Dataset Information

Selection for feed efficiency using the social effects animal model in growing Duroc pigs: evaluation by simulation.

ABSTRACT:

Background

Traits recorded on animals that are raised in groups can be analysed with the social effects animal model (SAM). For multiple traits, this model specifies the genetic correlation structure more completely than the animal model (AM). Our hypothesis was that by using the SAM for genetic evaluation of average daily gain (ADG) and backfat thickness (BF), a high rate of improvement in feed conversion ratio (FCR) might be achieved, since unfavourable genetic correlations between ADG and BF reported in a Duroc pig line could be partially avoided. We estimated genetic and non-genetic correlations between BF, ADG and FCR on 1144 pigs using Bayesian methods considering the SAM; and responses to selection indexes that combine estimates of indirect (IGE) and direct (DGE) genetic effects for ADG and BF by stochastic simulation.

Results

Estimates of the ratio of the variance of DGE to the phenotypic variance were 0.31, 0.39 and 0.25 and those of the total genetic variance to the phenotypic variance were 0.63, 0.74 and 0.93 for ADG, BF and FCR, respectively. In spite of this, when the SAM was used to generate data and for the genetic evaluations, the average economic response was worse than that obtained when BV predictions from the AM were considered. The achieved economic response was due to a direct reduction in BF and not to an improvement in FCR.

Conclusions

Our results show that although social genetic effects play an important role in the traits studied, their proper consideration in pig breeding programs to improve FCR indirectly is still difficult. The correlations between IGE and DGE that could help to overcome the unfavourable genetic correlations between DGE did not reach sufficiently high magnitudes; also, the genetic parameters estimates from the SAM have large errors. These two factors penalize the average response under the SAM compared to the AM.

SUBMITTER: Herrera-Caceres W

PROVIDER: S-EPMC7526410 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Publications

Selection for feed efficiency using the social effects animal model in growing Duroc pigs: evaluation by simulation.

Herrera-Cáceres William W Sánchez Juan Pablo JP

Genetics, selection, evolution : GSE 20200929 1

<h4>Background</h4>Traits recorded on animals that are raised in groups can be analysed with the social effects animal model (SAM). For multiple traits, this model specifies the genetic correlation structure more completely than the animal model (AM). Our hypothesis was that by using the SAM for genetic evaluation of average daily gain (ADG) and backfat thickness (BF), a high rate of improvement in feed conversion ratio (FCR) might be achieved, since unfavourable genetic correlations between ADG ...[more]

PMID: 32993480

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Project description:BACKGROUND:Increasing marker density was proposed to have potential to improve the accuracy of genomic prediction for quantitative traits; whole-sequence data is expected to give the best accuracy of prediction, since all causal mutations that underlie a trait are expected to be included. However, in cattle and chicken, this assumption is not supported by empirical studies. Our objective was to compare the accuracy of genomic prediction of feed efficiency component traits in Duroc pigs using single nucleotide polymorphism (SNP) panels of 80K, imputed 650K, and whole-genome sequence variants using GBLUP, BayesB and BayesRC methods, with the ultimate purpose to determine the optimal method to increase genetic gain for feed efficiency in pigs. RESULTS:Phenotypes of average daily feed intake (ADFI), average daily gain (ADG), ultrasound backfat depth (FAT), and loin muscle depth (LMD) were available for 1363 Duroc boars from a commercial breeding program. Genotype imputation accuracies reached 92.1% from 80K to 650K and 85.6% from 650K to whole-genome sequence variants. Average accuracies across methods and marker densities of genomic prediction of ADFI, FAT, LMD and ADG were 0.40, 0.65, 0.30 and 0.15, respectively. For ADFI and FAT, BayesB outperformed GBLUP, but increasing marker density had little advantage for genomic prediction. For ADG and LMD, GBLUP outperformed BayesB, while BayesRC based on whole-genome sequence data gave the best accuracies and reached up to 0.35 for LMD and 0.25 for ADG. CONCLUSIONS:Use of genomic information was beneficial for prediction of ADFI and FAT but not for that of ADG and LMD compared to pedigree-based estimates. BayesB based on 80K SNPs gave the best genomic prediction accuracy for ADFI and FAT, while BayesRC based on whole-genome sequence data performed best for ADG and LMD. We suggest that these differences between traits in the effect of marker density and method on accuracy of genomic prediction are mainly due to the underlying genetic architecture of the traits.

