Training genomic selection models across several breeding cycles increases genetic gain in oil palm in silico study

Cros, David; Tchounke, Billy; Nkague-Nkamba, Léontine

doi:10.1007/s11032-018-0850-x

Cited by 23 publications

(29 citation statements)

References 27 publications

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“…They posited that optimal number of F 1 hybrid genotypes in the training set should increase with heterozygosity of the parents of the F 1 hybrids, because more heterozygous parents may produce more within-cross variance than less heterozygous parents ( Cros et al, 2015 ). In a follow-up study, Cros et al (2018) also investigated whether prediction accuracies from a training set of genotypes from either only the previous breeding cycle, or both the previous two breeding cycles, was superior in RRGS. They found that training on two previous breeding cycles was superior because of both increased in prediction accuracy and slightly decreased loss of additive genetic variance ( Cros et al, 2018 ).…”

Section: Genomic Selection In Hybrid Breedingmentioning

confidence: 99%

“…In a follow-up study, Cros et al (2018) also investigated whether prediction accuracies from a training set of genotypes from either only the previous breeding cycle, or both the previous two breeding cycles, was superior in RRGS. They found that training on two previous breeding cycles was superior because of both increased in prediction accuracy and slightly decreased loss of additive genetic variance ( Cros et al, 2018 ).…”

Section: Genomic Selection In Hybrid Breedingmentioning

confidence: 99%

“…A key consideration in both studies was that dominance was not simulated even though RRGS was used, so the mean genetic values of the hybrids were equal to the mean of their parents ( Cros et al, 2015 , 2018 ). Further simulations of RRGS with dominance would be valuable.…”

Section: Genomic Selection In Hybrid Breedingmentioning

confidence: 99%

“…Hybrid crop varieties vastly outperform their inbred progenitors in economically important species ranging from maize ( Zea mays ) to oil palm ( Elaeis guineensis ; Duvick, 2005 ; Fu et al, 2014 ; Cros et al, 2015 ). However, hybrid breeding requires more time and resources than inbred breeding ( Troyer and Wellin, 2009 ; Longin et al, 2014 ; Cros et al, 2018 ). The effectiveness of hybrid breeding can be improved by genomic selection, in which marker information partially replaces phenotypes in estimation of breeding values ( Heffner et al, 2009 ).…”

Section: Introductionmentioning

confidence: 99%

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Heterosis and Hybrid Crop Breeding: A Multidisciplinary Review

2021

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Although hybrid crop varieties are among the most popular agricultural innovations, the rationale for hybrid crop breeding is sometimes misunderstood. Hybrid breeding is slower and more resource-intensive than inbred breeding, but it allows systematic improvement of a population by recurrent selection and exploitation of heterosis simultaneously. Inbred parental lines can identically reproduce both themselves and their F1 progeny indefinitely, whereas outbred lines cannot, so uniform outbred lines must be bred indirectly through their inbred parents to harness heterosis. Heterosis is an expected consequence of whole-genome non-additive effects at the population level over evolutionary time. Understanding heterosis from the perspective of molecular genetic mechanisms alone may be elusive, because heterosis is likely an emergent property of populations. Hybrid breeding is a process of recurrent population improvement to maximize hybrid performance. Hybrid breeding is not maximization of heterosis per se, nor testing random combinations of individuals to find an exceptional hybrid, nor using heterosis in place of population improvement. Though there are methods to harness heterosis other than hybrid breeding, such as use of open-pollinated varieties or clonal propagation, they are not currently suitable for all crops or production environments. The use of genomic selection can decrease cycle time and costs in hybrid breeding, particularly by rapidly establishing heterotic pools, reducing testcrossing, and limiting the loss of genetic variance. Open questions in optimal use of genomic selection in hybrid crop breeding programs remain, such as how to choose founders of heterotic pools, the importance of dominance effects in genomic prediction, the necessary frequency of updating the training set with phenotypic information, and how to maintain genetic variance and prevent fixation of deleterious alleles.

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Section: Genomic Selection In Hybrid Breedingmentioning

confidence: 99%

Section: Genomic Selection In Hybrid Breedingmentioning

confidence: 99%

Section: Genomic Selection In Hybrid Breedingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Heterosis and Hybrid Crop Breeding: A Multidisciplinary Review

2021

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“…reach sexual maturity after three years but require 13 to 15 years before phenotypic characteristics can be obtained. The transition of phenotypic selection to genomic selection reduced the time of one breeding cycle from 15 to three years ( Cros et al 2018 ). In time, genomic selection has further evolved and has become a powerful tool in animal and plant breeding ( Meuwissen et al 2001 ; Bernardo and Yu 2007 ; Crossa et al 2010 ).…”

mentioning

confidence: 99%

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

Vanavermaete

Fostier

Maenhout³

et al. 2020

G3 Genes|Genomes|Genetics

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Genomic selection has been successfully implemented in plant and animal breeding. The transition of parental selection based on phenotypic characteristics to genomic selection (GS) has reduced breeding time and cost while accelerating the rate of genetic progression. Although breeding methods have been adapted to include genomic selection, parental selection often involves truncation selection, selecting the individuals with the highest genomic estimated breeding values (GEBVs) in the hope that favorable properties will be passed to their offspring. This ensures genetic progression and delivers offspring with high genetic values. However, several favorable quantitative trait loci (QTL) alleles risk being eliminated from the breeding population during breeding. We show that this could reduce the mean genetic value that the breeding population could reach in the long term with up to 40%. In this paper, by means of a simulation study, we propose a new method for parental mating that is able to preserve the genetic variation in the breeding population, preventing premature convergence of the genetic values to a local optimum, thus maximizing the genetic values in the long term. We do not only prevent the fixation of several unfavorable QTL alleles, but also demonstrate that the genetic values can be increased by up to 15 percentage points compared with truncation selection.

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