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Genomics in Enhancing Crop Productivity Against Stresses

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Augmenting Crop Productivity in Stress Environment

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Abstract

Compared with the world’s 1.8 billion people, the population in the last century has quadrupled and may surpass five times by 2050. On the other hand, climate variability and climate change are further compounding problems toward achieving higher yield. High-throughput genotyping and phenotyping have enabled genetic dissection of crop plants. Mapping of agronomically important quantitative trait loci and genomic selection enabled the development of genomics-assisted breeding. The availability of whole-genome sequencing in many crops has provided opportunity for molecular marker development and genomic-assisted breeding for rapid development of stress resistant/tolerant genotypes. This present book chapter deals with recent advances in these genomic technologies for enhancing crop productivity in stress environment.

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References

  • Alqudah AM, Sallam A, Baenziger PS, Börner A (2020) GWAS: fast-forwarding gene identification and characterization in temperate cereals: lessons from barley—a review. J Adv Res 22:119–135

    Article  PubMed  Google Scholar 

  • An D, Yang J, Zhang P (2012) Transcriptome profiling of low temperature-treated cassava apical shoots showed dynamic responses of tropical plant to cold stress. BMC Genomics 13(1):64

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Ansari WA, Chandanshive SU, Bhatt V, Nadaf AB, Vats S, Katara JL et al (2020) Genome editing in cereals: approaches, applications and challenges. Int J Mol Sci 21(11):4040

    Article  CAS  PubMed Central  Google Scholar 

  • Ardui S, Ameur A, Vermeesch JR, Hestand MS (2018) Single molecule real-time (SMRT) sequencing comes of age: applications and utilities for medical diagnostics. Nucleic Acids Res 46(5):2159–2168

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Baute J, Herman D, Coppens F, De Block J, Slabbinck B, Dell’Acqua M, Pè ME, Maere S, Nelissen H, InzĂ© D (2015) Correlation analysis of the transcriptome of growing leaves with mature leaf parameters in a maize RIL population. Genome Biol 16(1):168

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Baute J, Herman D, Coppens F, De Block J, Slabbinck B, Dell’Acqua M, Pè ME, Maere S, Nelissen H, InzĂ© D (2016) Combined large-scale phenotyping and transcriptomics in maize reveals a robust growth regulatory network. Plant Physiol 170(3):1848–1867

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Berry CC (1998) Computationally efficient Bayesian QTL mapping in experimental crosses. In: Proceedings of the biometrics section of the American Statistical Association, pp 164–169

    Google Scholar 

  • Bhat JA, Ali S, Salgotra RK, Mir ZA, Dutta S, Jadon V, Tyagi A, Mushtaq M, Jain N, Singh PK, Singh GP (2016) Genomic selection in the era of next generation sequencing for complex traits in plant breeding. Front Genet 7:221

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Boerner V, Tier B (2016) BESSiE: a software for linear model BLUP and Bayesian MCMC analysis of large-scale genomic data. Genet Sel Evol 48:63

    Article  PubMed  PubMed Central  Google Scholar 

  • Bolger ME, Weisshaar B, Scholz U, Stein N, Usadel B, Mayer KFX (2014) Plant genome sequencing-applications for crop improvement. Plant Biotechnol J 8(1):2–9

    Google Scholar 

  • Botstein D, White RL, Skolnick M, Davis RW (1980) Construction of a genetic linkage map in man using restriction fragment length polymorphisms. Am J Hum Genet 32(3):314

    CAS  PubMed  PubMed Central  Google Scholar 

  • Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES (2007) TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23(19):2633–2635

    Article  CAS  PubMed  Google Scholar 

  • Broman KW, Sen Ĺš (2009) A guide to QTL mapping with R/qtl. Springer, New York

    Book  Google Scholar 

  • Bumgarner R (2013) Overview of DNA microarrays: types, applications, and their future. Curr Protoc Mol Biol 101:22–31

    Article  Google Scholar 

  • Chen Q, Zeng G, Hao M, Jiang H, Xiao Y (2020) Improvement of rice blast and brown planthopper resistance of PTGMS line C815S in two-line hybrid rice through marker-assisted selection. Mol Breed 40(2):21

