Login Register Cart 0
Generic placeholder image

Current Genomics

Editor-in-Chief

ISSN (Print): 1389-2029
ISSN (Online): 1875-5488

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Review Article

The Era of Plant Breeding: Conventional Breeding to Genomics-assisted Breeding for Crop Improvement

Author(s): Thumadath Palayullaparambil Ajeesh Krishna, Duraipandiyan Veeramuthu*, Theivanayagam Maharajan and Mariapackiam Soosaimanickam

Volume 24, Issue 1, 2023

Published on: 30 May, 2023

Page: [24 - 35] Pages: 12

DOI: 10.2174/1389202924666230517115912

Price: $65

Abstract

Plant breeding has made a significant contribution to increasing agricultural production. Conventional breeding based on phenotypic selection is not effective for crop improvement. Because phenotype is considerably influenced by environmental factors, which will affect the selection of breeding materials for crop improvement. The past two decades have seen tremendous progress in plant breeding research. Especially the availability of high-throughput molecular markers followed by genomic-assisted approaches significantly contributed to advancing plant breeding. Integration of speed breeding with genomic and phenomic facilities allowed rapid quantitative trait loci (QTL)/gene identifications and ultimately accelerated crop improvement programs. The advances in sequencing technology helps to understand the genome organization of many crops and helped with genomic selection in crop breeding. Plant breeding has gradually changed from phenotype-to-genotype-based to genotype-to-phenotype-based selection. High-throughput phenomic platforms have played a significant role in the modern breeding program and are considered an essential part of precision breeding. In this review, we discuss the rapid advance in plant breeding technology for efficient crop improvements and provide details on various approaches/platforms that are helpful for crop improvement. This review will help researchers understand the recent developments in crop breeding and improvements.

