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

Area of Science:

• Quantitative Genetics
• Genomics
• Evolutionary Biology

Background:

• Epistasis's effect on selection response is debated.
• Investigated epistasis in genomic best linear prediction (GBLUP) using Drosophila sequence data.
• Explored benefits of including epistasis in models and knowing causal mutations.

Purpose of the Study:

• To assess epistasis's impact on sequence-based selection response under strong, asymmetrical epistasis and divergent selection.
• To evaluate the advantage of incorporating epistasis into genomic evaluation models.
• To determine the benefit of identifying causal mutations for selection.

Main Methods:

• Simulated divergent selection on Drosophila sequence data.
• Utilized genomic best linear prediction (GBLUP) for genomic selection.
• Compared selection response with and without accounting for epistasis in the model.

Main Results:

• Selection response driven by few loci with large effects, showing high asymmetry due to skewed allele frequencies.
• Epistasis amplified selection asymmetry by modulating additive genetic variance, sustaining it longer under upward selection.
• Selection response was largely insensitive to the GBLUP model, but including epistasis when absent reduced accuracy over time.

Conclusions:

• Epistatic interactions modulate additive genetic variance, potentially increasing selection response beyond additive effects.
• Genomic evaluation models, including GBLUP, demonstrate robustness to model complexity.
• Accounting for epistasis is beneficial in the medium term when it is present.