Simultaneous fitting of genomic-BLUP and Bayes-C components in a genomic prediction model - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Genetics Selection Evolution Année : 2017

Simultaneous fitting of genomic-BLUP and Bayes-C components in a genomic prediction model

Oscar O. M. Iheshiulor
  • Fonction : Auteur correspondant
  • PersonId : 984834

Connectez-vous pour contacter l'auteur
John A. Woolliams
  • Fonction : Auteur
  • PersonId : 984359
Morten Svendsen
  • Fonction : Auteur
  • PersonId : 1002766
Trygve Solberg
  • Fonction : Auteur
  • PersonId : 984707
Theo H. E. Meuwissen

Résumé

AbstractBackgroundThe rapid adoption of genomic selection is due to two key factors: availability of both high-throughput dense genotyping and statistical methods to estimate and predict breeding values. The development of such methods is still ongoing and, so far, there is no consensus on the best approach. Currently, the linear and non-linear methods for genomic prediction (GP) are treated as distinct approaches. The aim of this study was to evaluate the implementation of an iterative method (called GBC) that incorporates aspects of both linear [genomic-best linear unbiased prediction (G-BLUP)] and non-linear (Bayes-C) methods for GP. The iterative nature of GBC makes it less computationally demanding similar to other non-Markov chain Monte Carlo (MCMC) approaches. However, as a Bayesian method, GBC differs from both MCMC- and non-MCMC-based methods by combining some aspects of G-BLUP and Bayes-C methods for GP. Its relative performance was compared to those of G-BLUP and Bayes-C.MethodsWe used an imputed 50 K single-nucleotide polymorphism (SNP) dataset based on the Illumina Bovine50K BeadChip, which included 48,249 SNPs and 3244 records. Daughter yield deviations for somatic cell count, fat yield, milk yield, and protein yield were used as response variables.ResultsGBC was frequently (marginally) superior to G-BLUP and Bayes-C in terms of prediction accuracy and was significantly better than G-BLUP only for fat yield. On average across the four traits, GBC yielded a 0.009 and 0.006 increase in prediction accuracy over G-BLUP and Bayes-C, respectively. Computationally, GBC was very much faster than Bayes-C and similar to G-BLUP.ConclusionsOur results show that incorporating some aspects of G-BLUP and Bayes-C in a single model can improve accuracy of GP over the commonly used method: G-BLUP. Generally, GBC did not statistically perform better than G-BLUP and Bayes-C, probably due to the close relationships between reference and validation individuals. Nevertheless, it is a flexible tool, in the sense, that it simultaneously incorporates some aspects of linear and non-linear models for GP, thereby exploiting family relationships while also accounting for linkage disequilibrium between SNPs and genes with large effects. The application of GBC in GP merits further exploration.
Fichier principal
Vignette du fichier
12711_2017_Article_339.pdf (2.37 Mo) Télécharger le fichier
Origine Publication financée par une institution
Loading...

Dates et versions

hal-01577077 , version 1 (24-08-2017)

Identifiants

Citer

Oscar O. M. Iheshiulor, John A. Woolliams, Morten Svendsen, Trygve Solberg, Theo H. E. Meuwissen. Simultaneous fitting of genomic-BLUP and Bayes-C components in a genomic prediction model. Genetics Selection Evolution, 2017, 49 (1), pp.63. ⟨10.1186/s12711-017-0339-9⟩. ⟨hal-01577077⟩
176 Consultations
72 Téléchargements

Altmetric

Partager

Gmail Mastodon Facebook X LinkedIn More