel weight (KW), kernel ratio (KR), and ear length (EL) under eight watering environments, and then to analyze quantitative trait locus (QTL) in a single environment by composite interval mapping (CIM) and in the eight environments by mixed linear model based on composite interval mapping (MCIM). Sixty-two QTLs for ear related traits were detected in two F2:3 populations in a single environment by CIM, among than 38 QTLs were mapped under water-stressed environments, and further analysis showed that ten stable QTLs (sQTLs) were simultaneously identified in two F2:3 populations under multiple water-stressed environments, these sQTLs were located in Bin 1.01-1.03, Bin 1.03-1.04, Bin 1.05, Bin 1.07, Bin 1.07-1.08, Bin 2.04, Bin 4.08, Bin 5.06-5.07, Bin 6.05, and Bin 9.04-9.06. Fifty-four joint QTLs for ear related traits were identified in the eight environments by joint analysis with MCIM, among than 24 had significant QTL by environment interaction (QTL×E), and 17 significant epistatic interactions with additive by additive/dominance (AA/AD) effects, with less phenotypic variation. These results lay a foundation for systematically revealing molecular genetic mechanism of ear related traits, these sQTLs detected in two F2:3 populations under multiple environments are important genomic regions that could be used in positional cloning and molecular breeding for drought tolerance and high yield, however, more attention should be paid to the effects of environment or epistatic interaction.

Key words: maize (Zea mays), drought, ear correlative traits, QTL, QTL by environment interaction (QTL×E), epistasis

Fig. 1

Meteorological data in four experimental sites (Wuwei, Zhangye, Gulang, and Jingtai)"

Table 1

Phenotypic value of six ear related traits in F2:3 population (LTPOP/CTPOP) under eight watering environments"