« Previous Next »
Graphical Abstract

[1]
Vetriventhan, M.; Azevedo, V.C.R.; Upadhyaya, H.D.; Nirmalakumari, A.; Kane-Potaka, J.; Anitha, S.; Ceasar, S.A.; Muthamilarasan, M.; Bhat, B.V.; Hariprasanna, K.; Bellundagi, A.; Cheruku, D.; Backiyalakshmi, C.; Santra, D.; Vanniarajan, C.; Tonapi, V.A. Genetic and genomic resources, and breeding for accelerating improvement of small millets: Current status and future interventions. Nucleus, 2020, 63(3), 217-239.
[http://dx.doi.org/10.1007/s13237-020-00322-3]
[2]
Khan, A.W.; Garg, V.; Roorkiwal, M.; Golicz, A.A.; Edwards, D.; Varshney, R.K. Super-pangenome by integrating the wild side of a species for accelerated crop improvement. Trends Plant Sci., 2020, 25(2), 148-158.
[http://dx.doi.org/10.1016/j.tplants.2019.10.012] [PMID: 31787539]
[3]
Arrones, A.; Vilanova, S.; Plazas, M.; Mangino, G.; Pascual, L.; Díez, M.J.; Prohens, J.; Gramazio, P. The dawn of the age of multi-parent MAGIC populations in plant breeding: novel powerful next-generation resources for genetic analysis and selection of recombinant elite material. Biology, 2020, 9(8), 229.
[http://dx.doi.org/10.3390/biology9080229] [PMID: 32824319]
[4]
Oladosu, Y.; Rafii, M.Y.; Samuel, C.; Fatai, A.; Magaji, U.; Kareem, I.; Kamarudin, Z.S.; Muhammad, I.; Kolapo, K. Drought resistance in rice from conventional to molecular breeding: A review. Int. J. Mol. Sci., 2019, 20(14), 3519.
[http://dx.doi.org/10.3390/ijms20143519] [PMID: 31323764]
[5]
Ajeesh Krishna, T.P.; Maharajan, T.; Ignacimuthu, S.; Antony Ceasar, S. Genomic-assisted breeding in finger millet (Eleusine Coracana (L.) Gaertn.) for abiotic stress tolerance. In: Genomic Des. Abiotic Stress Resist. Cereal Crop; Springer, 2021; pp. 291-317.
[http://dx.doi.org/10.1007/978-3-030-75875-2_8]
[6]
Krishna, T.P.A.; Theivanayagam, M.; Roch, G.V.; Duraipandiyan, V.; Ignacimuthu, S. Microsatellite marker: importance and implications of cross-genome analysis for finger millet (Eleusine coracana (L.) Gaertn). Curr. Biotechnol., 2020, 9(3), 160-170.
[http://dx.doi.org/10.2174/2211550109999200908090745]
[7]
Poland, J.A.; Rife, T.W. Genotyping‐by‐sequencing for plant breeding and genetics. Plant Genome, 2012, 5(3), 92-102.
[8]
Rashid, B.; Tariq, M.; Khalid, A.; Shams, F.; Ali, Q.; Ashraf, F.; Ghaffar, I.; Khan, M.I.; Rehman, R.; Husnain, T. Crop improvement: New approaches and modern techniques. Plant Gene Trait, 2017, 8(3), 18-30.
[http://dx.doi.org/10.5376/pgt.2017.08.0003]
[9]
Singh, R.K.; Prasad, A.; Muthamilarasan, M.; Parida, S.K.; Prasad, M. Breeding and biotechnological interventions for trait improvement: Status and prospects. Planta, 2020, 252(4), 54.
[http://dx.doi.org/10.1007/s00425-020-03465-4] [PMID: 32948920]
[10]
Ray, S.; Satya, P. Next generation sequencing technologies for next generation plant breeding. Front. Plant Sci., 2014, 5, 367.
[http://dx.doi.org/10.3389/fpls.2014.00367] [PMID: 25126091]
[11]
Varshney, R.K.; Bohra, A.; Yu, J.; Graner, A.; Zhang, Q.; Sorrells, M.E. Designing future crops: Genomics-assisted breeding comes of age. Trends Plant Sci., 2021, 26(6), 631-649.
[http://dx.doi.org/10.1016/j.tplants.2021.03.010] [PMID: 33893045]
[12]
Fu, Y.B.; Yang, M.H.; Zeng, F.; Biligetu, B. Searching for an accurate marker-based prediction of an individual quantitative trait in molecular plant breeding. Front. Plant Sci., 2017, 8, 1182.
[http://dx.doi.org/10.3389/fpls.2017.01182] [PMID: 28729875]
[13]
He, T.; Li, C. Harness the power of genomic selection and the potential of germplasm in crop breeding for global food security in the era with rapid climate change. Crop J., 2020, 8(5), 688-700.
[http://dx.doi.org/10.1016/j.cj.2020.04.005]
[14]
Ziervogel, G.; Ericksen, P.J. Adapting to climate change to sustain food security. Wiley Interdiscip. Rev. Clim. Change, 2010, 1(4), 525-540.
[http://dx.doi.org/10.1002/wcc.56]
[15]
Tajibayev, D.; Yusov, V.S.; Chudinov, V.A.; Mal’chikov, P.N.; Rozova, M.A.; Shamanin, V.P.; Shepelev, S.S.; Sharma, R.; Tsygankov, V.I.; Morgounov, A.I. Genotype by environment interactions for spring durum wheat in Kazakhstan and Russia. Ecol. Genet. Genom., 2021, 21, 100099.
[http://dx.doi.org/10.1016/j.egg.2021.100099]
[16]
Varshney, R.K.; Sinha, P.; Singh, V.K.; Kumar, A.; Zhang, Q.; Bennetzen, J.L. 5Gs for crop genetic improvement. Curr. Opin. Plant Biol., 2020, 56, 190-196.
[http://dx.doi.org/10.1016/j.pbi.2019.12.004] [PMID: 32005553]
[17]
Scheben, A.; Wolter, F.; Batley, J.; Puchta, H.; Edwards, D. Towards CRISPR/Cas crops-bringing together genomics and genome editing. New Phytol., 2017, 216(3), 682-698.
[http://dx.doi.org/10.1111/nph.14702] [PMID: 28762506]
[18]
Ndlovu, N. Application of genomics and phenomics in plant breeding for climate resilience. Asian. Plant. Res. J., 2020, 6, 53-66.
[http://dx.doi.org/10.9734/aprj/2020/v6i430137]
[19]
Atefi, A.; Ge, Y.; Pitla, S.; Schnable, J. Robotic technologies for high-throughput plant phenotyping: Contemporary reviews and future perspectives. Front. Plant Sci., 2021, 12, 611940.
