Application of Genomics and Phenomics in Plant Breeding for Climate Resilience

Main Article Content

Noel Ndlovu

Abstract

Advances in the fields of genomics and phenomics are currently creating significant foundations for the sustainable intensification of plant breeding initiatives targeting climate resilience. Genomics is a biological study that focuses on architecture, function, editing, mapping, and evolution of genomes. It can be applied extensively in climate resilience breeding for cost-effective, rapid, and high-through put genotyping, phenotyping, and trait mapping. The efficacy of genomics-assisted breeding (GAB) is strongly hinged on the high resolution and robustness of Next Generation Sequencing (NGS) and CRISPR/Cas9-based Gene Editing systems. The integration of genomics and phenomics in crop improvement can upscale the efficiency of breeding systems targeting climate resilience and hasten cultivar release cycle. Phenomics is an interdisciplinary field that focuses on the enhanced measurement of plant performance, growth, and composition. Similarly, phenomics has revolutionized the efficacy of plant breeding off-trial initiatives established to phenotypically characterize and study diversity levels of collected germplasm. Field phenomics tools such as the phenonet, phenomobile, and phenonetwork have proven to be efficient in capturing large sums of multiscale and multidimensional experimental data. The main purpose of this review article is to present a summarized account of the probable applications of integrated systems of genomics and phenomics in plant breeding for climate resilience in major crops.

Keywords:
Climate resilience, genomics, high-throughput, next-generation sequencing, phenomics.

Article Details

How to Cite
Ndlovu, N. (2020). Application of Genomics and Phenomics in Plant Breeding for Climate Resilience. Asian Plant Research Journal, 6(4), 53-66. https://doi.org/10.9734/aprj/2020/v6i430137
Section
Review Article

References

Jean-Christophe G. Plant breeding for climate-smart agriculture, in: Plant breeding for climate-smart agriculture (L2.4 breeding and protecting crops and livestock). UMR Amélioration Génétique et Adaptation des Plantes (Agap-DDSE), CIRAD, France; 2015.

Brown TB, Cheng R, Sirault XR, Rungrat T, Murray KD, Trtilek M, Furbank RT, Badger M, Pogson BJ, Borevitz JO. TraitCapture: genomic and environment modelling of plant phenomic data. Curr. Opin. Plant Biol. 2014;18:73–79. Available:https://doi.org/10.1016/j.pbi.2014.02.002

Scheben A, Yuan Y, Edwards D. Advances in genomics for adapting crops to climate change. Curr. Plant Biol. 2016;6:2–10. Available:https://doi.org/10.1016/j.cpb.2016.09.001

Ndlovu N, Mayaya T, Muitire C, Munyengwa N. Nanotechnology applications in crop production and food systems. Int. J. Plant Breed. Crop Sci. 2020;7:624–634.

Kole C, Muthamilarasan M, Henry R, Edwards D, Sharma R, Abberton M, Batley J, Bentley A, Blakeney M, Bryant J, Cai H, Cakir M, Cseke LJ, Cockram J, de Oliveira AC, De Pace C, Dempewolf H, Ellison S, Gepts P, Greenland A, Hall A, Hori K, Hughes S, Humphreys MW, Iorizzo M, Ismail AM, Marshall A, Mayes S, Nguyen HT, Ogbonnaya FC, Ortiz R, Paterson AH, Simon PW, Tohme J, Tuberosa R, Valliyodan B, Varshney RK, Wullschleger SD, Yano M, Prasad M. Application of genomics-assisted breeding for generation of climate resilient crops: Progress and prospects. Front. Plant Sci. 2015;6. Available:https://doi.org/10.3389/fpls.2015.00563

Caira S, Ferranti P. Innovation for sustainable agriculture and food production. In: Reference Module in Food Science. Elsevier. 2016;97800810059652-10000. Available:https://doi.org/10.1016/B978-0-08-100596-5.21018-4

Singh K, Kumar S, Kumar SR, Singh M, Gupta K. Plant genetic resources management and pre-breeding in genomics era. Indian J. Genet. Plant Breed. 2019;79:117–130. Available:https://doi.org/10.31742/IJGPB.79S.1.1

Yang W, Feng H, Zhang X, Zhang J, Doonan JH, Batchelor WD, Xiong L, Yan J. Crop Phenomics and High-Throughput Phenotyping: Past Decades, Current Challenges, and Future Perspectives. Mol. Plant. 2020;13:187–214. Available:https://doi.org/10.1016/j.molp.2020.01.008

Choubey M, Lama U, Chetri P, Bera B. Application of phenomics, genomic resources and bioinformatics tools for tea plant improvement. Int. J. Agric. Innov. Res. 2019;7:6.