    Article  CAS  Google Scholar 

  • Cui Y, Li R, Li G, Zhang F, Zhu T, Zhang Q, Ali J, Li Z, Xu S (2020) Hybrid breeding of rice via genomic selection. Plant Biotechnol J 18(1):57–67

    Article  CAS  PubMed  Google Scholar 

  • Curtis RE, Kinnaird P, Xing EP (2011) GenA-map: visualization strategies for structured association mapping. In: 2011 IEEE symposium on biological data visualization (BioVis), pp 87–94

    Google Scholar 

  • Davey JW, Hohenlohe PA, Etter PD, Boone JQ, Catchen JM, Blaxter ML (2011) Genome-wide genetic marker discovery and genotyping using next-generation sequencing. Nat Rev Genet 12:499–510

    Article  CAS  PubMed  Google Scholar 

  • Depristo MA, Banks E, Poplin R, Garimella KV, Maguire JR, Hartl C, Philippakis AA, Del AG, Rivas MA, Hanna M, McKenna A, Fennell TJ, Kernytsky AM, Sivachenko AY, Cibulskis K, Gabriel SB, Altshuler D, Daly MJ (2011) A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet 43:491

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Dreher K, Morris M, Khairallah M, Ribaut JM, Shivaji P, Ganesan S (2009) Is marker-assisted selection cost-effective compared with conventional plant breeding methods? The case of quality protein Maize. Proceedings of the 4th annual conference of the international consortium on agricultural biotechnology research (ICABR’00), pp 203–236

    Google Scholar 

  • Edwards D, Batley J (2010) Plant genome sequencing: applications for crop improvement. Plant Biotechnol J 8(1):2–9

    Article  CAS  PubMed  Google Scholar 

  • Elshire RJ, Jeffrey CG, Sun Q, Poland JA, Kawamoto K, Buckler ES, Mitchell SE (2011) A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS One 6(5):e19379

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Endelman JB (2011) Ridge regression and other kernels for genomic selection with R package rrBLUP. Plant Genome 4:250–255

    Article  Google Scholar 

  • Franke A, Wollstein A, Teuber M, Wittig M, Lu T, Hoffmann K, NĂĽrnberg P, Krawczak M, Schreiber S, Hampe J (2006) GENOMIZER: an integrated analysis system for genome-wide association data. Hum Mutat 27(6):583–588

    Article  CAS  PubMed  Google Scholar 

  • Gao H, Williamson S, Busta-mante CD (2007) A Markov chain Monte Carlo approach for joint inference of population structure and inbreeding rates from multilocus genotype data. Genetics 176(3):1635–1651

    Article  PubMed  PubMed Central  Google Scholar 

  • Gaunt TR, Rodriguez S, Zapata C, Day IN (2006) MIDAS: software for analysis and visualisation of interallelic disequilibrium between multiallelic markers. BMC Bioinformatics 7(1):1–11

    Article  CAS  Google Scholar 

  • Goddard M (2009) Genomic selection: prediction of accuracy and maximisation of long term response. Genetica 136:245–257

    Article  PubMed  Google Scholar 

  • González MN, Massa GA, Andersson M, Turesson H, Olsson N, Fält AS, Storani L, DĂ©cima Oneto CA, Hofvander P, Feingold SE (2020) Reduced enzymatic browning in potato tubers by specific editing of a polyphenol oxidase gene via ribonucleoprotein complexes delivery of the CRISPR/Cas9 system. Front Plant Sci 10:1649

    Article  PubMed  PubMed Central  Google Scholar 

  • Goodwin S, McPherson JD, McCombie WR (2016) Coming of age: ten years of next-generation sequencing technologies. Nat Rev Genet 17(6):333

    Article  CAS  PubMed  Google Scholar 

  • Grau A, MarĂ­a M, Montes R, Se-bastiani P (2006) Building chromosome-wide LD maps. Bioinformatics 22(16):1933–1934

    Article  CAS  Google Scholar 

  • Gumtow R, Wu D, Uchida J, Tian MA (2018) Phytophthora palmivora extracellular cystatin-like protease inhibitor targets papain to contribute to virulence on papaya. Mol Plant Microbe 31:363–373

    Article  CAS  Google Scholar 

  • Gupta PK, Roy JK (2002) Molecular markers in crop improvement: present status and future needs in India. Plant Cell Tiss Org Cult 70(3):229–234