性状Trait 环境
Env.
双亲
Parents
F1杂交种
F1 hybrid
LTPOP群体
LTPOP population
廊黄
Langhuang
TS141 F1LT 均值
Mean
变幅
Range
变异系数
CV (%)
偏度Skewness 峰度Kurtosis
EW E1 71.33±6.89 111.30±9.72 184.27±7.03 149.81±43.25 52.85-285.91 28.87 0.315 0.313
(g) E2 66.58±7.95 100.67±8.44 163.51±6.79 133.19±51.83 43.27-265.43 38.92 0.453 -0.327
E3 88.29±10.43 128.85±6.45 197.80±6.74 163.19±41.49 70.21-281.87 25.42 0.086 -0.338
E4 84.14±6.77 110.56±8.01 169.14±7.83 152.76±51.41 52.27-276.94 33.65 0.382 -0.577
CW E1 12.53±1.65 29.90±2.15 33.71±2.81 27.65±9.43 9.47-53.31 34.11 0.326 -0.490
(g) E2 9.85±1.23 21.81±1.89 26.58±2.44 25.11±8.23 7.91-50.34 32.77 0.230 -0.051
E3 15.75±2.05 33.78±3.13 37.83±3.15 30.79±10.85 10.87-60.03 35.22 0.636 -0.141
E4 11.66±2.78 23.04±2.61 31.09±3.07 28.17±9.56 8.50-51.23 33.92 0.294 -0.410
GW E1 59.81±4.70 82.40±4.82 150.56±5.11 123.16±33.98 65.07-208.15 27.59 0.815 0.206
(g) E2 56.73±3.96 71.92±3.17 136.93±5.09 108.22±24.72 61.14-186.27 22.84 -0.192 -0.887
E3 64.54±4.51 85.07±4.66 159.97±4.85 132.46±25.05 68.94-207.92 18.91 -0.620 0.753
E4 60.48±5.03 73.52±4.49 138.05±4.23 125.01±23.36 65.31-194.58 18.68 0.904 0.727
KW E1 19.70±2.51 25.15±1.67 40.06±3.40 27.05±7.52 18.40-50.15 27.80 0.853 0.728
(g) E2 15.80±1.38 20.05±1.41 33.95±4.18 25.65±6.39 12.00-45.50 24.91 0.308 -0.376
E3 20.55±1.62 32.70±1.72 43.97±3.79 33.80±7.20 13.75-60.25 21.30 0.617 -0.407
E4 25.70±1.70 24.80±2.03 31.20±3.73 25.90±6.45 13.35-45.50 24.90 0.467 -0.339
性状Trait 环境
Env.
双亲
Parents
F1杂交种
F1 hybrid
LTPOP群体
LTPOP population
廊黄
Langhuang
TS141 F1LT 均值
Mean
变幅
Range
变异系数
CV (%)
偏度Skewness 峰度Kurtosis
KR E1 83.43±1.84 77.34±1.67 83.93±2.01 81.59±4.94 70.63-91.08 6.05 0.711 -0.095
E2 81.02±1.35 73.08±1.55 82.15±1.84 78.80±5.79 65.97-88.84 7.35 -0.093 0.268
E3 85.14±1.42 79.62±1.73 85.92±1.85 83.01±5.05 71.89-92.51 6.08 -0.216 0.670
E4 82.26±1.61 73.98±1.89 83.93±1.79 80.56±5.93 68.05-89.93 7.36 -0.326 -0.431
EL E1 9.23±1.68 15.33±1.74 20.17±1.40 15.74±2.72 9.18-22.78 17.29 -0.069 0.000
(cm) E2 8.05±1.21 13.10±1.46 18.83±2.05 14.82±3.08 8.20-21.30 20.74 -0.203 -0.855
E3 9.55±2.02 15.62±1.31 20.84±1.87 15.81±2.92 9.10-23.20 18.46 0.073 -0.089
