Next Article in Journal
Effect of Rice-Straw Biochar Application on the Acquisition of Rhizosphere Phosphorus in Acidified Paddy Soil
Previous Article in Journal
Genome-Wide Identification and Expression Analysis of SnRK Gene Family under Abiotic Stress in Cucumber (Cucumis sativus L.)
Previous Article in Special Issue
Research Trends and Challenges of Using CRISPR/Cas9 for Improving Rice Productivity
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Editorial

Rice Genetics: Trends and Challenges for the Future Crops Production

1
Division of Horticultural Biotechnology, Hankyong National University, Anseong 17579, Korea
2
Department of Crop Science, Chungbuk National University, Cheongju 28644, Korea
*
Authors to whom correspondence should be addressed.
Submission received: 18 May 2022 / Revised: 21 June 2022 / Accepted: 27 June 2022 / Published: 28 June 2022
(This article belongs to the Special Issue Rice Genetics: Trends and Challenges for the Future Crops Production)
Twenty-first-century agriculture faces serious challenges in every country on the planet due to global population growth, declining genetic resources, climate change, farmland loss due to urbanization, and stagnant crop yields [1]. According to the United Nations’ population growth projections, the world’s population is projected to reach 9.6 billion by 2050, from 7 billion in 2021. For rice, it has become increasingly urgent to develop ultra-high-yielding varieties as well as varieties highly resistant to pathogens and climate change as a major staple food holding more than 3 billion people worldwide. As a potential solution to this problem, various genomic studies have been reported that may lead the future use of biotechnology directly in agriculture [2,3]. To date, many genes and single nucleotide polymorphisms (SNPs) involved in agronomically important traits have been identified by comparative genomics, GWAS, and OMICS-based approaches [4,5,6,7]. Recent studies provide in-depth technical and market insight into the different genetic technologies used in crop agriculture, including genetically modified organisms (GMOs), genome editing techniques (CRISPR, TALEN, ZFN, etc.), and breeding strategies [8]. Genome editing technology is revolutionizing crop improvement compared to conventional technologies as a fast, efficient, and simple strategy for modification of target genes. A deeper understanding of plant mechanisms that increase yields in diverse environments can be facilitated by genetic diversity analysis and implemented by genome-scale breeding, fine-tuned genetic engineering, and more precise agricultural management practices. Thus, trait-based analysis selection is essential to derive the benefits needed to improve crops. This Special Issue will provide a platform to present and discuss related topics of research progress and trends in the genetics, genomics, and breeding of rice.
Recent genetic research trends in rice aim to explore and apply important agronomical traits-related genes and molecular networks such as grain yield, grain quality, stress tolerance, disease resistance, nutrient use efficiency, and reproductive processes. In rice, various molecular research tools have been well established since the sequenced rice whole genome was reported in 2005 [9]. So far, a lot of information on factors affecting agricultural characteristics has been accumulated through field studies such as mutation breeding, MAB (marker assisted backcross) breeding, genetics, transcriptomics, proteomics, epigenetics, and metabolomics [10,11]. Through these achievements, it will be possible to develop new varieties with excellent agronomic characteristics to respond to climate change. The rice genome contains more than 37,000 annotated genes. However, despite the achievements of many studies, the individual molecular functions of most genes with a function are still unknown [10]. Therefore, functional analysis of individual genes controlling agronomically important traits is required to elucidate molecular mechanisms as well as networks of genes. Increasing grain yields is the overarching goal of any rice breeding program.
