IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v12y2022i11p1757-d951811.html
   My bibliography  Save this article

Integrated Genetic and Omics Approaches for the Regulation of Nutritional Activities in Rice ( Oryza sativa L.)

Author

Listed:
  • Muhammad Junaid Zaghum

    (Laboratory of Photosynthesis and Environmental Biology, CAS Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai 200032, China
    Seed Physiology Laboratory, Department of Agronomy, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Kashir Ali

    (Institute of Soil and Environmental Sciences, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan)

  • Sheng Teng

    (Laboratory of Photosynthesis and Environmental Biology, CAS Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai 200032, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

Abstract

The primary considerations in rice ( Oryza sativa L.) production evoke improvements in the nutritional quality as well as production. Rice cultivars need to be developed to tackle hunger globally with high yield and better nutrition. The traditional cultivation methods of rice to increase the production by use of non-judicious fertilizers to fulfill the nutritional requirement of the masses. This article provokes nutritional strategies by utilization of available omics techniques to increase the nutritional profiling of rice. Recent scientific advancements in genetic resources provide many approaches for better understanding the molecular mechanisms encircled in a specific trait for its up- or down-regulation for opening new horizons for marker-assisted breeding of new rice varieties. In this perspective, genome-wide association studies, genome selection (GS) and QTL mapping are all genetic analysis that help in precise augmentation of specific nutritional enrichment in rice grain. Implementation of several omics techniques are effective approaches to enhance and regulate the nutritional quality of rice cultivars. Advancements in different types of omics including genomics and pangenomics, transcriptomics, metabolomics, nutrigenomics and proteomics are also relevant to rice development initiatives. This review article compiles genes, locus, mutants and for rice yield and yield attribute enhancement. This knowledge will be useful for now and for the future regarding rice studies.

