Developing marker-assisted models for evaluating growth traits in Canadian beef cattle genetic improvement

Differential Expression of ACTL8 Gene and Association Study of Its Variations with Growth Traits in Chinese Cattle

Simple Summary

Marker-assisted selection has a great influence on livestock molecular breeding development. The discovery of key molecular markers that are significantly associated with body size data will accelerate molecular breeding in livestock. In this study, the cattle ACTL8 gene is a critical candidate gene. It was found that there are multiple mutations in the ACTL8 gene that may be used as molecular markers. Our results have shown that the mutations of the ACTL8 gene could have important reference value in molecular breeding for beef cattle.

Abstract

Mutations are heritable changes at the base level of genomic DNA. Furthermore, mutations lead to genetic polymorphisms and may alter animal growth phenotypes. Our previous study found that mutations in the bovine Actin-like protein 8 (ACTL8) gene may be involved in muscle growth and development. This study explored several mutations of the ACTL8 gene and their influence on body size in Chinese beef cattle, as well as tested the tissue expression profile of the ACTL8 gene in Qinchuan cattle at different ages. Five single nucleotide polymorphisms (SNPs) (including one synonymous mutation (c.2135552895G > A)) and two insertion/deletion polymorphisms (indels) were identified in the ACTL8 gene from 1138 cattle by DNA-seq, RFLP and other methods. Then, the expression profile of the ACTL8 gene in Qinchuan cattle showed that it was expressed in heart, spleen, lung, liver, muscle, and fat tissues. Moreover, the expression level of ACTL8 was increased with cattle growth (p < 0.01). The ACTL8 mRNA expression level in kidney and muscle tissues was the highest in the calves, while lowest in the fetal stage. Overall, we showed that the mutations could act as markers in beef molecular breeding and selection of the growth traits of cattle.

Possibilities to improve the genetic evaluation of a rare breed using limited genomic information and multivariate BLUP

The use of molecular genetic information in the evaluation of livestock has become more common. This study looks at the efficacy of using such information to improve the genetic evaluation of a rare breed of dual-purpose cattle. Data were available in the form of pedigree information on the Gloucester cattle breed in the United Kingdom and recorded milk and beef performance on a small number of animals. In addition, molecular genetic information in the form of multi-marker, multiple regression results converted to a 1 to 10 score (Igenity scores) and 123 single nucleotide polymorphism (SNP) genotypes for 199 non-recorded animals were available. Appropriate mixed-animal models were explored for the recorded traits and these were used to calculate estimated breeding values (EBV), and their accuracies, for 6527 animals in the breed’s pedigree file. Various ways to improve the accuracy of these EBV were explored. This involved using multivariate BLUP analyses, genomic estimated breeding values (GEBV) and combining Igenity scores with recorded traits in a series of bivariate genetic analyses. Using the milk recording traits as an example, the accuracy of a number of traits could be improved using multivariate analyses by up to 14%, depending on the combination of traits used. The level of increase in accuracy largely corresponded to the absolute difference between the genetic and residual correlations between two traits, but this was not always symmetrical. The use of GEBV did not increase the accuracy of milk trait EBV owing to the low proportion of variance explained by the 101 SNPs used. Using Igenity scores in bivariate analyses with the recorded data was more successful in increasing EBV accuracy. The largest increases were found in genotyped animals with no recorded performance (e.g. a 58% increase in fat weight in milk); however, the size of the increase depended on the level of the genetic correlation between the recorded trait and the Igenity score for that trait. Lower levels of improvements in accuracy were seen in animals that were recoded but not genotyped, and ancestors which were neither genotyped nor recorded. This study demonstrated that it was possible to improve the accuracy of EBV estimation by including Igenity score information in genetic analyses but it also concluded that increasing the level of performance recording in the breed would be beneficial.