Furbank RT, Jimenez‐Berni JA, George‐Jaeggli B, Potgieter AB, Deery DM. Field crop phenomics: Enabling breeding for radiation use efficiency and biomass in cereal crops. New Phytol. 2019;223:1714–1727. Available:https://doi.org/10.1111/nph.15817

Basu U, Bajaj D, Sharma A, Malik N, Daware A, Narnoliya L, Thakro V, Upadhyaya HD, Kumar R, Tripathi S, Bharadwaj C, Tyagi AK, Parida SK. Genetic dissection of photosynthetic efficiency traits for enhancing seed yield in chickpea: Dissecting chickpea photosynthetic efficiency. Plant Cell Environ. 2019;42:158–173. Avaialble:https://doi.org/10.1111/pce.13319

Schlautman B, Diaz-Garcia L, Barriball S,. Morphometric approaches to promote the use of exotic germplasm for improved food security and resilience to climate change: A kura clover example. Plant Sci. 2020; 290:110319. Available:https://doi.org/10.1016/j.plantsci.2019.110319

Latha VS, Prabhavathi K. Breeding climate resilient varieties for food and nutritional security: A perspective. J. Pharmacogn. Nat. Prod. 2019;8:324–327.

Mehrabi Z, Pironon S, Kantar M, Ramankutty N, Rieseberg L. Shifts in the abiotic and biotic environment of cultivated sunflower under future climate change. Oil Seeds Fats Crops Lipids OCL 2019;26(9). Available:https://doi.org/10.1051/ocl/2019003

Taranto F, Nicolia A, Pavan S, De Vita P, D’Agostino N. Biotechnological and digital revolution for climate-smart plant breeding. Agronomy 2018;8:277. Available:https://doi.org/10.3390/agronomy8120277

Varshney RK, Singh VK, Kumar A, Powell W, Sorrells ME. Can genomics deliver climate-change ready crops? Curr. Opin. Plant Biol. 2018;45:205–211. Available:https://doi.org/10.1016/j.pbi.2018.03.007

Mat-Sharani S, Quay DH, Chyan LN, Firdaus-Raih M. Structural Genomics. Encycl. Bioinforma. Comput. Biol; 2018.
Available:https://doi.org/10.1016/B978-0-12-809633-8.20155-6

Wambugu PW, Ndjiondjop MN, Henry RJ. Role of genomics in promoting the utilization of plant genetic resources in genebanks. Brief. Funct. Genomics 2018; 17:198–206. Available:https://doi.org/10.1093/bfgp/ely014

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, 34. Available:https://doi.org/10.3390/plants9010034

Varshney RK, Mohan SM, Gaur PM, Gangarao NVPR, Pandey MK, Bohra A, Sawargaonkar SL, Chitikineni A, Kimurto PK, Janila P, Saxena KB, Fikre A, Sharma M, Rathore A, Pratap A, Tripathi S, Datta S, Chaturvedi SK, Mallikarjuna N, Anuradha G, Babbar A, Choudhary AK, Mhase MB, Bharadwaj CH, Mannur DM, Harer PN, Guo B, Liang X, Nadarajan N, Gowda CLL. Achievements and prospects of genomics-assisted breeding in three legume crops of the semi-arid tropics. Biotechnol. Adv. 2013;31:1120–1134. Available:https://doi.org/10.1016/j.biotechadv.2013.01.001

Razzaq A, Saleem F, Kanwal M, Mustafa G, Yousaf S, Imran Arshad HM, Hameed MK, Khan MS, Joyia FA. Modern trends in plant genome editing: An Inclusive Review of the CRISPR/Cas9 Toolbox. Int. J. Mol. Sci. 2019;20:4045. Available:https://doi.org/10.3390/ijms20164045

Wang H, Cimen E, Singh N, Buckler E. Deep learning for plant genomics and crop improvement. Curr. Opin. Plant Biol. 2020; 54:34–41. Available:https://doi.org/10.1016/j.pbi.2019.12.010

Varshney RK, Sinha P, Singh VK, Kumar A, Zhang Q, Bennetzen JL. 5Gs for crop genetic improvement. Curr. Opin. Plant Biol. 2020;13:1–7. Available:https://doi.org/10.1016/j.pbi.2019.12.004

Neeraja CN, Voleti SR, Subrahmanyam D, Surekha K, Rao PR. Breeding rice for nitrogen use efficiency. Indian J. Genet. Plant Breed. 2019;79. Available:https://doi.org/10.31742/IJGPB.79S.1.11

Varshney RK, Singh VK, Hickey JM, Xun X, Marshall DF, Wang J, Edwards D, Ribaut JM. Analytical and decision support tools for genomics-assisted breeding. Trends Plant Sci. 2016;21:354–363. Available:https://doi.org/10.1016/j.tplants.2015.10.018

Baba T, Momen M, Campbell MT, Walia H, Morota G. Multi-trait random regression models increase genomic prediction accuracy for a temporal physiological trait derived from high-throughput phenotyping (preprint). Genetics; 2019. Available:https://doi.org/10.1101/772038

Yang Z, Qanmber G, Wang Z, Yang Zhaoen Li F.. Gossypium Genomics: Trends, Scope, and Utilization for Cotton Improvement. Trends Plant Sci. 2020; 136013851930336X. Available:https://doi.org/10.1016/j.tplants.2019.12.011

Leng P, Lübberstedt T, Xu M. Genomics-assisted breeding – A revolutionary strategy for crop improvement. J. Integr. Agric. 2017;16:2674–2685. Available:https://doi.org/10.1016/S2095-3119(17)61813-6

Wolter F, Schindele P, Puchta H. Plant breeding at the speed of light: the power of CRISPR/Cas to generate directed genetic diversity at multiple sites. BMC Plant Biol. 2019;19:176. Available:https://doi.org/10.1186/s12870-019-1775-1

Pickar-Oliver A, Gersbach CA. The next generation of CRISPR–Cas technologies and applications. Nat. Rev. Mol. Cell Biol. 2019;20:490–507. Available:https://doi.org/10.1038/s41580-019-0131-5

Wang Y, Geng L, Yuan M, Wei J, Jin C, Li, M, Yu K, Zhang Y, Jin H, Wang E, Chai Z, Fu X, Li X. Deletion of a target gene in Indica rice via CRISPR/Cas9. Plant Cell Rep 2017;36:133–1343. Available:https://doi.org/10.1007/s00299-017-2158-4

Kushwaha UKS, Deo I, Jaiswal JP, Prasad B. Role of bioinformatics in crop improvement. Glob. J. Sci. Front. Res. Agric. Vet. 2017;17:13.

Schmutzer T, Bolger ME, Rudd S, Chen J, Gundlach H, Arend D, Oppermann M, Weise S, Lange M, Spannagl M, Usadel B, Mayer, KFX, Scholz U. Bioinformatics in the plant genomic and phenomic domain: The german contribution to resources, services and perspectives. J. Biotechnol. 2017;261:37–45. Available:https://doi.org/10.1016/j.jbiotec.2017.07.006

Roshni P, Prajwala KA. Phenomics: Approaches and Application in Crop Improvement. Curr. J. Appl. Sci. Technol. 2019;33:1–10. Available:https://doi.org/10.9734/cjast/2019/v33i330080

Neveu P, Tireau A, Hilgert N, Nègre V, Mineau-Cesari J, Brichet N, Chapuis R, Sanchez I, Pommier C, Charnomordic B, Tardieu F, Cabrera-Bosquet L. Dealing with multi-source and multi-scale information in plant phenomics: the ontology-driven Phenotyping Hybrid Information System. New Phytol. 2019l; 221:588–601. Available:https://doi.org/10.1111/nph.15385

Borrill P, Harrington SA, Uauy C. Applying the latest advances in genomics and phenomics for trait discovery in polyploid wheat. Plant J. 2019;97:56–72. Available:https://doi.org/10.1111/tpj.14150

Zhao, C, Zhang Y, Du J, Guo X, Wen W, Gu S, Wang J, Fan J. Crop phenomics: Current status and perspectives. Front. Plant Sci. 2019;10:714. Available:https://doi.org/10.3389/fpls.2019.00714

York LM. Functional phenomics: An emerging field integrating high-throughput phenotyping, physiology, and bioinformatics. J. Exp. Bot. 2019;70:379–386. Available:https://doi.org/10.1093/jxb/ery379

