Molecular Breeding for Improving Drought Tolerance in Rice: Recent Progress and Future Perspectives

Majumder, Ratna Rani; Sakhale, Sandeep; Yadav, Shailesh; Sandhu, Nitika; Hassan, Lutful; Hossain, Md. Amir; Kumar, Arvind

doi:10.1002/9781119633174.ch3

Cited by 5 publications

(3 citation statements)

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“…Under this project, imperative efforts were made to incorporate the major drought-tolerant QTLs ( qDTY1.1, qDTY2.1, qDTY2.2, qDTY3.1, qDTY4.1, qDTY12.1 qDTY6.3, etc . ) into the background of the mega rice varieties like IR64, Swarna, and TDK-1, which led to the development of several high yielding drought-tolerant rice varieties (Bernier et al 2007 ; Venuprasad et al 2009 ; Vikram et al 2011 ; Mishra et al 2013 ; Yadaw et al 2013 ; Sandhu et al 2014 , 2019 , 2021 ; Kumar et al 2014 , 2020 ; Henry et al 2015 , 2019 ; Dixit et al 2017 , 2020 ; Sandhu and Kumar 2017 ; Bhandari et al 2020 ; Majumder et al 2021 ; Yadav et al 2021 ). The most popular drought-tolerant rice varieties include—DRR dhan 42, CR Dhan 801, Sahbhagi dhan in India, Sukha dhan 4, Bahuguni dhan 11 in Nepal, and Katihan 2, Katihan 3, Sahod Ulan 15, Sahod Ulan 19 in the Philippines, Yaenelo 4 in Myanmar, MPTSA and ATETE in Malawi, CAR 14 in Cambodia, and BRRI Dhan66, BRRI Dhan 71 in Bangladesh ( https://strasa.irri.org/varietal-releases/drought ).…”

Section: Introductionmentioning

confidence: 99%

Genetic Trends Estimation in IRRIs Rice Drought Breeding Program and Identification of High Yielding Drought-Tolerant Lines

Khanna

Mahender

Catolos

et al. 2022

Rice

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Estimating genetic trends using historical data is an important parameter to check the success of the breeding programs. The estimated genetic trends can act as a guideline to target the appropriate breeding strategies and optimize the breeding program for improved genetic gains. In this study, 17 years of historical data from IRRI’s rice drought breeding program was used to estimate the genetic trends and assess the breeding program's success. We also identified top-performing lines based on grain yield breeding values as an elite panel for implementing future population improvement-based breeding schemes. A two-stage approach of pedigree-based mixed model analysis was used to analyze the data and extract the breeding values and estimate the genetic trends for grain yield under non-stress, drought, and in combined data of non-stress and drought. Lower grain yield values were observed in all the drought trials. Heritability for grain yield estimates ranged between 0.20 and 0.94 under the drought trials and 0.43–0.83 under non-stress trials. Under non-stress conditions, the genetic gain of 0.21% (10.22 kg/ha/year) for genotypes and 0.17% (7.90 kg/ha/year) for checks was observed. The genetic trend under drought conditions exhibited a positive trend with the genetic gain of 0.13% (2.29 kg/ha/year) for genotypes and 0.55% (9.52 kg/ha/year) for checks. For combined analysis showed a genetic gain of 0.27% (8.32 kg/ha/year) for genotypes and 0.60% (13.69 kg/ha/year) for checks was observed. For elite panel selection, 200 promising lines were selected based on higher breeding values for grain yield and prediction accuracy of > 0.40. The breeding values of the 200 genotypes formulating the core panel ranged between 2366.17 and 4622.59 (kg/ha). A positive genetic rate was observed under all the three conditions; however, the rate of increase was lower than the required rate of 1.5% genetic gain. We propose a recurrent selection breeding strategy within the elite population with the integration of modern tools and technologies to boost the genetic gains in IRRI’s drought breeding program. The elite breeding panel identified in this study forms an easily available and highly enriched genetic resource for future recurrent selection programs to boost the genetic gains.

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Section: Introductionmentioning

confidence: 99%

Genetic Trends Estimation in IRRIs Rice Drought Breeding Program and Identification of High Yielding Drought-Tolerant Lines

Khanna

Mahender

Catolos

et al. 2022

Rice

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“…The DNA is healed by the cellular repair system either through error-prone, non-homologous end joining or homologous recombination, by which small DNA fragments can be inserted at the target site [13,14]. Genome editing provides information for creating new alleles, fixing faulty alleles, and pyramiding alleles to obtain the desired phenotype by eliminating the generic drug [15].…”

Section: Introductionmentioning

confidence: 99%

Research Trends and Challenges of Using CRISPR/Cas9 for Improving Rice Productivity

Kim

Jung

et al. 2022

Agronomy

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Nowadays, rice production faces significant challenges due to population pressure, global climate change, and outbreak of various pests and diseases. Breeding techniques used to improve rice traits include mutant breeding, cross breeding, heterogeneity, transformation, molecular markers, genome-wide association study (GWAS), and so on. Since the recently developed CRISPR/Cas9 technology can directly target a specific part of a desired gene to induce mutation, it can be used as a powerful means to expand genetic diversity of crops and develop new varieties. So far, CRISPR/Cas9 technology has been used for improving rice characteristics such as high yield, good quality, abundant nutrition, pest and disease resistance, herbicide resistance, and biotic and abiotic stress resistance. This review highlights the mechanisms and optimization of the CRISPR system and its application to rice crop, including resistance to biotic and abiotic stresses, and improved rice quality and yield.

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“…STRASA (Stress Tolerant Rice for Africa and South Asia) Project (2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019) at IRRI, funded by the Bill and Melinda Gates Foundation (BMGF) was one of the most successful research programs that led to the successful development and release of more than 30 high-yielding drought-tolerant rice varieties in Asia and Africa (https://strasa.irri.org/). Under this project, imperative efforts were made to incorporate the major drought-tolerant QTLs (qDTY1.1,qDTY2.1,qDTY2.2,qDTY3.1,qDTY4.1,qDTY12.1 qDTY6.3,etc.,) into the background of the mega rice varieties like IR64, Swarna, and TDK-1 which led to the development of several high yielding drought-tolerant rice varieties (Bernier et al 2007;Venuprasad et al 2009;Vikram et al 2011;Mishra et al 2013;Yadaw et al 2013;Sandhu et al 2014;Kumar et al 2014;Henry et al 2015;Dixit et al 2017;Sandhu and Kumar 2017;Henry et al 2019;Sandhu et al 2019;Bhandari et al 2020;Dixit et al 2020;Kumar et al 2020;Majumder et al 2021;Sandhu et al 2021;Yadav et al 2021). The most popular drought-tolerant rice varieties include -DRR dhan 42, CR Dhan 801, Sahbhagi dhan in India, Sukha dhan 4, Bahuguni dhan 11 in Nepal, and Katihan 2, Katihan 3, Sahod Ulan 15, Sahod Ulan 19 in the Philippines, Yaenelo 4 in Myanmar, MPTSA ...…”

Section: Introductionmentioning

confidence: 99%

Genetic Trends Estimation in IRRIs Rice Drought Breeding Program and Identification of High Yielding Drought-Tolerant Lines

Khanna

Mahender

Catolos

et al. 2021

Preprint

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BackgroundEstimation of genetic trends using historical data is an important parameter to check the success of the breeding programs. The estimated genetic trends can act as a guideline to target the appropriate breeding strategies and optimize the breeding program for improved genetic gains. In this study, 17 years of historical data from IRRI’s rice drought breeding program was used to estimate the genetic trends and assess the success of the breeding program. We also identified top-performing lines based on grain yield breeding values as an elite panel for implementing future population improvement-based breeding schemes.ResultsA two-stage approach of pedigree-based mixed model analysis was used to analyze the data and extract the breeding values and estimate the genetic trends for grain yield under non-stress, drought, and in combined data of non-stress and drought. Lower grain yield values were observed in all the drought trials. Heritability for grain yield estimates ranged between 0.20-0.94 under the drought trials, and 0.43-0.83 under non-stress trials. Under non-stress conditions the genetic gain of 0.44% (21.20 kg/ha/year) for genotypes and 0.17 % (7.90 kg/ha/year) for checks was observed. The genetic trend under the drought conditions exhibited a positive trend with the genetic gain of 0.11% (1.98kg/ha/year) for genotypes and 0.55% (9.52kg/ha/year) for checks. For combined analysis showed a genetic gain of 0.39% (12.13 kg/ha/year) for genotypes and 0.60% (13.69 kg/ha/year) for checks was observed. For elite panel selection, 200 promising lines were selected based on higher breeding values for grain yield and prediction accuracy of >0.40. The breeding values of the 200 genotypes formulating the core panel ranged between 2366.17 and 4622.59 (kg/ha).ConclusionsA positive genetic rate was observed under all the three conditions; however, the rate of increase was lower than the required rate of 1.5% genetic gain. We propose a recurrent selection breeding strategy within the elite population with the integration of modern tools and technologies to boost the genetic gains in IRRI’s drought breeding program. The elite breeding panel identified in this study forms an easily available and highly enriched genetic resource for future recurrent selection programs to boost the genetic gains.

show abstract

Molecular Breeding for Improving Drought Tolerance in Rice: Recent Progress and Future Perspectives

Cited by 5 publications

References 104 publications

Genetic Trends Estimation in IRRIs Rice Drought Breeding Program and Identification of High Yielding Drought-Tolerant Lines

Genetic Trends Estimation in IRRIs Rice Drought Breeding Program and Identification of High Yielding Drought-Tolerant Lines

Research Trends and Challenges of Using CRISPR/Cas9 for Improving Rice Productivity

Genetic Trends Estimation in IRRIs Rice Drought Breeding Program and Identification of High Yielding Drought-Tolerant Lines

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