[http://dx.doi.org/10.3389/fpls.2021.611940] [PMID: 34249028]
[20]
Hillary, V.E.; Ceasar, S.A. Application of CRISPR/Cas9 genome editing system in cereal crops. Open Biotechnol. J., 2019, 13(1), 173-179.
[http://dx.doi.org/10.2174/1874070701913010173]
[21]
Gosa, S.C.; Lupo, Y.; Moshelion, M. Quantitative and comparative analysis of whole-plant performance for functional physiological traits phenotyping: New tools to support pre-breeding and plant stress physiology studies. Plant Sci., 2019, 282, 49-59.
[http://dx.doi.org/10.1016/j.plantsci.2018.05.008] [PMID: 31003611]
[22]
Dar, Z.A.; Dar, S.A.; Khan, J.A.; Lone, A.A.; Langyan, S.; Lone, B.A.; Kanth, R.H.; Iqbal, A.; Rane, J.; Wani, S.H.; Alfarraj, S.; Alharbi, S.A.; Brestic, M.; Ansari, M.J. Identification for surrogate drought tolerance in maize inbred lines utilizing high-throughput phenomics approach. PLoS One, 2021, 16(7), e0254318.
[http://dx.doi.org/10.1371/journal.pone.0254318] [PMID: 34314420]
[23]
Esposito, S.; Carputo, D.; Cardi, T.; Tripodi, P. Applications and trends of machine learning in genomics and phenomics for next-generation breeding. Plants, 2019, 9(1), 34.
[http://dx.doi.org/10.3390/plants9010034] [PMID: 31881663]
[24]
Yang, W.; Feng, H.; Zhang, X.; Zhang, J.; Doonan, J.H.; Batchelor, W.D.; Xiong, L.; Yan, J. Crop phenomics and high-throughput phenotyping: Past decades, current challenges, and future perspectives. Mol. Plant, 2020, 13(2), 187-214.
[http://dx.doi.org/10.1016/j.molp.2020.01.008] [PMID: 31981735]
[25]
Houle, D.; Govindaraju, D.R.; Omholt, S. Phenomics: The next challenge. Nat. Rev. Genet., 2010, 11(12), 855-866.
[http://dx.doi.org/10.1038/nrg2897] [PMID: 21085204]
[26]
Mueller-Sim, T.; Jenkins, M.; Abel, J.; Kantor, G. The Robotanist: A ground-based agricultural robot for high-throughput crop phenotyping. IEEE Int. Conf. Robot. Autom., IEEE, 2017, p. 3634-3639.
[http://dx.doi.org/10.1109/ICRA.2017.7989418]
[27]
Li, D.; Quan, C.; Song, Z.; Li, X.; Yu, G.; Li, C.; Muhammad, A. High-throughput plant phenotyping platform (HT3P) as a novel tool for estimating agronomic traits from the lab to the field. Front. Bioeng. Biotechnol., 2021, 8, 623705.
[http://dx.doi.org/10.3389/fbioe.2020.623705] [PMID: 33520974]
[28]
Shafiekhani, A.; Kadam, S.; Fritschi, F.; DeSouza, G. Vinobot and vinoculer: two robotic platforms for high-throughput field phenotyping. Sensors, 2017, 17(12), 214.
[http://dx.doi.org/10.3390/s17010214] [PMID: 28124976]
[29]
Biber, P.; Weiss, U.; Dorna, M.; Albert, A. Navigation system of the autonomous agricultural robot Bonirob In: Work. Agric. Robot. Enabling Safe, Effic. Afford. Robot. Food Prod. (Collocated with IROS 2012); Vilamoura Port, 2012; p. 1-7.
[30]
Fan, J.; Zhang, Y.; Wen, W.; Gu, S.; Lu, X.; Guo, X. The future of Internet of Things in agriculture: Plant high-throughput phenotypic platform. J. Clean. Prod., 2021, 280, 123651.
[http://dx.doi.org/10.1016/j.jclepro.2020.123651]
[31]
Zhang, Y.; Zhang, N. Imaging technologies for plant high-throughput phenotyping: A review. Front. Agric. Sci. Eng., 2018, 0(0), 0.
[http://dx.doi.org/10.15302/J-FASE-2018242]
[32]
Jin, X.; Zarco-Tejada, P.J.; Schmidhalter, U.; Reynolds, M.P.; Hawkesford, M.J.; Varshney, R.K.; Yang, T.; Nie, C.; Li, Z.; Ming, B.; Xiao, Y.; Xie, Y.; Li, S. High-throughput estimation of crop traits: A review of ground and aerial phenotyping platforms. IEEE Geosci. Remote Sens. Mag., 2021, 9(1), 200-231.
[http://dx.doi.org/10.1109/MGRS.2020.2998816]
[33]
Xie, C.; Yang, C. A review on plant high-throughput phenotyping traits using UAV-based sensors. Comput. Electron. Agric., 2020, 178, 105731.
[http://dx.doi.org/10.1016/j.compag.2020.105731]
[34]
Varshney, R.K.; Nayak, S.N.; May, G.D.; Jackson, S.A. Next-generation sequencing technologies and their implications for crop genetics and breeding. Trends Biotechnol., 2009, 27(9), 522-530.
[http://dx.doi.org/10.1016/j.tibtech.2009.05.006] [PMID: 19679362]
[35]
Varshney, R.K.; Terauchi, R.; McCouch, S.R. Harvesting the promising fruits of genomics: Applying genome sequencing technologies to crop breeding. PLoS Biol., 2014, 12(6), e1001883.
[http://dx.doi.org/10.1371/journal.pbio.1001883] [PMID: 24914810]
[36]
He, J.; Zhao, X.; Laroche, A.; Lu, Z.X.; Liu, H.; Li, Z. Genotyping-by-sequencing (GBS), an ultimate marker-assisted selection (MAS) tool to accelerate plant breeding. Front. Plant Sci., 2014, 5, 484.
[http://dx.doi.org/10.3389/fpls.2014.00484] [PMID: 25324846]
[37]
Moorthie, S.; Mattocks, C.J.; Wright, C.F. Review of massively parallel DNA sequencing technologies. HUGO J., 2011, 5(1-4), 1-12.
[http://dx.doi.org/10.1007/s11568-011-9156-3] [PMID: 23205160]
[38]
Pareek, C.S.; Smoczynski, R.; Tretyn, A. Sequencing technologies and genome sequencing. J. Appl. Genet., 2011, 52(4), 413-435.