    Article  Google Scholar 

  • Heffner EL, Sorrells ME, Jannink JL (2009) Genomic selection for crop improvement. Crop Sci 49(1):1–12

    Article  CAS  Google Scholar 

  • Iwata H, Ninomiya S (2006) AntMap: constructing genetic linkage maps using an ant colony optimization algorithm. Breed Sci 56(4):371–377

    Article  Google Scholar 

  • Jiang M, Liu Y, Liu Y, Tan Y, Huang J, Shu Q (2019) Mutation of inositol 1, 3, 4-trisphosphate 5/6-kinase6 impairs plant growth and phytic acid synthesis in rice. Plant Theory 8:114

    Article  CAS  Google Scholar 

  • Jouanin A, Gilissen LJ, Schaart JG, Leigh FJ, Cockram J, Wallington EJ, Boyd LA, Van Den Broeck HC, Van der Meer IM, America AHP, RGF V (2020) CRISPR/Cas9 gene editing of gluten in wheat to reduce gluten content and exposure-Reviewing methods to screen for Coeliac safety. Front Nutr 2020:7

    Google Scholar 

  • Juliana P, Singh RP, Braun HJ, Huerta-Espino J, Crespo-Herrera L, Govindan V, Mondal S, Poland J, Shrestha S (2020) Genomic selection for grain yield in the CIMMYT wheat breeding program-status and perspectives. Front Plant Sci 11:1418

    Article  Google Scholar 

  • Kang WH, Sim YM, Koo N, Nam JY, Lee J, Kim N, Jang H, Kim YM, Yeom SI (2020) Transcriptome profiling of abiotic responses to heat, cold, salt, and osmotic stress of Capsicum annuum L. Sci Data 7(1):1–7

    Article  CAS  Google Scholar 

  • Khan MA, Naeem M, Iqbal M (2014) Breeding approaches for bacterial leaf blight resistance in rice (Oryza sativa L.), current status and future directions. Eur J Plant Pathol 139(1):27–37

    Article  CAS  Google Scholar 

  • Kono N, Arakawa K (2019) Nanopore sequencing: review of potential applications in functional genomics. Dev Growth Differ 61(5):316–326

    Article  PubMed  Google Scholar 

  • Koseoglou E (2017) The study of SlPMR4 CRISPR/Cas9-mediated tomato allelic series for resistance against powdery mildew. Master’s thesis, Wageningen University, Wageningen

    Google Scholar 

  • Kulski JK (2016) Next-generation sequencing—an overview of the history, tools, and omic applications. In: Next generation sequencing–advances, applications and challenges. InTech, Rijeka, pp 3–60

    Chapter  Google Scholar 

  • Kumar S, Banks TW, Cloutier S (2012) SNP discovery through next-generation sequencing and its applications. Int J Plant Genomics 2012:831460. https://doi.org/10.1155/2012/831460

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Lander ES, Green P, Abrahamson J, Barlow A, Daly MJ, Lincoln SE, Newburg L (1987) MAPMAKER: an interactive computer package for constructing primary genetic linkage maps of experimental and natural populations. Genomics 1(2):174–181

    Article  CAS  PubMed  Google Scholar 

  • Leng PF, LĂĽbberstedt T, Xu ML (2017) Genomics-assisted breeding–a revolutionary strategy for crop improvement. J Integr Agric 16(12):2674–2685

    Article  Google Scholar 

  • Li J, Meng X, Zong Y, Chen K, Zhang H, Liu J, Li J, Gao C (2016b) Gene replacements and insertions in rice by intron targeting using CRISPR-Cas9. Nat Plants 2:16139

    Article  CAS  PubMed  Google Scholar 

  • Li M, Li X, Zhou Z, Wu P, Fang M, Pan X, Lin Q, Luo W, Wu G, Li H (2016c) Reassessment of the four yield-related genes Gn1a, DEP1, GS3, and IPA1 in rice using a CRISPR/Cas9 system. Front Plant Sci 7:377

    PubMed  PubMed Central  Google Scholar 

  • Li Z, Hu G, Liu X, Zhou Y, Li Y, Zhang X, Yuan X, Zhang Q, Yang D, Wang T, Zhang Z (2016a) Transcriptome sequencing identified genes and gene ontologies associated with early freezing tolerance in maize. Front Plant Sci 7:1477

