Forneris Natalia S, Vitezica Zulma G, Legarra Andres, Pérez-Enciso Miguel
Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB Consortium, 08193, Bellaterra, Barcelona, Spain.
Departamento de Producción Animal, Facultad de Agronomía, Universidad de Buenos Aires, C1417DSE, Buenos Aires, Argentina.
Genet Sel Evol. 2017 Aug 25;49(1):66. doi: 10.1186/s12711-017-0340-3.
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.
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.
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).
上位性对选择响应的影响是一个备受争议的话题。在此,我们利用真实的果蝇序列数据,在非对称上位性较强的分歧选择条件下,通过基因组最佳线性预测(GBLUP)研究了上位性对基于序列的选择响应的影响。我们还探讨了在评估模型中纳入上位性和/或知晓因果突变的潜在优势。
选择响应几乎完全归因于少数几个具有较大效应的位点上等位基因频率的变化。由于位点频率谱高度偏斜,响应高度不对称(向上选择比向下选择高约四个表型标准差)。上位性加剧了这种不对称性,并通过调节加性遗传方差影响选择响应,向上选择时加性遗传方差维持时间更长,而向下选择时则迅速衰减。选择响应对评估模型相当不敏感,尤其是在加性模型下。然而,在不存在上位性的情况下将其纳入模型最终会导致随着选择进行准确性降低。如果模型中存在上位性并加以考虑,则是有益的,但仅在中期有益。与使用序列数据相比,已知因果突变时在选择响应方面没有太大提升,这可能是由于强连锁不平衡、高遗传力以及候选个体表型的可获得性。
上位性相互作用通过调节用于选择的加性遗传方差来影响基因组选择响应。与纯加性遗传作用相比,上位性释放出可能增加选择响应的加性方差。此外,基因组评估模型,特别是GBLUP是稳健的,即增加模型的复杂性并不会显著改变响应(对于给定的架构)。