欢迎访问作物学报,今天是

作物学报 ›› 2013, Vol. 39 ›› Issue (11): 1935-1943.doi: 10.3724/SP.J.1006.2013.01935

• 作物遗传育种·种质资源·分子遗传学 • 上一篇    下一篇

基于图像处理的冬小麦植被覆盖率测定及其遗传解析

肖永贵1,刘建军2,夏先春1,陈新民1,Matthew REYNOLDS3,何中虎1,4,*   

  1. 1中国农业科学院作物科学研究所/国家小麦改良中心,北京100081;2山东省农业科学院作物研究所,山东济南 250100;3 CIMMYT, Apartado Postal 6-641, 06600 México, DF, Mexico;4国际玉米小麦改良中心(CIMMYT)中国办事处,北京 100081
  • 收稿日期:2013-01-30 修回日期:2013-06-24 出版日期:2013-11-12 网络出版日期:2013-08-12
  • 通讯作者: 何中虎, E-mail: zhhecaas@163.com
  • 基金资助:

    本研究由国家自然科学基金项目(31161140346和31201207)和旱区作物逆境生物学国家重点实验室(西北农林科技大学)开放课题资助。

Genetic Analysis of Vegetative Ground Cover Rate in Winter Wheat Using Digital Imaging

XIAO Yong-Gui1,LIU Jian-Jun2,XIA Xian-Chun1,CHEN Xin-Min1,Matthew REYNOLDS3,HE Zhong-Hu1,4,*   

  1. 1 Institute of Crop Science / National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100081, China; 2 Crop Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, China; 3 CIMMYT, Apartado Postal 6-641, 06600 México, DF, Mexico;
    4 CIMMYT-China Office, c/o CAAS, Beijing 100081, China
  • Received:2013-01-30 Revised:2013-06-24 Published:2013-11-12 Published online:2013-08-12
  • Contact: 何中虎, E-mail: zhhecaas@163.com

摘要:

植被覆盖率是反映植株生长势的重要生理性状,在旱作地区尤为重要。图像处理技术能够快速有效地对苗期和孕穗期植被覆盖率进行量化分析。以28份山东小麦主栽品种和品系为材料,在240株 m-2和360株 m-2密度下,连续2年测定了孕穗前不同发育阶段的植被覆盖率,并利用921个DArT标记和83个SSR标记分析了与植被覆盖率相关的遗传区段。结果表明。不同密度下,冬小麦植被覆盖率在越冬期、返青期和孕穗期存在显著差异,而起身期基本一致。起身期植被覆盖率与春季最高分蘖数、抽穗后群体叶面积指数、单位面积穗数和籽粒产量均呈显著正相关,r = 0.73~0.76 (P<0.01),表明起身期植被覆盖率可用于预测上述性状。共检测出12个遗传区段与植被覆盖率相关联,大部分区段直接参与调控苗期和孕穗期的生长势。10个遗传区段与已报道的苗期性状、产量性状及抗病位点一致,其中5BL、6AS和6BL染色体上携带的植被覆盖率相关遗传区段与已报道的苗期比叶面积和生物量等位点完全相同。建议将植被覆盖率作为生长势量化指标,用于育种选择和遗传研究。

关键词: 普通小麦, 植被覆盖率, 关联分析, 遗传区段

Abstract:

Vegetative vigour is an important physiological trait and selection for greater seedling vigour is a goal of breeding programs, especially in rain-fed regions. This study aimed to identify the genetic variation of early vigour, determine the agronomic traits most closely associated with seedling growth, and detect the major gene-containing region of early vigour in winter wheat. Twenty-eight cultivars and advanced lines at two planting densities (240 plants m-2 and 360 plants m-2) were grown in Jinan during 2009–2010 and 2010–2011 cropping seasons, with randomized complete block design of three replications. Whole-genome association mapping was employed to identify the chromosome region controlling early vigour using 921 Diversity Array Technology (DArT) and 83 SSR markers. Early vigour was evaluated with vegetative ground cover rate via implementation of photographic image analysis, whereby computer analysis was used to determine percentage ground cover. Significant differences of ground cover rate between two planting densities were detected in pre-winter period, erecting and booting stages, but not in early stem elongation stage. Ground cover rate in erecting stage was significantly and positively associated with maximum tiller number (r = 0.76, P < 0.01), leaf area index (r = 0.74, P < 0.01), spike number (r = 0.73, P < 0.01), and grain yield (r = 0.73, P < 0.01). Twelve gene-containing regions for vegetative ground cover rate were detected in two seasons. Most of the regions conditioning the vegetative ground cover rate were not affected by the developmental stages. Ten gene-containing regions identified were consistent with previously reported QTLs for seedling traits, grain yield and disease resistance. Three regions on 5BL, 6AS, and 6BL were the same as previously reported loci for seedling traits. Therefore, there is sufficient genetic variation to increase early vigour in winter wheat, and early vigour could be quickly measured through digital image analysis.

