Welcome to Acta Agronomica Sinica，

Acta Agron Sin ›› 2010, Vol. 36 ›› Issue (11): 1832-1842.doi: 10.3724/SP.J.1006.2010.01832

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

QTL Analysis for Yield Components and Kernel-Related Traits in Maize under Different Water Regimes

PENG Bo1,WANG Yang1,**,LI Yong-Xiang1,LIU Cheng2,LIU Zhi-Zhai1,WANG Di1,TAN Wei-Wei1,ZHANG Yan1,SUN Bao-Cheng2,SHI Yun-Su1,SONG Yan-Chun1,WANG Tian-Yu1,*,LI Yu1,*   

  1. 1 Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; 2 Institute of Food Crops, Xinjiang Academy of Agricultural Sciences, Urumqi 830000, China; 3 Southwest University, Chongqing 400716, China
  • Received:2010-03-03 Revised:2010-05-25 Online:2010-11-12 Published:2010-08-30
  • Contact: WANG Tian-Yu,E-mail: wangtianyu@263.net, Tel: 010-62186632; 黎裕, E-mail: yuli@mail.caas.net.cn, Tel: 010-62131196

PDF

1924

Cited

10

Abstract: Maize yield and yield-related traits are seriously affected by water stress. Therefore, detecting quantitative trait locus (QTL) for yield components and kernel-related traits, analyzing the stability of QTLs and exploiting constitutive QTLs under different water regimes are of great importance in marker-assisted breeding for drought tolerance in maize. In this study two F2:3 populations derived from Qi319×Huangzaosi (Q/H) and Ye478×Huangzaosi (Y/H) were used to investigate the genetic basis of yield components and kernel-related traits under different water regimes in Xinjiang (including well-water and water-stress environments) by stepwise joint QTL mapping method. The results showed that above 70% of the QTLs for yield components and kernel-related traits expressed stably under the same water regime across the two years. The QTLs detected in water-stress environments were less stable than those in well-water environments across the two years in Xinjiang. The joint analysis combining data of all environments indicated that the stability of the QTLs for all traits decreased, but above 60% of them still expressed stably. A total of 11 constitutive QTLs (with contribution rate more than10% in at least one environment, detected in more than two environments based on single environment analysis) distributed on bin1.10, 2.00, 4.09, 7.02, 9.02, 10.04 and 10.07 were detected in the two populations, and all of them except bin10.04 were stable across all environments. Consequently, most of the QTLs for yield components and kernel-related traits stably expressed under the same water regime across different years, and even under different water regimes in Xinjiang. These constitutive QTLs may provide references for molecular breeding and further basic studies.

Key words: Maize, Water stress, Yield, Kernel, QTL×E

[1]Ribaut J M, Jiang C, Gonzalet-de-Leon D, Edmeades G O, Hoisington D A. Identification of quantitative traits loci under drought conditions in tropical maize: II. Yield components and marker-assisted selection strategies. Theor Appl Genet, 1997, 94: 887–896
[2]Peleg Z, Fahima T, Krugman T, Abbo S, Yakir D, Korol A B, Saranga Y. Genomic dissection of drought resistance in durum wheat×wild emmer wheat recombinant inbreed line population. Plant Cell Environ, 2009, 32: 758–779
[3]Passioura J B. Environmental biology and crop improvement. Funct Plant Biol, 2002, 29: 537–546
[4]Bernardo R. Molecular markers and selection for complex traits in plants: learning from the last 20 years. Crop Sci, 2008, 48: 1649–1664
[5]Schaeffer M, Byrne P, Coe J E H. Consensus quantitative trait maps in maize: a database strategy. Maydica, 2006, 51: 357–367
[6]Veldboom L R, Lee M. Molecular marker-facilitated studies of morphological traits in maize: II. Determination of QTLs for grain yield and yield components. Theor Appl Genet, 1994, 89: 451–458
[7]Veldboom L R, Lee M. Genetic mapping of quantitative trait loci in maize in stress and nonstress environment: I. Grain yield and yield components. Crop Sci, 1996, 36: 1310–1319
[8]Veldboom L R, Lee M, Woodman W L. Molecular marker-facilitated studies in an elite maize population: I. Linkage analysis and determination of QTL for morphological traits. Theor Appl Genet, 1994, 88: 7–16
[9]Austin D F, Lee M. Comparative mapping in F2:3 and F6:7 generations of quantitative trait loci for grain yield and yield components in maize. Theor Appl Genet, 1996, 92: 817–826
[10]Beavis W D, Smith O S, Grant D, Fincher R R. Identification of quantitative trait loci using a small sample of topcrossed and F4 progeny from maize. Crop Sci, 1994, 34: 882–896
[11]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)
[12]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
[13]Hallauer A R, Miranda J B. Quantitative Genetics in Maize Breeding. Iowa: Iowa State University Press, 1981
[14]Chen D H, Ronald P C. A rapid DNA minipreparation method suitable for AFLP and other PCR applications. Plant Mol Biol Rep, 1999, 17: 53–57
[15]Lander E S, Green P, Abrahamson 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
[16]Zeng Z B. Precision mapping of quantitative trait loci. Genetics, 1994, 136: 1457–1468
[17]Yang J, Zhu J, Williams R W. Mapping the genetic architecture of complex traits in experimental populations. Bioinformatics, 2007, 23: 1527–1536
[18]Malosetti M, Ribaut J M, Vargas M, Crossa J, van Eeuwijk F A. A multi-trait multi-environment QTL mixed model with an application to drought and nitrogen stress trials in maize (Zea mays L.). Euphytica, 2008, 161: 241–257
[19]Gao S-B(高世斌), Feng Z-L(冯质雷), Li W-C(李晚忱), Rong T-Z(荣廷昭). Mapping QTLs for root and yield under drought stress in maize. Acta Agron Sin (作物学报), 2005, 31(6): 718–722 (in Chinese with English abstract)
[20]Ribaut J-M, Hoisington D A, Deutsch J A, Jiang C, Gonzalez-de-Leon D. Identification of quantitative trait loci under drought conditions in tropical maize: 1. Flowering parameters and the anthesis silking interval. Theor Appl Genet, 1996, 92: 905–914
[21]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
[22]Vargas M, van Eeuwijk F A, Crossa J, Ribaut J M. Mapping QTLs and QTL × environment interaction for CIMMYT maize drought stress program using factorial regression and partial least squares methods. Theor Appl Genet, 2006, 112: 1009–1023
[23]Li H, Ribaut J M, Li Z, Wang J. Inclusive composite interval mapping (ICIM) for digenic epistasis of quantitative traits in biparental populations. Theor Appl Genet, 2008, 116:243–260
[24]Melchinger A E, Utz H F, Schon C C. Quantitative Trait Locus (QTL) mapping using different testers and independent population samples in maize reveals low power of QTL detection and large bias in estimates of QTL effects. Genetics, 1998, 149: 383–403
[25]Schön C C, Melchinger A E, Boppenmaier J, Brunklaus-Jung E, Herrmann R G. RFLP mapping in maize: quantitative trait loci affecting testcross performance of elite European flint lines. Crop Sci, 1994, 34: 378–389
