Welcome to Acta Agronomica Sinica，

Acta Agron Sin ›› 2011, Vol. 37 ›› Issue (02): 235-248.doi: 10.3724/SP.J.1006.2011.00235

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

QTL Mapping of Ear Traits of Maize under Different Water Regimes

TAN Wei-Wei1,2,WANG Yang2,LI Yong-Xiang2,LIU Cheng3,LIU Zhi-Zhai2,4,PENG Bo2,WANG Di2,ZHANG Yan2,SUN Bao-Cheng3,SHI Yun-Su2,SONG Yan-Chun2,YANG De-Guang1,*,WANG Tian-Yu2, and LI Yu2,*   

  1. 1 College of Agronomy, Northeast Agricultural University, Harbin 150030, China; 2 Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; 3Institute of Food Crops, Xinjiang Academy of Agricultural Sciences, Urumqi 830000, China; 4 Southwest University, Chongqing 400716, China
  • Received:2010-06-13 Revised:2010-09-25 Online:2011-02-12 Published:2010-12-12
  • Contact: 杨德光, E-mail: ydgl@tom.com; 黎裕, E-mail: yuli@mail.caas.net.cn, Tel: 010-62131196

PDF

1937

Cited

14

Abstract: Ear traits are closely associated with maize yield. Therefore, genetic dissection of ear traits can provide clues to maize high-yield breeding and is especially important in breeding for drought tolerance. In this study, seven ear related traits including ear length (EL), ear diameter (ED), row number (KRN), kernel number per row (KRE), grain yield per ear (GW), cob diameter (AD) and weight per ear (EW), were investigated using two sets of F2:3 populations, which were derived from crosses of Ye478×Huangzaosi (Y/H) and Qi319×Huangzaosi (Q/H), respectively. The two populations were evaluated under different water conditions in Xinjiang in 2007 and 2008. The results showed that those ear traits under drought were phenotypically lower than those under normal water regime and ear length, ear diameter, weight per ear had positive correlation with GW. A total of 75 QTL were identified under different water regimes using mixed linear model with two methods of single-experiment analysis and water regime joint analysis, including 20 QTL detected under normal water regime and 55 QTL detected under drought stress. The QTL detected in Y/H were located on chromosome 1, 2, 5, 6, 7, and 10, and the QTL detected in Q/H were distributed on chromosome 2, 3, 4, 5, 6, 7, 9, and 10. However, only four and nineteen QTL was identified in the two populations under drought stress respectively, significantly lower than that detected under normal water regime. Meanwhile, in the joint analysis, only three QTL had significant interaction with environments and six QTL had epistatic interaction, showing that the genetic feature of ear traits was complex. In Y/H, two congruent QTL, qKRE1-5-1 and qKRE1-7-1, were detected under different water levels, with the phenotypic contribution from 6.15% to 19.48%, while in Q/H, three congruent QTL, qKRE2-5-1, qGW2-10-1, and qKRE2-3-1, were detected under different water levels, with the phenotypic contribution from 7.14% to 16.65%. These results implied that these QTL were little influenced by environment and could stably expressed, which can be used in marker-assisted selection.

