Univariate or bivariate animal models were used to estimate the variance components and co-variance components for eight reproductive traits: total number born (TNB), number born alive (NBA), total litter weight of piglets born alive (BALWT), number of healthy births (NHB), number of weak births (NWB), number of deformed fetuses (NDF), number of stillborn (NSB), and number of mummified pigs (MUMM). In addition, the phenotypic and genetic correlations between traits at different parities were also estimated. The results showed that the heritabilities of the eight reproductive traits were lower than 0.10. Genetic correlations between NHB and TNB, NBA, or BALWT were 0.68, 0.84 and 0.89 respectively; whereas genetic correlations between NHB and NWB, NDF, NSB or MUMM were negative or close to 0, ranging from −0.28 to 0.13. NHB was relatively identified as an ideal informative trait for selection for improved reproduction. Furthermore, genetic correlations between different parities for all traits, except for NDF were strongly positive, showing that it was reasonable to consider different parities as the same trait. For NDF, genetic correlations between the first and the other parities were low, indicating that it was probably unreasonable to cull pigs according to the NDF at first parity. Optimum reproductive traits were observed at the third parity, and reinforcing the management of sows in the first and > 4 parities can be a practical method for improving reproductive traits.

(1) Background: Litter size traits are critical for pig breeding efficiency but pose challenges due to low heritability and sex-limited influences. This study aimed to elucidate the genetic architecture and identify candidate genes for these traits in Large White pigs using genomic selection (GS) and genome-wide association analyses (GWAS). (2) Methods: This study utilized phenotypic data from nine litter size traits in Large White sows. Genotyping-by-sequencing (GBS) was performed to obtain genotype data, retaining 153,782 high-quality SNPs after quality control. Genetic evaluation was conducted using single-step genomic best linear unbiased prediction (ssGBLUP), with genetic parameters (heritability and genetic correlations) estimated via an animal model (repeatability model). To assess prediction accuracy, 10-fold cross-validation was employed to compare traditional BLUP with ssGBLUP. Furthermore, a single-step genome-wide association study (ssGWAS) integrated genomic information and pedigree-based relationship matrices to screen for significant SNPs associated with litter size traits across the genome. Functional analysis of key candidate genes was subsequently conducted based on ssGWAS results. (3) Results: Heritabilities for litter traits ranged from 0.01 to 0.06. ssGBLUP improved genomic prediction accuracy by 6.38–13.33% over BLUP. Six genomic windows explaining 1.07–1.77% of genetic variance were identified via ssGWAS, highlighting GPR12 on SSC11 as a key candidate gene linked to oocyte development. (4) Conclusions: This study demonstrates the efficacy of ssGBLUP for low-heritability traits and identifies GPR12 as a pivotal gene for litter size. Prioritizing NHB and LBWT in breeding programs could enhance genetic gains while mitigating adverse effects on piglet health. These findings advance genomic strategies for improving reproductive efficiency in swine.

Duroc pigs are popular crossbred terminal sires, and accurate assessment of genetic parameters in the population can help to rationalize breeding programmes. The principle aim of this study were to evaluate the genetic parameters of production (birth weight, BW; age at 115 kg, AGE; feed conversion ratio, FCR) and body size (body length, BL; body height, BH; front cannon circumference, FCC) traits of Duroc pigs. The second objective was to analyze the fit of different genetic assessment models. The variance components and correlations of BW (28,348 records), AGE (28,335 records), FCR (11,135 records), BL (31,544 records), BH (21,862 records), and FCC (14,684 records) traits were calculated by using DMU and AIREMLF90 from BLUPF90 package. In the common environment model, the heritability of BW, AGE, FCR, BL, BH, and FCC traits were 0.17 ± 0.014, 0.30 ± 0.019, 0.28 ± 0.024, 0.16 ± 0.013, 0.14 ± 0.017, and 0.081 ± 0.016, with common litter effect values of 0.25, 0.20, 0.18, 0.23, 0.19, and 0.16, respectively. According to the results of the Akaike information criterion (AIC) calculations, models with smaller AIC values have a better fit. We found that the common environment model with litter effects as random effects for estimating genetic parameters had a better fit. In this Model, the estimated genetic correlations between AGE with BW, FCR, BL, BH, and FCC traits were −0.28 (0.040), 0.76 (0.038), −0.71 (0.036), −0.44 (0.060), and −0.60 (0.073), respectively, with phenotypic correlations of −0.17, 0.52, −0.22, −0.13 and −0.24, respectively. In our analysis of genetic trends for six traits in the Duroc population from 2012 to 2021, we observed significant genetic trends for AGE, BL, and BH. Particularly noteworthy is the rapid decline in the genetic trend for AGE, indicating an enhancement in the pig's growth rate through selective breeding. Therefore, we believe that some challenging-to-select traits can benefit from the genetic correlations between traits. By selecting easily measurable traits, they can gain from synergistic selection effects, leading to genetic progress. Conducting population genetic parameter analysis can assist us in devising breeding strategies.

Genomic prediction (GP), which uses genome-wide markers to estimate breeding values, is a crucial tool for accelerating genetic progress in livestock and plant breeding. The accuracy of GP depends on several factors, including the statistical model, marker density, and cross-validation strategy. This study evaluated these factors to optimize GP accuracy for eight economically important carcass and body traits in a Duroc × (Landrace × Yorkshire) (DLY) pig population. This study used 50 K SNP chip data from 1494 DLY pigs, which were imputed to the whole genome sequence (WGS) level. Seven different models were compared, including GBLUP, ssGBLUP, and five Bayesian models. The ssGBLUP model consistently outperformed other models across all traits, with prediction accuracies ranging from 0.371 to 0.502. Further analyses showed that prediction accuracy improved with increasing cross-validation folds and marker density, particularly in the low-density panel. However, the improvement plateaued in medium-to-high-density scenarios. These findings underscore the importance of carefully selecting the model, marker density, and cross-validation strategy to optimize GP accuracy for carcass and body traits in commercial pigs. The insights from this study can guide breeders and researchers in maximizing genetic progress in pig breeding programs.