Gene networks for total number born in pigs across divergent environments

Association of IL-4 and IL-4R Polymorphisms with Litter Size Traits in Pigs

Simple Summary

The IL-4 and IL-4R cytokine genes are responsible for immune response in the reproductive system and are related to embryonic implantation and fetal survival during pregnancy in females. However, to date, their effects on litter size traits in pigs have been not elucidated. Therefore, the present study was conducted to verify the porcine IL-4 and IL-4R polymorphisms and assess how they affect litter size traits in commercial pigs. The findings suggested that the porcine IL-4 g.134993898T > C and IL-4R c.1577A > T polymorphisms were associated with the litter size traits. Therefore, the porcine IL-4 and IL-4R genes may be potentially effective genetic markers to improve the litter size traits in pigs.

Abstract

The interleukin-4 (IL-4) and interleukin-4 receptor (IL-4R) are cytokines that are involved in the immune and reproductive systems. This study aimed to verify the polymorphisms in the porcine IL-4 and IL-4R genes and to assess their effects on litter size traits in commercial pigs. Single nucleotide polymorphisms (SNPs) in the porcine IL-4 and IL-4R genes were genotyped by the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method. A non-coding SNP of IL-4 g.134993898T > C and a non-synonymous SNP of IL-4R c.1577A > T (amino acid change at position 526, Q526L) were found to be segregating in Landrace sows. The IL-4 g.134993898T > C polymorphism was significantly associated with the number of piglets weaned alive (NWA) trait. The IL-4R c.1577A > T polymorphism was significantly associated with the number born alive (NBA) and NWA traits. Moreover, the accumulation of favorable alleles of these two SNP markers revealed significant associations with the NBA, NWA, and mean weight of piglets at weaning (MWW) traits. These findings indicate that the porcine IL-4 and IL-4R genes may contribute to the reproductive traits of pigs and could be used as candidate genes to improve litter size traits in the pig breeding industry.

Invited review: Advances and applications of random regression models: From quantitative genetics to genomics

An important goal in animal breeding is to improve longitudinal traits; that is, traits recorded multiple times during an individual's lifetime or physiological cycle. Longitudinal traits were first genetically evaluated based on accumulated phenotypic expression, phenotypic expression at specific time points, or repeatability models. Until now, the genetic evaluation of longitudinal traits has mainly focused on using random regression models (RRM). Random regression models enable fitting random genetic and environmental effects over time, which results in higher accuracy of estimated breeding values compared with other statistical approaches. In addition, RRM provide insights about temporal variation of biological processes and the physiological implications underlying the studied traits. Despite the fact that genomic information has substantially contributed to increase the rates of genetic progress for a variety of economically important traits in several livestock species, less attention has been given to longitudinal traits in recent years. However, including genomic information to evaluate longitudinal traits using RRM is a feasible alternative to yield more accurate selection and culling decisions, because selection of young animals may be based on the complete pattern of the production curve with higher accuracy compared with the use of traditional parent average (i.e., without genomic information). Moreover, RRM can be used to estimate SNP effects over time in genome-wide association studies. Thus, by analyzing marker associations over time, regions with higher effects at specific points in time are more likely to be identified. Despite the advances in applications of RRM in genetic evaluations, more research is needed to successfully combine RRM and genomic information. Future research should provide a better understanding of the temporal variation of biological processes and their physiological implications underlying the longitudinal traits.

Plasticity effect of rider-horse interaction on genetic evaluations for Show Jumping discipline in sport horses

To obtain a sport horse that excels in the highest levels of competition, breeders must take into account certain genetic and environmental factors that could influence the sport horse's performance, such as the rider-horse interaction (RHI). The main aim of this study was to describe this interaction in a genetic model by modelling it in relation to the horse's age. A total of 31,129 sport results from Spanish Sport Horses were used from a total of 1,101 animals evaluated, and these were grouped in three age levels and had been ridden by 606 different riders. Only riders who had ridden more than one horse (and vice-versa) were considered for the analyses. Five linear models with different random effects were analysed according to the covariates, the homogeneity/heterogeneity of the RHI and the relevant residual random effects. The model of best fit was then selected for the genetic evaluation of the animal. In general, models including the RHI effect (M2, M4 and M5) fitted better than the other models, and the best fit was obtained for M4 (with heterogeneous residual variance). The genetic variance increased constantly with age, whereas heritability showed a response on three intervals. This study revealed the varied evolution of the RHI with age, showing the different "plastic abilities" of this relationship.