Key words: Common wheat, Vegetative ground cover rate, Association study, Gene-containing region

[1]Rebetzke G J, Condon A G, Richards R A, Farquhar G D. Selection for reduced carbon-isotope discrimination increases aerial biomass and grain yield of rainfed bread wheat. Crop Sci, 2002, 42: 739–745



[2]Botwright T L, Condon A G, Rebetzke G J, Richards R A. Field evaluation of early vigour for genetic improvement of grain yield in wheat. Aust J Agric Res, 2002, 53: 1137–1145



[3]Rebetzke G J, Botwright T L, Moore C S, Richards R A, Condon A G. Genotypic variation in specific leaf area for genetic improvement of early vigour in wheat. Field Crops Res, 2004, 88: 179–189



[4]Ryan J. Crop nutrients for sustainable agricultural production in the drought stressed Mediterranean region. J Agric Sci Technol, 2008, 10: 295–306



[5]López-Castañeda C, Richards C, Richards R A, Farquhar G D. Variation in early vigor between wheat and barley. Crop Sci, 1995, 35: 472–479



[6]López-Castaóeda C, Richards R A. Variation in temperate cereals in rainfed environments: III. Water use and water-use efficiency. Field Crops Res, 1994, 39: 85–98



[7]Rebetzke G J, Richards R A. Genetic improvement of early vigour in wheat. Aust J Agric Res, 1999, 50: 291–301



[8]Zhang G Y, Guo Y, Chen S L, Chen S Y. RFLP tagging of a salt tolerance gene in rice. Plant Sci, 1995, 110: 227–234



[9]Prasad S R, Bagali P G, Hittalmani S, Shashidhar H E. Molecular mapping of quantitative trait loci associated with seedling tolerance to salt stress in rice (Oryza sativa L.). Curr Sci, 2000, 78: 162–164



[10]Lin H X, Zhu M Z, Yano M, Gao J P, Liang Z W, Su W A, Hu X H, Ren Z H, Chao D Y. QTLs for Na+ and K+ uptake of the shoots and roots controlling rice salt tolerance. Theor Appl Genet, 2004, 108: 253–260



[11]Ellis R P, Forster B P, Gordon D C, Handley L L, Keith R P, Lawrence P, Meyer R, Powell W, Robinson D, Scrimgeour C M, Young G, Thomas W T. Phenotype genotype associations for yield and salt tolerance in a barley mapping population segregating for two dwarfing genes. J Exp Bot, 2002, 53: 1163–1176



[12]Mano Y, Takeda K. Mapping quantitative trait loci for salt tolerance at germination and the seedling stage in barley (Hordeum vulgare L.). Euphytica, 1997, 94: 263–272



[13]Xue D, Huang Y Z, Zhang X Q, Wei K, Westcott S, Li C, Chen M, Zhang G, Lance R. Identification of QTLs associated with salinity tolerance at late growth stage in barley. Euphytica, 2009, 169: 187–196



[14]Genc Y, Oldach K, Verbyla A P, Lott G, Hassan M, Tester M, Wallwork H, McDonald G K. Sodium exclusion QTL associated with improved seedling growth in bread wheat under salinity stress. Theor Appl Genet, 2010, 121: 877–894



[15]Spielmeyer W, Hyles J, Joaquim P, Azanza F, Bonnett D, Ellis M E, Moore C, Richards R A. A QTL on chromosome 6A in bread wheat (Triticum aestivum) is association with longer coleoptiles, grater seedling vigour and final plan height. Theor Appl Genet, 2007, 115: 59–66



[16]Botwright T L, Rebetzke G J, Condon A G, Richards R A. Influence of the gibberellin-sensitive Rht8 dwarfing gene on leaf epidermal cell dimensions and early vigour in wheat (Triticum aestivum L.). Ann Bot, 2005, 95: 631–639



[17]Richards R A, Lukacs Z. Seedling vigour in wheat-sources of variation for genetic and agronomic improvement. Aust J Agric Res, 2002, 53: 41–50



[18]Prasad B, Carver B F, Stone M L, Babar M A, Raun W R, Klatt A R. Genetic analysis of indirect selection for winter wheat grain yield using spectral reflectance indices. Crop Sci, 2007, 47: 1416–1425



[19]Mullan D J, Reynolds M P. Quantifying genetic effects of ground cover on soil water evaporation using digital imaging. Funct Plant Biol, 2010, 37: 703–712



[20]Wang G-Q(王桂琴), Zheng L-M(郑丽敏), Zhu H(朱虹), Liang Z-X(梁振兴), Liao S-H(廖树华). Application of image processing technology in wheat canopy leaf area index measuring. J Triticeae Crops (麦类作物学报), 2004, 24(4): 108–112 (in Chinese with English abstract)



[21]Preussa C P, Huanga C Y, Louhaichib M, Ogbonnayab F C. Genetic variation in the early vigour of spring bread wheat under phosphate stress as characterised through digital charting. Field Crops Res, 2012, 127: 71–78



[22]Crossa J, Burgueño J, Dreisigacker S, Vargas M, Herrera-Foessel S A, Lillemo M, Singh R P, Trethowan R, Warburton M, Franco J, Reynolds M, Crouch J H, Ortiz R. Association analysis of historical bread wheat germplasm using additive genetic covariance of relatives and population structure. Genetics, 2007, 177: 1889–1913



[23]Wang G, Leonard J M, Ross A S, Peterson C J, Zemetra R S, Campbell K G, Riera-Lizarazu O. Identification of genetic factors controlling kernel hardness and related traits in a recombinant inbred population derived from a soft 3 ‘extra-soft’ wheat (Triticum aestivum L.) cross. Theor Appl Genet, 2012, 124: 207–221



[24]Yang J, Sears R G, Gill B S, Paulsen G M. Quantitative and molecular characterization of heat tolerance in hexaploid wheat. Euphytica, 2002, 126: 275–282



[25]Ren Y-Z(任永哲), Xu Y-H(徐艳花), Gui X-W(贵祥卫), Wang S-P(王素平), Ding J-P(丁锦平), Zhang Q-C(张庆琛), Ma Y-S(马原松), Pei D-L(裴冬丽). QTLs analysis of wheat seedling traits under salt stress. Sci Agric Sin (中国农业科学), 2012, 45(14): 2793–2800 (in Chinese with English abstract)



[26]Heidari B, Sayed-Tabatabaei B E, Saeidi G, Kearsey M, Suenaga K. Mapping QTL for grain yield, yield components, and spike features in a doubled haploid population of bread wheat. Genome, 2011, 54: 517–527



[27]Maccaferri M, Sanguineti M C, Demontis A, El-Ahmed A, Garcia del Moral L, Maalouf F, Nachit M, Nserallah N, Ouabbou H, Rhouma S, Royo C, Villegas D, Tuberosa R. Association mapping in durum wheat grown across a broad range of water regimes. J Exp Bot, 2011, 62: 409–438



[28]Wang Y, Sun X, Zhao Y, Kong F, Guo Y, Zhang G, Pu Y, Wu K, Li S. Enrichment of a common wheat genetic map and QTL mapping for fatty acid content in grain. Plant Sci, 2011, 181: 65–75



[29]Mir R R, Kumar N, Jaiswal V, Girdharwal N, Prasad M, Balyan H S, Gupta P K. Genetic dissection of grain weight in bread wheat through quantitative trait locus interval and association mapping. Mol Breed, 2012, 29: 963–972



[30]Kobayashi F, Takumi S, Handa H. Identification of quantitative trait loci for ABA responsiveness at the seedling stage associated with ABA-regulated gene expression in common wheat. Theor Appl Genet, 2010, 121: 629–641



[31]Marone D, Laido G, Gadaleta A, Colasuonno P, Ficco D B M, Giancaspro A, Giove S, Panio G, Russo M A, De Vita P, Cattivelli L, Papa R. A high-density consensus map of A and B wheat genomes. Theor Appl Genet, 2012, 125: 1619–1638



[32]Nik M M, Babaeian M, Tavassoli A. Effect of seed size and genotype on germination characteristic and seed nutrient content of wheat. Sci Res Essays, 2011, 6: 2019–2025



[33]Hallauer A R, Miranda J B. Quantitative Genetics in Maize Breeding. 2nd edn. Ames: Iowa State University Press, Ames. 1988, pp 380–420

[1] 习千辉, 徐梓瑗, 刘梦梦, 王宏艺, 郎凯琳, 井震海, 陈锋, 赵磊. 小麦籽粒铜含量的全基因组关联分析及候选基因预测[J]. 作物学报, 2026, 52(6): 1604-1617.
[2] 毛嘉琦, 黄朋雨, 赵佳佳, 郑兴卫, 武棒棒, 郝宇琼, 屈非, 刘成, 马朋涛, 郑军. 山西小麦品种白粉病抗性评价及抗病基因分子检测[J]. 作物学报, 2026, 52(6): 1669-1681.
[3] 唐宽强, 李公允, 宋美毅, 赵雪, 常春玲. 大豆株高性状全基因组关联分析及预测模型构建[J]. 作物学报, 2026, 52(6): 1743-1756.
[4] 杨欣雨, 崔文涛, 迪力尼格尔·阿力木, 汪凯翔, 吴鹏昊, 任姣姣. 玉米穗上叶片数全基因组关联分析和全基因组选择[J]. 作物学报, 2026, 52(5): 1573-1590.
[5] 闫安, 蒋昆炜, 王蓉圆, 田林, 张璐, 王韵, 徐建龙. 水稻剑叶小维管束数基因SVN7的鉴定与克隆[J]. 作物学报, 2026, 52(5): 1364-1372.
[6] 张超, 郭欢, 李忠玲, 岳淑宁, 赵娜. 基于BSA-seq技术定位玉米籽粒花青素关联基因[J]. 作物学报, 2026, 52(3): 780-789.
[7] 鲁雅妮, 丁超杰, 张煜, 杜习军, 齐学礼, 胡琳, 许为钢. 河南省200份小麦品种苗期茎基腐病抗性鉴定与全基因组关联分析[J]. 作物学报, 2026, 52(2): 363-375.
[8] 李诗晴, 王茜, 王素华, 张耀文, 王丽侠. 绿豆种质资源苗期耐盐性鉴定及相关基因发掘[J]. 作物学报, 2026, 52(2): 376-388.
[9] 李云香, 郭千纤, 侯万伟, 张小娟. 引进ICARDA小麦苗期根系抗旱性状的全基因组关联分析[J]. 作物学报, 2025, 51(9): 2387-2398.
[10] 李璐琪, 程宇坤, 白斌, 雷斌, 耿洪伟. 小麦叶片气孔相关性状全基因组关联分析[J]. 作物学报, 2025, 51(9): 2266-2284.
[11] 蔡金珊, 李超男, 王景一, 李宁, 柳玉平, 景蕊莲, 李龙, 孙黛珍. 小麦幼苗根系性状全基因组关联分析及TaSRL-3B优异等位基因发掘[J]. 作物学报, 2025, 51(8): 2020-2032.
[12] 李宜谦, 徐守振, 刘萍, 马麒, 谢斌, 陈红. 基于40K SNP芯片的陆地棉产量构成因素全基因组关联分析及单铃重位点挖掘[J]. 作物学报, 2025, 51(8): 2128-2138.
[13] 高梦娟, 赵贺莹, 陈家辉, 陈晓倩, 牛萌康, 钱琪润, 崔陆飞, 邢江敏, 银庆淼, 郭雯, 张宁, 孙丛苇, 阳霞, 裴丹, 贾奥琳, 陈锋, 余晓东, 任妍. 小麦抗纹枯病新位点Qse.hnau-5AS的定位及其候选基因鉴定[J]. 作物学报, 2025, 51(8): 2240-2250.
[14] 赵超男, 王金凤, 张玉, 张丽, 李瑞琦, 王鹏飞, 李鸽子, 张宏军, 虞波, 康国章. 全基因组关联分析定位与挖掘小麦氮高效基因[J]. 作物学报, 2025, 51(7): 1801-1813.
[15] 梁红凯, 赵苏蒙, 陆琼, 周鹏, 智慧, 刁现民, 贺强. 谷子微核心种质的构建[J]. 作物学报, 2025, 51(6): 1435-1444.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!