[26]Goldman I L, Rocheford T R, Dudley J W. Molecular markers associated with maize kernel oil concentration in an Illinois high protein x Illinois low protein cross. Crop Sci, 1994, 34: 908–915
[27]Goldman I L, Rocheford T R, Dudley J W. Quantitative trait loci influencing protein and starch concentration in the Illinois long term selection maize strains. Theor Appl Genet, 1993, 87: 217–224
[28]Ajmone-Marsan P, Monfredini G, Ludwig W F, Melchinger A E, Franceschini P, Pagnotto G, Motto M. Identification of genomic regions affecting plant height and their relationship with grain yield in an elite maize cross. Maydica, 1994, 39: 133–139
[29]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
[30]Ajmone-Marsan P, Mmonfredini G, Brandolini A, Melchinger A E, Garay G, Motto M. Identification of QTL for grain yield in an elite hybrid of maize: Repeatability of map position and effects in independent samples derived from the same population. Maydica, 1996, 41: 49–57
[31]Maccaferri M, Sanguineti M C, Corneti S, Ortega J L A, Salem M B, Bort J, DeAmbrogio E, Garcia del Moral L F, Demontis A, El-Ahmed A, Maalouf F, Machlab H, Martos V, Moragues M, Motawaj J, Nachit M, Nserallah N, Ouabbou H, Royo C, Slama A, Tuberosa R. Quantitative trait loci for grain yield and adaptation of durum wheat (Triticum durum Desf.) across a wide range of water availability. Genetics, 2008, 178: 489–511  
[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] WANG Wang-Nian, GE Jun-Zhu, YANG Hai-Chang, YIN Fa-Ting, HUANG Tai-Li, KUAI Jie, WANG Jing, WANG Bo, ZHOU Guang-Sheng, FU Ting-Dong. Adaptation of feed crops to saline-alkali soil stress and effect of improving saline-alkali soil [J]. Acta Agronomica Sinica, 2022, 48(6): 1451-1462.
[3] YAN Jia-Qian, GU Yi-Biao, XUE Zhang-Yi, ZHOU Tian-Yang, GE Qian-Qian, ZHANG Hao, LIU Li-Jun, WANG Zhi-Qin, GU Jun-Fei, YANG Jian-Chang, ZHOU Zhen-Ling, XU Da-Yong. Different responses of rice cultivars to salt stress and the underlying mechanisms [J]. Acta Agronomica Sinica, 2022, 48(6): 1463-1475.
[4] 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.
[5] 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.
[6] LI Yi-Jun, LYU Hou-Quan. Effect of agricultural meteorological disasters on the production corn in the Northeast China [J]. Acta Agronomica Sinica, 2022, 48(6): 1537-1545.
[7] 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.
[8] SHI Yan-Yan, MA Zhi-Hua, WU Chun-Hua, ZHOU Yong-Jin, LI Rong. Effects of ridge tillage with film mulching in furrow on photosynthetic characteristics of potato and yield formation in dryland farming [J]. Acta Agronomica Sinica, 2022, 48(5): 1288-1297.
[9] YAN Xiao-Yu, GUO Wen-Jun, QIN Du-Lin, WANG Shuang-Lei, NIE Jun-Jun, ZHAO Na, QI Jie, SONG Xian-Liang, MAO Li-Li, SUN Xue-Zhen. Effects of cotton stubble return and subsoiling on dry matter accumulation, nutrient uptake, and yield of cotton in coastal saline-alkali soil [J]. Acta Agronomica Sinica, 2022, 48(5): 1235-1247.
[10] KE Jian, CHEN Ting-Ting, WU Zhou, ZHU Tie-Zhong, SUN Jie, HE Hai-Bing, YOU Cui-Cui, ZHU De-Quan, WU Li-Quan. Suitable varieties and high-yielding population characteristics of late season rice in the northern margin area of double-cropping rice along the Yangtze River [J]. Acta Agronomica Sinica, 2022, 48(4): 1005-1016.
[11] 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.
[12] 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.
[13] LI Rui-Dong, YIN Yang-Yang, SONG Wen-Wen, WU Ting-Ting, SUN Shi, HAN Tian-Fu, XU Cai-Long, WU Cun-Xiang, HU Shui-Xiu. Effects of close planting densities on assimilate accumulation and yield of soybean with different plant branching types [J]. Acta Agronomica Sinica, 2022, 48(4): 942-951.
[14] WANG Lyu, CUI Yue-Zhen, WU Yu-Hong, HAO Xing-Shun, ZHANG Chun-Hui, WANG Jun-Yi, LIU Yi-Xin, LI Xiao-Gang, QIN Yu-Hang. Effects of rice stalks mulching combined with green manure (Astragalus smicus L.) incorporated into soil and reducing nitrogen fertilizer rate on rice yield and soil fertility [J]. Acta Agronomica Sinica, 2022, 48(4): 952-961.
[15] 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.
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