Grain yield is composed of very complex networks such as the number of panicles, the number of grains per panicle, and grain weight by controlling the tiller and panicle [12,13]. Moreover, the grain quality depends on four factors such as appearance, cooking, milling and nutritional quality [14]. In addition, the grain shape is a key factor in determining quality and yield characteristics. Grain chalkiness in rice is an undesirable trait that negatively affects the appearance, cooking, milling, and nutrition qualities as well as the head rice rate [15]. Eating and cooking quality of rice grain is generally determined by three physicochemical indices containing amylase content (AC), gel consistency (GC), and gelatinization temperature (GT) [14,16]. Rice grain nutritional quality mainly comprises the grain protein content (GPC), and contents of fats, amino acids, vitamins, and other micronutrients [17,18,19]. These research efforts will be able to contribute to improving the quality of rice grains in future breeding programs by acquiring the basic genetic knowledge of grain components. In addition to grain yield and quality, resistance characteristics to biotic and abiotic stresses to climate change are important goals for crop breeders [20]. A large number of pathogens, such as fungi, bacteria, viruses, and nematodes, cause diseases in rice, resulting in serious yield losses worldwide [21,22]. In the past decades, more than 100 stress-responsive genes and QTLs have been identified in rice through either forward or reverse genetics approach [2,23,24]. Recent research trends have revealed important knowledge about the biological interactions between rice and pathogens. Resistance genes to blast disease and bacterial blight disease have been identified in rice [25]. Many studies have been reported to elucidate the molecular mechanisms of immune response processes such as pathogen recognition, signal transduction and susceptibility to pathogens in rice [26]. As a result of overexpression of the OsAAA-ATPase1 gene, it was reported that the expression of pathogenesis related genes, brassinosteroid signaling response and the salicylic acid (SA)-mediated defense response and resistance to rice blast disease were enhanced [27,28]. The pyramiding lines developed using five bacterial blight resistance genes (xa4, xa5, xa7, xa13, xa21) showed not only high levels of resistance to bacterial blight disease, but also improved grain quantity and quality [29]. In addition, many researchers have isolated and functionally characterized salinity and osmosis-related genes (DEP1, qLTG3-1, OsSAP16, qDOM3.1, OsWRKY, and OsCIPK) to significantly improve grain yield and quality [30,31,32]. To date, the great progress has been achieved in uncovering the mechanisms of how rice senses and responds to external nutrients [33]. Rice absorbs and transports ammonium nitrogen and nitrate nitrogen through ammonium transporters (AMTs) and nitrate transporters (NRTs), respectively [34,35]. Male sterility is a major subject of research on reproductive development in rice for both basic biology and breeding application [36,37].
Functional genomic understanding of an agronomic trait refers to characterization of the genes (including non-coding sequences) and their regulatory networks, which collectively determine the formation and development of the trait [38,39]. The formation of any trait involves a large array of genes, and the majority of the genes that participate in many processes thus affect the development of many traits (or pleiotropic effects) [40,41]. Data and literature accumulated to date have already clearly depicted such a ‘‘net-like’’ structure between genes and traits [42]. In addition, formation and development of traits are greatly influenced by environmental conditions and also to some extent by field management practices [42]. For functional genomic understanding of agronomic traits, a complex trait such as yield may be divided into sub traits, which in turn are subdivided into components and biological processes, which may be specified by pathways [38]. Genes and regulatory networks then would be characterized for each component trait and process [43]. Thus, it is necessary to plan and pursue systematic efforts and strategies in future studies that are solidly based on the present findings, keeping in mind the net-like structure of the relationship between the genome and traits. The important research mentioned in this Special Issue demonstrates experimental results that will help us understand and explain the molecular basis of agronomically important characteristics in rice. To develop new rice varieties that can respond to climate change in the future, it is necessary to identify more important genes, explain their molecular functions, and design desirable genotypes.