    Article  PubMed  PubMed Central  Google Scholar 

  • Liang Z, Chen K, Li T, Zhang Y, Wang Y, Zhao Q, Liu J, Zhang H, Liu C, Ran Y et al (2017) Efficient DNA-free genome editing of bread wheat using CRISPR/Cas9 ribonucleoprotein complexes. Nat Commun 8:14261

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Lippert C, Listgarten J, Liu Y, Kadie CM, Davidson RI, Heckerman D (2011) FaST linear mixed models for genome-wide association studies. Nat Methods 8:833

    Article  CAS  PubMed  Google Scholar 

  • Liu X, Hu X, Li K, Liu Z, Wu Y, Wang H, Huang C (2020) Genetic mapping and genomic selection for maize stalk strength. BMC Plant Biol 20:1–16

    CAS  Google Scholar 

  • Loh PR, Lipson M, Patterson N, Moorjani P, Pickrell JK, Reich D, Berger B (2013) Inferring admixture histories of human populations using linkage disequilibrium. Genetics 2013:14

    Google Scholar 

  • Lu Y, Zhu JK (2017) Precise editing of a target base in the rice genome using a modified CRISPR/Cas9 system. Mol Plant 10:523–525

    Article  CAS  PubMed  Google Scholar 

  • Luo W, Huang M, Guo T, Xiao W, Wang J, Yang G, Liu Y, Wang H, Chen Z, Zhuang C (2017) Marker-assisted selection for rice blast resistance genes Pi2 and Pi9 through high-resolution melting of a gene-targeted amplicon. Plant Breed 136(1):67–73

    Article  CAS  Google Scholar 

  • Ma W, Qiu Z, Song J, Li J, Cheng Q, Zhai J, Ma C (2018) A deep convolutional neural network approach for predicting phenotypes from genotypes. Planta 248:1307–1318

    Article  CAS  PubMed  Google Scholar 

  • Mace ES, Rami JF, Bouchet S, Klein PE, Klein RR, Kilian A, Wenzl P, Xia L, Halloran K, Jordan DR (2009) A consensus genetic map of sorghum that integrates multiple component maps and high-throughput diversity array technology (DArT) markers. BMC Plant Biol 9(1):13

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Mailund T, Schierup MH, Pedersen CN, Madsen JN, Hein J, Schauser L (2006) GeneRecon-A coalescent based tool for fine-scale association mapping. Bioinformatics 22(18):2317–2318

    Article  CAS  PubMed  Google Scholar 

  • Maioli A, Gianoglio S, Moglia A, Acquadro A, Valentino D, Milani AM, Prohens J, Orzaez D, Granell A, Lanteri S, Comino C (2020) Simultaneous CRISPR/Cas9 editing of three PPO genes reduces fruit flesh browning in Solanum melongena L. Front Plant Sci 11:1883

    Article  Google Scholar 

  • Malnoy M, Viola R, Jung MH, Koo OJ, Kim S, Kim JS, Velasco R, Nagamangala Kanchiswamy C (2016) DNA-free genetically edited grapevine and apple protoplast using CRISPR/Cas9 ribonucleoproteins. Front Plant Sci 7:1904

    Article  PubMed  PubMed Central  Google Scholar 

  • Mammadov J, Aggarwal R, Buyyarapu R, Kumpatla S (2012) SNP markers and their impact on plant breeding. Int J Plant Genomics 2012:1–11

    Article  CAS  Google Scholar 

  • Manly KF, Cudmore JRH, Meer JM (2014) Map manager QTX, cross-platform software for genetic mapping. Mamm Genome 12(12):930–932

    Article  CAS  Google Scholar 

  • Meuwissen TH, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Milos PM (2009) Emergence of single-molecule sequencing and potential for molecular diagnostic applications. Expert Rev Mol Diagn 9(7):659–666

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Muthu V, Abbai R, Nallathambi J, Rahman H, Ramasamy S, Kambale R, Thulasinathan T, Ayyenar B, Muthurajan R (2020) Pyramiding QTLs controlling tolerance against drought, salinity, and submergence in rice through marker assisted breeding. PLoS One 15(1):e0227421

