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Influence of epistasis on response to genomic selection using complete sequence data

Colaborador(es): Forneris, Natalia Soledad. Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB Consortium, 08193 Bellaterra, Barcelona, Spain y Departamento de Producción Animal, Facultad de Agronomía, Universidad de Buenos Aires, C1417DSE Buenos Aires, Argentina. - e-mail : forneris@agro.uba.ar | Vitezica, Zulma Gladis. GenPhySE, INRA, INPT, ENVT, Université de Toulouse, 31326 Castanet‑Tolosan, France | Legarra, Andres. GenPhySE, INRA, INPT, ENVT, Université de Toulouse, 31326 Castanet‑Tolosan, France | Pérez Enciso, Miguel. Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB Consortium, 08193 Bellaterra, Barcelona, Spain y Departament de Ciència Animal i dels Aliments, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain y ICREA, Passeig de Lluís Companys 23, 08010 Barcelona, Spain. - E-mail : miguel.perez@uab.es.
ISSN: 0999-193X.Tipo de material: Recurso electrónico. Artículos y capítulos.Tema(s): ANIMAL | BIOLOGICAL MODEL | DROSOPHILA | EPISTASIS | GENETIC DATABASE | GENETIC SELECTION | GENETICS | GENOME | Recursos en línea: Haga clic para acceso en línea | LINK AL EDITOR En: Genetics selection evolution Vol.47, no.66 (2017), 14 p., grafs.Resumen: Background: The effect of epistasis on response to selection is a highly debated topic. Here, we investigated the impact of epistasis on response to sequence-based selection via genomic best linear prediction (GBLUP) in a regime of strong non-symmetrical epistasis under divergent selection, using real Drosophila sequence data. We also explored the possible advantage of including epistasis in the evaluation model and/or of knowing the causal mutations. Results: Response to selection was almost exclusively due to changes in allele frequency at a few loci with a large effect. Response was highly asymmetric (about four phenotypic standard deviations higher for upward than downward selection) due to the highly skewed site frequency spectrum. Epistasis accentuated this asymmetry and affected response to selection by modulating the additive genetic variance, which was sustained for longer under upward selection whereas it eroded rapidly under downward selection. Response to selection was quite insensitive to the evaluation model, especially under an additive scenario. Nevertheless, including epistasis in the model when there was none eventually led to lower accuracies as selection proceeded. Accounting for epistasis in the model, if it existed, was beneficial but only in the medium term. There was not much gain in response if causal mutations were known, compared to using sequence data, which is likely due to strong linkage disequilibrium, high heritability and availability of phenotypes on candidates. Conclusions: Epistatic interactions affect the response to genomic selection by modulating the additive genetic variance used for selection. Epistasis releases additive variance that may increase response to selection compared to a pure additive genetic action. Furthermore, genomic evaluation models and, in particular, GBLUP are robust, i.e. adding complexity to the model did not modify substantially the response (for a given architecture).
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Background: The effect of epistasis on response to selection is a highly debated topic. Here, we investigated the impact of epistasis on response to sequence-based selection via genomic best linear prediction (GBLUP) in a regime of strong non-symmetrical epistasis under divergent selection, using real Drosophila sequence data.
We also explored the possible advantage of including epistasis in the evaluation model and/or of knowing the causal mutations.
Results: Response to selection was almost exclusively due to changes in allele frequency at a few loci with a large effect. Response was highly asymmetric (about four phenotypic standard deviations higher for upward than downward selection) due to the highly skewed site frequency spectrum.
Epistasis accentuated this asymmetry and affected response to selection by modulating the additive genetic variance, which was sustained for longer under upward selection whereas it eroded rapidly under downward selection.
Response to selection was quite insensitive to the evaluation model, especially under an additive scenario. Nevertheless, including epistasis in the model when there was none eventually led to lower accuracies as selection proceeded. Accounting for epistasis in the model, if it existed, was beneficial but only in the medium term.
There was not much gain in response if causal mutations were known, compared to using sequence data, which is likely due to strong linkage disequilibrium, high heritability and availability of phenotypes on candidates.
Conclusions: Epistatic interactions affect the response to genomic selection by modulating the additive genetic variance used for selection. Epistasis releases additive variance that may increase response to selection compared to a pure additive genetic action. Furthermore, genomic evaluation models and, in particular, GBLUP are robust, i.e. adding complexity to the model did not modify substantially the response (for a given architecture).

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