[http://dx.doi.org/10.1007/s13353-011-0057-x] [PMID: 21698376]
[39]
Varshney, R.K.; Ribaut, J.M.; Buckler, E.S.; Tuberosa, R.; Rafalski, J.A.; Langridge, P. Can genomics boost productivity of orphan crops? Nat. Biotechnol., 2012, 30(12), 1172-1176.
[http://dx.doi.org/10.1038/nbt.2440] [PMID: 23222781]
[40]
Krishna, T.P.A.; Maharajan, T.; Ceasar, S.A. The role of membrane transporters in the biofortification of zinc and iron in plants. Biol. Trace Elem. Res., 2023, 201(1), 464-478.
[http://dx.doi.org/10.1007/s12011-022-03159-w] [PMID: 35182385]
[41]
Ajeesh Krishna, T.P.; Maharajan, T.; Ceasar, S.A. Improvement of millets in the post-genomic era. Physiol. Mol. Biol. Plants, 2022, 28(3), 669-685.
[http://dx.doi.org/10.1007/s12298-022-01158-8] [PMID: 35465206]
[42]
Mannur, D.M.; Babbar, A.; Thudi, M.; Sabbavarapu, M.M.; Roorkiwal, M.; Yeri, S.B.; Bansal, V.P.; Jayalakshmi, S.K.; Singh Yadav, S.; Rathore, A.; Chamarthi, S.K.; Mallikarjuna, B.P.; Gaur, P.M.; Varshney, R.K. Super Annigeri 1 and improved JG 74: Two Fusarium wilt-resistant introgression lines developed using marker-assisted backcrossing approach in chickpea (Cicer arietinum L.). Mol. Breed., 2019, 39(1), 2.
[http://dx.doi.org/10.1007/s11032-018-0908-9] [PMID: 30631246]
[43]
Sandhu, N.; Yadav, S.; Catolos, M.; Cruz, M.T.S.; Kumar, A. Developing climate-resilient, direct-seeded, adapted multiple-stress-tolerant rice applying genomics-assisted breeding. Front. Plant Sci., 2021, 12, 637488.
[http://dx.doi.org/10.3389/fpls.2021.637488] [PMID: 33936127]
[44]
Varshney, R.K.; Mohan, S.M.; Gaur, P.M.; Gangarao, N.V.P.R.; Pandey, M.K.; Bohra, A.; Sawargaonkar, S.L.; Chitikineni, A.; Kimurto, P.K.; Janila, P.; Saxena, K.B.; Fikre, A.; Sharma, M.; Rathore, A.; Pratap, A.; Tripathi, S.; Datta, S.; Chaturvedi, S.K.; Mallikarjuna, N.; Anuradha, G.; Babbar, A.; Choudhary, A.K.; Mhase, M.B.; Bharadwaj, C.; Mannur, D.M.; Harer, P.N.; Guo, B.; Liang, X.; Nadarajan, N.; Gowda, C.L.L. Achievements and prospects of genomics-assisted breeding in three legume crops of the semi-arid tropics. Biotechnol. Adv., 2013, 31(8), 1120-1134.
[http://dx.doi.org/10.1016/j.biotechadv.2013.01.001] [PMID: 23313999]
[45]
Kaiser, N.; Douches, D.; Dhingra, A.; Glenn, K.C.; Herzig, P.R.; Stowe, E.C.; Swarup, S. The role of conventional plant breeding in ensuring safe levels of naturally occurring toxins in food crops. Trends Food Sci. Technol., 2020, 100, 51-66.
[http://dx.doi.org/10.1016/j.tifs.2020.03.042]
[46]
Ajeesh Krishna, T.P.; Ceasar, S.A.; Maharajan, T.; Ramakrishnan, M.; Duraipandiyan, V.; Al-Dhabi, N.A.; Ignacimuthu, S. Improving the zinc-use efficiency in plants: A review. SABRAO J. Breed. Genet., 2017, 49(3), 211-230.
[47]
Tuberosa, R. Phenotyping for drought tolerance of crops in the genomics era. Front. Physiol., 2012, 3, 347.
[http://dx.doi.org/10.3389/fphys.2012.00347] [PMID: 23049510]
[48]
Varshney, R.; Graner, A.; Sorrells, M. Genomics-assisted breeding for crop improvement. Trends Plant Sci., 2005, 10(12), 621-630.
[http://dx.doi.org/10.1016/j.tplants.2005.10.004] [PMID: 16290213]
[49]
Tiwari, S.; Yadav, S.K.; Sahu, V.K.; Tripathi, M.K. Current status and future prospects of marker assisted breeding for genetic improvement of minor millets. Int. J. Curr. Microbiol. Appl. Sci., 2018, 7(12), 2587-2590.
[http://dx.doi.org/10.20546/ijcmas.2018.712.293]
[50]
Dai, D.; Ma, Z.; Song, R. Maize kernel development. Mol. Breed., 2021, 41(1), 2.
[http://dx.doi.org/10.1007/s11032-020-01195-9]
[51]
Haussmann, B.I.G.; Parzies, H.K.; Presterl, T.; Susic, Z.; Miedaner, T. Plant genetic resources in crop improvement. Plant Genet. Resour., 2004, 2, 3-21.
[http://dx.doi.org/10.1079/PGR200430]
[52]
Madhumati, B. Potential and application of molecular markers techniques for plant genome analysis. Int. J. Pure. App. Biosci., 2014, 2, 169-188.
[53]
Kage, U.; Kumar, A.; Dhokane, D.; Karre, S.; Kushalappa, A.C. Functional molecular markers for crop improvement. Crit. Rev. Biotechnol., 2016, 36(5), 917-930.
[http://dx.doi.org/10.3109/07388551.2015.1062743] [PMID: 26171816]
[54]
Kumar, P.; Gupta, V.K.; Misra, A.K.; Modi, D.R.; Pandey, B.K. Potential of molecular markers in plant biotechnology. Plant Omics, 2009, 2, 141-162.
[55]
Bai, H.; Cao, Y.; Quan, J.; Dong, L.; Li, Z.; Zhu, Y.; Zhu, L.; Dong, Z.; Li, D. Identifying the genome-wide sequence variations and developing new molecular markers for genetics research by re-sequencing a Landrace cultivar of foxtail millet. PLoS One, 2013, 8(9), e73514.
[http://dx.doi.org/10.1371/journal.pone.0073514] [PMID: 24039970]
[56]
Gujaria, N.; Kumar, A.; Dauthal, P.; Dubey, A.; Hiremath, P.; Bhanu Prakash, A.; Farmer, A.; Bhide, M.; Shah, T.; Gaur, P.M.; Upadhyaya, H.D.; Bhatia, S.; Cook, D.R.; May, G.D.; Varshney, R.K. Development and use of genic molecular markers (GMMs) for construction of a transcript map of chickpea (Cicer arietinum L.). Theor. Appl. Genet., 2011, 122(8), 1577-1589.
