Abstract
In the current post-genomic era, the genetic basis of pig growth can be understood by assessing SNP marker effects and genomic breeding values (GEBV) based on estimates of these growth curve parameters as phenotypes. Although various statistical methods, such as random regression (RR-BLUP) and Bayesian LASSO (BL), have been applied to genomic selection (GS), none of these has yet been used in a growth curve approach. In this work, we compared the accuracies of RR-BLUP and BL using empirical weight-age data from an outbred F2 (Brazilian Piau X commercial) population. The phenotypes were determined by parameter estimates using a nonlinear logistic regression model and the halothane gene was considered as a marker for evaluating the assumptions of the GS methods in relation to the genetic variation explained by each locus. BL yielded more accurate values for all of the phenotypes evaluated and was used to estimate SNP effects and GEBV vectors. The latter allowed the construction of genomic growth curves, which showed substantial genetic discrimination among animals in the final growth phase. The SNP effect estimates allowed identification of the most relevant markers for each phenotype, the positions of which were coincident with reported QTL regions for growth traits.
Highlights
The success of pig production systems, including the evaluation of alternative management and marketing strategies, requires knowledge of the body weight behavior over time, commonly referred to as the growth curve
The phenotypes were defined by parameter estimates obtained with a nonlinear logistic regression model and the halothane gene was considered a single nucleotide polymorphism (SNP) marker in order to evaluate the assumptions of the genomic selection (GS) methods in relation to the genetic variation explained by each locus
There has been no evaluation of the accuracy of genomic selection for growth traits in pigs using real data, Akanno (2012) reported a simulation study in which a performance trait was simulated for different populations
Summary
The success of pig production systems, including the evaluation of alternative management and marketing strategies, requires knowledge of the body weight behavior over time, commonly referred to as the growth curve. The main difference between these two very popular GS methods is that the first one assumes, a priori, that all loci explain an equal amount of genetic variation, while the second one allows the assumption that each locus explains its own amount of this variation These two methods have already been compared in other studies, so far there has been no comparison of these methods using a major gene, such as the halothane gene in pigs (Fujii et al, 1991), as a marker. We compared the accuracies of RRBLUP and BL for predicting genetic merit in an empirical application using weight-age data from an outbred F2 (Brazilian Piau X commercial) pig population (Silva et al, 2011) In this approach, the phenotypes were defined by parameter estimates obtained with a nonlinear logistic regression model and the halothane gene was considered a single nucleotide polymorphism (SNP) marker in order to evaluate the assumptions of the GS methods in relation to the genetic variation explained by each locus. Genomic growth curves based on genomic estimated breeding values were constructed and the most relevant SNPs associated with growth parameters were identified
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