Senapati N, Brown HE, Semenov MA. Raising genetic yield potential in high productive countries: Designing wheat ideotypes under climate change. Agric. For. Meteorol. 2019;271:33–45. Available:https://doi.org/10.1016/j.agrformet.2019.02.025

Bassi FM, Bentley AR, Charmet G, Ortiz R, Crossa J. Breeding schemes for the implementation of genomic selection in wheat (Triticum spp.). Plant Sci. 2016; 242:23–36. Available:https://doi.org/10.1016/j.plantsci.2015.08.021

Hawkins C, Yu LX. Recent progress in alfalfa (Medicago sativa L.) genomics and genomic selection. Crop J. 2018;6:565–575. Available:https://doi.org/10.1016/j.cj.2018.01.006

Heun JT, Attalah S, French AN, Lehner KR, McKay JK, Mullen JL, Ottman MJ, Andrade-Sanchez P. Deployment of lidar from a ground platform: Customizing a Low-cost, information-rich and user-friendly application for field phenomics research. Sensors. 2019;19:5358. Available:https://doi.org/10.3390/s19245358

Roitsch T, Cabrera-Bosquet L, Fournier A, Ghamkhar K, Jiménez-Berni J, Pinto F, Ober ES. Review: New sensors and data-driven approaches—A path to next generation phenomics. Plant Sci. 2019; 282:2–10. Available:https://doi.org/10.1016/j.plantsci.2019.01.011

Shakoor N, Northrup D, Murray S, Mockler TC. Big data driven agriculture: Big data analytics in plant breeding, genomics, and the use of remote sensing technologies to Advance Crop Productivity. Plant Phenome J. 2019;2:1–8. Available:https://doi.org/10.2135/tppj2018.12.0009

Kumar J, Pratap A, Kumar S. Plant Phenomics: An Overview, in: Kumar, J., Pratap, A., Kumar, S. (Eds.), Phenomics in crop plants: Trends, options and limitations. Springer India, New Delhi. 2015;1–10. Available:https://doi.org/10.1007/978-81-322-2226-2_1

Lobos GA, Camargo AV, del Pozo A, Araus JL, Ortiz R, Doonan JH. Editorial: Plant Phenotyping and phenomics for plant breeding. Front. Plant Sci. 2017;8: 2181. Available:https://doi.org/10.3389/fpls.2017.02181

Lamichhaney S, Card DC, Grayson P, Tonini JFR, Bravo GA, Näpflin K, Termignoni-Garcia F, Torres C, Burbrink F, Clarke JA, Sackton TB, Edwards SV. Integrating natural history-derived phenomics with comparative genomics to study the genetic architecture of convergent evolution (preprint). Evolutionary Biology; 2019. https://doi.org/10.1101/574756

Mba C, Guimaraes EP, Ghosh K. Re-orienting crop improvement for the changing climatic conditions of the 21st century. Agric. Food Secur. 2012;1:7. Available:https://doi.org/10.1186/2048-7010-1-7

Momen M, Campbell MT, Walia H, Morota G. Predicting longitudinal traits derived from high-throughput phenomics in contrasting environments using genomic legendre polynomials and b-splines. G3 - Genes Genomes Genetics 2019;9:12. Available:https://doi.org/10.1534/g3.119.400346

Pratap A, Gupta Sanjeev Nair R, Gupta S, Schafleitner R, Basu P, Singh C, Prajapati U, Gupta A, Nayyar H, Mishra A, Baek KH. Using Plant phenomics to exploit the gains of genomics. Agronomy 2019;9: 126. Available:https://doi.org/10.3390/agronomy9030126

Bolger AM, Poorter H, Dumschott K, Bolger ME, Arend D, Osorio S, Gundlach H, Mayer KFX, Lange M, Scholz U, Usadel B. Computational aspects underlying genome to phenome analysis in plants. Plant J. 2019;97:182–198.
Available:https://doi.org/10.1111/tpj.14179

Gentzbittel L, Ben C, Mazurier M, Shin MG, Lorenz T, Rickauer M, Marjoram P, Nuzhdin SV, Tatarinova TV. WhoGEM: An admixture-based prediction machine accurately predicts quantitative functional traits in plants. Genome Biol. 2019;20:106. Available:https://doi.org/10.1186/s13059-019-1697-0