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Article

Genotype Imputation to Improve the Cost-Efficiency of Genomic Selection in Rabbits

1
Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, viale dell’Università 16, 35020 Legnaro, PD, Italy
2
Institute for Animal Science and Technology, Universitat Politècnica de València, 46022 Valencia, Spain
*
Authors to whom correspondence should be addressed.
E.M. and B.S.S.-M. share the first author position.
Academic Editors: Rosa M. García-Garcia and Maria Arias Alvarez
Animals 2021, 11(3), 803; https://doi.org/10.3390/ani11030803
Received: 2 February 2021 / Revised: 4 March 2021 / Accepted: 5 March 2021 / Published: 13 March 2021
(This article belongs to the Special Issue Challenges and New Strategies on Rabbit Breeding)
Genotyping costs are still the major limitation for the uptake of genomic selection by the rabbit meat industry, as a large number of genetic markers are needed for improving the prediction of breeding values by genomic data. In this study, several genotyping strategies were examined through simulation scenarios to disentangle the best feasible options of implementing genomic selection in rabbit breeding programs. Most scenarios emphasized the genotyping of candidate animals with a low Single Nucleotide Polymorphism (SNP) density platform. Imputation accuracies were high for the scenarios with ancestors genotyped at high or medium SNP-densities. However, the scenario with male ancestors genotyped at high SNP-density and only dams genotyped at medium SNP-density showed the best economically feasible strategy, taking into account the trade-off among genotyping costs, the accuracy of breeding values and response to selection. The results confirmed that by combining the imputation technique with a mindful selection of the animals to be genotyped, it is possible to achieve better performance than Best Linear Unbiased Prediction (BLUP), reducing genotyping cost at the same time.
Genomic selection uses genetic marker information to predict genomic breeding values (gEBVs), and can be a suitable tool for selecting low-hereditability traits such as litter size in rabbits. However, genotyping costs in rabbits are still too high to enable genomic prediction in selective breeding programs. One method for decreasing genotyping costs is the genotype imputation, where parents are genotyped at high SNP-density (HD) and the progeny are genotyped at lower SNP-density, followed by imputation to HD. The aim of this study was to disentangle the best imputation strategies with a trade-off between genotyping costs and the accuracy of breeding values for litter size. A selection process, mimicking a commercial breeding rabbit selection program for litter size, was simulated. Two different Quantitative Trait Nucleotide (QTN) models (QTN_5 and QTN_44) were generated 36 times each. From these simulations, seven different scenarios (S1–S7) and a further replicate of the third scenario (S3_A) were created. Scenarios consist of a different combination of genotyping strategies. In these scenarios, ancestors and progeny were genotyped with a mix of three different platforms, containing 200,000, 60,000, and 600 SNPs under a cost of EUR 100, 50 and 11 per animal, respectively. Imputation accuracy (IA) was measured as a Pearson’s correlation between true genotype and imputed genotype, whilst the accuracy of gEBVs was the correlation between true breeding value and the estimated one. The relationships between IA, the accuracy of gEBVs, genotyping costs, and response to selection were examined under each QTN model. QTN_44 presented better performance, according to the results of genomic prediction, but the same ranks between scenarios remained in both QTN models. The highest IA (0.99) and the accuracy of gEBVs (0.26; QTN_44, and 0.228; QTN_5) were observed in S1 where all ancestors were genotyped at HD and progeny at medium SNP-density (MD). Nevertheless, this was the most expensive scenario compared to the others in which the progenies were genotyped at low SNP-density (LD). Scenarios with low average costs presented low IA, particularly when female ancestors were genotyped at LD (S5) or non-genotyped (S7). The S3_A, imputing whole-genomes, had the lowest accuracy of gEBVs (0.09), even worse than Best Linear Unbiased Prediction (BLUP). The best trade-off between genotyping costs and the accuracy of gEBVs (0.234; QTN_44 and 0.199) was in S6, in which dams were genotyped with MD whilst grand-dams were non-genotyped. However, this relationship would depend mainly on the distribution of QTN and SNP across the genome, suggesting further studies on the characterization of the rabbit genome in the Spanish lines. In summary, genomic selection with genotype imputation is feasible in the rabbit industry, considering only genotyping strategies with suitable IA, accuracy of gEBVs, genotyping costs, and response to selection. View Full-Text
Keywords: genomic selection; imputation; litter size; rabbits; genomic simulation genomic selection; imputation; litter size; rabbits; genomic simulation
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MDPI and ACS Style

Mancin, E.; Sosa-Madrid, B.S.; Blasco, A.; Ibáñez-Escriche, N. Genotype Imputation to Improve the Cost-Efficiency of Genomic Selection in Rabbits. Animals 2021, 11, 803. https://doi.org/10.3390/ani11030803

AMA Style

Mancin E, Sosa-Madrid BS, Blasco A, Ibáñez-Escriche N. Genotype Imputation to Improve the Cost-Efficiency of Genomic Selection in Rabbits. Animals. 2021; 11(3):803. https://doi.org/10.3390/ani11030803

Chicago/Turabian Style

Mancin, Enrico, Bolívar S. Sosa-Madrid, Agustín Blasco, and Noelia Ibáñez-Escriche. 2021. "Genotype Imputation to Improve the Cost-Efficiency of Genomic Selection in Rabbits" Animals 11, no. 3: 803. https://doi.org/10.3390/ani11030803

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