E4 8.72±1.04 12.60±1.76 17.68±1.92 15.07±2.52 8.21-19.80 16.69 -0.497 0.047
双亲
Parents
F1杂交种
F1 hybrid
CTPOP群体
CTPOP population
昌7-2
Chang 7-2
TS141 F1CT 均值
Mean
变幅
Range
变异系数
CV (%)
偏度Skewness 峰度Kurtosis
EW E5 63.68±4.57 107.64±7.99 235.32±7.15 121.00±39.89 27.07-224.56 32.97 0.528 0.041
(g) E6 59.05±6.22 96.47±6.80 215.76±6.98 111.20±38.62 22.34-211.91 34.73 0.596 0.152
E7 54.79±3.16 101.26±5.43 228.43±7.68 105.30±38.29 13.20-200.00 36.37 -0.033 -0.191
E8 50.00±2.69 90.43±5.90 205.37±8.03 90.03±41.93 12.07-198.34 46.26 0.238 -0.220
CW E5 6.86±3.30 21.41±3.88 30.28±1.89 17.51±6.71 4.24-41.21 38.33 0.577 0.619
(g) E6 5.90±5.28 17.68±4.72 26.77±2.01 15.81±5.84 4.16-35.99 36.94 0.701 1.037
E7 6.22±2.19 18.64±3.61 28.95±1.68 16.81±5.74 5.09-32.56 34.14 0.258 -0.119
E8 5.03±3.00 15.11±2.45 23.72±1.74 14.49±6.12 4.03-32.08 42.26 0.425 -0.368
GW E5 56.82±4.01 86.25±3.77 205.07±7.56 104.49±33.19 25.17-184.38 31.76 -0.911 0.150
(g) E6 52.12±3.65 73.79±4.09 189.99±6.70 95.39±28.05 19.78-180.21 29.41 0.228 1.006
E7 49.57±4.22 82.63±4.73 199.48±5.79 89.87±30.82 10.92-174.89 34.29 0.858 0.417
E8 43.97±3.99 70.32±4.36 180.55±6.04 77.54±31.21 9.95-173.24 40.25 0.325 0.271
KW E5 15.15±1.24 23.40±2.14 34.77±2.13 25.20±6.27 6.15-46.14 24.88 0.275 0.761
(g) E6 13.95±1.90 20.10±1.00 29.94±1.78 22.00±7.50 5.05-45.60 34.09 0.435 1.069
E7 14.05±1.05 21.21±1.12 31.26±1.69 22.80±6.29 8.40-42.71 27.58 0.483 0.407
E8 10.80±1.76 14.97±1.33 28.78±1.55 21.60±5.37 6.15-44.43 24.86 0.438 0.696
KR E5 87.22±2.16 80.19±2.32 88.36±1.69 85.50±4.89 73.47-90.80 5.72 0.916 0.188
E6 85.81±2.02 73.73±1.85 86.03±1.57 83.17±5.63 65.13-87.96 6.77 -0.470 0.735
E7 84.64±1.90 79.42±2.13 84.75±1.96 84.04±5.30 71.90-91.83 6.31 0.438 -0.307
E8 80.09±2.11 70.68±2.26 80.77±2.01 79.99±6.37 62.38-90.48 7.96 0.691 -0.740
EL E5 8.15±2.36 13.15±1.77 21.86±2.04 13.55±2.50 7.30-21.20 18.47 0.522 0.903
(cm) E6 7.02±2.69 10.44±3.10 17.99±1.21 12.49±2.32 6.80-20.60 18.59 0.164 0.455
E7 7.08±1.57 12.53±2.85 19.78±11.36 12.91±2.14 7.30-20.30 16.57 0.357 0.723
E8 6.00±2.31 10.48±2.08 16.04±1.02 12.01±2.59 5.70-18.30 21.52 -0.283 -0.218