[http://dx.doi.org/10.1007/s00122-011-1556-1] [PMID: 21384113]
[57]
Robertsen, C.; Hjortshøj, R.; Janss, L. Genomic selection in cereal breeding. Agronomy, 2019, 9(2), 95.
[http://dx.doi.org/10.3390/agronomy9020095]
[58]
Niaz, S.; Nawaz, S.; Butt, A.; Bilal, M.Q.; Mubin, M.; Akram, A.; Latif, M.F.; Iqbal, M.A.; Tabassum, S.; Saleem, F. Genetic variability estimation in wheat using random amplified polymorphic DNA based markers. Pak. J. Agric. Sci., 2020, 57(3), 685-690.
[59]
Shamsuzzaman, M.; Bhattacharjya, D.K.; Islam, M.S.; Hoque, M.E. Molecular diversity analysis of somaclonal variants of potato (Solanum tuberosum L.) by random amplified polymorphic DNA markers. Annu. Res. Rev. Biol., 2021, 63-76.
[http://dx.doi.org/10.9734/arrb/2021/v36i330353]
[60]
Nkongolo, K.; Alamri, S.; Michael, P. Assessment of genetic variation in Soybean (<i>Glycine max</i>) accessions from international gene pools using RAPD Markers: Comparison with the ISSR System. Am. J. Plant Sci., 2020, 11(9), 1414-1428.
[http://dx.doi.org/10.4236/ajps.2020.119102]
[61]
Christov, N.K.; Tsonev, S.; Todorova, V.; Todorovska, E.G. Genetic diversity and population structure analysis-a prerequisite for constructing a mini core collection of Balkan Capsicum annuum germplasm. Biotechnol. Biotechnol. Equip., 2021, 35(1), 1010-1023.
[http://dx.doi.org/10.1080/13102818.2021.1946428]
[62]
Wang, C.; Li, G.; Zhang, Z.; Peng, M.; Shang, Y.; Luo, R.; Chen, Y. Genetic diversity of castor bean (Ricinus communis L.) in Northeast China revealed by ISSR markers. Biochem. Syst. Ecol., 2013, 51, 301-307.
[http://dx.doi.org/10.1016/j.bse.2013.09.017]
[63]
Gonias, E.D.; Ganopoulos, I.; Mellidou, I.; Bibi, A.C.; Kalivas, A.; Mylona, P.V.; Osanthanunkul, M.; Tsaftaris, A.; Madesis, P.; Doulis, A.G. Exploring genetic diversity of tomato (Solanum lycopersicum L.) germplasm of genebank collection employing SSR and SCAR markers. Genet. Resour. Crop Evol., 2019, 66(6), 1295-1309.
[http://dx.doi.org/10.1007/s10722-019-00786-6]
[64]
Lee, H.M.; Park, Y.M.; Jun, T.H.; Kwon, S.W.; Choi, I.S.; Kim, Y.C.; Gupta, R.; Chung, M.N.; Kim, S.H.; Yang, P. Direct sequencing of RAPD products provides a set of SCAR markers for discrimination of sweet potato cultivars. Plant Omics, 2015, 8(3), 195-200.
[65]
Satish, L.; Shilpha, J.; Pandian, S.; Rency, A.S.; Rathinapriya, P.; Ceasar, S.A.; Largia, M.J.V.; Kumar, A.A.; Ramesh, M. Analysis of genetic variation in sorghum (Sorghum bicolor (L.) Moench) genotypes with various agronomical traits using SPAR methods. Gene, 2016, 576(1), 581-585.
[http://dx.doi.org/10.1016/j.gene.2015.10.056] [PMID: 26515517]
[66]
Pandian, S.; Marichelvam, K.; Satish, L.; Ceasar, S.A.; Pandian, S.K.; Ramesh, M. SPAR markers-assisted assessment of genetic diversity and population structure in finger millet (Eleusine Coracana (L.) Gaertn) mini-core collection. J. Crop Sci. Biotechnol., 2018, 21(5), 469-481.
[http://dx.doi.org/10.1007/s12892-018-0034-0]
[67]
Krishna, T.P.A.; Maharajan, T.; Antony David, R.H.; Ramakrishnan, M.; Ceasar, S.A.; Duraipandiyan, V.; Roch, G.V.; Ignacimuthu, S. Microsatellite markers of finger millet (Eleusine coracana (L.) Gaertn) and foxtail millet (Setaria italica (L.) Beauv) provide resources for cross-genome transferability and genetic diversity analyses in other millets. Biocatal. Agric. Biotechnol., 2018, 16, 493-501.
[http://dx.doi.org/10.1016/j.bcab.2018.09.009]
[68]
Molosiwa, O.O.; Aliyu, S.; Stadler, F.; Mayes, K.; Massawe, F.; Kilian, A.; Mayes, S. SSR marker development, genetic diversity and population structure analysis of Bambara groundnut [Vigna subterranea (L.) Verdc. landraces. Genet. Resour. Crop Evol., 2015, 62(8), 1225-1243.
[http://dx.doi.org/10.1007/s10722-015-0226-6]
[69]
Kaur, G.; Joshi, A.; Jain, D. SSR-Marker assisted evaluation of Genetic Diversity in Mungbean (Vigna radiata (L.) Wilcezk) genotypes. Braz. Arch. Biol. Technol., 2018, 61(0), e180613.
[http://dx.doi.org/10.1590/1678-4324-2016160613]
[70]
Haina, K.V.J.; Krishna, T.P.A.; Dash, M.; Thiyageshwari, S.; Ceasar, S.A.; Selvi, D. Food and Nutritional Security: Innovative approaches for improving micronutrient use efficiency in Soybean (Glycine max (L.) Merrill) under hostile soils. J. Soil Sci. Plant Nutr., 2022, 1-15.
[71]
Kebriyaee, D.; Kordrostami, M.; Rezadoost, M.H.; Lahiji, H.S. QTL analysis of agronomic traits in rice using SSR and AFLP markers. Not. Sci. Biol., 2012, 4(2), 116-123.
[http://dx.doi.org/10.15835/nsb427501]
[72]
Sandhu, N.; Singh, A.; Dixit, S.; Sta Cruz, M.T.; Maturan, P.C.; Jain, R.K.; Kumar, A. Identification and mapping of stable QTL with main and epistasis effect on rice grain yield under upland drought stress. BMC Genet., 2014, 15(1), 63.
[http://dx.doi.org/10.1186/1471-2156-15-63] [PMID: 24885990]
[73]
Liang, Y.; Zhan, X.; Gao, Z.; Lin, Z.; Yang, Z.; Zhang, Y.; Shen, X.; Cao, L.; Cheng, S. Mapping of QTLs associated with important agronomic traits using three populations derived from a super hybrid rice Xieyou9308. Euphytica, 2012, 184(1), 1-13.
