Genetic gains with genomic versus phenotypic selection for drought and waterlogging tolerance in tropical maize (Zea mays L.)

Das, Reshmi; Vinayan, M.T.; Seetharam, K.; Phagna, Ramesh Kumar; Singh, Shyam Bir; Shahi, J. P.; Sarma, Ananthakrishna; Barua, Nagendra Sarma; Babu, Raman; Zaidi, P.H.

doi:10.1016/j.cj.2021.03.012

Cited by 13 publications

(10 citation statements)

References 33 publications

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“…The effectiveness of RCGS was initially assessed in a temperate maize biparental mapping population, with higher realized genetic gains for indices of stover and grain yield from RCGS in comparison to MARS . Superior response was also reported in tropical maize multiparental populations with cycles of RCGS for drought and waterlogging stress (Zhang et al 2017, Das et al 2020, Das et al 2021. For example, Zhang et al (2017) performed four cycles and obtained genetic gain for grain yield of 0.225 t ha −1 per cycle, which is equivalent to 0.100 t ha −1 year −1 over 4.5 years from the initial cross to the last cycle.…”

Section: Ca Wartha and Aj Lorenzmentioning

confidence: 69%

“…Other studies demonstrated similar responses to selection per cycle with GS and PS, but with a shorter cycle and reduced costs under GS (Table 1). With the goal of conserving genetic diversity while achieving high genetic gains, Zhang et al (2017) implemented GS in multi-parental tropical maize populations originating from crossing among 18 CIMMYT (International Center for Maize and Wheat Improvement) elite lines (Zhang et al 2017) and CIMMYT-Asia synthetic populations (Das et al 2020, Das et al 2021. In this sense, the CIMMYT maize breeding program is developing a phased GS implementation with a test-half-predict-half (THPH) strategy that has been cost effective since 2018 (Beyene et al 2019, Atanda et al 2021.…”

Section: Genomic Selection Implementation In the Public Sector For Cu...mentioning

confidence: 99%

“…An extreme form of parental selection based solely on predicted genotypic values is year-round rapid cycling GS (RCGS) (Asoro et al 2013, Combs and Bernardo 2013b, Beyene et al 2015, Rutkoski et al 2015, Vivek et al 2017, Das et al 2020, Das et al 2021. Conventional recurrent selection is amenable to GS by using genome-wide markers to select the individuals that will be recombined instead of phenotypic measurements in the target environment.…”

Section: Parental Selection Based Solely On the Prediction Of Genotyp...mentioning

confidence: 99%

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Implementation of genomic selection in public-sector plant breeding programs: Current status and opportunities

Wartha

Lorenz

2021

Crop Breed. Appl. Biotechnol.

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The usefulness of genomic selection (GS) for improvement of complex traits has been demonstrated in plant and animal breeding. While fully adopted in the livestock sector and commercial plant breeding, the implementation of GS in public-sector plant breeding programs lags. The goal of this review is to discuss advancements in GS implementation and opportunities for near-term and long-term adoption in public-sector plant breeding programs. We also highlighted specific applications of GS for cultivar development ordered by what we believe to be their feasibility, where feasibility is defined by cost, disruption to current breeding practices, and risk.

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Section: Ca Wartha and Aj Lorenzmentioning

confidence: 69%

Section: Genomic Selection Implementation In the Public Sector For Cu...mentioning

confidence: 99%

Section: Parental Selection Based Solely On the Prediction Of Genotyp...mentioning

confidence: 99%

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Implementation of genomic selection in public-sector plant breeding programs: Current status and opportunities

Wartha

Lorenz

2021

Crop Breed. Appl. Biotechnol.

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“…Those lines with high yield and high GEBVs in the safflower collection could be selected directly to achieve yield gain. Selection in the stressed environment, especially for drought resistance, has been reported to improve the selection in optimal conditions ( Das et al, 2021 ; Kumar et al, 2021 ). However, we observed the selection in the RF and LR sites leads to the compromised gain for GY in IR.…”

Section: Discussionmentioning

confidence: 99%

Genomic prediction and selection response for grain yield in safflower

et al. 2023

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In plant breeding programs, multiple traits are recorded in each trial, and the traits are often correlated. Correlated traits can be incorporated into genomic selection models, especially for traits with low heritability, to improve prediction accuracy. In this study, we investigated the genetic correlation between important agronomic traits in safflower. We observed the moderate genetic correlations between grain yield (GY) and plant height (PH, 0.272–0.531), and low correlations between grain yield and days to flowering (DF, −0.157–0.201). A 4%–20% prediction accuracy improvement for grain yield was achieved when plant height was included in both training and validation sets with multivariate models. We further explored the selection responses for grain yield by selecting the top 20% of lines based on different selection indices. Selection responses for grain yield varied across sites. Simultaneous selection for grain yield and seed oil content (OL) showed positive gains across all sites with equal weights for both grain yield and oil content. Combining g×E interaction into genomic selection (GS) led to more balanced selection responses across sites. In conclusion, genomic selection is a valuable breeding tool for breeding high grain yield, oil content, and highly adaptable safflower varieties.

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“…In this study the authors reported realized grain yield from cycle 1 to cycle 4 reached 0.100 tons ha -1 yr -1 over a 4.5-yr breeding period from the initial cross to the last cycle with a minimal loss of genetic diversity during the last cycle of genomic selection. Recently, Das et al (2020) used rapid cycle genomic selection to improve drought and waterlogging tolerance in two multi-parent yellow synthetic maize populations in Asia. This study showed that realized genetic gains after two cycles of rapid cycle recombination of genomic selection under drought stress were 0.110-and 0.135-tons ha −1 yr −1 , respectively, and 0.038-and 0.113-tons ha −1 yr −1 under waterlogging in two populations, respectively.…”

Section: Current Status Of Rapid Cycle Genomic Selectionmentioning

confidence: 99%

Genomic selection with rapid cyclingcycling: Current insights and future prospects

Volpato

Bernardeli

Gomez

2021

Crop Breed. Appl. Biotechnol.

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Enhancing the rate of genetic gain in plant breeding program is critical to address global food security in the face of climate change and a growing population. Rapid cycling genomic selection offers a powerful breeding strategy to reduce the breeding cycle and obtain rapid genetic gains in plant breeding programs. In this paper, we discuss theoretical and empirical approaches to deploy and optimize rapid cycling genomic selection in crop improvement programs. We highlight major advantages and challenges associated with rapid cycling genomic selection. Finally, we discuss the trends and general conclusion on this breeding strategy and provide recommendations for future discussions and continued adoption in plant breeding programs.

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Genetic gains with genomic versus phenotypic selection for drought and waterlogging tolerance in tropical maize (Zea mays L.)

Cited by 13 publications

References 33 publications

Implementation of genomic selection in public-sector plant breeding programs: Current status and opportunities

Implementation of genomic selection in public-sector plant breeding programs: Current status and opportunities

Genomic prediction and selection response for grain yield in safflower

Genomic selection with rapid cyclingcycling: Current insights and future prospects

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