Suggested Citation

  • Muhammad Junaid Zaghum & Kashir Ali & Sheng Teng, 2022. "Integrated Genetic and Omics Approaches for the Regulation of Nutritional Activities in Rice ( Oryza sativa L.)," Agriculture, MDPI, vol. 12(11), pages 1-17, October.
  • Handle: RePEc:gam:jagris:v:12:y:2022:i:11:p:1757-:d:951811
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/12/11/1757/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/12/11/1757/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Shilei Liu & Wenli Zou & Xiang Lu & Jianmin Bian & Haohua He & Jingguang Chen & Guoyou Ye, 2021. "Genome-Wide Association Study Using a Multiparent Advanced Generation Intercross (MAGIC) Population Identified QTLs and Candidate Genes to Predict Shoot and Grain Zinc Contents in Rice," Agriculture, MDPI, vol. 11(1), pages 1-14, January.
    2. Wensheng Wang & Ramil Mauleon & Zhiqiang Hu & Dmytro Chebotarov & Shuaishuai Tai & Zhichao Wu & Min Li & Tianqing Zheng & Roven Rommel Fuentes & Fan Zhang & Locedie Mansueto & Dario Copetti & Millicen, 2018. "Genomic variation in 3,010 diverse accessions of Asian cultivated rice," Nature, Nature, vol. 557(7703), pages 43-49, May.
    3. Cécile Grenier & Tuong-Vi Cao & Yolima Ospina & Constanza Quintero & Marc Henri Châtel & Joe Tohme & Brigitte Courtois & Nourollah Ahmadi, 2015. "Accuracy of Genomic Selection in a Rice Synthetic Population Developed for Recurrent Selection Breeding," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-25, August.
    4. Jennifer Spindel & Hasina Begum & Deniz Akdemir & Parminder Virk & Bertrand Collard & Edilberto Redoña & Gary Atlin & Jean-Luc Jannink & Susan R McCouch, 2015. "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," PLOS Genetics, Public Library of Science, vol. 11(2), pages 1-25, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Aditi Bhandari & Jérôme Bartholomé & Tuong-Vi Cao-Hamadoun & Nilima Kumari & Julien Frouin & Arvind Kumar & Nourollah Ahmadi, 2019. "Selection of trait-specific markers and multi-environment models improve genomic predictive ability in rice," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-21, May.
    2. Julien Frouin & Axel Labeyrie & Arnaud Boisnard & Gian Attilio Sacchi & Nourollah Ahmadi, 2019. "Genomic prediction offers the most effective marker assisted breeding approach for ability to prevent arsenic accumulation in rice grains," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-22, June.
    3. Hideki Yoshida & Ko Hirano & Kenji Yano & Fanmiao Wang & Masaki Mori & Mayuko Kawamura & Eriko Koketsu & Masako Hattori & Reynante Lacsamana Ordonio & Peng Huang & Eiji Yamamoto & Makoto Matsuoka, 2022. "Genome-wide association study identifies a gene responsible for temperature-dependent rice germination," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    4. Yu Chen & Amy Y. Wang & Courtney A. Barkley & Yixin Zhang & Xinyang Zhao & Min Gao & Mick D. Edmonds & Zechen Chong, 2023. "Deciphering the exact breakpoints of structural variations using long sequencing reads with DeBreak," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    5. Rujia Chen & Ning Xiao & Yue Lu & Tianyun Tao & Qianfeng Huang & Shuting Wang & Zhichao Wang & Mingli Chuan & Qing Bu & Zhou Lu & Hanyao Wang & Yanze Su & Yi Ji & Jianheng Ding & Ahmed Gharib & Huixin, 2023. "A de novo evolved gene contributes to rice grain shape difference between indica and japonica," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    6. Charles‐Elie Rabier & Simona Grusea, 2021. "Prediction in high‐dimensional linear models and application to genomic selection under imperfect linkage disequilibrium," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(4), pages 1001-1026, August.
    7. Charles-Elie Rabier & Philippe Barre & Torben Asp & Gilles Charmet & Brigitte Mangin, 2016. "On the Accuracy of Genomic Selection," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-23, June.
    8. Yingyan Yu & Zhen Zhang & Xiaorui Dong & Ruixin Yang & Zhongqu Duan & Zhen Xiang & Jun Li & Guichao Li & Fazhe Yan & Hongzhang Xue & Du Jiao & Jinyuan Lu & Huimin Lu & Wenmin Zhang & Yangzhen Wei & Sh, 2022. "Pangenomic analysis of Chinese gastric cancer," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    9. Daiqi Wang & Hongru Wang & Xiaomei Xu & Man Wang & Yahuan Wang & Hong Chen & Fei Ping & Huanhuan Zhong & Zhengkun Mu & Wantong Xie & Xiangyu Li & Jingbin Feng & Milan Zhang & Zhilan Fan & Tifeng Yang , 2023. "Two complementary genes in a presence-absence variation contribute to indica-japonica reproductive isolation in rice," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    10. Shin-Fu Tsai & Chih-Chien Shen & Chen-Tuo Liao, 2021. "Bayesian Optimization Approaches for Identifying the Best Genotype from a Candidate Population," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(4), pages 519-537, December.
    11. Shiori Yabe & Masanori Yamasaki & Kaworu Ebana & Takeshi Hayashi & Hiroyoshi Iwata, 2016. "Island-Model Genomic Selection for Long-Term Genetic Improvement of Autogamous Crops," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-21, April.
    12. Xiaojuan Fan & Yongtao Cui & Jian Song & Honghuan Fan & Liqun Tang & Jianjun Wang, 2024. "Preliminary Exploration of Physiology and Genetic Basis Underlying High Yield in Indica–Japonica Hybrid Rice," Agriculture, MDPI, vol. 14(4), pages 1-12, April.
    13. Prabin Bajgain & James A. Anderson, 2021. "Multi-Allelic Haplotype-Based Association Analysis Identifies Genomic Regions Controlling Domestication Traits in Intermediate Wheatgrass," Agriculture, MDPI, vol. 11(7), pages 1-15, July.
    14. Marco Scutari & Ian Mackay & David Balding, 2016. "Using Genetic Distance to Infer the Accuracy of Genomic Prediction," PLOS Genetics, Public Library of Science, vol. 12(9), pages 1-19, September.
    15. Ting Wang & Shiyao Duan & Chen Xu & Yi Wang & Xinzhong Zhang & Xuefeng Xu & Liyang Chen & Zhenhai Han & Ting Wu, 2023. "Pan-genome analysis of 13 Malus accessions reveals structural and sequence variations associated with fruit traits," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    16. Laviola, Bruno Galvêas & Rodrigues, Erina Vitório & Teodoro, Paulo Eduardo & Peixoto, Leonardo de Azevedo & Bhering, Leonardo Lopes, 2017. "Biometric and biotechnology strategies in Jatropha genetic breeding for biodiesel production," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 894-904.
    17. Yakun Wang & Shengjia Tang & Naihui Guo & Ruihu An & Zongliang Ren & Shikai Hu & Xiangjin Wei & Guiai Jiao & Lihong Xie & Ling Wang & Ying Chen & Fengli Zhao & Peisong Hu & Zhonghua Sheng & Shaoqing T, 2023. "Base Editing of EUI1 Improves the Elongation of the Uppermost Internode in Two-Line Male Sterile Rice Lines," Agriculture, MDPI, vol. 13(3), pages 1-13, March.
    18. Md. S. Islam & Per McCord & Quentin D. Read & Lifang Qin & Alexander E. Lipka & Sushma Sood & James Todd & Marcus Olatoye, 2022. "Accuracy of Genomic Prediction of Yield and Sugar Traits in Saccharum spp. Hybrids," Agriculture, MDPI, vol. 12(9), pages 1-22, September.
    19. Sylvain Aubry, 2023. "Genebanking plant genetic resources in the postgenomic era," Agriculture and Human Values, Springer;The Agriculture, Food, & Human Values Society (AFHVS), vol. 40(3), pages 961-971, September.
    20. Jian Sun & Guangchen Zhang & Zhibo Cui & Ximan Kong & Xiaoyu Yu & Rui Gui & Yuqing Han & Zhuan Li & Hong Lang & Yuchen Hua & Xuemin Zhang & Quan Xu & Liang Tang & Zhengjin Xu & Dianrong Ma & Wenfu Che, 2022. "Regain flood adaptation in rice through a 14-3-3 protein OsGF14h," Nature Communications, Nature, vol. 13(1), pages 1-15, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:12:y:2022:i:11:p:1757-:d:951811. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.