[http://dx.doi.org/10.1007/s10681-011-0456-4]
[74]
Lei, L.; Zheng, H.L.; Wang, J.G.; Liu, H.L.; Sun, J.; Zhao, H.W.; Yang, L.M.; Zou, D.T. Genetic dissection of rice (Oryza sativa L.) tiller, plant height, and grain yield based on QTL mapping and metaanalysis. Euphytica, 2018, 214(7), 109.
[http://dx.doi.org/10.1007/s10681-018-2187-2]
[75]
Zheng, Z.P.; Liu, X.H. Genetic analysis of agronomic traits associated with plant architecture by QTL mapping in maize. Genet. Mol. Res., 2013, 12(2), 1243-1253.
[http://dx.doi.org/10.4238/2013.April.17.3] [PMID: 23661449]
[76]
Choi, J.K.; Sa, K.J.; Park, D.H.; Lim, S.E.; Ryu, S.H.; Park, J.Y.; Park, K.J.; Rhee, H.I.; Lee, M.; Lee, J.K. Construction of genetic linkage map and identification of QTLs related to agronomic traits in DH population of maize (Zea mays L.) using SSR markers. Genes Genomics, 2019, 41(6), 667-678.
[http://dx.doi.org/10.1007/s13258-019-00813-x] [PMID: 30953340]
[77]
Park, K.J.; Sa, K.J.; Kim, B.W.; Koh, H.J.; Lee, J.K. Genetic mapping and QTL analysis for yield and agronomic traits with an F2:3 population derived from a waxy corn × sweet corn cross. Genes Genomics, 2014, 36(2), 179-189.
[http://dx.doi.org/10.1007/s13258-013-0157-6]
[78]
Wang, J.; Yang, J.; McNeil, D.L.; Zhou, M. Identification and molecular mapping of a dwarfing gene in barley (Hordeum vulgare L.) and its correlation with other agronomic traits. Euphytica, 2010, 175(3), 331-342.
[http://dx.doi.org/10.1007/s10681-010-0175-2]
[79]
Wang, J.; Yang, J.; Jia, Q.; Zhu, J.; Shang, Y.; Hua, W.; Zhou, M. A new QTL for plant height in barley (Hordeum vulgare L.) showing no negative effects on grain yield. PLoS One, 2014, 9(2), e90144.
[http://dx.doi.org/10.1371/journal.pone.0090144] [PMID: 24587247]
[80]
Ren, X.; Sun, D.; Sun, G.; Li, C.; Dong, W. Molecular detection of QTL for agronomic and quality traits in a doubled haploid barley population. Aust. J. Crop Sci., 2013, 7, 878-886.
[81]
Mansour, E.; Casas, A.M.; Gracia, M.P.; Molina-Cano, J.L.; Moralejo, M.; Cattivelli, L.; Thomas, W.T.B.; Igartua, E. Quantitative trait loci for agronomic traits in an elite barley population for Mediterranean conditions. Mol. Breed., 2014, 33(2), 249-265.
[http://dx.doi.org/10.1007/s11032-013-9946-5]
[82]
Pinto, R.S.; Reynolds, M.P.; Mathews, K.L.; McIntyre, C.L.; Olivares-Villegas, J.J.; Chapman, S.C. Heat and drought adaptive QTL in a wheat population designed to minimize confounding agronomic effects. Theor. Appl. Genet., 2010, 121(6), 1001-1021.
[http://dx.doi.org/10.1007/s00122-010-1351-4] [PMID: 20523964]
[83]
Gahlaut, V.; Jaiswal, V.; Tyagi, B.S.; Singh, G.; Sareen, S.; Balyan, H.S.; Gupta, P.K. QTL mapping for nine drought-responsive agronomic traits in bread wheat under irrigated and rain-fed environments. PLoS One, 2017, 12(8), e0182857.
[http://dx.doi.org/10.1371/journal.pone.0182857] [PMID: 28793327]
[84]
Lv, C.; Song, Y.; Gao, L.; Yao, Q.; Zhou, R.; Xu, R.; Jia, J. Integration of QTL detection and marker assisted selection for improving resistance to Fusarium head blight and important agronomic traits in wheat. Crop J., 2014, 2(1), 70-78.
[http://dx.doi.org/10.1016/j.cj.2013.10.004]
[85]
Rajkumar; Fakrudin, B.; Kavil, S.P.; Girma, Y.; Arun, S.S.; Dadakhalandar, D.; Gurusiddesh, B.H.; Patil, A.M.; Thudi, M.; Bhairappanavar, S.B.; Narayana, Y.D.; Krishnaraj, P.U.; Khadi, B.M.; Kamatar, M.Y. Molecular mapping of genomic regions harbouring QTLs for root and yield traits in sorghum (Sorghum bicolor L. Moench). Physiol. Mol. Biol. Plants, 2013, 19(3), 409-419.
[http://dx.doi.org/10.1007/s12298-013-0188-0] [PMID: 24431509]
[86]
Murali Mohan, S.; Madhusudhana, R.; Mathur, K.; Chakravarthi, D.V.N.; Rathore, S.; Nagaraja Reddy, R.; Satish, K.; Srinivas, G.; Sarada Mani, N.; Seetharama, N. Identification of quantitative trait loci associated with resistance to foliar diseases in sorghum [Sorghum bicolor (L.) Moench]. Euphytica, 2010, 176(2), 199-211.
[http://dx.doi.org/10.1007/s10681-010-0224-x]
[87]
Nagaraja Reddy, R.; Madhusudhana, R.; Murali Mohan, S.; Chakravarthi, D.V.N.; Mehtre, S.P.; Seetharama, N.; Patil, J.V. Mapping QTL for grain yield and other agronomic traits in post-rainy sorghum [Sorghum bicolor (L.) Moench]. Theor. Appl. Genet., 2013, 126(8), 1921-1939.
[http://dx.doi.org/10.1007/s00122-013-2107-8] [PMID: 23649648]
[88]
Ramakrishnan, M.; Antony Ceasar, S.; Duraipandiyan, V.; Vinod, K.K.; Kalpana, K.; Al-Dhabi, N.A.; Ignacimuthu, S. Tracing QTLs for leaf blast resistance and agronomic performance of finger millet (Eleusine coracana (L.) Gaertn.) genotypes through association mapping and in silico comparative genomics analyses. PLoS One, 2016, 11(7), e0159264.
[http://dx.doi.org/10.1371/journal.pone.0159264] [PMID: 27415007]
[89]
