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作物学报 ›› 2011, Vol. 37 ›› Issue (02): 235-248.doi: 10.3724/SP.J.1006.2011.00235

• 作物遗传育种·种质资源·分子遗传学 • 上一篇    下一篇

在干旱和正常水分条件下玉米穗部性状QTL分析

谭巍巍1,2,李永祥2,**,王阳2,刘成3,刘志斋2,4,彭勃2,王迪2,张岩2,孙宝成3,石云素2,宋燕春2,杨德光1,*,王天宇2,黎裕2,*   

  1. 1东北农业大学农学院, 黑龙江哈尔滨 150030; 2中国农业科学院作物科学研究所, 北京 100081; 3新疆农业科学院粮食作物研究所，新疆乌鲁木齐 830000; 4西南大学农学院, 重庆 400716
  • 收稿日期:2010-06-13 修回日期:2010-09-25 出版日期:2011-02-12 网络出版日期:2010-12-12
  • 通讯作者: 杨德光, E-mail: ydgl@tom.com; 黎裕, E-mail: yuli@mail.caas.net.cn, Tel: 010-62131196
  • 基金资助:

    本研究由国家重点基础研究发展计划(973计划)项目(2011CB100100, 2009CB118401)，国家高技术研究发展计划(863计划)项目(2006AA10Z188, 2009AA10AA03)和国家自然科学基金重点项目(30730063)资助。

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 Published:2011-02-12 Published online:2010-12-12
  • Contact: 杨德光, E-mail: ydgl@tom.com; 黎裕, E-mail: yuli@mail.caas.net.cn, Tel: 010-62131196

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被引次数

34

摘要： 穗部性状与产量密切相关，因此对其进行遗传剖析可为玉米高产育种提供理论基础，尤其是对干旱胁迫下的稳产有重要意义。本研究以玉米骨干亲本黄早四分别与自交系掖478和齐319进行杂交，构建了两套F2:3群体(分别记为Y/H和Q/H)。在正常水分灌溉和干旱胁迫下对穗长、穗粗、轴粗、穗行数、行粒数、穗粒重和穗重等7个穗部性状进行了表型鉴定，采用基于混合线性模型的单环境分析和相同处理水平的联合分析方法进行了QTL分析。结果表明，在干旱胁迫下，2个群体的亲本及F2:3家系的各性状值均低于正常水分条件，且穗粒重与穗长、穗重、穗粗呈正相关。在干旱胁迫下和正常水分条件下，通过两种检测方法共定位到75个玉米穗部性状QTL，其中Y/H群体共定位了20个QTL，分布在第1、第2、6、第5、第7、第10染色体上；Q/H群体共定位了55个QTL，分布在第2、第3、第4、第5、第6、第7、第9、第10染色体上；但是在干旱条件下两群体分别只检测到4个和19个QTL，明显低于正常水分条件下检测到的QTL数目。通过联合分析只检测到3个QTL与环境发生显著互作和6对QTL存在上位性互作效应，说明玉米穗部性状的遗传基础较为复杂。同时还发现，Y/H群体在正常灌溉与干旱条件下检测到2个一致性的QTL，分别是qKRE1-5-1和qKRE1-7-1，对表型变异解释的变化范围是6.15%~19.48%；Q/H群体检测到3个一致性QTL，分别是qKRE2-5-1、qGW2-10-1和qKRE2-3-1，对表型变异解释的变化范围是7.14%~16.65%，说明这些QTL受环境影响较小，能够稳定遗传，可以作为分子标记辅助选择的候选区间应用于玉米穗部性状抗旱性改良。

关键词: 玉米, 穗部性状, 干旱胁迫, QTL分析

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
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