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Nayak SN, Singh VK, Varshney RK (2017) Marker-assisted selection. In: Encyclopedia of applied plant sciences. Academic Press, Oxford, pp 183–197

    Chapter  Google Scholar 

  • Nejat N, Ramalingam A, Mantri N (2018) Advances in transcriptomics of plants. In: Plant genetics and molecular biology. Springer, Cham, pp 161–185

    Google Scholar 

  • Nelson JC (1997) QGENE: software for marker-based genomic analysis and breeding. Mol Breed 3(3):239–245

    Article  CAS  Google Scholar 

  • Nielsen NH, Jahoor A, Jensen JD, Orabi J, Cericola F, Edriss V, Jensen J (2016) Genomic prediction of seed quality traits using advanced barley breeding lines. PLoS One 11(10):e0164494

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Powell W, Morgante M, Andre C, McNicol JW, Machray GC, Doyle JJ, Tingey SV, Rafalski JA (1995) Hypervariable microsatellites provide a general source of polymorphic DNA markers for the chloroplast genome. Curr Biol 5(9):1023–1029

    Article  CAS  PubMed  Google Scholar 

  • Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38(8):904–909

    Article  CAS  PubMed  Google Scholar 

  • Prihatna C, Barbetti MJ, Barker SJ (2018) A novel tomato Fusarium Wilt tolerance gene. Front Microbiol 9:1226

    Article  PubMed  PubMed Central  Google Scholar 

  • Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155(2):945–959

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, De Bakker PI, Daly MJ, Sham PC (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81(3):559–575

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Ran FA, Hsu PD, Wright J, Agarwala V, Scott DA, Zhang F (2013) Genome engineering using the CRISPR-Cas9 system. Nat Protoc 8(11):2281–2308

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Rana MM, Takamatsu T, Baslam M, Kaneko K, Itoh K, Harada N, Sugiyama T, Ohnishi T, Kinoshita T, Takagi H, Mitsui T (2019) Salt tolerance improvement in rice through efficient SNP marker-assisted selection coupled with speed-breeding. Intf Mol 20(10):2585

    CAS  Google Scholar 

  • Raney JA (2012) Transcriptome analysis of drought induced stress in Chenopodium quinoa. Theses and Dissertations, p 3915. https://scholarsarchive.byu.edu/etd/3915

  • Septiningsih EM, Pamplona AM, Sanchez DL, Neeraja CN, Vergara GV, Heuer S, Ismail AM, Mackill DJ (2009) Development of submergence-tolerant rice cultivars: the Sub1 locus and beyond. Ann Bot 103(2):151–160

    Article  CAS  PubMed  Google Scholar 

  • Seren Ăś, Vilhjálmsson BJ, Horton MW, Meng D, Forai P, Huang YS, Long Q, Segura V, Nordborg M (2012) GWAPP: a web application for genome-wide association mapping in Arabidopsis. Plant Cell 24(12):4793–4805

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Shamshad M, Sharma A (2018) The usage of genomic selection strategy in plant breeding. In: Next generation plant breeding. InTech, Rijeka, p 93. https://doi.org/10.5772/intechopen.76247

    Chapter  Google Scholar 

  • Shan Q, Wang Y, Li J, Gao C (2014) Genome editing in rice and wheat using the CRISPR/Cas system. Nat Protoc 9:2395

    Article  CAS  PubMed  Google Scholar 

  • Shi J, Gao H, Wang H, Lafitte HR, Archibald RL, Yang M, Hakimi SM, Mo H, Habben JE (2017) ARGOS 8 variants generated by CRISPR-Cas9 improve maize grain yield under field drought stress conditions. Plant Biotechnol J 15:207–216

    Article  CAS  PubMed  Google Scholar 

  • Siangliw JL, Jongdee B, Pantuwan G, Toojinda T (2007) Developing KDML105 backcross introgression lines using marker-assisted selection for QTLs associated with drought tolerance in rice. Sci Asia 33:207–214

    Article  Google Scholar 

  • Skol AD, Scott L, Abecasis GR, Boehnke M (2006) Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies. Nat Genet 38(2):209–213

    Article  CAS  PubMed  Google Scholar 

  • Soyk S, MĂĽller NA, Park SJ, Schmalenbach I, Jiang K, Hayama R, Zhang L, Van Eck J, JimĂ©nez-GĂłmez JM, Lippman ZB (2017) Variation in the flowering gene SELF PRUNING 5G promotes day-neutrality and early yield in tomato. Nat Genet 49(1):162–168

    Article  CAS  PubMed  Google Scholar 

  • Sprenger H, Erban A, Seddig S, Rudack K, Thalhammer A, Le MQ et al (2018) Metabolite and transcript markers for the prediction of potato drought tolerance. Plant Biotechnol J 16(4):939–950

    Article  CAS  PubMed  Google Scholar 

  • Stam P (1993) Construction of integrated genetic linkage maps by means of a new computer package: join map. Plant J 3(5):739–744

    Article  CAS  Google Scholar 

  • Steele KA, Price AH, Shashidhar HE, Witcombe JR (2006) Marker-assisted selection to introgress rice QTLs controlling root traits into an Indian upland rice variety. Theor Appl Genet 112(2):208–221

    Article  CAS  PubMed  Google Scholar 

  • Steiner B, Buerstmayr M, Michel S, Schweiger W, Lemmens M, Buerstmayr H (2017) Breeding strategies and advances in line selection for fusarium head blight resistance in wheat. Trop Plant Pathol 42(3):165–174

    Article  Google Scholar 

  • Svitashev S, Young JK, Schwartz C, Gao H, Falco SC, Cigan AM (2015) Targeted mutagenesis, precise gene editing, and site-specific gene insertion in maize using Cas9 and guide RNA. Plant Physiol 169:931–945

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Tashkandi M, Ali Z, Aljedaani F, Shami A, Mahfouz MM (2018) Engineering resistance against tomato yellow leaf curl virus via the CRISPR/Cas9 system in tomato. Plant Signal Behav 13:e1525996

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Tautz D (1989) Hypervariability of simple sequences as a general source for polymorphic DNA markers. Nucleic Acids Res 17(16):6463–6471

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Thomson MJ (2014) High-throughput SNP genotyping to accelerate crop improvement. Plant Breed Biotechnol 2(3):195–212

    Article  Google Scholar 

  • Thottathil GP, Jayasekaran K, Othman AS (2016) Sequencing crop genomes: a gateway to improve tropical agriculture. Trop Life Sci Res 27(1):93

    PubMed  PubMed Central  Google Scholar 

  • Tripathi JN (2019) CRISPR/Cas9 editing of endogenous banana streak virus in the B genome of Musa spp. overcomes a major challenge in banana breeding. Commun Biol 2:46

    Article  PubMed  PubMed Central  Google Scholar 

  • Utz HF, Melchinger AE (1996) PLABQTL: a program for composite interval mapping of QTL. J Agric Genomics 2:1–6

    Google Scholar 

  • van Ooijen JW, Maliepaard C (1996) MapQTL version 3.0: software for the calculation of QTL positions on genetic maps. Proceedings of the 4th plant genome conference, San Diego, pp 316. http://www.intl-ag.org/4/abstracts/p316.html

  • Vos P, Hogers R, Bleeker M, Reijans M, Van De Lee T, Hornes M, Friters A, Pot J, Paleman J, Kuiper M, Zabeau M (1995) AFLP: a new technique for DNA fingerprinting. Nucleic Acids Res 23(21):4407–4414

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wang FJ, Wang CL, Liu PQ, Lei CL, Hao W, Gao Y, Liu YG, Zhao KJ (2016) Enhanced rice blast resistance by CRISPR/Cas9-targeted mutagenesis of the erf transcription factor gene OsERF922. PLoS One 11:e0154027

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Wang J, Zhou Z, Zhang Z, Li H, Liu D, Zhang Q, Bradbury PJ, Buckler ES, Zhang Z (2018) Expanding the BLUP alphabet for genomic prediction adaptable to the genetic architectures of complex traits. Heredity 121:648

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wang S, Basten CJ, Zeng ZB (2005) Windows QTL cartographer 2.5. Department of Statistics, North Carolina State University, Raleigh

    Google Scholar 

  • Wang T, Zhang H, Zhu H (2019) CRISPR technology is revolutionizing the improvement of tomato and other fruit crops. Hortic Res 6:77. https://doi.org/10.1038/s41438-019-0159-x