Fig. 2

RC (rate of change for each trait under water-stressed environment) and heterosis analysis of six ear related traits"

"

"

Fig.3

Distribution of QTLs for six ear related traits in in F2:3 populatiom (LTpop) under different watering environments by CIM and MCIM"

Table 4

Joint QTLs and QTL×E for six ear related traits detected in F2:3 population (LTPOP/CTPOP) across multiple environments with MCIM"

性状Trait QTL Chr. QTL位置 QTL position A AE1/
AE5
AE2/
AE6
AE3/
AE7
AE4/
AE8
h2A
(%)
h2AE
(%)
cM Mb 标记区间
Marker interval
LTPOP群体 LTPOP population
EW qEW-Ch.1-2 1 60.7 0.05 umc2025-umc1395 -2.07 12.56
qEW-J2-1 2 89.2 4.40 bnlg1520-umc1736 -1.55 -0.81 -0.95 9.43 7.08
qEW-Ch.4-1 4 183.6 46.16 umc2041-umc2287 -0.98 7.18
qEW-Ch.9-1 9 54.4 16.69 umc1120-umc2134 -1.70 6.33
qEW-J10-1 10 2.7 0.26 umc1319-bnlg1451 1.41 0.66 0.89 12.95 6.59
CW qCW-Ch.1-1 1 35.8 22.79 umc2224-bnlg1484 1.09 0.86 10.20 8.12
qCW-Ch.2-1 2 23.0 2.71 umc1555-umc1024 0.30 4.98
qCW-J2-1 2 87.5 4.40 bnlg1520-umc1736 -1.37 -0.79 -0.65 9.03 6.84
qCW-Ch.4-1 4 179.9 46.16 umc2041-umc2287 0.24 6.16
qCW-Ch.9-1 9 67.3 23.41 umc1120-umc2346 -1.58 8.54
GW qGW-J1-1 1 114.2 5.03 phi308707-umc1847 -1.20 -0.64 -0.37 0.45 8.14 5.01
qGW-J2-1 2 10.5 0.39 umc2363-umc2403 -0.53 4.85
qGW-J2-2 2 101.8 0.24 bnlg1520-umc1736 -1.55 -0.86 -0.40 -0.71 9.31 6.86
qGW-Ch.4-1 4 181.9 1.32 umc2041-umc2287 -1.31 8.32
qGW-Ch.8-1 8 44.2 0.01 bnlg1863-umc2075 -0.35 -0.26 -0.17 2.79 1.24
KW qKW-Ch.1-2 1 114.2 5.03 phi308707-umc1847 -1.06 -0.63 -0.20 -0.48 8.02 2.87
qKW-Ch.4-1 4 181.0 4.16 umc2041-umc2287 -1.28 8.17
qKW-J6-1 6 94.9 20.76 bnlg2191-mmc0523 0.72 5.48
qKW-Ch.6-1 6 119.7 11.12 umc2040-bnlg1174a 0.41 3.56
性状Trait QTL Chr. QTL位置 QTL position A AE1/
AE5
AE2/
AE6
AE3/
AE7
AE4/
AE8
h2A
(%)
h2AE
(%)
cM Mb 标记区间
Marker interval
KR qKR-Ch.1-1 1 40.7 1.56 bnlg1484-umc1917 1.11 11.69
qKR-J1-1 1 95.4 2.30 bnlg1025-mmc0041 0.60 0.37 5.30 2.73
qKR-Ch.6-1 6 119.8 0.03 umc2040-bnlg1174a 0.47 3.51
qKR-Ch.7-1 7 110.5 1.01 umc1708-umc1768 0.65 5.37
qKR-J8-1 8 88.3 0.54 umc2356-umc1607 0.84 0.26 0.43 -0.31 8.14 2.20
EL qEL-Ch.9-1 9 66.5 23.41 umc1120-umc2346 0.64 5.36
qEL-Ch.10-1 10 50.1 2.85 umc1345-umc2016 0.82 6.49
CTPOP群体 CTPOP population
EW qEW-Ch.1-1 1 138.4 17.57 bnlg1025-mmc0041 -0.90 5.06
qEW-J2-1 2 124.3 4.40 bnlg1520-umc1736 -1.76 -0.88 -0.74 10.93 6.00
qEW-Ch.5-1 5 236.0 2.96 umc2216-umc1072 1.03 4.71
qEW-Ch.6-1 6 81.3 22.97 mmc0523-umc2141 -0.77 4.84
qEW-J8-1 8 8.8 4.96 umc1327-bnlg1194 1.19 0.95 4.90 4.63
qEW-J10-1 10 4.9 0.26 umc1319-bnlg1451 1.01 0.81 0.58 0.66 10.25 7.11
CW qCW-Ch.1-1 1 27.1 22.79 umc2224-bnlg1484 1.10 0.73 0.58 6.49 4.57
qCW-J1-1 1 159.0 19.78 phi308707-umc2289 1.13 -0.79 -0.43 0.68 5.40 4.34
qCW-J5-1 5 113.8 15.76 umc1226-umc1815 -0.88 -0.54 4.92 3.59
qCW-Ch.8-1 8 107.0 32.89 umc2218-umc2356 -1.20 7.93
qCW-Ch.9-1 9 51.8 16.69 umc1120-umc2134 -1.82 9.29
qCW-J10-1 10 3.3 0.26 umc1319-bnlg1451 1.07 0.55 6.05 4.18
GW qGW-J1-1 1 154.9 17.51 mmc0041-phi308707 -2.03 -0.84 -0.66 -0.41 -0.68 11.07 5.50
qGW-J2-1 2 38.6 2.01 umc2363-umc1024 -0.88 7.31
qGW-J2-2 2 127.1 0.24 bnlg1520-umc1736 -1.63 -0.89 -0.41 -0.64 -0.50 9.95 4.19
qGW-Ch.4-1 4 120.3 0.66 umc2041-umc2188 -1.77 10.14
qGW-J6-1 6 97.4 0.03 umc2040-bnlg1174a -1.05 8.96
qGW-J8-1 8 40.7 0.01 bnlg1863-umc2075 -0.84 -0.36 -0.52 7.20 4.47
KW qCW-J1-1 1 155.0 17.51 mmc0041-phi308707 -0.78 -0.58 7.10 6.03
qKW-Ch.4-1 4 `120.5 0.66 umc2041-umc2188 -1.02 8.95
qKW-Ch.6-1 6 81.0 22.97 mmc0523-umc2141 0.44 4.06
KR qKR-J1-1 1 139.3 2.30 bnlg1025-mmc0041 0.51 0.26 0.39 4.15 2.93
qKR-J6-1 6 88.1 0.17 umc2141-umc2040 0.58 4.20
qKR-J7-1 7 118.0 1.01 umc1708-umc1768 0.73 6.12
qKR-Ch.8-1 8 114.9 1.96 umc2356-phi233376 0.62 0.37 0.18 5.04 2.11
EL qEL-J1-1 1 154.6 17.51 mmc0041-phi308707 -0.79 -0.66 7.04 6.18
qEL-Ch.4-1 4 120.3 0.66 umc2041-umc2188 -1.32 9.16
qEL-Ch.10-1 10 142.6 0.75 bnlg1839-umc1249 0.36 3.41