Acknowledgments

We thank the support of “Cooperative Research Program for Agriculture Science & Technology Development (PJ016548022022)” Rural Development Administration (RDA) and basic science research program through the National Research Foundation of Korea (NRF) funded by the ministry education (2021R1I1A4A01057295) Republic of Korea.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. FAO. The Future of Food and Agriculture-Trends and Challenges; FAO: Rome, Italy, 2017. [Google Scholar]
  2. Yang, Y.; Li, Y.; Wu, C. Genomic resources for functional analyses of the rice genome. Curr. Opin. Plant Biol. 2013, 16, 157–163. [Google Scholar] [CrossRef] [PubMed]
  3. Le, V.T.; Kim, M.S.; Jung, Y.J.; Kang, K.K.; Cho, Y.G. Research Trends and Challenges of CRISPR/Cas9 for Improving Rice Productivity. Agronomy 2022, 12, 164. [Google Scholar] [CrossRef]
  4. Takahagi, K.; Uehara-Yamaguchi, Y.; Yoshida, T.; Sakurai, T.; Shinozaki, K.; Mochida, K.; Saisho, D. Analysis of single nucleotide polymorphisms based on RNA sequencing data of diverse bio-geographical accessions in barley. Sci. Rep. 2016, 6, 33199. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Lu, K.; Peng, L.; Zhang, C.; Lu, J.; Yang, B.; Xiao, Z.; Liang, Y.; Xu, X.; Qu, C.; Zhang, K.; et al. Genome-Wide Association and Transcriptome Analyses Reveal Candidate Genes Underlying Yield-determining Traits in Brassica napus. Front. Plant Sci. 2017, 8, 206. [Google Scholar] [CrossRef] [Green Version]
  6. Jaiswal, V.; Gupta, S.; Gahlaut, V.; Muthamilarasan, M.; Bandyopadhyay, T.; Ramchiary, N.; Prasad, M. Genome-Wide Association Study of Major Agronomic Traits in Foxtail Millet (Setaria italica L.) Using ddRAD Sequencing. Sci. Rep. 2019, 9, 5020. [Google Scholar] [CrossRef]
  7. Sheoran, S.; Jaiswal, S.; Raghav, N.; Sharma, R.; Sabhyata, S.; Gaur, A.; Jagannadham, J.; Tandon, G.; Singh, S.; Sharma, P.; et al. Genome-Wide Association Study and Post-Genome-Wide Association Study Analysis for Spike Fertility and Yield Related Traits in Bread Wheat. Front. Plant Sci. 2022, 11, 3452. [Google Scholar] [CrossRef]
  8. Mishra, R.; Joshi, R.K.; Zhao, K. Genome Editing in Rice: Recent Advances, Challenges, and Future Implications. Front. Plant Sci. 2018, 9, 1361. [Google Scholar] [CrossRef]
  9. International Rice Genome Sequencing Project. The map-based sequence of the rice genome. Nature 2005, 436, 793–800. [Google Scholar] [CrossRef]
  10. Song, S.; Tian, D.; Zhang, Z.; Hu, S.; Yu, J. Rice genomics: Over the past two decades and into the future. Genom. Proteom. Bioinf. 2018, 16, 397–404. [Google Scholar] [CrossRef]
  11. Dhawan, G.; Kumar, A.; Dwivedi, P.; Gopala Krishnan, S.; Pal, M.; Vinod, K.K.; Nagarajan, M.; Bhowmick, P.K.; Bollinedi, H.; Ellur, R.K.; et al. Introgression of qDTY1.1 Governing Reproductive Stage Drought Tolerance into an Elite Basmati Rice Variety “Pusa Basmati 1” through Marker Assisted Backcross Breeding. Agronomy 2021, 11, 202. [Google Scholar] [CrossRef]
  12. Kang, J.-W.; Nuulu, R.K.; Zarchi, P.; Park, S.-Y.; Lee, S.-M.; Lee, J.-Y.; Shin, D.; Cho, J.-H.; Park, D.-S.; Ko, J.-M.; et al. Combined Linkage Mapping and Genome-Wide Association Study Identified QTLs Associated with Grain Shape and Weight in Rice (Oryza sativa L.). Agronomy 2020, 10, 1532. [Google Scholar] [CrossRef]
  13. Hori, K. Genetic dissection and breeding for grain appearance quality in rice. In Rice Genomics, Genetics and Breeding; Sasaki, T., Ashilari, M., Eds.; Springer: Singapore, 2018; pp. 435–451. [Google Scholar]
  14. Kim, M.S.; Yang, J.Y.; Yu, J.K.; Lee, Y.; Park, Y.J.; Kang, K.K.; Cho, Y.G. Breeding of High Cooking and Eating Quality in Rice by Marker-Assisted Backcrossing (MABc) Using KASP Markers. Plants 2021, 10, 804. [Google Scholar] [CrossRef]