    Article  PubMed  PubMed Central  Google Scholar 

  • Waziri A, Kumar P, Purty R (2016) Saltol QTL and their role in salinity tolerance in rice. Austin J Biotechnol Bioeng 3(3):1067

    Google Scholar 

  • Welsh J, Mcclelland M (1990) Fingerprinting genomes using PCR with arbitrary primers. Nucleic Acids Res 18(24):7213–7218

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wu Y, Bhat PR, Close TJ, Lonardi S (2008b) Efficient and accurate construction of genetic linkage maps from the minimum spanning tree of a graph. PLoS Genet 4(10):e1000212

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Wu Y, Close TJ, Lonardi S (2008a) The accurate construction of consensus genetic maps. Comput Syst Bioinfo Conf 7:285–296

    Google Scholar 

  • Xie K, Yang Y (2013) RNA-guided genome editing in plants using a CRISPR–Cas system. Mol Plant 6:1975–1983

    Article  CAS  PubMed  Google Scholar 

  • Xu Y, Li P, Yang Z, Xu C (2017) Genetic mapping of quantitative trait loci in crops. Crop J 5(2):175–184

    Article  Google Scholar 

  • Ye X, Zhang S, Li S, Wang J, Chen H, Wang K, Lin Z, Wei Y, Du L, Yan Y (2019) Improvement of three commercial spring wheat varieties for powdery mildew resistance by marker-assisted selection. Crop Prot 125:104889

    Article  CAS  Google Scholar 

  • Yin Y et al (2018) BZR1 transcription factor regulates heat stress tolerance through FERONIA receptor-like kinase-mediated reactive oxygen species signaling in tomato. Plant Cell Physiol 59:2239–2254

    CAS  PubMed  Google Scholar 

  • Zhang A, Liu Y, Wang F, Li T, Chen Z, Kong D, Bi J, Zhang F, Luo X, Wang J, Tang J (2019) Enhanced rice salinity tolerance via CRISPR/Cas9-targeted mutagenesis of the OsRR22 gene. Mol Breed 39(3):47

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zhang X, Wang J, Huang J, Lan H, Wang C, Yin C, Wu Y, Tang H, Qian Q, Li Jet al. (2012) Rare allele of OsPPKL1 associated with grain length causes extra-large grain and a significant yield increase in rice. Proc Natl Acad Sci U S A 109:21534–21539

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zhang Y, Bai Y, Wu G, Zou S, Chen Y, Gao C, Tang D (2017) Simultaneous modification of three homoeologs of ta EDR 1 by genome editing enhances powdery mildew resistance in wheat. Plant J 91:714–724

    Article  CAS  PubMed  Google Scholar 

  • Zhou H, He M, Li J, Chen L, Huang Z, Zheng S, Zhu L, Ni E, Jiang D, Zhao B, Zhuang C (2016) Development of commercial thermo-sensitive genic male sterile rice accelerates hybrid rice breeding using the CRISPR/Cas9-mediated TMS5 editing system. Sci Rep 6(1):1–12

    CAS  Google Scholar 

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Authors and Affiliations

  1. S.R. Institute of Management and Technology, Lucknow, Uttar Pradesh, India

    V. K. Mishra, Deepali Yadav, Yuvraj Srivatava, Chandra Prakash, Rohit Kashyap, Rahul Rawat & Pawan Kashyap

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  1. V. K. Mishra
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  2. Deepali Yadav
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  3. Yuvraj Srivatava
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  4. Chandra Prakash
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  5. Rohit Kashyap
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  6. Rahul Rawat
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  7. Pawan Kashyap
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Editors and Affiliations

  1. Institute of Forest Productivity, Ranchi, Jharkhand, India

    Shamim Akhtar Ansari

  2. Department of Botany, University of Lucknow, Lucknow, Uttar Pradesh, India

    Mohammad Israil Ansari

  3. Wolaita Sodo University, Wolaita, Ethiopia

    Azamal Husen

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© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Mishra, V.K. et al. (2022). Genomics in Enhancing Crop Productivity Against Stresses. In: Ansari, S.A., Ansari, M.I., Husen, A. (eds) Augmenting Crop Productivity in Stress Environment. Springer, Singapore. https://doi.org/10.1007/978-981-16-6361-1_3

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