Project description:Interactions among genomic loci have often been overlooked in genome-wide association studies, revealing the combinatorial effects of variants on phenotype or disease manifestation. Unexplained genetic variance, interactions among causal genes of small effects, and biological pathways could be identified using a network biology approach. The main objective of this study was to determine the genome-wide epistatic variants affecting feed efficiency traits [feed conversion ratio (FCR) and residual feed intake (RFI)] based on weighted interaction SNP hub (WISH-R) method. Herein, we detected highly interconnected epistatic SNP modules, pathways, and potential biomarkers for the FCR and RFI in Duroc and Landrace purebreds considering the whole population, and separately for low and high feed efficient groups. Highly interacting SNP modules in Duroc (1,247 SNPs) and Landrace (1,215 SNPs) across the population and for low feed efficient (Duroc-80 SNPs, Landrace-146 SNPs) and high feed efficient group (Duroc-198 SNPs, Landrace-232 SNPs) for FCR and RFI were identified. Gene and pathway analyses identified ABL1, MAP3K4, MAP3K5, SEMA6A, KITLG, and KAT2B from chromosomes 1, 2, 5, and 13 underlying ErbB, Ras, Rap1, thyroid hormone, axon guidance pathways in Duroc. GABBR2, GNA12, and PRKCG genes from chromosomes 1, 3, and 6 pointed towards thyroid hormone, cGMP-PKG and cAMP pathways in Landrace. From Duroc low feed efficient group, the TPK1 gene was found involved with thiamine metabolism, whereas PARD6G, DLG2, CRB1 were involved with the hippo signaling pathway in high feed efficient group. PLOD1 and SETD7 genes were involved with lysine degradation in low feed efficient group in Landrace, while high feed efficient group pointed to genes underpinning valine, leucine, isoleucine degradation, and fatty acid elongation. Some SNPs and genes identified are known for their association with feed efficiency, others are novel and potentially provide new avenues for further research. Further validation of epistatic SNPs and genes identified here in a larger cohort would help to establish a framework for modelling epistatic variance in future methods of genomic prediction, increasing the accuracy of estimated genetic merit for FE and helping the pig breeding industry.

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Project description:Feed efficiency is one of the most important issues for sustainable pig production. Daily-phase feeding (DPF) is a form of precision feeding that could improve feed efficiency in pigs. Gut microbiota can regulate host nutrient digestion, absorption, and metabolism. However, which key microbes may play a vital role in improving the feed efficiency during DPF remains unclear. In the present study, we used a DPF program compared to a three-phase feeding (TPF) program in growing-finishing pigs to investigate the effects of gut microbiota on feed efficiency. A total of 204 Landrace × Yorkshire pigs (75 d) were randomly assigned into 2 treatments. Each treatment was replicated 8 times with 13 to 15 pigs per replicate pen. Pigs in the TPF group were fed with a commercial feeding program that supplied fixed feed for phases I, II, and III, starting at 81, 101, and 132 d of age, respectively, and pigs in the DPF group were fed a blend of adjacent phase feed from 81 to 155 d at a gradual daily ratio and phase III feed from 155 to 180 d of age. Daily feed intake and body weight were recorded by a computerized device in the feeders. Feces and blood samples were collected from 1 pig per replicate at 155 and 180 d of age. The results showed that the DPF program remarkably improved the feed efficiency at 155 d (P < 0.001) and 180 d of age (P < 0.001), with a significant reduction of the intake of crude protein (P < 0.01), net energy (P < 0.001), crude fiber (P < 0.001), ether extract (P < 0.01), and ash (P < 0.001). The daily-phase feeding program increased the abundance of Prevotella copri (P < 0.05) and Paraprevotella clara (P < 0.05), while it decreased the abundance of Ocilibacter (P < 0.05) at 155 d of age. The results of correlation analysis indicated that the differentially abundant microbiota communities were closely associated with 20 metabolites which enriched amino acid and phenylalanine metabolism. Our results suggest that 2 key microbes may contribute to feed efficiency during daily-phase feeding strategies in pigs.

Dataset Information

Selection for feed efficiency using the social effects animal model in growing Duroc pigs: evaluation by simulation.

Background

Results

Conclusions

Publications

Selection for feed efficiency using the social effects animal model in growing Duroc pigs: evaluation by simulation.

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