A practical guide to genetic gain

Rutkoski, Jessica

doi:10.1016/bs.agron.2019.05.001

Cited by 65 publications

(60 citation statements)

References 57 publications

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“…The average reduction of 3.18 per year in selection gain is expressive since the evaluated lines do not come from a breeding programme that exclusively aims the selection of disease‐resistant genotypes but consider, concomitantly, other agronomically important traits, such as productivity, plant height, tolerance to water deficit, culinary quality, among many others of interest to the upland rice crop, as mentioned by Botelho et al (2018). The SG is important within the breeding programme because, besides enabling the breeder to predict the selection response even before it is performed, it provides an indication of the success of the selection over the trait under evaluation (Rutkoski, 2019). The interference of multi‐character selection on selection for disease resistance can be noticed by the reduction in the magnitude of the SG values from around 50 to around 3.…”

Section: Discussionmentioning

confidence: 99%

Genetic progress of upland rice (Oryza sativa L.) lines for disease resistance

et al. 2020

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The rice crop is affected by diseases throughout its cycle, impacting negatively on grain yield and quality. The control of the disease impact can be accomplished via crop breeding, using highly multiple resistant genotypes. This study aimed to evaluate the efficiency of multiple‐character and specific selection of multiple resistance to major culture‐associated diseases (neck blast, leaf scald and grain discoloration) in rice lines of the Upland Rice Genetic Breeding Program. The experiments were conducted in 35 sites during 12 agricultural years, where 124 lines were evaluated for the severity of fungal diseases, under natural field conditions. Multiple parameters were calculated based on the diseases´ scores: genetic, phenotypic and environmental variances, heritability, selection gain, renewal rate, and genetic and renewal progress. Genetic variance for the disease resistance was identified in the population, and the selection gain for multiple‐character selection was of 3.16 year−1 throughout the breeding process with a renewal rate of over 35%. The programme has showed efficiency in selecting multiple resistant genotypes to the mentioned diseases, highlighting genotypes with high potential for market release.

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Section: Discussionmentioning

confidence: 99%

Genetic progress of upland rice (Oryza sativa L.) lines for disease resistance

et al. 2020

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“…where R is the response to selection per year, i is the selection intensity, r is the accuracy of selection, σ a is the additive genetic standard deviation for the trait of interest, and L is the generation interval (Lush, 1936). Assuming that breeding objectives, selection criteria, available germplasm, and target environments are well defined, the success of a breeding program is largely dependent on the optimal use of available resources to maximize the response to selection (Rutkoski, 2019). Effective breeding programs must re-evaluate breeding strategies as technology, environments, access to germplasm, and consumer needs are constantly changing.…”

Section: Introductionmentioning

confidence: 99%

Strategies for Effective Use of Genomic Information in Crop Breeding Programs Serving Africa and South Asia

et al. 2020

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Much of the world's population growth will occur in regions where food insecurity is prevalent, with large increases in food demand projected in regions of Africa and South Asia. While improving food security in these regions will require a multi-faceted approach, improved performance of crop varieties in these regions will play a critical role. Current rates of genetic gain in breeding programs serving Africa and South Asia fall below rates achieved in other regions of the world. Given resource constraints, increased genetic gain in these regions cannot be achieved by simply expanding the size of breeding programs. New approaches to breeding are required. The Genomic Open-source Breeding informatics initiative (GOBii) and Excellence in Breeding Platform (EiB) are working with public sector breeding programs to build capacity, develop breeding strategies, and build breeding informatics capabilities to enable routine use of new technologies that can improve the efficiency of breeding programs and increase genetic gains. Simulations evaluating breeding strategies indicate cost-effective implementations of genomic selection (GS) are feasible using relatively small training sets, and proof-of-concept implementations have been validated in the International Maize and Wheat Improvement Center (CIMMYT) maize breeding program. Progress on GOBii, EiB, and implementation of GS in CIMMYT and International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) breeding programs are discussed, as well as strategies for routine implementation of GS in breeding programs serving Africa and South Asia.

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“…where GEBV F 10 denotes the average GEBV of the resulting 2,700 F 10 RILs; and GEBV P denotes the average GEBV of 10 parental lines selected using each strategy [23]. The larger the absolute value of the genetic gain, the greater the improvement in the target trait.…”

Section: Calculation Of Genetic Gainmentioning

confidence: 99%

Identification of superior parental lines for biparental crossing via genomic prediction

Chung¹,

Liao²

2020

PLoS ONE

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A parental selection approach based on genomic prediction has been developed to help plant breeders identify a set of superior parental lines from a candidate population before conducting field trials. A classical parental selection approach based on genomic prediction usually involves truncation selection, i.e., selecting the top fraction of accessions on the basis of their genomic estimated breeding values (GEBVs). However, truncation selection inevitably results in the loss of genomic diversity during the breeding process. To preserve genomic diversity, the selection of closely related accessions should be avoided during parental selection. We thus propose a new index to quantify the genomic diversity for a set of candidate accessions, and analyze two real rice (Oryza sativa L.) genome datasets to compare several selection strategies. Our results showed that the pure truncation selection strategy produced the best starting breeding value but the least genomic diversity in the base population, leading to less genetic gain. On the other hand, strategies that considered only genomic diversity resulted in greater genomic diversity but less favorable starting breeding values, leading to more genetic gain but unsatisfactorily performing recombination inbred lines (RILs) in progeny populations. Among all strategies investigated in this study, compromised strategies, which considered both GEBVs and genomic diversity, produced the best or second-best performing RILs mainly because these strategies balance the starting breeding value with the maintenance of genomic diversity.

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A practical guide to genetic gain

Cited by 65 publications

References 57 publications

Genetic progress of upland rice (Oryza sativa L.) lines for disease resistance

Genetic progress of upland rice (Oryza sativa L.) lines for disease resistance

Strategies for Effective Use of Genomic Information in Crop Breeding Programs Serving Africa and South Asia

Identification of superior parental lines for biparental crossing via genomic prediction

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