Fang, X.; Dong, K.; Wang, X.; Liu, T.; He, J.; Ren, R.; Zhang, L.; Liu, R.; Liu, X.; Li, M.; Huang, M.; Zhang, Z.; Yang, T. A high density genetic map and QTL for agronomic and yield traits in Foxtail millet [Setaria italica (L.) P. Beauv. BMC Genomics, 2016, 17(1), 336.
[http://dx.doi.org/10.1186/s12864-016-2628-z] [PMID: 27146360]
[90]
Chelpuri, D.; Sharma, R.; Durga, K.K.; Katiyar, P.; Mahendrakar, M.D.; Singh, R.B.; Yadav, R.S.; Gupta, R.; Srivastava, R.K. Mapping quantitative trait loci (QTLs) associated with resistance to major pathotype-isolates of pearl millet downy mildew pathogen. Eur. J. Plant Pathol., 2019, 154(4), 983-994.
[http://dx.doi.org/10.1007/s10658-019-01718-x]
[91]
Spindel, J.; Begum, H.; Akdemir, D.; Virk, P.; Collard, B.; Redoña, E.; Atlin, G.; Jannink, J.L.; McCouch, S.R. Genomic selection and association mapping in rice (Oryza sativa): effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines. PLoS Genet., 2015, 11(2), e1004982.
[http://dx.doi.org/10.1371/journal.pgen.1004982] [PMID: 25689273]
[92]
Huang, X.; Wei, X.; Sang, T.; Zhao, Q.; Feng, Q.; Zhao, Y.; Li, C.; Zhu, C.; Lu, T.; Zhang, Z.; Li, M.; Fan, D.; Guo, Y.; Wang, A.; Wang, L.; Deng, L.; Li, W.; Lu, Y.; Weng, Q.; Liu, K.; Huang, T.; Zhou, T.; Jing, Y.; Li, W.; Lin, Z.; Buckler, E.S.; Qian, Q.; Zhang, Q.F.; Li, J.; Han, B. Genome-wide association studies of 14 agronomic traits in rice landraces. Nat. Genet., 2010, 42(11), 961-967.
[http://dx.doi.org/10.1038/ng.695] [PMID: 20972439]
[93]
Jansen, M.; Gilmer, F.; Biskup, B.; Nagel, K.A.; Rascher, U.; Fischbach, A.; Briem, S.; Dreissen, G.; Tittmann, S.; Braun, S.; De Jaeger, I.; Metzlaff, M.; Schurr, U.; Scharr, H.; Walter, A. Simultaneous phenotyping of leaf growth and chlorophyll fluorescence via GROWSCREEN FLUORO allows detection of stress tolerance in Arabidopsis thaliana and other rosette plants. Funct. Plant Biol., 2009, 36(11), 902-914.
[http://dx.doi.org/10.1071/FP09095] [PMID: 32688701]
[94]
Massonnet, C.; Vile, D.; Fabre, J.; Hannah, M.A.; Caldana, C.; Lisec, J.; Beemster, G.T.S.; Meyer, R.C.; Messerli, G.; Gronlund, J.T.; Perkovic, J.; Wigmore, E.; May, S.; Bevan, M.W.; Meyer, C.; Rubio-Díaz, S.; Weigel, D.; Micol, J.L.; Buchanan-Wollaston, V.; Fiorani, F.; Walsh, S.; Rinn, B.; Gruissem, W.; Hilson, P.; Hennig, L.; Willmitzer, L.; Granier, C. Probing the reproducibility of leaf growth and molecular phenotypes: A comparison of three Arabidopsis accessions cultivated in ten laboratories. Plant Physiol., 2010, 152(4), 2142-2157.
[http://dx.doi.org/10.1104/pp.109.148338] [PMID: 20200072]
[95]
Lu, Y.; Hao, Z.; Xie, C.; Crossa, J.; Araus, J.L.; Gao, S.; Vivek, B.S.; Magorokosho, C.; Mugo, S.; Makumbi, D.; Taba, S.; Pan, G.; Li, X.; Rong, T.; Zhang, S.; Xu, Y. Large-scale screening for maize drought resistance using multiple selection criteria evaluated under water-stressed and well-watered environments. Field Crops Res., 2011, 124(1), 37-45.
[http://dx.doi.org/10.1016/j.fcr.2011.06.003]
[96]
Montes, J.M.; Technow, F.; Dhillon, B.S.; Mauch, F.; Melchinger, A.E. High-throughput non-destructive biomass determination during early plant development in maize under field conditions. Field Crops Res., 2011, 121(2), 268-273.
[http://dx.doi.org/10.1016/j.fcr.2010.12.017]
[97]
Mohd Asaari, M.S.; Mishra, P.; Mertens, S.; Dhondt, S.; Inzé, D.; Wuyts, N.; Scheunders, P. Close-range hyperspectral image analysis for the early detection of stress responses in individual plants in a high-throughput phenotyping platform. ISPRS J. Photogramm. Remote Sens., 2018, 138, 121-138.
[http://dx.doi.org/10.1016/j.isprsjprs.2018.02.003]
[98]
Zhang, Z.; Kayacan, E.; Thompson, B.; Chowdhary, G. High precision control and deep learning-based corn stand counting algorithms for agricultural robot. Auton. Robots, 2020, 44(7), 1289-1302.
[http://dx.doi.org/10.1007/s10514-020-09915-y]
[99]
Qiu, Q.; Sun, N.; Bai, H.; Wang, N.; Fan, Z.; Wang, Y.; Meng, Z.; Li, B.; Cong, Y. Field-based high-throughput phenotyping for maize plant using 3D LiDAR point cloud generated with a “Phenomobile”. Front. Plant Sci., 2019, 10, 554.
[http://dx.doi.org/10.3389/fpls.2019.00554] [PMID: 31134110]
[100]
Bao, Y.; Tang, L.; Srinivasan, S.; Schnable, P.S. Field-based architectural traits characterisation of maize plant using time-of-flight 3D imaging. Biosyst. Eng., 2019, 178, 86-101.
[http://dx.doi.org/10.1016/j.biosystemseng.2018.11.005]
[101]
Vázquez-Arellano, M.; Paraforos, D.S.; Reiser, D.; Garrido-Izard, M.; Griepentrog, H.W. Determination of stem position and height of reconstructed maize plants using a time-of-flight camera. Comput. Electron. Agric., 2018, 154, 276-288.
[http://dx.doi.org/10.1016/j.compag.2018.09.006]
[102]
Fukatsu, T.; Watanabe, T.; Hu, H.; Yoichi, H.; Hirafuji, M. Field monitoring support system for the occurrence of Leptocorisa chinensis Dallas (Hemiptera: Alydidae) using synthetic attractants, Field Servers, and image analysis. Comput. Electron. Agric., 2012, 80, 8-16.