Key words: Maize, Ear traits, Drought stress, Quantitative trait loci

[1]Qi W(齐伟), Zhang J-W(张吉旺), Wang K-J(王空军), Liu P(刘鹏), Dong S-T(董树亭). Effects of drought stress on the grain yield and root physiological traits of maize varieties with different drought tolerance. Chin J Appl Ecol (应用生态学报), 2010, 21(1): 48-52 (in Chinese with English abstract)
[2]Xiao J-F(肖俊夫), Liu Z-D(刘战东), Liu Z-G(刘祖贵), Chen Y-M(陈玉民), Liu X-F(刘小飞). Analysis on irrigation input problems and drought in growth period of maize in China. J Irrigation & Drainage (灌溉排水学报), 2009, 5: 21-24 (in Chinese with English abstract)
[3]Liu Z-H(刘宗华), Tang J-H(汤继华), Wei X-Y(卫晓轶), Wang C-L(王春丽), Tian G-W(田国伟), Hu Y-M(胡彦民), Chen W-C(陈伟程). QTL mapping of ear traits under low and high nitrogen conditions in maize. Sci Agric Sin (中国农业科学), 2007, 40(11): 2409-2417 (in Chinese with English abstract)
[4]Blum A. Plant Breeding for Stress Environments. In: Boca Raton, USA: FL, CRC Press, 1988
[5]Tang H (汤华), Huang Y-Q(黄益勤), Yan J-B(严建兵), Liu Z-H(刘宗华), Tang J-H(汤继华), Zheng Y-L(郑用琏), Li J-S(李建生). Genetic analysis of yield traits with elite maize hybrid—Yuyu 22. Acta Agron Sin (作物学报), 2004, 30(9): 922-926 (in Chinese with English abstract)
[6]Cui Z-H(崔震海), Zhang L-J(张立军), Fan J-J(樊金娟), Ruan Y-Y(阮燕晔), Ma X-L(马兴林). Correlation analysis of grain yield and ear characters of maize during seedling stage with different water supply. Acta Agric Boreali-Sin (华北农学报), 2008, 23(1): 123-127 (in Chinese with English abstract)
[7]Frova C, Krajewski P, N di Fonzo, Villa M, Sari-Gorla M. Genetic analysis of drought tolerance in maize by molecular markers: I. Yield components. Theor Appl Genet, 1999, 99: 280-288
[8]Guo J F, Su G Q, Zhang J P, Wang GY. Genetic analysis and QTL mapping of maize yield and associate agronomic traits under semi-arid land condition. African J Biotech, 2008, 12: 1829-1838
[9]Lu G H, Tang J H, Yan J B, Ma X Q, Li J S, Chen S J, Ma J C, Liu Z X, E L Z, 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
[10]Shi Y-S石云素), Li Y(黎裕), Wang T-Y(王天宇), Song Y-C(宋燕春). Standard of Description for Maize Germplasm and Data. Beijing: China Agriculture Press, 2006. pp 1-98 (in Chinese)
[11]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
[12]Lander E S, Green P, Abrahanson J, Barlow A, Daly M J, Lincoln S E, Newberg L A. MAPMAKER: an interactive computer package for constructing primary genetic linkage maps of experimental and natural populations. Genomics, 1987, 1: 174-181
[13]Yang J, Zhu J, Williams R W. Mapping the genetic architecture of complex traits in experimental populations. Bioinformatics, 2007, 23: 1527-1536
[14]Wang C S, Rutledge J J, Gianola D. Bayesian analysis of mixed linear models via Gibbs sampling with an application to little size in Iberian pigs. J Genet Sel Evol, 1994, 26: 91-115
[15]Stuber C W, Edwards M D, Wendel J F. Molecular marker facilitated investigations of quantitative trait loci in maize: II. Factors influencing yield and its component traits. Crop Sci, 1987, 27: 639-648
[16]McCouch S R, Cho Y G, Yano M, Paul E, Blinstrub M, Morishima H, Kinoshita T. Report on QTL nomenclature. Rice Genet Newsl, 1997, 14: 11-13
[17]Yan J B, Tang H, Huang Y Q, Zheng Y L, Li J S. Quantitative trait loci mapping and epistatic analysis for grain yield and yield components using molecular markers with an elite maize hybrid. Euphytica, 2006, 149: 121-131
[18]Messmer R, Fracheboud Y, Bänziger M, Vargas M, Stamp P, Ribaut J M. Drought stress and tropical maize: QTL-by-environment interactions and stability of QTL across environments for yield components and secondary traits. Theor Appl Genet, 2009, 119: 913-930
[19]Sabadin P K, de Souza C L J, de Souza A P, Franco A A G. QTL mapping for yield components in a tropical maize population using microsatellite markers. Hereditas, 2008, 145: 194-203