"

[1] 王崇桃, 李少昆 . 玉米生产限制因素评估与技术优先序. 中国农业科学, 2010,43:1136-1146.
Wang C T, Li S K . Assessment of limiting factors and techniques prioritization for maize production in China. Sci Agric Sin, 2010,43:1136-1146 (in Chinese with English abstract).
[2] Zhao X Q, Peng Y L, Zhang J W, Fang P, Wu B Y . Identification of QTLs and meta-QTLs for seven agronomic traits in multiple maize populations under well-watered and water-stressed conditions. Crop Sci, 2018,58:507-520.
[3] 谭巍巍, 李永祥, 王阳, 刘成, 刘志斋, 彭勃, 王迪, 张岩, 孙宝成, 石云素, 宋燕春, 杨德光, 王天宇, 黎裕 . 在干旱和正常水分条件下玉米穗部性状QTL分析. 作物学报, 2011,37:235-248.
Tan W W, Li Y X, Wang Y, Liu C, Liu Z Z, Peng B, Wang D, Zhang Y, Sun B C, Shi Y S, Song Y C, Yang D G, Wang T Y, Li Y . QTL mapping of ear traits of maize under different water regimes. Acta Agron Sin, 2011,37:235-248 (in Chinese with English abstract).
[4] Nikolic A, Andjelkovic A, Dodig D, Ignjatovic-Micic D . Quantitative trait loci for yield and morphological traits in maize under drought stress. Genetika-Belgrade, 2011,43:263-276.
[5] Almeida G D, Nair S, Borém A, Cairns J, Trachsel S, Ribaut J M, Bänziger M, Prasanna B M, Crossa J, Babu R . Molecular mapping across three populations reveals a QTL hotspot region on chromosome 3 for secondary traits associated with drought tolerance in tropical maize. Mol Breed, 2014,34:701-715.
doi: 10.1007/s11032-014-0068-5 pmid: 4092235
[6] Guo J F, Su G Q, Zhang J P, Wang G Y . Genetic analysis and QTL mapping of maize yield and associate agronomic traits under semi-arid land condition. Afr J Biotech, 2008,7:1829-1838.
[7] Lu G H, Tang J H, Yan J B, Ma X Q, Li J S, Chen S J, Ma J C, Liu Z X, Zhang Y R, Dai J R . Quantitative trait loci mapping of maize yield and its components under different water treatments at flowering time. J Integr Plant Biol, 2006,48:1233-1243.
doi: 10.1111/j.1744-7909.2006.00289.x
[8] Sabadin P K, de-Souza C L, de-Souza A P, Franco G A A . QTL mapping for yield components in a tropical maize population using microsatellite markers. Hereditas, 2008,145:194-203.
doi: 10.1111/j.0018-0661.2008.02065.x
[9] Calderón C I, Yandell B S, Doebley J F . Fine mapping of a QTL associated with kernel row number on chromosome 1 of maize. PLoS One, 2016,11:e0150276.
doi: 10.1371/journal.pone.0150276 pmid: 26930509
[10] Liu L, Du Y F, Shen X M, Li M F, Sun W, Huang J, Liu Z J, Tao Y S, Zheng Y L, Yan J B, Zhang Z X . KRN4 controls quantitative variation in maize kernel row number. PLoS Genet, 2015,11:e1005670.
[11] 李雪华, 李新海, 郝传芳, 田清震, 张世煌 . 干旱条件下玉米耐旱相关性状的QTL一致性图谱构建. 中国农业科学, 2005,38:882-890.
Li X H, Li X H, Hao C F, Tian Q Z, Zhang S H . Consensus map of the QTL relevant to drought tolerance of maize under drought conditions. Sci Agric Sin, 2005,38:882-890 (in Chinese with English abstract).
[12] 赵小强, 方鹏, 彭云玲, 高巧红, 曾文静, 任斌 . 基于两个相关群体的玉米6个穗部性状QTL定位. 农业生物技术学报, 2018,26:729-742.
Zhao X Q, Fang P, Peng Y L, Gao Q H, Zeng W J, Ren B . QTL mapping for six ear-related traits based on two maize ( Zea mays) related populations. J Agric Biotech, 2018,26:729-742 (in Chinese with English abstract).
[13] 赵小强, 方鹏, 彭云玲, 张金文, 曾文静, 任斌, 高巧红 . 基于两个相关群体的玉米7个主要农艺性状遗传分析和QTL定位. 草业学报, 2018,27(9):152-165.
Zhao X Q, Fang P, Peng Y L, Zhang J W, Zeng W J, Ren B, Gao Q H . Genetic analysis and QTL mapping for seven agronomic traits in maize ( Zea mays) using two connected populations. Acta Pratacult Sin 2018,27(9):152-165 (in Chinese with English abstract).
[14] 彭云玲, 赵小强, 任续伟, 李健英 . 开花期干旱胁迫对不同基因型玉米生理特性和产量的影响. 干旱地区农业研究, 2014,32(3):9-14.
Peng Y L, Zhao X Q, Ren X W, Li J Y . Genotypic differences in response of physiological characteristics and grain yield of maize inbred lines to drought stress at flowering stage. Agric Res Arid Areas, 2014,32(3):9-14 (in Chinese with English abstract).
[15] 彭云玲, 赵小强, 任续伟, 李健英 . 干旱胁迫对不同株型玉米大喇叭口期生长的影响. 中国沙漠, 2013,33:1064-1070.
Peng Y L, Zhao X Q, Ren X W, Li J Y . Effect of drought stress on growth of different plant type maize ( Zea mays) in the bell-mouthed period. J Desert Res, 2013,33:1064-1070 (in Chinese with English abstract).
[16] 赵小强, 彭云玲, 李建英, 任续伟 . 16份玉米自交系的耐盐性评价. 干旱地区农业研究, 2014,32(5):40-45.
Zhao X Q, Peng Y L, Li J Y, Ren X W . Comprehensive evaluation of salt tolerance in 16 maize inbred lines. Agric Res Arid Areas, 2014,32(5):40-45 (in Chinese with English abstract).
[17] 彭云玲, 赵小强, 闫慧萍, 武金欢 . 不同玉米自交系耐深播性评价及遗传多样性分析. 草业学报, 2016,25(7):73-86.
Peng Y L, Zhao X Q, Yan H P, Wu J H . Deep-sowing tolerance and genetic diversity of maize inbred lines. Acta Pratacult Sin, 2016,25(7):73-86 (in Chinese with English abstract).
[18] Zhao X Q, Peng Y L, Zhang J W, Fang P ,Wu B Y. Mapping QTLs and meta-QTLs for two inflorescence architecture traits in multiple maize populations under different watering environments.Mol Breed, 2017, 37: 91.
[19] Zhao X Q, Fang P, Zhang J W, Peng Y L . QTL mapping for six ear leaf architecture traits under water-stressed and well-watered conditions in maize (Zea mays L.). Plant Breed, 2018,137:60-72.
doi: 10.1111/pbr.12559
[20] 石云素, 黎裕, 王天宇 . 玉米种质资源描述规范和数据标准. 北京: 中国农业出版社, 2006, pp 1-98.
Shi Y S, Li Y, Wang T Y. Descriptors and Data Standard for Maize (Zea mays). Beijing: China Agriculture Press, 2006, pp 1-98(in Chinese).
[21] Knapp S J, Stroup W W, Ross W M . Exact confidence intervals for heritability on a progeny mean basis. Crop Sci, 1985,25:192-194.
doi: 10.2135/cropsci1985.0011183X002500010046x
[22] Churchill G A, Doerge R W . Empirical threshold values for quantitative trait mapping. Genetics, 1994,138:963-971
doi: 10.1101/gad.8.21.2653 pmid: 7851788
[23] Stuber C W, Edwards M D, Wendel J . F1 molecular marker facilitated investigations of quantitative trait loci in maize: II. Factors influencing yield and its component traits. Crop Sci, 1987,27:639-648.
[24] McCouch S R, Cho Y G, Yano P E, Paul E, Blinstrub M, Morishima H, Kinoshita T . Report on QTL nomenclature. Rice Genet Newslett, 1997,14:11-13.
[25] Yang J, Zhu J, Williams R W . Mapping the genetic architecture of complex traits in experimental populations. Bioinformatics, 2007,23:1527-1536.
doi: 10.1093/bioinformatics/btm143 pmid: 17459962
[26] Tuberosa R, Salvi S, Sanguineti M C, Landi P, Maccaferri M, Conti S . Mapping QTL regulating morpho-physiological traits and yields: case studies, shortcomings and perspectives in drought-stress maize. Ann Bot, 2002,89:941-963.
doi: 10.1093/aob/mcf134 pmid: 12102519
[27] 李叶蓓, 陶洪斌, 王若男, 张萍, 吴春江, 雷鸣, 张巽, 王璞 . 干旱对玉米穗发育及产量的影响. 中国生态农业学报, 2015,23:383-391.
Li Y B, Tao H B, Wang R N, Zhang P, Wu C J, Lei M, Zhang X, Wang P . Effect of drought on ear development and yield of maize. Chin J Eco-Agric, 2015,23:383-391 (in Chinese with English abstract).
[28] Robinson H F, Comstock R E, Harvey P H . Genotypic and phenotypic correlations in corn and their implications in selection. Agron J, 1951,43:282-287.
doi: 10.2134/agronj1951.00021962004300060007x
[29] 李忠南, 王越人, 邬生辉, 张淑萍, 李光发 . 玉米品种穗轴重和出籽率性状的相关及遗传关系研究. 玉米科学, 2012,20(5):33-39.
Li Z N, Wang Y R, Wu S H, Zhang S P, Li G F . Character analysis of maize variety and the genetic relationship. J Maize Sci, 2012,20(5):33-39 (in Chinese with English abstract).