[http://dx.doi.org/10.1016/j.compag.2011.10.005]
[103]
Kim, S.L.; Kim, N.; Lee, H.; Lee, E.; Cheon, K.S.; Kim, M.; Baek, J.; Choi, I.; Ji, H.; Yoon, I.S.; Jung, K.H.; Kwon, T.R.; Kim, K.H. High-throughput phenotyping platform for analyzing drought tolerance in rice. Planta, 2020, 252(3), 38.
[http://dx.doi.org/10.1007/s00425-020-03436-9] [PMID: 32779032]
[104]
Wasson, A.P.; Richards, R.A.; Chatrath, R.; Misra, S.C.; Prasad, S.V.S.; Rebetzke, G.J.; Kirkegaard, J.A.; Christopher, J.; Watt, M. Traits and selection strategies to improve root systems and water uptake in water-limited wheat crops. J. Exp. Bot., 2012, 63(9), 3485-3498.
[http://dx.doi.org/10.1093/jxb/ers111] [PMID: 22553286]
[105]
Andrade-Sanchez, P.; Gore, M.A.; Heun, J.T.; Thorp, K.R.; Carmo-Silva, A.E.; French, A.N.; Salvucci, M.E.; White, J.W. Development and evaluation of a field-based high-throughput phenotyping platform. Funct. Plant Biol., 2014, 41(1), 68-79.
[http://dx.doi.org/10.1071/FP13126] [PMID: 32480967]
[106]
Hu, P.; Chapman, S.C.; Wang, X.; Potgieter, A.; Duan, T.; Jordan, D.; Guo, Y.; Zheng, B. Estimation of plant height using a high throughput phenotyping platform based on unmanned aerial vehicle and self-calibration: Example for sorghum breeding. Eur. J. Agron., 2018, 95, 24-32.
[http://dx.doi.org/10.1016/j.eja.2018.02.004]
[107]
Vijayarangan, S.; Sodhi, P.; Kini, P.; Bourne, J.; Du, S.; Sun, H.; Poczos, B.; Apostolopoulos, D.; Wettergreen, D. High-throughput robotic phenotyping of energy Sorghum Crops. In: BT-field and service robotics; Hutter, M.; Siegwart, R., Eds.; Springer: Cham, 2018; pp. 99-113.
[108]
Grenier, C.; Cao, T.V.; Ospina, Y.; Quintero, C.; Châtel, M.H.; Tohme, J.; Courtois, B.; Ahmadi, N. Accuracy of genomic selection in a rice synthetic population developed for recurrent selection breeding. PLoS One, 2015, 10(8), e0136594.
[http://dx.doi.org/10.1371/journal.pone.0136594] [PMID: 26313446]
[109]
Yang, W.; Guo, Z.; Huang, C.; Duan, L.; Chen, G.; Jiang, N.; Fang, W.; Feng, H.; Xie, W.; Lian, X.; Wang, G.; Luo, Q.; Zhang, Q.; Liu, Q.; Xiong, L. Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice. Nat. Commun., 2014, 5(1), 5087.
[http://dx.doi.org/10.1038/ncomms6087] [PMID: 25295980]
[110]
Yang, M.; Lu, K.; Zhao, F.J.; Xie, W.; Ramakrishna, P.; Wang, G.; Du, Q.; Liang, L.; Sun, C.; Zhao, H.; Zhang, Z.; Liu, Z.; Tian, J.; Huang, X.Y.; Wang, W.; Dong, H.; Hu, J.; Ming, L.; Xing, Y.; Wang, G.; Xiao, J.; Salt, D.E.; Lian, X. Genome-wide association studies reveal the genetic basis of ionomic variation in rice. Plant Cell, 2018, 30(11), 2720-2740.
[http://dx.doi.org/10.1105/tpc.18.00375] [PMID: 30373760]
[111]
Zhang, X.; Pérez-Rodríguez, P.; Semagn, K.; Beyene, Y.; Babu, R.; López-Cruz, M.A.; San Vicente, F.; Olsen, M.; Buckler, E.; Jannink, J-L.; Prasanna, B.M.; Crossa, J. Genomic prediction in biparental tropical maize populations in water-stressed and well-watered environments using low-density and GBS SNPs. Heredity, 2015, 114(3), 291-299.
[http://dx.doi.org/10.1038/hdy.2014.99] [PMID: 25407079]
[112]
Crossa, J.; Beyene, Y.; Kassa, S.; Pérez, P.; Hickey, J.M.; Chen, C.; de los Campos, G.; Burgueño, J.; Windhausen, V.S.; Buckler, E.; Jannink, J.L.; Lopez Cruz, M.A.; Babu, R. Genomic prediction in maize breeding populations with genotyping-by-sequencing. G3, 2013, 3(11), 1903-1926.
[http://dx.doi.org/10.1534/g3.113.008227] [PMID: 24022750]
[113]
dos Santos, J.P.R.; Pires, L.P.M.; de Castro Vasconcellos, R.C.; Pereira, G.S.; Von Pinho, R.G.; Balestre, M. Genomic selection to resistance to Stenocarpella maydis in maize lines using DArTseq markers. BMC Genet., 2016, 17(1), 86.
[http://dx.doi.org/10.1186/s12863-016-0392-3] [PMID: 27316946]
[114]
Rutkoski, J.E.; Poland, J.A.; Singh, R.P.; Huerta-Espino, J.; Bhavani, S.; Barbier, H.; Rouse, M.N.; Jannink, J.L.; Sorrells, M.E. Genomic selection for quantitative adult plant stem rust resistance in wheat. Plant Genome, 2014, 7(3), 1-10.
[http://dx.doi.org/10.3835/plantgenome2014.02.0006]
[115]
Lado, B.; Barrios, P.G.; Quincke, M.; Silva, P.; Gutiérrez, L. Modeling genotype × environment interaction for genomic selection with unbalanced data from a wheat breeding program. Crop Sci., 2016, 56(5), 2165-2179.
[http://dx.doi.org/10.2135/cropsci2015.04.0207]
[116]
Isidro, J.; Jannink, J.L.; Akdemir, D.; Poland, J.; Heslot, N.; Sorrells, M.E. Training set optimization under population structure in genomic selection. Theor. Appl. Genet., 2015, 128(1), 145-158.
[http://dx.doi.org/10.1007/s00122-014-2418-4] [PMID: 25367380]
[117]
Arruda, M.P.; Lipka, A.E.; Brown, P.J.; Krill, A.M.; Thurber, C.; Brown-Guedira, G.; Dong, Y.; Foresman, B.J.; Kolb, F.L. Comparing genomic selection and marker-assisted selection for Fusarium head blight resistance in wheat (Triticum aestivum L.). Mol. Breed., 2016, 36(7), 84.
[http://dx.doi.org/10.1007/s11032-016-0508-5]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy