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作物学报 ›› 2016, Vol. 42 ›› Issue (11): 1592-1600.doi: 10.3724/SP.J.1006.2016.01592

• 作物遗传育种·种质资源·分子遗传学 • 上一篇    下一篇

不同密度下玉米穗部性状的QTL分析

王辉,梁前进,胡小娇,李坤,黄长玲,王琪,何文昭,王红武*,刘志芳*   

  1. 中国农业科学院作物科学研究所,北京 100081
  • 收稿日期:2016-03-03 修回日期:2016-06-20 出版日期:2016-11-12 网络出版日期:2016-07-04
  • 通讯作者: 刘志芳,E-mail: liuzhifang@caas.cn; 王红武,E-mail: wanghongwu@caas.cn
  • 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(2014CB138200),北京市科技计划项目(D141100005014003)和中国农业科学院科技创新工程项目资助。

QTL Mapping for Ear Architectural Traits under Three Plant Densities in Maize

WANG Hui,LIANG Qian-Jin,HU Xiao-Jiao,LI Kun,HUANG Chang-Ling,WANG Qi,HE Wen-Zhao,WANG Hong-Wu*,LIU Zhi-Fang*   

  1. Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China?
  • Received:2016-03-03 Revised:2016-06-20 Published:2016-11-12 Published online:2016-07-04
  • Contact: Liu Zhifang,E-mail: liuzhifang@caas.cn; Wang hongwu,E-mail: wanghongwu@caas.cn
  • Supported by:

    This study was supported by the China National 973 Project (2014CB138200), Program of Beijing Municipal Science and Technology (D141100005014003), and Agricultural Science and Technology Innovation Program (ASTIP) of CAAS.

摘要:

为研究玉米穗部性状对不同种植密度的遗传响应,以郑58和HD568为亲本构建的220个重组自交系群体为材料,于2014年春、2014年冬及2015年春分别在北京和海南进行3个种植密度的田间试验,调查玉米穗长、穗粗、穗行数和行粒数等表型性状。利用SAS软件计算穗部性状的最优线性无偏估计值(BLUP),并采用完备区间作图法进行QTL定位。结果表明,在3个种植密度下共检测到42个QTL,单个QTL可解释4.20%~14.07%的表型变异。3个种植密度下同时检测到位于第2染色体上控制穗行数的QTL。2个种植密度下同时检测到4个与穗粗、穗行数和行粒数有关的QTL,其中第4染色体上1个与穗行数有关的主效QTL,在低、中种植密度下可分别解释表型变异的10.88%和14.07%。此外,在第2、4和9染色体上检测到3个同时调控不同穗部性状的QTL。研究结果表明玉米穗部性状在不同种植密度下的遗传调控发生变化,在不同密度下共同检测到的稳定QTL可应用于精细定位或开发玉米耐密性分子标记用于辅助育种。

关键词: 玉米, 穗部性状, 密度, 数量性状位点(QTL), 最优线性无偏估计(BLUP)

Abstract:

To identify genetic factors of ear architectural traits response to plant density, we developed a recombination inbred line (RIL) mapping population with 220 families from a cross between two maize inbred lines, Zheng 58 and HD568. The filed experiments were performed in 2014 and 2015 seasons of Beijing and Hainan. The ear architectural traits including ear length, ear diameter, ear row number and kernel number per row were evaluated under three plant densities in each environment. With the BLUP value estimated by SAS software, QTLs for ear architectural traits were detected by inclusive composite interval mapping (ICIM) using Windows QTL ICI-Mapping software. In total, 42 QTLs were detected under three plant densities, each QTL explained phenotypic variation ranging from 4.20% to 14.07%. One QTL related to ear row number on chromosome 2 was repeatedly detected under three plant densities. Four QTLs related to ear diameter, ear row number and kernel number per row were commonly detected under two plant densities, among them an ear row number QTL was located on chromosome 4 with explained 10.88% and 14.07% of phenotypic variance under plant density of 52 500 plants ha-1 and 67 500 plants ha-1. In addition, we found three QTLs for different ear architectural traits on chromosomes 2, 4 and 9 simultaneously. This study revealed the genetic mechanisms of ear architectural traits changed under different plant densities. The QTLs stably expressed under different plant densities can be applied in fine mapping and marker assisted selection in density tolerance breeding of maize.

