Background: For genomic selection in populations with a small reference population, combining populations of the same breed or populations of related breeds is an effective way to increase the size of the reference population. However, genomic predictions based on single nucleotide polymorphism (SNP)-chip genotype data using combined populations with different genetic backgrounds or from different breeds have not shown a clear advantage over using within-population or within-breed predictions. The increasing availability of whole-genome sequencing (WGS) data provides new opportunities for combined population genomic prediction. Our objective was to investigate the accuracy of genomic prediction using imputation-based WGS data from combined populations in pigs. Using 80K SNP panel genotypes, WGS genotypes, or genotypes on WGS variants that were pruned based on linkage disequilibrium (LD), three methods [genomic best linear unbiased prediction (GBLUP), single-step (ss)GBLUP, and genomic feature (GF)BLUP] were implemented with different prior information to identify the best method to improve the accuracy of genomic prediction for combined populations in pigs. Results: In total, 2089 and 2043 individuals with production and reproduction phenotypes, respectively, from three Yorkshire populations with different genetic backgrounds were genotyped with the PorcineSNP80 panel. Imputation accuracy from 80K to WGS variants reached 92%. The results showed that use of the WGS data compared to the 80K SNP panel did not increase the accuracy of genomic prediction in a single population, but using WGS data with LD pruning and GFBLUP with prior information did yield higher accuracy than the 80K SNP panel. For the 80K SNP panel genotypes, using the combined population resulted in a slight improvement, no change, or even a slight decrease in accuracy in comparison with the single population for GBLUP and ssGBLUP, while accuracy increased by 1 to 2.4% when using WGS data. Notably, the GFBLUP method did not perform well for both the combined population and the single populations. Conclusions: The use of WGS data was beneficial for combined population genomic prediction. Simply increasing the number of SNPs to the WGS level did not increase accuracy for a single population, while using pruned WGS data based on LD and GFBLUP with prior information could yield higher accuracy than the 80K SNP panel.

Genomic selection (GS) has great potential to increase genetic gain in aquaculture breeding; however, its implementation is hindered owing to high genotyping cost and the large number of individuals to genotype. This study investigated the efficiency of genomic prediction in four aquaculture species. In total, 749 to 1481 individuals with records for disease resistance and growth traits were genotyped using SNP arrays ranging from 12K to 40K. We compared the prediction accuracies and bias of breeding values obtained from BLUP, genomic BLUP (GBLUP), Bayesian mixture (BayesR), weighted GBLUP (WGBLUP), and genomic feature BLUP (GFBLUP). For GFBLUP, the genomic feature matrix was constructed based on prior information from genome‐wide association studies. Fivefold cross‐validation was performed with 20 replicates. Moreover, to reduce the cost of GS, we reduced the SNP density based on linkage disequilibrium as well as the reference population size. The results showed that the methods with marker information produced more accurate predictions than the pedigree‐based BLUP method. For the genomic model, BayesR performed prediction with a similar or higher accuracy compared to GBLUP. For the four traits, WGBLUP yielded an average of 1.5% higher accuracy than GBLUP. However, the accuracy of genomic prediction decreased by an average of 6.2% for GFBLUP compared to GBLUP. When the density of SNP panels was reduced to 3K, which was sufficient to obtain accuracies similar to those using the whole dataset in the four species, the cost of GS was estimated to be 50% lower than that of genotyping all animals with high‐density panels. In addition, when the reference population size was reduced by 10%, evenly from full‐sib family, the accuracy of genomic prediction was almost unchanged, and the cost reduction was 8% in the four populations. Our results have important implications for translating the benefits of GS to most aquaculture species.

