Abstract
BackgroundIn this paper, we simulate deleterious load in an animal breeding program, and compare the efficiency of genome editing and selection for decreasing it. Deleterious variants can be identified by bioinformatics screening methods that use sequence conservation and biological prior information about protein function. However, once deleterious variants have been identified, how can they be used in breeding?ResultsWe simulated a closed animal breeding population that is subject to both natural selection against deleterious load and artificial selection for a quantitative trait representing the breeding goal. Deleterious load was polygenic and was due to either codominant or recessive variants. We compared strategies for removal of deleterious alleles by genome editing (RAGE) to selection against carriers. When deleterious variants were codominant, the best strategy for prioritizing variants was to prioritize low-frequency variants. When deleterious variants were recessive, the best strategy was to prioritize variants with an intermediate frequency. Selection against carriers was inefficient when variants were codominant, but comparable to editing one variant per sire when variants were recessive.ConclusionsGenome editing of deleterious alleles reduces deleterious load, but requires the simultaneous editing of multiple deleterious variants in the same sire to be effective when deleterious variants are recessive. In the short term, selection against carriers is a possible alternative to genome editing when variants are recessive. Our results suggest that, in the future, there is the potential to use RAGE against deleterious load in animal breeding.
Highlights
In this paper, we simulate deleterious load in an animal breeding program, and compare the efficiency of genome editing and selection for decreasing it
Our results show that both genome editing of deleterious alleles and selection against carriers can reduce deleterious load in some cases, but is inefficient at reducing it in other cases
Each individual carried on average a load of 52 (SD = 7.6) deleterious alleles in the codominant case and 89 (SD = 9.7) deleterious alleles in the recessive case
Summary
We simulate deleterious load in an animal breeding program, and compare the efficiency of genome editing and selection for decreasing it. Deleterious variants can be identified by bioinformatics screening methods that use sequence conservation and biological prior information about protein function. Once deleterious variants have been identified, how can they be used in breeding?. Deleterious load is an unavoidable fact of genetics that has a sizeable impact on the fitness of populations [1]. Reducing the number of deleterious variants in livestock populations could improve fitness traits, with subsequent benefits for animal welfare and profitability. We use simulation of deleterious variants in an animal breeding program to evaluate the efficiency of genome editing and selection against carriers for improving fitness traits in livestock.
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