On the relevance of three genetic models for the description of genetic variance in small populations undergoing selection

Fournet-Hanocq, Florence; Elsen, Jean-Michel

doi:10.1186/1297-9686-30-1-59

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…When it comes to genetic variance, the observed drastic decay in the finite locus models matches the findings of [18]. We ascribe the lower genetic gain and variance in the finite locus models mainly to the inhomogeneity of the allele effects.…”

Section: Discussionsupporting

confidence: 85%

“…The different realized distributions of allele effects within a finite locus model showed only minor effects on the outcomes. We conclude that a single realization will suffice for many purposes and that more diverse results are to be expected with the introduction of more distributions rather than more realizations of the same distribution [12, 18].…”

Section: Discussionmentioning

confidence: 99%

“…Although existing software, such as ADAM [17], can run simulations based on either model, comparisons of the two models’ properties in stochastic simulations appears to be scarce in the literature. Fournet-Hanocq & Elsen [18] compared Monte Carlo simulations based on the finite locus model with two deterministic simulations, one of which relied on the infinitesimal model, and found greater losses of genetic variance in the finite locus models. Further studies focused on only one of the models and found dependencies of the simulation outcomes on the population size [19] or the distribution of QTL effects [12].…”

Section: Introductionmentioning

confidence: 99%

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Comparison of infinitesimal and finite locus models for long-term breeding simulations with direct and maternal effects at the example of honeybees

et al. 2019

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Stochastic simulation studies of animal breeding have mostly relied on either the infinitesimal genetic model or finite polygenic models. In this study, we investigated the long-term effects of the chosen model on honeybee breeding schemes. We implemented the infinitesimal model, as well as finite locus models, with 200 and 400 gene loci and simulated populations of 300 and 1000 colonies per year over the course of 100 years. The selection was of a directly and maternally influenced trait with maternal heritability of , direct heritability of , and a negative correlation between the effects of r md = − 0.18. Another set of simulations was run with parameters , , and r md = − 0.53. All models showed similar behavior for the first 20 years. Throughout the study, we observed a higher genetic gain in the direct than in the maternal effects and a smaller gain with a stronger negative covariance. In the long-term, however, only the infinitesimal model predicted sustainable linear genetic progress, while the finite locus models showed sublinear behavior and, after 100 years, only reached between 58% and 62% of the mean breeding values in the infinitesimal model. While the infinitesimal model suggested a reduction of genetic variance by 33% to 49% after 100 years, the finite locus models saw a more drastic loss of 76% to 92%. When designing sustainable breeding strategies, one should, therefore, not blindly trust the infinitesimal model as the predictions may be overly optimistic. Instead, the more conservative choice of the finite locus model should be favored.

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Section: Discussionsupporting

confidence: 85%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison of infinitesimal and finite locus models for long-term breeding simulations with direct and maternal effects at the example of honeybees

et al. 2019

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Measuring and managing genetic variability in small populations

2000

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-Genetic variability in small populations is affected by specific phenomena. The joint effects of genetic drift and selection, in addition to the decrease in genetic variance due to the mere selection (Bulmer effect), enhance the risk of losing alleles at selected or unselected genes and increase the inbreeding in the population by changing the family structure. Criteria for measuring this change in genetic variability are derived from the three approaches to describe the genetic variability. At the genealogical level, the kinship and inbreeding coefficients, or the effective population size, can be used. At the trait level, the estimation of its heritability is a good measure of remaining genetic variance. At the genome level, studying the polymorphism of known genetic markers can inform on the degree of genetic diversity. These criteria are to be integrated in specific tools for the management of the genetic variability. After a short introduction on the basic concepts needed for the study of genetic variability in small populations, the main criteria available to measure its change in populations is exposed and their relative efficiencies discussed. The strategies for monitoring genetic variability, deriving from the previous criteria, are illustrated through different examples. small population / genetic variability / genetic drift / genetic management / conservation programme Résumé -Mesure et gestion de la variabilité génétique dans les petites populations. Plusieurs phénomènes spécifiques modifient la variabilité génétique dans les petites populations. Les effets combinés de la dérive génétique et de la sélection, auxquels s'ajoutent la réduction de la variance géné-tique due spécifiquement à la sélection (Effet Bulmer), renforcent le risque de perdre des allèles à des loci sélectionnés et non sélectionnés et augmentent la consanguinité de la population du fait de la modification de la structure familiale. Les critères de mesure de la variabilité génétique dérivent des 3 approches utilisées pour la décrire. Les coefficients de consanguinité et de parenté ou l'effectif génétique résument l'information généalogique. L'estimation de l'héritabilité d'un caractère synthétise la variabilité génétique restante. L'étude du polymorphisme pour des marqueurs génétiques Ann. Zootech. 49 (2000) 77-93 77

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