[30] Messmer R, Fracheboud Y, Banziger M, Vargas M, Stamp P, Ribaut J M . Drought stress and tropical maize: QTL-by-environment interactions and stability of QTLs across environments for yield components and secondary traits. Theor Appl Genet, 2009,119:913-930.
[31] 马金亮, 张春荣, 董华芳, 席章营, 夏宗良, 丁俊强, 吴建宇 . 玉米果穗出子率QTL定位及上位性分析. 河南农业大学学报, 2010,44:233-237.
Ma J L, Zhang C R, Dong H F, Xi Z Y, Xia Z L, Ding J Q, Wu J Y . QTL mapping and epistasis analysis for rate of kernel production in maize. J Henan Agric Univ, 2010,44:233-237 (in Chinese with English abstract).
[32] 张伟强, 库丽霞, 张君, 韩赞平, 陈彦惠 . 玉米出籽率、籽粒深度和百粒重的QTL分析. 作物学报, 2013,39:455-463.
Zhang W Q, Ku L X, Zhang J, Han Z P, Chen Y H . QTL analysis of kernel ratio, kernel depth, and 100-kernel weight in maize (Zea mays L.). Acta Agron Sin, 2013,39:455-463 (in Chinese with English abstract).
[33] Li J Z, Zhang Z W, Li Y L, Wang Q L, Zhou Y G . QTL consistency and meta-analysis for grain yield components in three generations in maize. Theor Appl Genet, 2011,122:771-782.
[34] Wang Y J, Xu J, Deng D X, Ding H D, Bian Y L, Yin Z T, Wu Y R, Zhou B, Zhao Y . A comprehensive meta-analysis of plant morphology, yield, and virus disease resistance QTL in maize (Zea mays L.). Planta, 2016,243:459-471.
doi: 10.1007/s00425-015-2419-9 pmid: 26474992
[35] 兰进好, 李新海, 高树仁, 张宝石, 张世煌 . 不同生态环境下玉米产量性状QTL分析. 作物学报, 2005,31:1253-1259.
Lan J H, Li X H, Gao S R, Zhang B S, Zhang S H . QTL analysis of yield components in maize under different environments. Acta Agron Sin, 2005,31:1253-1259 (in Chinese with English abstract).
[36] Zhuang J Y, Lin H X, Lu J . Analysis of QTL× environment interaction for yield components and plant height in rice. Theor Appl Genet, 1997,55:799-808.
doi: 10.1007/s001220050628
[37] 谭巍巍, 王阳, 李永祥, 刘成, 刘志斋, 彭勃, 王迪, 张岩, 孙宝成, 石云素, 宋燕春, 杨德光, 王天宇, 黎裕 . 不同环境下多个玉米穗部性状的QTL分析. 中国农业科学, 2011,44:233-244.
Tan W W, Wang Y, Li Y X, Liu C, Liu Z Z, Peng B, Wang D, Zhang Y, Sun B C, Shi Y S, Song Y C, Yang D G, Wang T Y, Li Y . QTL analysis of ear traits in maize across multiple environments. Sci Agric Sin, 2011,44:233-244 (in Chinese with English abstract).
[38] Tanksley S D, Ahn N, Causse M. RFLP mapping of the rice genome. In: Proceeding of Second International Rice Genetics Symposium. Rice Genetics II. Los Banos, Laguna, the Philippines: International Rice Research Institute, 1991. pp 435-442
[39] 赵小强 . 玉米株型相关耐旱遗传机制研究. 甘肃农业大学博士学位论文, 甘肃兰州, 2018.
Zhao X Q . Genetic mechanisms study of drought tolerance related to plant architecture in maize (Zea mays L.). PhD Dissertation of Gansu Agricultural University ,Gansu, Lanzhou, China, 2018 (in Chinese with English abstract).
[40] Hagiwara W E, Qnish K, Takamure I, Sano Y . Transgressive segregation due to linked QTLs for grain characteristics of rice. Euphytica, 2006,150:27-35.
doi: 10.1007/s10681-006-9085-8
[41] 常立国, 何坤辉, 崔婷婷, 薛吉全, 刘建超 . 玉米出籽率的QTL定位及其与环境互作分析. 农业生物技术学报, 2017,25:517-525.
Chang L G, He K H, Cui T T, Xue J Q, Liu J C . QTL mapping and QTL × environment interaction analysis of kernel ratio in maize (Zea mays). J Agric Biotechnol, 2017,25:517-525 (in Chinese with English abstract).
[1] Zheng Yu-Zhen, Qi Fei-Yan, Sun Zi-Qi, Liu Hua, Qin Li, Shi Lei, Wang Juan, Wang Meng-Meng, Han Suo-Yi, Xu Jing, Miao Li-Juan, Huang Bing-Yan, Dong Wen-Zhao, Zheng Zheng, Zhang Xin-You. QTL mapping of total very long-chain fatty acids and seven fatty acid components in peanut seeds [J]. Acta Agronomica Sinica, 2026, 52(6): 1646-1657.
[2] Zhang Ning-Ning, Teng Yu-Fei, Ren Na-Na, Wei Xing-Zhuo, Yan Shu-Hao, Fan Ke-Xin, Wang Yong-Hong, Chen Wen-Kang, Zhang Xing-Hua, Zhu Wan-Chao, Xu Shu-Tu, Xue Ji-Quan. Phenotypic evaluation and plasticity analysis of drought resistance in 201 maize inbred lines [J]. Acta Agronomica Sinica, 2026, 52(5): 1309-1325.
