Preservation of Genetic Variation in a Breeding Population for Long-Term Genetic Gain

Vanavermaete, David; Fostier, Jan; Maenhout, Steven; Baets, Bernard De

doi:10.1534/g3.120.401354

Cited by 24 publications

(46 citation statements)

References 37 publications

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“…As mentioned earlier, the works of Gaynor et al [ 13 ] and Yao et al [ 14 ] support that the strategy for selecting better parental lines through GP with Monte Carlo simulation should prove useful in plant breeding. The study of Vanavermaete et al [ 15 ] supports our theory that considering both GEBV and genomic diversity in parental selection is a promising strategy.…”

Section: Discussionsupporting

confidence: 61%

“…By selecting parental lines with the highest GEBVs, breeders hope to maximally pass the favorable traits of parental lines on to their progeny. However, the truncation selection approach risks the elimination of several favorable QTLs from the breeding population because of a lack of genomic diversity [ 15 ]. Therefore, in this study, we took both GEBV and genomic diversity into account for identifying superior parents in a biparental crossing program.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionsupporting

confidence: 61%

Section: Introductionmentioning

confidence: 99%

Identification of superior parental lines for biparental crossing via genomic prediction

Chung¹,

Liao²

2020

PLoS ONE

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“…Unfortunately, truncation selection is also associated with a loss in genetic variation (Jannink 2010). Besides entailing the loss of favorable QTL alleles from the breeding population, truncation selection causes a premature convergence of the genetic value, reducing the long-term genetic gain (Vanavermaete et al 2020). Therefore, truncation selection can only promise a temporary, short-term increase in the genetic gain.…”

Section: Introductionmentioning

confidence: 99%

“…In another strategy, the GEBV was replaced by the criterion of usefulness (UC), which not only takes into account the mean predicted genetic value of the offspring, but also the selection intensity, prediction accuracy and genetic variation of the offspring (Lehermeier et al 2017). The scoping method combines pre-selection with a score function to avoid the selection of individuals with a too low GEBV while preserving genetic variation of the breeding population, thus maximizing the long-term genetic gain (Vanavermaete et al 2020). Whereas the GEBV is based on the total sum of the additive marker effects, the optimal haploid value (OHV) scores individuals based on their haplotypes, and can therefore better preserve favorable QTL alleles in the breeding population, increasing the long-term genetic gain (Daetwyler et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Deep scoping: a breeding strategy to preserve, reintroduce and exploit genetic variation

Vanavermaete

Fostier

Maenhout³

et al. 2021

Theor Appl Genet

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Key message The deep scoping method incorporates the use of a gene bank together with different population layers to reintroduce genetic variation into the breeding population, thus maximizing the long-term genetic gain without reducing the short-term genetic gain or increasing the total financial cost. Abstract Genomic prediction is often combined with truncation selection to identify superior parental individuals that can pass on favorable quantitative trait locus (QTL) alleles to their offspring. However, truncation selection reduces genetic variation within the breeding population, causing a premature convergence to a sub-optimal genetic value. In order to also increase genetic gain in the long term, different methods have been proposed that better preserve genetic variation. However, when the genetic variation of the breeding population has already been reduced as a result of prior intensive selection, even those methods will not be able to avert such premature convergence. Pre-breeding provides a solution for this problem by reintroducing genetic variation into the breeding population. Unfortunately, as pre-breeding often relies on a separate breeding population to increase the genetic value of wild specimens before introducing them in the elite population, it comes with an increased financial cost. In this paper, on the basis of a simulation study, we propose a new method that reintroduces genetic variation in the breeding population on a continuous basis without the need for a separate pre-breeding program or a larger population size. This way, we are able to introduce favorable QTL alleles into an elite population and maximize the genetic gain in the short as well as in the long term without increasing the financial cost.

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“…Selecting the parental lines with the highest GEBVs (truncation selection), breeders hope to maximally pass favorable properties of the parental lines on to their progeny populations. However, several favorable QTLs can risk being eliminated from the breeding population using the truncation selection (Vanavermaete et al 2020). We therefore take both GEBV and genomic diversity into account for identifying superior parents in a biparental crossing program.…”

Section: Introductionmentioning

confidence: 99%

Identify Superior Parental Lines for Biparental Crossing via Genomic Prediction: Rice as an Example

Chung

Liao

2020

Preprint

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Background A set of superior parental lines is the key to high-performing recombinant inbred lines (RILs) for biparental crossing in a rice breeding program. The number of possible crosses in such a breeding program is often far greater than the number that breeders can handle in the field. A practical parental selection method via genomic prediction (GP) is therefore developed to help breeders identify a set of superior parental lines from a candidate population before field trials. Results The parental selection via GP often involves truncation selection, selecting the top fraction of accessions based on their genomic estimated breeding values (GEBVs). However, the truncation selection inevitably causes a loss of genomic diversity in the breeding population. To preserve genomic variation, the selection of closely related accessions should be avoided. We first proposed a new index to quantify the genomic diversity for a set of candidate accessions. Then, we compared the performance of three classes of strategy for the parental selection, including those consider (a) GEBV only, (b) genomic diversity only, and (c) both GEBV and genomic diversity. We analyzed two rice (Oryza sativa L.) genome datasets for the comparison. The results show that the strategies considering both GEBV and genomic diversity have the best or second-best performance for all the traits analyzed in this study. Conclusion Combining GP with Monte Carlo simulation can be a useful means of parental selection for rice pre-breeding programs. Different strategies can be implemented to identify a set of superior parental lines from a candidate population. In consequence, the strategies considering both GEBV and genomic diversity that can balance the starting GEBV average with maintenance of genomic diversity should be recommended for practical use.

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Preservation of Genetic Variation in a Breeding Population for Long-Term Genetic Gain

Cited by 24 publications

References 37 publications

Identification of superior parental lines for biparental crossing via genomic prediction

Identification of superior parental lines for biparental crossing via genomic prediction

Deep scoping: a breeding strategy to preserve, reintroduce and exploit genetic variation

Identify Superior Parental Lines for Biparental Crossing via Genomic Prediction: Rice as an Example

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