Marker-assisted selection strategy to pyramid two or more QTLs for quantitative trait-grain yield under drought

被引:57
作者
Kumar, Arvind [1 ]
Sandhu, Nitika [1 ]
Dixit, Shalabh [1 ]
Yadav, Shailesh [1 ]
Swamy, B. P. M. [1 ]
Shamsudin, Noraziyah Abd Aziz [1 ,2 ]
机构
[1] Int Rice Res Inst, DAPO Box 7777, Manila, Philippines
[2] Univ Kebangsaan Malaysia, Fac Sci & Technol, Bangi 43600, Selangor, Malaysia
基金
比尔及梅琳达.盖茨基金会;
关键词
Drought; Drought yield QTLs; Marker-assisted selection breeding strategy; Pyramiding; Rice; BLIGHT RESISTANCE GENES; RICE CULTIVAR; WHEAT; TOLERANCE; STRESS; REGISTRATION; COMPONENTS; REVEALS; IDENTIFICATION; ASSOCIATION;
D O I
10.1186/s12284-018-0227-0
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
Background: Marker-assisted breeding will move forward from introgressing single/multiple genes governing a single trait to multiple genes governing multiple traits to combat emerging biotic and abiotic stresses related to climate change and to enhance rice productivity. MAS will need to address concerns about the population size needed to introgress together more than two genes/QTLs. In the present study, grain yield and genotypic data from different generations (F-3 to F-8) for five marker-assisted breeding programs were analyzed to understand the effectiveness of synergistic effect of phenotyping and genotyping in early generations on selection of better progenies. Results: Based on class analysis of the QTL combinations, the identified superior QTL classes in F-3/BC1F3/BC2F3 generations with positive QTL x QTL and QTL x background interactions that were captured through phenotyping maintained its superiority in yield under non-stress (NS) and reproductive-stage drought stress (RS) across advanced generations in all five studies. The marker-assisted selection breeding strategy combining both genotyping and phenotyping in early generation significantly reduced the number of genotypes to be carried forward. The strategy presented in this study providing genotyping and phenotyping cost savings of 25-68% compared with the traditional marker-assisted selection approach. The QTL classes, Sub1 + qDTY(1.1) + qDTY(2.1) + qDTY(3.1) and Sub1 + qDTY(2.1) + qDTY(3.1) in Swarna-Sub1, Sub1 + qDTY(1.1) + qDTY(1.2), Sub1 + qDTY(1.1) + qDTY(2.2) and Sub1 + qDTY(2.2) + qDTY(12.1) in IR64-Sub1, qDTY(2.2) + qDTY4.1 in Samba Mahsuri, Sub1 + qDTY(3.1) + qDTY(6.1) + qDTY(6.2) and Sub1 + qDTY(6.1) + qDTY(6.2) in TDK1-Sub1 and qDTY(12.1) + qDTY(3.1) and qDTY(2.2) + qDTY(3.1) in MR219 had shown better and consistent performance under NS and RS across generations over other QTL classes. Conclusion: "Deployment of this procedure will save time and resources and will allow breeders to focus and advance only germplasm with high probability of improved performance. The identification of superior QTL classes and capture of positive QTL x QTL and QTL x background interactions in early generation and their consistent performance in subsequent generations across five backgrounds supports the efficacy of a combined MAS breeding strategy".
引用
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页数:16
相关论文
共 73 条
[1]   QTL mapping in three tropical maize populations reveals a set of constitutive and adaptive genomic regions for drought tolerance [J].
Almeida, Gustavo Dias ;
Makumbi, Dan ;
Magorokosho, Cosmos ;
Nair, Sudha ;
Borem, Aluizio ;
Ribaut, Jean-Marcel ;
Baenziger, Marianne ;
Prasanna, Boddupalli M. ;
Crossa, Jose ;
Babu, Raman .
THEORETICAL AND APPLIED GENETICS, 2013, 126 (03) :583-600
[2]   Development and evaluation of a field-based high-throughput phenotyping platform [J].
Andrade-Sanchez, Pedro ;
Gore, Michael A. ;
Heun, John T. ;
Thorp, Kelly R. ;
Carmo-Silva, A. Elizabete ;
French, Andrew N. ;
Salvucci, Michael E. ;
White, Jeffrey W. .
FUNCTIONAL PLANT BIOLOGY, 2014, 41 (01) :68-79
[3]  
Antle JM, 2016, AGR SYST AGSY, P1
[4]   Registration of 'NE01643' Wheat [J].
Baenziger, P. S. ;
Beecher, B. ;
Graybosch, R. A. ;
Ibrahim, A. M. H. ;
Baltensperger, D. D. ;
Nelson, L. A. ;
Jin, Y. ;
Wegulo, S. N. ;
Watkins, J. E. ;
Hatchett, J. H. ;
Chen, Ming-Shun ;
Bai, Guihua .
JOURNAL OF PLANT REGISTRATIONS, 2008, 2 (01) :36-42
[5]   A multi-sensor system for high throughput field phenotyping in soybean and wheat breeding [J].
Bai, Geng ;
Ge, Yufeng ;
Hussain, Waseem ;
Baenziger, P. Stephen ;
Graef, George .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2016, 128 :181-192
[6]   Historical Perspective, Development and Applications of Next-Generation Sequencing in Plant Virology [J].
Barba, Marina ;
Czosnek, Henryk ;
Hadidi, Ahmed .
VIRUSES-BASEL, 2014, 6 (01) :106-136
[7]   Genome-Wide Association Mapping for Yield and Other Agronomic Traits in an Elite Breeding Population of Tropical Rice (Oryza sativa) [J].
Begum, Hasina ;
Spindel, Jennifer E. ;
Lalusin, Antonio ;
Borromeo, Teresita ;
Gregorio, Glenn ;
Hernandez, Jose ;
Virk, Parminder ;
Collard, Bertrand ;
McCouch, Susan R. .
PLOS ONE, 2015, 10 (03)
[8]   Genetic dissection of grain yield and physical grain quality in bread wheat (Triticum aestivum L.) under water-limited environments [J].
Bennett, Dion ;
Izanloo, Ali ;
Reynolds, Matthew ;
Kuchel, Haydn ;
Langridge, Peter ;
Schnurbusch, Thorsten .
THEORETICAL AND APPLIED GENETICS, 2012, 125 (02) :255-271
[9]   A large-effect QTL for grain yield under reproductive-stage drought stress in upland rice [J].
Bernier, Jerome ;
Kumar, Arvind ;
Ramaiah, Venuprasad ;
Spaner, Dean ;
Atlin, Gary .
CROP SCIENCE, 2007, 47 (02) :507-518
[10]   Genomic Selection in the Era of Next Generation Sequencing for Complex Traits in Plant Breeding [J].
Bhat, Javaid A. ;
Ali, Sajad ;
Salgotra, Romesh K. ;
Mir, Zahoor A. ;
Dutta, Sutapa ;
Jadon, Vasudha ;
Tyagi, Anshika ;
Mushtaq, Muntazir ;
Jain, Neelu ;
Singh, Pradeep K. ;
Singh, Gyanendra P. ;
Prabhu, K. V. .
FRONTIERS IN GENETICS, 2016, 7