[3] Liu Chang-You, Wang Shen, Shi Hui-Ying, Shen Ying-Chao, Sun Lei, Wang Yan, Zhang Zhi-Xiao, Su Qiu-Zhu, Tian Jing, Fan Bao-Jie. QTL mapping for bruchid resistance in an adzuki bean distant hybridization population using rice bean genetic resources [J]. Acta Agronomica Sinica, 2026, 52(3): 936-944.
[4] Yang Biao, Du Shuai-Kang, Zhang Ji-Wang, Shi Ying, Zhang Li-Li. Genome-wide identification of class III POD gene family in potato and its expression profile analysis [J]. Acta Agronomica Sinica, 2026, 52(2): 405-420.
[5] Hu Cheng-Zhen, Gao Wei-Dong, Kong Bin-Xue, Wang Jian-Fei, Che Zhuo, Yang De-Long, Chen Tao. Genome-wide identification of the TaAPC11 gene family in wheat and functional characterization of TaAPC11-5B in drought stress responses [J]. Acta Agronomica Sinica, 2026, 52(1): 148-164.
[6] Wang Ya-Zhi, Yang Biao, Ji Xiang-Lin, Shi Ying, Zhang Li-Li. Identification of drought-resistant resources and preliminary screening of drought resistant genes in diploid potatoes [J]. Acta Agronomica Sinica, 2026, 52(1): 72-84.
[7] Kong Na, Liu Tao, Liu Wen-Ting, Chen Gang, Wen Li-Chao, Deng Zhi-Chao, Guo Mei, Li Wei, Guo Yong-Feng. Cloning of the NtCEP7 gene in tobacco and functional analysis of its encoded peptide in seedling-stage drought resistance [J]. Acta Agronomica Sinica, 2026, 52(1): 249-261.
[8] Liu Hai-Bo, Zhang Lei, Wang Li-Qi, Shi Xiao-Li, Zhou Wen-Ying, Cui Guo-Xian, She Wei. Functional study of the BnGCL1 gene in ramie (Boehmeria nivea L.) in response to drought stress [J]. Acta Agronomica Sinica, 2026, 52(1): 14-27.
[9] Ji Xuan-Tong, Bian Chun-Song, Jin Li-Ping, Li Sen, Qin Jun-Hong, Li Guang-Cun. Responses of root-associated microorganisms of different drought-tolerant potato varieties to drought conditions [J]. Acta Agronomica Sinica, 2026, 52(1): 165-177.
[10] Zhu Jin-Juan, Wang Hui-Ping, Yang Guo-Dong, Wang Yu-Cheng, Yang Chen, Wang Bin, Agustiani Nurwulan, Tu Jun-Ming, Bi Jun-Guo, Cui Ke-Hui, Huang Jian-Liang, Peng Shao-Bing, Yuan Shen. Effects of water management and variety type on grain yield and quality in ratoon rice [J]. Acta Agronomica Sinica, 2026, 52(1): 295-315.
[11] LI Yun-Xiang, GUO Qian-Qian, HOU Wan-Wei, ZHANG Xiao-Juan. Genome-wide association analysis of drought resistance traits in wheat seedlings introduced from ICARDA [J]. Acta Agronomica Sinica, 2025, 51(9): 2387-2398.
[12] HU Run-Hui, WANG Jun-Cheng, SI Er-Jing, ZHANG Hong, LI Xing-Mao, MA Xiao-Le, MENG Ya-Xiong, WANG Hua-Jun, LIU Qing, YAO Li-Rong, LI Bao-Chun. Screening of drought and salt tolerant germplasm during wheat seedling stage and comprehensive evaluation of drought and salt tolerance [J]. Acta Agronomica Sinica, 2025, 51(9): 2371-2386.
[13] HE Peng-Xu, YAO Li-Rong, CHEN Yuan-Ling, YAN Yan, ZHANG Hong, WANG Jun-Cheng, LI Bao-Chun, YANG Ke, SI Er-Jing, MENG Ya-Xiong, MA Xiao-Le, WANG Hua-Jun. Differences and correlations in physiological and molecular mechanisms of barley germination under drought stress [J]. Acta Agronomica Sinica, 2025, 51(9): 2412-2432.
[14] ZHANG Fei-Fei, HE Wan-Long, JIAO Wen-Juan, BAI Bin, GENG Hong-Wei, CHENG Yu-Kun. Meta-analysis of stripe rust resistance-associated traits and candidate gene identification in wheat [J]. Acta Agronomica Sinica, 2025, 51(8): 2111-2127.
[15] XU Yi-Wei, ZHANG Ying-Ying, LI Rui, YAN Yong-Liang, LIU Yun-Jun, KONG Zhao-Sheng, ZHENG Jun, WANG Yi-Ru. csp2 gene of Deinococcus gobiensis improves drought tolerance in maize [J]. Acta Agronomica Sinica, 2025, 51(8): 1981-1990.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!