Abstract
BackgroundThe 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.ResultsResponse 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.ConclusionsEpistatic 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).
Highlights
The effect of epistasis on response to selection is a highly debated topic
This study examined the impact of epistasis on the shortto-medium-term response to genomic selection (GS) in an extreme scenario of non-symmetrical epistasis under divergent selection, as well as the possible advantage of including non-additive effects in the prediction model
Some studies predict gains in accuracy when accounting for epistasis in GS [19], our results show that this advantage can be eroded as selection proceeds, likely because the substitution effects change over time
Summary
The effect of epistasis on response to selection is a highly debated topic. 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. Quantitative genetics is able to predict breeding values and response to selection with surprising accuracy based on highly simplified assumptions. Epistasis is one of the most widely studied and controversial [1]. The effect of epistasis on response to selection is a highly debated topic, both from the perspective of shortterm response and from an evolutionary perspective [4]. Some authors claim that its effect on the long term may be substantial, others argue that interaction effects contribute very little to the total genetic variance of a population, and to its short-term response, because most of the variance is additive [5].
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