  15. Sun, M.-M.; Abdula, S.E.; Lee, H.-J.; Cho, Y.-C.; Han, L.-Z. Molecular Aspect of Good Eating Quality Formation in Japonica Rice. PLoS ONE 2011, 6, e18385. [Google Scholar] [CrossRef] [Green Version]
  16. Fiaz, S.; Ahmad, S.; Noor, M.A.; Wang, X.; Younas, A.; Riaz, A.; Riaz, A.; Ali, F. Applications of the CRISPR/Cas9 System for Rice Grain Quality Improvement: Perspectives and Opportunities. Int. J. Mol. Sci. 2019, 20, 888. [Google Scholar] [CrossRef] [Green Version]
  17. Das, P.; Adak, S.; Lahiri Majumder, A. Genetic Manipulation for Improved Nutritional Quality in Rice. Front. Genet. 2020, 11, 776. [Google Scholar] [CrossRef]
  18. Gaikwad, K.B.; Rani, S.; Kumar, M.; Gupta, V.; Babu, P.H.; Bainsla, N.K.; Yadav, R. Enhancing the Nutritional Quality of Major Food Crops Through Conventional and Genomics-Assisted Breeding. Front. Nutr. 2020, 7, 533453. [Google Scholar] [CrossRef]
  19. Shelenga, T.V.; Kerv, Y.A.; Perchuk, I.N.; Solovyeva, A.E.; Khlestkina, E.K.; Loskutov, I.G.; Konarev, A.V. The Potential of Small Grains Crops in Enhancing Biofortification Breeding Strategies for Human Health Benefit. Agronomy 2021, 11, 1420. [Google Scholar] [CrossRef]
  20. Sheteiwy, M.S.; Shao, H.; Qi, W.; Hamoud, Y.A.; Shaghaleh, H.; Khan, N.U.; Yang, R.; Tang, B. GABA-alleviated oxidative injury induced by salinity, osmotic stress and their combination by regulating cellular and molecular signals in rice. Int. J. Mol. Sci. 2019, 20, 5709. [Google Scholar] [CrossRef] [Green Version]
  21. Deb, S.; Madhavan, V.N.; Gokulan, C.G.; Patel, H.K.; Sonti, R.V. Arms and ammunitions: Effectors at the interface of rice and it’s pathogens and pests. Rice 2021, 14, 94. [Google Scholar] [CrossRef]
  22. Pandit, M.A.; Kumar, J.; Gulati, S.; Bhandari, N.; Mehta, P.; Katyal, R.; Rawat, C.D.; Mishra, V.; Kaur, J. Major Biological Control Strategies for Plant Pathogens. Pathogens 2022, 11, 273. [Google Scholar] [CrossRef]
  23. Wu, Y.B.; Li, G.; Zhu, Y.J.; Cheng, Y.C.; Yang, J.Y.; Chen, H.Z.; Song, X.J.; Ying, J.Z. Genome-wide identification of QTLs for grain protein content based on genotyping-by-resequencing and verification of qGPC1-1 in rice. Int. J. Mol. Sci. 2020, 21, 408. [Google Scholar] [CrossRef] [Green Version]
  24. Ester, S.; Eva, M.; Luis, M. Breeding for Low Temperature Germinability in Temperate Japonica Rice Varieties: Analysis of Candidate Genes in Associated QTLs. Agronomy 2021, 11, 2125. [Google Scholar] [CrossRef]
  25. Du, X.-X.; Park, J.-R.; Kim, H.; Saleah, S.-A.; Yun, B.-J.; Jeon, M.; Kim, K.-M. Quantitative Trait Locus Analysis of Microscopic Phenotypic Characteristic Data Obtained Using Optical Coherence Tomography Imaging of Rice Bacterial Leaf Blight Infection in the Field. Agronomy 2021, 11, 1630. [Google Scholar] [CrossRef]
  26. Kanda, Y.; Nakagawa, H.; Nishizawa, Y.; Kamakura, T.; Mori, M. Broad-spectrum disease resistance conferred by the overexpression of rice RLCK BSR1 results from an enhanced immune response to multiple MAMPs. Int. J. Mol. Sci. 2019, 20, 5523. [Google Scholar] [CrossRef] [Green Version]
  27. Liu, X.; Inoue, H.; Tang, X.; Tan, Y.; Xu, X.; Wang, C.; Jiang, C.J. Rice OsAAA-ATPase1 is induced during blast infection in a salicylic acid-dependent manner, and promotes blast fungus resistance. Int. J. Mol. Sci. 2020, 21, 1443. [Google Scholar] [CrossRef] [Green Version]
  28. Hwang, H.; Ryu, H.; Cho, H. Brassinosteroid Signaling Pathways Interplaying with Diverse Signaling Cues for Crop Enhancement. Agronomy 2021, 11, 556. [Google Scholar] [CrossRef]