In genomic prediction, single-step method has been demonstrated to outperform multi-step methods. This study investigated the efficiency of genomic prediction for 7 body measurement traits in Yorkshire population using single-step method. In total, 592 individuals were genotyped with Illumina PorcineSNP80 marker panel. We compared the prediction accuracy of a traditional pedigree-based method (BLUP) with the single-step genomic BLUP (ssGBLUP) through 20 replicates of five-fold cross-validation (CV). In addition, we also compared the performance of two-trait ssGBLUP and single-trait ssGBLUP for the traits with different gradients of genetic correlation. Our results indicated the accuracies of ssGBLUP for 7 body measurement traits ranged from 0.543 to 0.785, and the unbiasedness of ssGBLUP ranged from 0.834 to 1.026, respectively. ssGBLUP generally generated 1 percentage on average higher prediction accuracy than traditional BLUP, the improvement was lower than expected mainly due to the small number of genotyped animals. Our results also demonstrated that the accuracies of genomic prediction can be further improved by implementing two-trait ssGBLUP model, the maximum gain on accuracy was 2 and 2.6 percentage for trait of chest width compared to single-trait ssGBLUP and traditional BLUP, while the gain was decreased with the weakness of genetic correlation. Two-trait ssGBLUP even performed worse than single trait analysis in low genetic correlation.

The body shape of a pig is the most direct production index, which can fully reflect the pig’s growth status and is closely related to important economic traits. In this study, a genome-wide association study on seven body size traits, the body length (BL), height (BH), chest circumference (CC), abdominal circumference (AC), cannon bone circumference (CBC), rump width (RW), and chest width (CW), were conducted in Yorkshire pigs. Illumina Porcine 80K SNP chips were used to genotype 589 of 5,572 Yorkshire pigs with body size records, and then the chip data was imputed to sequencing data. After quality control of imputed sequencing data, 784,267 SNPs were obtained, and the averaged linkage disequilibrium (r²) was 0.191. We used the single-trait model and the two-trait model to conduct single-step genome wide association study (ssGWAS) on seven body size traits; a total of 198 significant SNPS were finally identified according to the P-value and the contribution to the genetic variance of individual SNP. 11 candidate genes (CDH13, SIL1, CDC14A, TMRPSS15, TRAPPC9, CTNND2, KDM6B, CHD3, MUC13, MAPK4, and HMGA1) were found to be associated with body size traits in pigs; KDM6B and CHD3 jointly affect AC and CC, and MUC13 jointly affect RW and CW. These genes are involved in the regulation of bone growth and development as well as the absorption of nutrients and are associated with obesity. HMGA1 is proposed as a strong candidate gene for body size traits because of its important function and high consistency with other studies regarding the regulation of body size traits. Our results could provide valuable information for pig breeding based on molecular breeding.

Improvement in growth and fatness traits are the main objectives in pig all breeding programs. Tenth rib backfat thickness (10RIBBFT) and days to 100 kg (D100), which are good predictors of carcass lean content and growth rate, respectively, are economically important traits and also main breeding target traits in pigs. To investigate the genetic mechanisms of 10RIBBFT and D100 of pigs, we sampled 1,137 and 888 pigs from 2 Yorkshire populations of American and British origin, respectively, and conducted genome-wide association study (GWAS) through combined analysis and meta-analysis, to identify SNPs associated with 10RIBBFT and D100. A total of 11 and 7 significant SNPs were identified by combined analysis for 10RIBBFT and D100, respectively. And in meta-analysis, 8 and 7 significant SNPs were identified for 10RIBBFT and D100, respectively. Among them, 6 and 5 common significant SNPs in two analysis results were, respectively, identified associated with 10RIBBFT and D100, and correspondingly explained 2.09% and 0.52% of the additive genetic variance of 10RIBBFT and D100. Further bioinformatics analysis revealed 10 genes harboring or close to these common significant SNPs, 5 for 10RIBBFT and 5 for D100. In particular, Gene Ontology analysis highlighted 6 genes, PCK1, ANGPTL3, EEF1A2, TNFAIP8L3, PITX2, and PLA2G12, as promising candidate genes relevant with backfat thickness and growth. PCK1, ANGPTL3, EEF1A2, and TNFAIP8L3 could influence backfat thickness through phospholipid transport, regulation of lipid metabolic process through the glycerophospholipid biosynthesis and metabolism pathway, the metabolism of lipids and lipoproteins pathway. PITX2 has a crucial role in skeletal muscle tissue development and animal organ morphogenesis, and PLA2G12A plays a role in the lipid catabolic and phospholipid catabolic processes, which both are involved in the body weight pathway. All these candidate genes could directly or indirectly influence fat production and growth in Yorkshire pigs. Our findings provide novel insights into the genetic basis of growth and fatness traits in pigs. The candidate genes for D100 and 10RIBBFT are worthy of further investigation.