[20]Li Y L, Li X H, Li J Z, Fu J F, Wang Y Z, Wei M G. Dent corn genetic background influences QTL detection for grain yield and yield components in high-oil maize. Euphytica, 2009, 169: 273-284
[21]Beavis W D, Smith O S, Grant D, Fincher R. Identification of quantitative trait loci using a small sample of topcrosses and F4 progeny from maize. Crop Sci, 1994, 34: 882-896
[22]Ribaut J M, Jiang C, Gonzalez-de-Leon D, Edmeades G O, Hoisington D A. Identification of quantitative trait loci under drought conditions in tropical maize: 2. Yield components and marker-assisted selection strategies. Theor Appl Genet, 1997, 94: 887-896
[23]Ajmone-Marsan P, Monfredini G, Ludwig W F, Melchinger A E, Franceschini P, Pagnotto G, Motto M. In an elite cross of maize a major quantitative trait locus controls one-fourth of the genetic variation for grain yield. Theor Appl Genet, 1995, 90: 415-424
[24]Mihaljevic R, Utz H F, Melchinger A E. Congruency of quantitative trait loci detected for agronomic traits in testcrosses of five populations of European maize. Crop Sci, 2004, 44: 114-124
[25]Upadyayula N, da Silva H S, Bohn M O, Rocheford T R. Genetic and QTL analysis of maize tassel and ear inflorescence architecture. Theor Appl Genet, 2006, 112: 592-606
[26]Li Y-X(李永祥), Wang Y(王阳), Shi Y-S(石云素), Song Y-C(宋燕春), Wang T-Y(王天宇), Li Y(黎裕). Correlation analysis and QTL mapping for traits of kernel structure and yield components in maize. Sci Agric Sin (中国农业科学), 2009, 42(2): 408-418 (in Chinese with English abstract)
[27]Tuberosa R, Salvi S, Sanguineti M C, Landi P, Maccaferri M. Conti S. Mapping QTL regulating morpho-physiological traits and yield: case studies, shortcomings and perspectives in drought-stress maize. Annu Bot, 2002, 89: 941-963
[28]Moreau L, Charcosset A, Gallais A. Use of trial clustering to study QTL × environment effects for grain yield and related traits in maize. Theor Appl Genet, 2004, 110: 92-10
[29]Cockerham C C, Zeng Z B. Design III with marker loci. Genetics, 1996, 143: 1437-1456
[30]Luo L J, Li Z K, Mei H W, Shu Q Y, Tabien R, Zhong D B, Ying C S, Stansel J W, Khush G S, Paterson A H. Overdominant epistatic loci are the primary genetic basis of inbreeding depression and heterosis in rice: II. Grain yield components. Genetics, 2001, 158: 1755-1771
[31]Johnson W C, Gepts P. The role of epistasis in controlling seed yield and other agronomic traits in an Andean×Mesoamerican cross of common bean (Phaseolus vulgrais L.). Euphytica, 2004, 125: 69-79
[32]Wang J, van Ginkel M, Trethowan R, Ye G, Delacy I, Podlich D, Cooper M. Simulating the effects of dominance and epistasis on selection response in the CIMMYT wheat breeding program using QuCim. Crop Sci, 2004, 44: 1889-1892
[33]Doebley J, Stec A, Gustus C. Teosinte branched-1 and the origin of maize: evidence for epistasis and the evolution of dominance. Genetics, 1995, 141: 333-346
[34]Eta-Ndu J T, Openshaw S J. Epistasis for grain yield in two F2 populations of maize. Crop Sci, 1999, 39: 346-352
[35]Ma X Q, Tang J H, Teng W T, Yan J B, Meng Y J, Li J S. Epistatic interaction is an important genetic basis of grain yield and its components in maize. Mol Breed, 2007, 20: 41-51
[36]Xiang D-Q(向道权), Cao H-H(曹海河), Cao Y-G(曹永国), Yang J-P(杨俊品), Huang L-J(黄烈健), Wang S-C(王守才) , Dai J-R(戴景瑞). Constructure of SSR genetic linkage map and mapping of maize yield. Acta Genet Sin (遗传学报), 2001, 28(8): 778-784 (in Chinese with English abstract)
[37]Albler B S B, Edwards M D, Stuber C W. Isoenzymatic identification of quantitative trait loci in across of elite maize inbreds. Crop Sci, 1991, 31: 267-274
[1] WANG Dan, ZHOU Bao-Yuan, MA Wei, GE Jun-Zhu, DING Zai-Song, LI Cong-Feng, ZHAO Ming. Characteristics of the annual distribution and utilization of climate resource for double maize cropping system in the middle reaches of Yangtze River [J]. Acta Agronomica Sinica, 2022, 48(6): 1437-1450.