Key words: Maize, Ear architectural traits, Plant density, QTL, BLUP

[1]Ku L X, Zhao W M, Zhang J, Wu L C, Wang C L, Wang P A, Zhang W Q, Chen Y H. Quantitative trait loci mapping of leaf angle and leaf orientation value in maize (Zea mays L.). Theor Appl Genet, 2010, 121: 951–959 [2]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 [3]Li C, Li Y, Sun B, Peng B, Liu C, Liu Z Z, Yang Z Z, Li Q C, Tan W W, Zhang Y, Wang D, Shi Y S, Song Y C, Wang T Y, Li Y. Quantitative trait loci mapping for yield components and kernel-related traits in multiple connected RIL populations in maize. Euphytica, 2013, 19: 303–316 [4]Nikoli? A, An?elkovi? V, Dodig D, Drini? S M, Kravi? N, Mici?-Ignjatovi? D. Identification of QTLs for drought tolerance in maize: II. yield and yield components. Genetika, 2013, 45: 341–350 [5]Liu L, Du Y F, Huo D A, Wang M, Shen X M, Yue B, Qiu F Z, Zheng Y L, Yan J B, Zhang Z X. Genetic architecture of maize kernel row number and whole genome prediction. Theor Appl Genet, 2015, 128: 2243–2254 [6]Zhang Z H, Wu X Y, Shi C N, Wang R N, Li S F, Wang Z H, Liu Z H, Xue Y D, Tang G L, Tang J H. Genetic dissection of the maize kernel development process via conditional QTL mapping for three developing kernel-related traits in an immortalized F2 population. Mol General Genet, 2015, 291: 437–454 [7]Peter B, Namiko S N, David J. Quantitative variation in maize kernel row number is controlled by the FASCIATED EAR2 locus. Nat Genet, 2013, 45: 334–337 [8]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(11): e1005670 [9]Guo J, Su G, Zhang J, Wang G. Genetic analysis and QTL mapping of maize yield and associate agronomic traits under semi-arid land condition. Afr J Biotechnol, 2008, 7: 1829–1838 [10]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 [11]Gonzalo M, Holland J B, Vyn T J, Mclntyre L M. Direct mapping of density response in a population of B73 × Mo17 recombinant inbred lines of maize (Zea mays L.). Heredity, 2010, 104: 583–599 [12]Guo J, Chen Z, Liu Z, Wang B B, Song W B, Li W, Chen J, Dai J R, Lai J S. Identification of genetic factors affecting plant density response through QTL mapping of yield component traits in maize (Zea mays L.). Euphytica, 2011, 182: 409–422 [13]刘小刚. 玉米茎秆强度QTL定位研究. 中国农业科学院硕士学位论文, 北京, 2014 Liu X G. Quantitative Trait Locus Analysis of Stalk Strength in Maize. MS Thesis of Chinese Academy of Agricultural Sciences, Beijing, China, 2014 (in Chinese with English abstract) [14]马飞前. 玉米茎秆纤维性状QTL定位. 中国农业科学院硕士学位论文, 北京, 2014 Ma F Q. Mapping of Quantitative Trait Loci (QTL) for Stalk Fiber Traits in Maize. MS Thesis of Dissertation of Chinese Academy of Agricultural Sciences, Beijing, China, 2014 (in Chinese with English abstract) [15]Ganal M W, Gregor D, Andreas P, Aurélie B, Buckler E S, Alain C, Clarke J D, Graner E M, Hansen M, Joets J, Paslier M-C L, McMullen M D, Montalent P, Rose M, Sch?n C C, Sun Q, Walter H, Martin O C, Matthieu F. A large maize (Zea mays L.) SNP genotyping array: development and germplasm genotyping, and genetic mapping to compare with the B73 reference genome. Plos One, 2011, 6(12): e28334 [16]Van Ooijen J: JoinMap4.software for the Calculation of Genetic Linkage Maps in Experimental Populations. Kyazma B V, Wageningen, Netherlands, 2006. p 56 [17]Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira M A R, Bender D, Maller J, Sklar P, Bakker P I W, Daly M J, Sham P C. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Human Genet, 2007, 81: 559–575 [18]Wang J K. QTL IciMapping: Integrated Software for Building Linkage Maps and Mapping Quantitative Trait Genes. International Plant and Animal Genome Conference XXI 2013. Scherago International, San Diego, CA, 2013 [19]Kosambi D D. The estimation of map distance from recombination valves. Annu Eugenia, 1994, 12: 172–175 [20]Knapp S, Stroup W, Ross W. Exact confidence intervals for heritability on a progeny mean basis1. Crop Sci, 1985, 25: 192–194 [21]Piepho H P, M?hring J, Melchinger A E, Büchse A. BLUP for phenotypic selection in plant breeding and variety testing. Euphytica, 2008, 161: 209–228 [22]王建康. 