Genetic parameters and accuracy of genomic prediction for production traits in a Duroc population were estimated. Data were on 24 828 purebred Duroc pigs born in 2000–2016. After quality control procedures, 30 263 single nucleotide polymorphism markers and 560 animals remained that were used to predict the genomic breeding values of individuals. Accuracies of predicted breeding values for average daily gain (ADG), backfat thickness (BF), loin muscle area (LMA), lean percentage (LP) and age at 90 kg (D90) between pedigree-based and single-step methods were compared. Analyses were carried out with a multivariate animal model to estimate genetic parameters for production traits while univariate analyses were performed to predict the genomic breeding values of individuals. Heritability estimates from pedigree analysis were moderate to high. Heritability estimates and standard error for ADG, BF, LMA, LP and D90 were 0.35 ± 0.01, 0.35 ± 0.11, 0.24 ± 0.04, 0.42 ± 0.11 and 0.37 ± 0.03, respectively. Genetic correlations of ADG with BF and LP were low and negative. Genetic correlations of LMA with ADG, BF, LP and D90 were –0.37, –0.27, 0.48 and 0.31, respectively. High correlations were observed between ADG and D90 (–0.98), and also between BF and LP (–0.93). Accuracies of genomic breeding values for ADG, BF, LMA, LP and D90 were 0.30, 0.33, 0.38, 0.40 and 0.28, respectively. Corresponding accuracies using pedigree-based method were 0.29, 0.32, 0.38, 0.39 and 0.27, respectively. The results showed that the single-step method did not show significant advantage compared to the pedigree-based method.

ObjectiveGenome wide association study was conducted to identify and validate candidate genes associated with fatty acid composition of pork.MethodsA total of 480 purebreed Duroc pigs were genotyped using IlluminaPorcine60k bead chips while the association test was implemented following genome-wide rapid association using Mixed Model and Regression-Genomic Control (GRAMMAR-GC) approach.ResultsA total of 25, 29, and 16 single nucleotide polymorphisms (SNPs) were significantly associated with stearic (18:0), oleic (18:1) and saturated fatty acids (SFA), respectively. Genome wide significant variants were located on the same region of swine chromosome 14 (SSC14) that spanned from 120 to 124 Mb. Top SNP ALGA008191 was located at 5 kb near the stearoyl-CoA desaturase (SCD) gene. This gene is directly involved in desaturation of stearic acid into oleic acid. General relationship of significant SNPs showed high linkage disequilibrium thus genome-wide signals was attributed to SCD gene. However, understanding the role of other genes like elongation of very long chain fatty acids-3 (ELOVL3) located on this chromosomal segment might help in further understanding of metabolism and biosynthesis of fatty acids.ConclusionOverall, this study provides evidence that validates SCD gene as strong candidate gene associated with fatty acid composition in Duroc pigs. Moreover, this study confirms significant SNPs near ELOVL3 gene.

The objective of this study was to evaluate the present conventional selection program of a swine nucleus farm and compare it with a new selection strategy employing genomic enhanced breeding value (GEBV) as the selection criteria. The ZPLAN+ software was employed to calculate and compare the genetic gain, total cost, return and profit of each selection strategy. The first strategy reflected the current conventional breeding program, which was a progeny test system (CS). The second strategy was a selection scheme based strictly on genomic information (GS1). The third scenario was the same as GS1, but the selection by GEBV was further supplemented by the performance test (GS2). The last scenario was a mixture of genomic information and progeny tests (GS3). The results showed that the accuracy of the selection index of young boars of GS1 was 26% higher than that of CS. On the other hand, both GS2 and GS3 gave 31% higher accuracy than CS for young boars. The annual monetary genetic gain of GS1, GS2 and GS3 was 10%, 12%, and 11% higher, respectively, than that of CS. As expected, the discounted costs of genomic selection strategies were higher than those of CS. The costs of GS1, GS2 and GS3 were 35%, 73%, and 89% higher than those of CS, respectively, assuming a genotyping cost of $120. As a result, the discounted profit per animal of GS1 and GS2 was 8% and 2% higher, respectively, than that of CS while GS3 was 6% lower. Comparison among genomic breeding scenarios revealed that GS1 was more profitable than GS2 and GS3. The genomic selection schemes, especially GS1 and GS2, were clearly superior to the conventional scheme in terms of monetary genetic gain and profit.