Optimized breeding strategies to harness Genetic Resources with different performance levels

Allier, Antoine; Teyssèdre, Simon; Lehermeier, Christina; Moreau, Laurence; Charcosset, Alain

doi:10.1101/2019.12.20.885087

Cited by 11 publications

(13 citation statements)

References 61 publications

(3 reference statements)

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“…In our simulation study, the genetic value of the individuals of the gene bank is low. In such case, to allow for a fair comparison between the HUC method and the deep scoping method, the HUC method should be extended with a bridging population to assist the introduction of the individuals of the gene bank into the elite population (Allier et al 2020b). This means that the breeding population is (5) U =̂p + îp , split into two parts: an elite population and a pre-breeding population.…”

Section: Huc Methods With Bridgingmentioning

confidence: 99%

“…This means that the breeding population is (5) U =̂p + îp , split into two parts: an elite population and a pre-breeding population. According to Allier et al (2020b), 75% of the parental population is used to select the elite population, while the remaining 25% is used to select the pre-breeding individuals. Because the recurrent breeding scheme used in our simulation study requires the selection of an even number of parents, 80% of the parental population is used to select the elite population and the remaining 20% is used to select the pre-breeding population.…”

Section: Huc Methods With Bridgingmentioning

confidence: 99%

“…However, when a gene bank is used containing individuals with low GEBVs, the HUC method fails to increase the genetic value of the breeding population. For this case, Allier et al (2020b) designed a new breeding scheme that incorporates a bridging population. However, this breeding scheme uses the GEBVs to select the parental population.…”

Section: Comparison Of the Huc Methods And The Deep Scoping Methodsmentioning

confidence: 99%

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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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Section: Huc Methods With Bridgingmentioning

confidence: 99%

Section: Huc Methods With Bridgingmentioning

confidence: 99%

Section: Comparison Of the Huc Methods And The Deep Scoping Methodsmentioning

confidence: 99%

See 1 more Smart Citation

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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“…With the development of high-throughput genotyping techniques, the use of the genome-wide prediction (or genomic selection: GS) approach to increase breeding progress by shortening generation intervals has been proposed, in which a large number of molecular markers is employed. Their effects are estimated on a training set (TS) of phenotyped and genotyped individuals [ 243 ]. GS is a method proposed by Meuwissen et al [ 244 ] to increase dairy cattle improvement programs efficiency.…”

Section: Gwas Genomic and Phenomic Predictionmentioning

confidence: 99%

OMICs, Epigenetics, and Genome Editing Techniques for Food and Nutritional Security

Gogolev

Ahmar

Akpınar³

et al. 2021

Plants

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The incredible success of crop breeding and agricultural innovation in the last century greatly contributed to the Green Revolution, which significantly increased yields and ensures food security, despite the population explosion. However, new challenges such as rapid climate change, deteriorating soil, and the accumulation of pollutants require much faster responses and more effective solutions that cannot be achieved through traditional breeding. Further prospects for increasing the efficiency of agriculture are undoubtedly associated with the inclusion in the breeding strategy of new knowledge obtained using high-throughput technologies and new tools in the future to ensure the design of new plant genomes and predict the desired phenotype. This article provides an overview of the current state of research in these areas, as well as the study of soil and plant microbiomes, and the prospective use of their potential in a new field of microbiome engineering. In terms of genomic and phenomic predictions, we also propose an integrated approach that combines high-density genotyping and high-throughput phenotyping techniques, which can improve the prediction accuracy of quantitative traits in crop species.

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“…On the other hand, the role of epistasis is recently reconsidered for the long-term response to selection (Paixão and Barton 2016). Several strategies based on the additive model are proposed for long-term genetic gain (Beukelaer et al 2017;Gorjanc et al 2018;Allier et al 2019). Non-additive models may be considered in future studies if they contribute to the enhancement of the genetic response through more accurate estimation of additive breeding values (Varona et al 2018).…”

Section: )mentioning

confidence: 99%

Dissecting the Genetic Architecture of Biofuel-Related Traits in a Sorghum Breeding Population

Ishimori

Takanashi

Hamazaki

et al. 2020

G3 Genes|Genomes|Genetics

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In sorghum [Sorghum bicolor (L.) Moench], hybrid cultivars for the biofuel industry are desired. Along with selection based on testcross performance, evaluation of the breeding population per se is also important for the success of hybrid breeding. In addition to additive genetic effects, non-additive (i.e., dominance and epistatic) effects are expected to contribute to the performance of early generations. Unfortunately, studies on early generations in sorghum breeding programs are limited. In this study, we analyzed a breeding population for bioenergy sorghum, which was previously developed based on testcross performance, to compare genomic selection models both trained on and evaluated for the per se performance of the 3rd generation S0 individuals. Of over 200 ancestral inbred accessions in the base population, only 13 founders contributed to the 3rd generation as progenitors. Compared to the founders, the performances of the population per se were improved for target traits. The total genetic variance within the S0 generation progenies themselves for all traits was mainly additive, although non-additive variances contributed to each trait to some extent. For genomic selection, linear regression models explicitly considering all genetic components showed a higher predictive ability than other linear and non-linear models. Although the number and effect distribution of underlying loci was different among the traits, the influence of priors for marker effects was relatively small. These results indicate the importance of considering non-additive effects for dissecting the genetic architecture of early breeding generations and predicting the performance per se.

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Optimized breeding strategies to harness Genetic Resources with different performance levels

Cited by 11 publications

References 61 publications

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

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

OMICs, Epigenetics, and Genome Editing Techniques for Food and Nutritional Security

Dissecting the Genetic Architecture of Biofuel-Related Traits in a Sorghum Breeding Population

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