数量性状基因的完备区间作图方法. 作物学报, 2009, 35: 239–245 Wang J K. Inclusive composite interval mapping of quantitative trait genes. Acta Agron Sin, 2009, 35: 239–245 (in Chinese with English abstract) [23]Cai L, Li K, Yang X, Li J. Identification of large-effect QTL for kernel row number has potential for maize yield improvement. Mol Breed, 2014, 34: 1087–1096 [24]Yang C, Liu J, Rong T Z. Detection of quantitative trait loci for ear row number in F2 populations of maize. Genet Mol Res, 2015, 14: 14229–14238 [25]Li K, Yan J, Li J, Yang X. Genetic architecture of rind penetrometer resistance in two maize recombinant inbred line populations. BMC Plant Biol, 2014, 14: 152 [26]秦伟伟, 李永祥, 李春辉, 陈林, 吴迅, 白娜, 石云素, 宋燕春, 张登峰, 王天宇, 黎裕. 基于高密度遗传图谱的玉米籽粒性状QTL定位. 作物学报, 2015, 41: 1510–1518 Qin W W, Li Y X , Li C H, Chen L, Wu X, Bai N, Shi Y S, Song Y C, Zhang D F, Wang T Y, Li Y. QTL mapping for kernel related traits based on a high-density genetic map. Acta Agron Sin, 2015, 41: 1510–1518 (in Chinese with English abstract) [27]吕学高, 蔡一林, 陈天青, 徐德林, 王伟林, 刘志斋, 王久光. 玉米穗部性状QTL定位. 西南大学学报: 自然科学版, 2008, 30(2): 64–70 Lü X G, Cai Y L, Chen T Q, Xu D L, Wang W L, Liu Z Z, Wang J G. QTL mapping for ear traits in maize (Zea mays L.). J Southwest Univ (Nat Sci Edn), 2008, 30(2): 64–70 (in Chinese with English abstract) [28]Veldboom L R, Lee M. Genetic mapping of quantitative trait loci in maize in stress and nonstress environments: I. Grain yield and yield components. Crop Sci, 1996, 36: 1310–1319 [29]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) [30]Brown P J, Upadyayula N, Mahone G S, Tian F, Bradbury P J, Myles S, Holland J B, Flint-Garcia S, McMullen M D, Buckler E S, Rocheford T R. Distinct genetic architectures for male and female inflorescence traits of maize. PloS Genet, 2011, 7: 1276–1280 [31]Würschum T. Mapping QTL for agronomic traits in breeding population. Theor Appl Genet, 2012, 125: 201–210 [32]Cai L C, Li K, Yang X H, Li J S. Identification of large-effect QTL for kernel row number has potential for maize yield improvement. Mol Breed, 2014, 34: 1087–1096 [33]Tuberosa R, Salvi S, Sanguineti M C, Landi P, Maccaferri M, Conti S. Mapping QTLs regulating morpho-physiological traits and yield: case studies, shortcomings and perspectives in drought-stressed maize. Ann Bot, 2002, 89: 941–963 [34]Zhuang J Y, Lin H X, Lu J, Qian H R, Hittalmani S, Huang N, Zheng K L. Analysis of QTL environment interaction for yield components and plant height in rice. Theor Appl Genet, 1997, 95: 799–808 [35]Chen J, Zhu J. Genetic effects and genotype × environment interactions for cooking quality traits in Indica-japonica crosses of rice (Oryza sativa L.). Euphytica, 1999, 109: 9–15 [36]Shi C H, He C X, Zhu J, Chen J G. Analysis of genetic effects and genotype × environment interaction effects for apparent quality traits of indica rice. Chin J Rice Sci, 1999, 13: 179–182 (in English with Chinese abstract) [37]Huang N, Angeles E R, Domingo J, Magpantay S, Singh S, Zhang G, Kumaravadivel N, Bennett J, Khush G S. Pyramiding of bacterial blight resistance genes in rice: marker-assisted selection using RFLP and PCR. Theor Appl Genet, 1997, 95: 313–320 [38]Tanksley S D, Ahn N, Causse M, Coffman R, Fulton T, McCouch S R, Second G, Tai T, Wang Z, Wu K, Yu Z. RFLP mapping of the rice genome. In: Rice Genetics II. Los Banos, Laguna: IRRI, 1991. pp 435–442

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