Genomic prediction of reproduction traits for Merino sheep

Abstract

Economically important reproduction traits in sheep, such as number of lambs weaned and litter size, are expressed only in females and later in life after most selection decisions are made, which makes them ideal candidates for genomic selection. Accurate genomic predictions would lead to greater genetic gain for these traits by enabling accurate selection of young rams with high genetic merit. The aim of this study was to design and evaluate the accuracy of a genomic prediction method for female reproduction in sheep using daughter trait deviations (DTD) for sires and ewe phenotypes (when individual ewes were genotyped) for three reproduction traits: number of lambs born (NLB), litter size (LSIZE) and number of lambs weaned. Genomic best linear unbiased prediction (GBLUP), BayesR and pedigree BLUP analyses of the three reproduction traits measured on 5340 sheep (4503 ewes and 837 sires) with real and imputed genotypes for 510 174 SNPs were performed. The prediction of breeding values using both sire and ewe trait records was validated in Merino sheep. Prediction accuracy was evaluated by across sire family and random cross-validations. Accuracies of genomic estimated breeding values (GEBVs) were assessed as the mean Pearson correlation adjusted by the accuracy of the input phenotypes. The addition of sire DTD into the prediction analysis resulted in higher accuracies compared with using only ewe records in genomic predictions or pedigree BLUP. Using GBLUP, the average accuracy based on the combined records (ewes and sire DTD) was 0.43 across traits, but the accuracies varied by trait and type of cross-validations. The accuracies of GEBVs from random cross-validations (range 0.17-0.61) were higher than were those from sire family cross-validations (range 0.00-0.51). The GEBV accuracies of 0.41-0.54 for NLB and LSIZE based on the combined records were amongst the highest in the study. Although BayesR was not significantly different from GBLUP in prediction accuracy, it identified several candidate genes which are known to be associated with NLB and LSIZE. The approach provides a way to make use of all data available in genomic prediction for traits that have limited recording.

Selección genómica en ganado ovino de aptitud lechera = Genomic selection in dairy sheep livestock

“…However, it is important to consider that Puledda et al (2017) reported moderate-high phenotypic correlations between ILCY and MCP related traits, positive with the RCT/logK20 traits (range 0.32-0.64) and negative with A30 (-0.34, -0.67, respectively for the phenotypic and genotypic correlations).…”

“…En España, la mejora genómica todavía no ha sido completamente implementada en el ganado ovino de leche, aunque hay razas que ya la tienen en marcha. Jurado (2017) expuso y habló sobre la situación y problemas principales de la implementación de la selección genómica en el ganado ovino de leche en España. Jurado refirió a que se habían realizado varios estudios preliminares de selección genómica en los candidatos a futuros sementales.…”

“…Jurado (2017) explicó que el problema de la implementación de la selección genómica en ganado ovino de leche es que el precio del genotipado es alto en relación al precio del animal.…”

“…La capacidad económica de las razas de ovino, por lo general, no tiene el suficiente soporte para crear una población de referencia inicial y el poder económico de las asociaciones de ovino es menor que el de vacuno. Jurado (2017) alegó que, en el ganado vacuno, específicamente en la raza Holstein, los programas de mejora genética genómica están bien consolidados, ya que tienen grandes poblaciones de referencias, debido a que el precio del genotipado es bajo en relación al precio de los animales (alrededor de un 5%). A eso se le debe añadir que el número efectivo de la población en Holstein es bajo mientras que en ovino por lo general es grande.…”

“…Genomic prediction has been fully implemented in dairy cattle, poultry, pigs and some major beef cattle breeds (Daetwyler et al, 2010;. Similarly, in sheep breeding, genomic prediction has been quickly implemented in several large and economically important breeds/populations Baloche et al, 2014;Bolormaa et al, 2017;, but at a slower rate in less economically important breeds, like the Spanish Churra sheep. One reason for the differences in the level of implementation is the high genotyping cost that makes breeders of smaller breeds less able to afford the initial cost for creating the reference population.…”