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Autoregressive single-step model for genomic evaluation of longitudinal reproductive traits in portuguese holstein cattle.

Hugo Teixeira SilvaPaulo Sávio LopesClaudio Nápolis CostaAlessandra Alves da SilvaDelvan Alves SilvaFabyano Fonseca E SilvaRenata VeronezeGertrude ThompsonJulio Carvalheira
Published in: Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie (2020)
We investigated the applicability of ssGBLUP methodology under the autoregressive model (H-AR) for genomic evaluation of longitudinal reproductive traits in Portuguese Holstein cattle. The genotype data of 1,230 bulls and 1,645 cows were considered in our study. The reproductive traits evaluated were interval from calving to first service (ICF), calving interval (CI) and daughter pregnancy rate (DPR) measured during the first four parities. Reliability and rank correlation were used to compare the H-AR with the traditional pedigree-based autoregressive models (A-AR). In addition, a validation study was performed considering different scenarios. Higher genomic estimated breeding values (GEBV) reliabilities were obtained for genotyped bulls when evaluated under the H-AR model, with emphasis on bulls with less than 9 daughters. For this group, the averages of GEBV reliabilities corresponded to 0.62, 0.69 and 0.62 for ICF, CI and DPR, respectively, while the averages obtained by the A-AR model were 0.27, 0.15 and 0.16. The validation study was favourable to H-AR. The best results were observed in the scenario where genotyped cows were combined with contributing bulls (genotyped bulls with daughter or relationship information in the population). Overall, the results suggest that ssGBLUP methodology under the autoregressive model is a feasible and applicable approach to be used in genomic analyses of longitudinal reproductive traits in Portuguese Holstein cattle.
Keyphrases
  • genome wide
  • copy number
  • healthcare
  • heat stress
  • mental health
  • machine learning
  • pregnant women
  • deep learning
  • big data
  • data analysis
  • atomic force microscopy