[2] YANG Huan, ZHOU Ying, CHEN Ping, DU Qing, ZHENG Ben-Chuan, PU Tian, WEN Jing, YANG Wen-Yu, YONG Tai-Wen. Effects of nutrient uptake and utilization on yield of maize-legume strip intercropping system [J]. Acta Agronomica Sinica, 2022, 48(6): 1476-1487.
[3] CHEN Jing, REN Bai-Zhao, ZHAO Bin, LIU Peng, ZHANG Ji-Wang. Regulation of leaf-spraying glycine betaine on yield formation and antioxidation of summer maize sowed in different dates [J]. Acta Agronomica Sinica, 2022, 48(6): 1502-1515.
[4] SHAN Lu-Ying, LI Jun, LI Liang, ZHANG Li, WANG Hao-Qian, GAO Jia-Qi, WU Gang, WU Yu-Hua, ZHANG Xiu-Jie. Development of genetically modified maize (Zea mays L.) NK603 matrix reference materials [J]. Acta Agronomica Sinica, 2022, 48(5): 1059-1070.
[5] WANG Xia, YIN Xiao-Yu, Yu Xiao-Ming, LIU Xiao-Dan. Effects of drought hardening on contemporary expression of drought stress memory genes and DNA methylation in promoter of B73 inbred progeny [J]. Acta Agronomica Sinica, 2022, 48(5): 1191-1198.
[6] XU Jing, GAO Jing-Yang, LI Cheng-Cheng, SONG Yun-Xia, DONG Chao-Pei, WANG Zhao, LI Yun-Meng, LUAN Yi-Fan, CHEN Jia-Fa, ZHOU Zi-Jian, WU Jian-Yu. Overexpression of ZmCIPKHT enhances heat tolerance in plant [J]. Acta Agronomica Sinica, 2022, 48(4): 851-859.
[7] LIU Lei, ZHAN Wei-Min, DING Wu-Si, LIU Tong, CUI Lian-Hua, JIANG Liang-Liang, ZHANG Yan-Pei, YANG Jian-Ping. Genetic analysis and molecular characterization of dwarf mutant gad39 in maize [J]. Acta Agronomica Sinica, 2022, 48(4): 886-895.
[8] YAN Yu-Ting, SONG Qiu-Lai, YAN Chao, LIU Shuang, ZHANG Yu-Hui, TIAN Jing-Fen, DENG Yu-Xuan, MA Chun-Mei. Nitrogen accumulation and nitrogen substitution effect of maize under straw returning with continuous cropping [J]. Acta Agronomica Sinica, 2022, 48(4): 962-974.
[9] XU Ning-Kun, LI Bing, CHEN Xiao-Yan, WEI Ya-Kang, LIU Zi-Long, XUE Yong-Kang, CHEN Hong-Yu, WANG Gui-Feng. Genetic analysis and molecular characterization of a novel maize Bt2 gene mutant [J]. Acta Agronomica Sinica, 2022, 48(3): 572-579.
[10] DING Hong, XU Yang, ZHANG Guan-Chu, QIN Fei-Fei, DAI Liang-Xiang, ZHANG Zhi-Meng. Effects of drought at different growth stages and nitrogen application on nitrogen absorption and utilization in peanut [J]. Acta Agronomica Sinica, 2022, 48(3): 695-703.
[11] SONG Shi-Qin, YANG Qing-Long, WANG Dan, LYU Yan-Jie, XU Wen-Hua, WEI Wen-Wen, LIU Xiao-Dan, YAO Fan-Yun, CAO Yu-Jun, WANG Yong-Jun, WANG Li-Chun. Relationship between seed morphology, storage substance and chilling tolerance during germination of dominant maize hybrids in Northeast China [J]. Acta Agronomica Sinica, 2022, 48(3): 726-738.
[12] QU Jian-Zhou, FENG Wen-Hao, ZHANG Xing-Hua, XU Shu-Tu, XUE Ji-Quan. Dissecting the genetic architecture of maize kernel size based on genome-wide association study [J]. Acta Agronomica Sinica, 2022, 48(2): 304-319.
[13] ZHANG Yan-Bo, WANG Yuan, FENG Gan-Yu, DUAN Hui-Rong, LIU Hai-Ying. QTLs analysis of oil and three main fatty acid contents in cottonseeds [J]. Acta Agronomica Sinica, 2022, 48(2): 380-395.
[14] YAN Yan, ZHANG Yu-Shi, LIU Chu-Rong, REN Dan-Yang, LIU Hong-Run, LIU Xue-Qing, ZHANG Ming-Cai, LI Zhao-Hu. Variety matching and resource use efficiency of the winter wheat-summer maize “double late” cropping system [J]. Acta Agronomica Sinica, 2022, 48(2): 423-436.
[15] ZHANG Qian, HAN Ben-Gao, ZHANG Bo, SHENG Kai, LI Lan-Tao, WANG Yi-Lun. Reduced application and different combined applications of loss-control urea on summer maize yield and fertilizer efficiency improvement [J]. Acta Agronomica Sinica, 2022, 48(1): 180-192.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Add: No. 80 Xueyuan South Rd, Beijing 100081, P. R. China E-mail: zwxb301@caas.cn
Supported by Beijing Magtech Co.Ltd