Prediction accuracy for a simulated maternally affected trait of beef cattle using different genomic evaluation models

Different methods for genomic evaluation were compared for accuracy and feasibility of evaluation using phenotypic, pedigree, and genomic information for a trait influenced by a maternal effect. A simulated population was constructed that included 15,800 animals in 5 generations. Genotypes from 45,000 SNP were available for 1,500 animals in the last 3 generations. Genotyped animals in the last generation had no phenotypes. Weaning weight data were simulated using an animal model with direct and maternal effects. Additive direct and maternal effects were considered either noncorrelated (formula in text) or negatively correlated (formula in text). Methods of analysis were traditional BLUP, BayesC using phenotypes and ignoring maternal effects (BayesCPR), BayesC using deregressed EBV (BayesCDEBV), and single-step genomic BLUP (ssGBLUP). Whereas BayesCPR can be used when phenotypes of only genotyped animals are available, BayesCDEBV can be used when BLUP EBV of genotyped animals are available, and ssGBLUP is suitable when genotypes, phenotypes, and pedigrees are jointly available. For all genotyped and young genotyped animals, mean accuracies from BayesCPR and BayesCDEBV were lower than accuracies from BLUP for direct and maternal effects. The differences in mean accuracy were greater when genetic correlation was negative. Gains in accuracy were observed when ssGBLUP was compared with BLUP; for the direct (maternal) effect the average gain was 0.01 (0.02) for all genotyped animals and 0.03 (0.02) for young genotyped animals without phenotypes. Similar gains were observed for 0 and negative genetic correlation. Accuracy with BayesCPR was affected by ignoring phenotypes of nongenotyped animals and maternal effect and by not accounting for parent average. Accuracy with BayesCDEBV was affected by approximations needed for deregression, not accounting for parent average, and sequential rather than simultaneous fitting of genomic and nongenomic information. Whereas BayesCDEBV presented a considerable bias, especially for maternal effect, ssGBLUP was unbiased for both effects. The computing time was 1 s for BLUP, 44 s for ssGBLUP, and over 2,000 s for BayesC. Greatest computational efficiency and accuracy of genomic prediction for a maternally affected trait was obtained when information from all nongenotyped but related individuals was included and phenotypes, pedigree, and genotypes were available and considered jointly. Increasing the gain in accuracy of genomic predictions obtained by ssGBLUP over BLUP may require an increase in the number of genotyped animals.

The polymorphism analysis of CD169 and CD163 related with the risk of porcine reproductive and respiratory syndrome virus (PRRSV) infection

Porcine reproductive and respiratory syndrome virus (PRRSV) could infect porcine alveolar macrophages (PAM), and the CD169 and CD163 are identified as critical receptors on the surface of PAM, but whether the single nucleotide polymorphisms (SNPs) of these genes could influence the infection is remain unclear. In this study, we identified totally 6 SNPs for CD169 (G1640T, C1654A, C4175T) and CD163 (G2277A, A2552G and C2700A), and evaluated their associations with PRRSV infection using two classified methods in a 524 pig population to investigate the effects of mutations on the PRRSV receptors. The pigs with genotypes of AA of CD169-C1654A, CT of CD169-C4175T and AA of CD163-A2552G appeared to resistant to the PRRSV infection by the combination of two classified results. The results provided fundamental molecular investigation to promote pig breeding with disease resistance. However, the identification of functional changes induced by SNPs and molecular mechanism were need further research.

Analysis of genetic variability at codon 42 within caprine prion protein gene in relation to production traits in Chinese domestic breeds

In this study, genetic variability at codon 42 within prion protein (PRNP) gene and its associations with production traits were investigated in 2002 goats from four Chinese domestic breeds. The frequencies of allele "A" ranged from 0.353 to 0.562 in analyzed goat breeds with Hardy-Weinberg equilibrium (P > 0.05) except Xinong Sannen (XNSN) dairy breed. The establishment of relationships between different genotypes and growth traits was performed in Inner Mongolia white Cashmere (IMWC) breed and revealed an association of the polymorphism with body weight at 7-year-old goats (P = 0.033). The individuals with genotype GG showed heavier body weight than those with genotype AA. Moreover, association analysis detected two significant associations between different genotypes and cashmere yield and fiber length in IMWC breed (P = 0.009, P = 0.048, respectively). In addition, three significant associations of different genotypes with density of milk (a.m. and p.m.), solids-not-fat of milk (P = 0.013, P = 0.009 and P = 0.002), respectively, were found in XNSN breed. Genotype GG had better milk quality than others. These findings suggested that the polymorphism of codon 42 within PRNP was a useful DNA marker for eliminating or selecting excellent individuals in relation to production traits in marker-assist selection breeding of goat.