  29. Hsu, Y.C.; Chiu, C.H.; Yap, R.; Tseng, Y.C.; Wu, Y.P. Pyramiding bacterial blight resistance genes in Tainung82 for broad-spectrum resistance using marker-assisted selection. Int. J. Mol. Sci. 2020, 21, 1281. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  30. Jiang, S.; Yang, C.; Xu, Q.; Wang, L.; Yang, X.; Song, X.; Wang, J.; Zhang, X.; Li, B.; Li, H.; et al. Genetic dissection of germinability under low temperature by building a resequencing linkage map in japonica Rice. Int. J. Mol. Sci. 2020, 21, 1284. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  31. Yuan, S.; Wang, Y.; Zhang, C.; He, H.; Yu, S. Genetic dissection of seed dormancy using chromosome segment substitution lines in rice (Oryza sativa L.). Int. J. Mol. Sci. 2020, 21, 1344. [Google Scholar] [CrossRef] [Green Version]
  32. Lee, S. Recent Advances on Nitrogen Use Efficiency in Rice. Agronomy 2021, 11, 753. [Google Scholar] [CrossRef]
  33. Islam, M.S. Sensing and uptake of nitrogen in rice plant: A molecular view. Rice Sci. 2019, 26, 343–355. [Google Scholar] [CrossRef]
  34. Xuan, W.; Beeckman, T.; Xu, G. Plant nitrogen nutrition: Sensing and signaling. Curr. Opin. Plant Biol. 2017, 39, 57–65. [Google Scholar] [CrossRef]
  35. Wang, K.; Peng, X.; Ji, Y.; Yang, P.; Zhu, Y.; Li, S. Gene, protein, and network of male sterility in rice. Front. Plant Sci. 2013, 4, 92. [Google Scholar] [CrossRef] [Green Version]
  36. Zhang, D.; Yuan, Z. Molecular control of grass inflorescence development. Annu. Rev. Plant Biol. 2014, 65, 553–578. [Google Scholar] [CrossRef]
  37. Rech, G.E.; Sanz-Martín, J.M.; Anisimova, M.; Sukno, S.A.; Thon, M.R. Natural selection on coding and noncoding DNA sequences is associated with virulence genes in a plant pathogenic fungus. Genome Biol. Evol. 2014, 6, 2368–2379. [Google Scholar] [CrossRef] [Green Version]
  38. Li, Y.; Xiao, J.; Chen, L.; Huang, X.; Cheng, Z.; Han, B.; Zhang, Q.; Wu, C. Rice functional genomics research: Past decade and future. Mol. Plant 2018, 11, 359–380. [Google Scholar] [CrossRef] [Green Version]
  39. Gabur, I.; Chawla, H.S.; Snowdon, R.J.; Parkin, I.A. Connecting genome structural variation with complex traits in crop plants. Theor. Appl. Genet. 2019, 132, 733–750. [Google Scholar] [CrossRef] [PubMed]
  40. Zhang, M.; Liu, Y.H.; Xu, W.; Smith, C.W.; Murray, S.C.; Zhang, H.B. Analysis of the genes controlling three quantitative traits in three diverse plant species reveals the molecular basis of quantitative traits. Sci. Rep. 2020, 10, 10074. [Google Scholar] [CrossRef]
  41. Snape, J. The influence of genetics on future crop production strategies: From traits to genes, and genes to traits. Ann. Appl. Biol. 2001, 138, 203–206. [Google Scholar] [CrossRef]
  42. Bloomfield, K.J.; Prentice, I.C.; Cernusak, L.A.; Eamus, D.; Medlyn, B.E.; Rumman, R.; Wright, I.J.; Boer, M.M.; Cale, P.; Cleverly, J.; et al. The validity of optimal leaf traits modelled on environmental conditions. New Phytol. 2019, 221, 1409–1423. [Google Scholar] [CrossRef]
  43. Liu, C.; Cheng, Y.J.; Wang, J.W.; Weigel, D. Prominent topologically associated domains differentiate global chromatin packing in rice from Arabidopsis. Nat. Plants 2017, 3, 742–748. [Google Scholar] [CrossRef] [PubMed]
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Kang, K.-K.; Cho, Y.-G. Rice Genetics: Trends and Challenges for the Future Crops Production. Agronomy 2022, 12, 1555. https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy12071555

AMA Style

Kang K-K, Cho Y-G. Rice Genetics: Trends and Challenges for the Future Crops Production. Agronomy. 2022; 12(7):1555. https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy12071555

Chicago/Turabian Style

Kang, Kwon-Kyoo, and Yong-Gu Cho. 2022. "Rice Genetics: Trends and Challenges for the Future Crops Production" Agronomy 12, no. 7: 1555. https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy12071555

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop