Marcos Soares Lopes

Seasonal infertility remains a major challenge in pig production and is influenced by environmental conditions and sow parity. This study aimed to identify in-barn heat load (HL), light variability (LV), and parity as farm-specific predictors of farrowing success (FS) and litter size (LS) in purebred Large White sows raised under subtropical and tropical conditions in Brazil. Reproductive records from 2021 were obtained from two commercial farms, and in-barn temperature and illuminance were recorded using data loggers installed in gestation units. Services between March and August 2021 were analyzed using logistic regression for FS and linear regression for LS, resulting in 732 service records from Farm 1 and 233 from Farm 2. In the combined analysis, parity was associated with higher odds of FS (OR = 1.20; p = 0.003) and greater LS (β = 0.47; p < 0.001). HL was not associated with FS but showed a small positive association with LS (β = 0.28; p = 0.031). In Farm 2, LV was negatively associated with FS (OR = 0.72; p = 0.028) and LS (β = −0.59; p = 0.020). Overall, parity structure strongly shaped reproductive performance, and in-barn environmental effects were context-dependent.

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This study evaluated the effects of dietary macauba pulp on the growth performance, carcass, and pork traits of growing-finishing pigs; and whether differences in residual feed intake breeding values could influence the pigs’ growth responses to macauba pulp inclusion in the diet. A total of 282 (34.8 ± 4.40 kg) pigs (barrows and females), progeny of sires with high (HRFI) or low (LRFI) breeding value for residual feed intake, were pair-housed on the basis of sex, breeding value, and initial BW. Diets with (macauba) or without (control) of 50 g/kg of macauba pulp inclusion were randomly assigned to the experimental pens. There were eight treatment groups: HRFI barrows were fed the control diet; HRFI barrows were fed the macauba diet; LRFI barrows were fed the control diet; LRFI barrows were fed the macauba diet; HRFI female pigs were fed the control diet; HRFI female pigs were fed the macauba diet; LRFI female pigs were fed the control diet; and LRFI female pigs were fed the macauba diet. The trial lasted 90 days and was divided into four phases: growing phase 1 (G1) and 2 (G2); and finishing phases 1 (F1) and 2 (F2). There was no interaction between sex, breeding value, and diet for performance, carcass, and pork traits. Barrows had higher feed intake (ADFI), daily gain (ADG), feed conversion rate (FCR), and final body weight than female pigs. The breeding value had no effect on performance measurements. The inclusion of macauba pulp in the pigs’ diets did not affect any growth parameter during G1, G2 and F1 phases. However, reduced ADFI and improved FCR were observed in F2. Female pigs had lower backfat thickness (BF) and higher loin eye area (LEA) than barrows. HRFI pigs had higher hot carcass weight and LEA, and lower BF than LRFI pigs. There was no effect of macauba pulp inclusion on carcass traits. Pork from barrows presented lower Warner–Bratzler shear force and higher fat content than pork from the females. There was no effect of breeding value on pork traits. Pork from pigs fed the macauba diet showed lower moisture content and water-holding capacity. In conclusion, macauba pulp can partially replace corn without reducing the performance of pigs. Regardless of sex and breeding value for RFI, pigs responded similarly to macauba pulp inclusion in diets.

Nearly 2000 SNPs associated with pig litter size traits have been reported based on genome-wide association studies (GWASs). The aims of this study were to gather and integrate previously reported associations between SNPs and five litter traits: total number born (TNB), number born alive (NBA), number of stillborn (SB), litter birth weight (LWT), and corpus luteum number (CLN), in order to evaluate their common genetic background and to perform a meta-analysis (MA) of GWASs for total number born (TNB) recorded for animals from five pig populations. In this study, the genes with the largest number of associations with evaluated litter traits were GABRG3, RBP7, PRKD1, and STXBP6. Only 21 genes out of 233 associated with the evaluated litter traits were reported in more than one population or for more than one trait. Based on this evaluation, the most interesting candidate gene is PRKD1, which has an association with SB and TNB traits. Based on GO term analysis, PRKD1 was shown to be involved in angiogenesis as well. As a result of the MA, two new genomic regions, which have not been previously reported, were found to be associated with the TNB trait. One SNP was located on Sus scrofa chromosome (SSC) 14 in the intron of the FAM13C gene. The second SNP was located on SSC9 within the intron of the AGMO gene. Functional analysis revealed a strong candidate causal gene underlying the QTL on SSC9. The third best hit and the most promising candidate gene for litter size was found within the SOSTDC1 gene, associated with lower male fertility in rats. We showed that litter traits studied across pig populations have only a few genomic regions in common based on candidate gene comparison. PRKD1 could be an interesting candidate gene with a wider association with fertility. The MA identified new genomic regions on SSC9 and SSC14 associated with TNB. Further functional analysis indicated the most promising gene was SOSTDC1, which was confirmed to affect male fertility in other mammals. This is an important finding, as litter traits are by default linked with females rather than males.

Backfat is an important trait in pork production, and it has been included in the breeding objectives of genetic companies for decades. Although adipose tissue is a good energy storage, excessive fat results in reduced efficiency and economical losses. A large QTL for backfat thickness on chromosome 5 is still segregating in different commercial pig breeds. We fine mapped this QTL region using a genome-wide association analysis (GWAS) with 133,358 genotyped animals from five commercial populations (Landrace, Pietrain, Large White, Synthetic, and Duroc) imputed to the porcine 660K SNP chip. The lead SNP was located at 5:66103958 (G/A) within the third intron of theCCND2gene, with the G allele associated with more backfat, while the A allele is associated with less backfat. We further phased the QTL region to discover a core haplotype of five SNPs associated with low backfat across three breeds. Linkage disequilibrium analysis using whole-genome sequence data revealed three candidate causal variants within intronic regions and downstream of theCCND2gene, including the lead SNP. We evaluated the association of the lead SNP with the expression of the genes in the QTL region (includingCCND2) in a large cohort of 100 crossbred samples, sequenced in four different tissues (lung, spleen, liver, muscle). Results show that the A allele increases the expression ofCCND2in an additive way in three out of four tissues. Our findings indicate that the causal variant for this QTL region is a regulatory variant within the third intron of theCCND2gene affecting the expression ofCCND2.

In pig farming, measurements of production parameters play a fundamental role in the success of the activity. Minimal differences in fertility between breeders can lead to less reproductive efficiency and, less productivity. However, assessing the fertility of each male and the early identification of subfertile males is a difficult task to be performed. Thus, the aim of this study was to evaluate the use of in vitro and in vivo parameters in the identification of subfertile males of the Landrace breed, aiming to collaborate with genetic improvement programs, routine optimization in the Genetic Diffusion Units (GDUs) and the results of performance. In experiment 1, an approach to identify males with subfertility was evaluated based on retrospective data. For this, the results (averages of birth rates, number of total births and average percentages of female and male piglets per litter) were evaluated for a total of 996 matings and 847 parturitions. The inseminations came from ejaculates of 32 males, who had at least 19 females inseminated with homospermic doses in the concentration of 2.5 x 109 total sperm from the same male. As for the birth rate (BR), an average of 85.47% ± 6.05 was observed with a group of median males, seven males that stood out and one individual (M32) with a performance of 58.06% ± 9.0. For the total number of piglets born (PB) the average was 13.41 ± 0.56, with three males with better performance and one (M32) with very poor performance (8.62 ± 0.59). In experiment 2, it was verified whether evaluations of inseminating doses (ID) of semen in vitro (motility and sperm morphology) after 96 hours of storage had correlations with fertility in vivo, which can be used to identify subfertile males. The evaluations were performed on 30 ejaculates regarding the means of BR and PB, considering only those who had at least 7 females inseminated. There were no correlations between the motility assessments and semen morphological changes and the reproductive parameters evaluated. The results obtained in vivo, referring to BR and PB, demonstrated that it was possible to identify differences between males, the individual (M32) had the worst results for the percentages of BR and PB. It is concluded that there are males of high and low fertility and that only the in vitro analyzes carried out in this study are not enough to categorize them, however, the evaluation of retrospective data was efficient for this purpose.

Background: The genetic background of trait variability has captured the interest of ecologists and animal breeders because the genes that control it could be involved in buffering various environmental effects. Phenotypic variability of a given trait can be assessed by studying the heterogeneity of the residual variance, and the quantitative trait loci (QTL) that are involved in the control of this variability are described as variance QTL (vQTL). This study focuses on litter size (total number born, TNB) and its variability in a Large White pig population. The variability of TNB was evaluated either using a simple method, i.e. analysis of the log-transformed variance of residuals (LnVar), or the more complex double hierarchical generalized linear model (DHGLM). We also performed a single-SNP (single nucleotide polymorphism) genome-wide association study (GWAS). To our knowledge, this is only the second study that reports vQTL for litter size in pigs and the first one that shows GWAS results when using two methods to evaluate variability of TNB: LnVar and DHGLM. Results: Based on LnVar, three candidate vQTL regions were detected, on Sus scrofa chromosomes (SSC) 1, 7, and 18, which comprised 18 SNPs. Based on the DHGLM, three candidate vQTL regions were detected, i.e. two on SSC7 and one on SSC11, which comprised 32 SNPs. Only one candidate vQTL region overlapped between the two methods, on SSC7, which also contained the most significant SNP. Within this vQTL region, two candidate genes were identified, ADGRF1 , which is involved in neurodevelopment of the brain, and ADGRF5 , which is involved in the function of the respiratory system and in vascularization. The correlation between estimated breeding values based on the two methods was 0.86. Three-fold cross-validation indicated that DHGLM yielded EBV that were much more accurate and had better prediction of missing observations than LnVar. Conclusions: The results indicated that the LnVar and DHGLM methods resulted in genetically different traits. Based on their validation, we recommend the use of DHGLM over the simpler method of log-transformed variance of residuals. These conclusions can be useful for future studies on the evaluation of the variability of any trait in any species.

Biological information regarding markers and gene association may be used to attribute different weights for single nucleotide polymorphism (SNP) in genome‐wide selection. Therefore, we aimed to evaluate the predictive ability and the bias of genomic prediction using models that allow SNP weighting in the genomic relationship matrix (G) building, with and without incorporating biological information to obtain the weights. Firstly, we performed a genome‐wide association studies (GWAS) in data set containing single‐ (SL) or a multi‐line (ML) pig population for androstenone, skatole and indole levels. Secondly, 1%, 2%, 5%, 10%, 30% and 50% of the markers explaining the highest proportions of the genetic variance for each trait were selected to build gene networks through the association weight matrix (AWM) approach. The number of edges in the network was computed and used to derive weights for G (AWM‐WssGBLUP). The single‐step GBLUP (ssGBLUP) and weighted ssGBLUP (WssGBLUP) were used as standard scenarios. All scenarios presented predictive abilities different from zero; however, the great overlap in their confidences interval suggests no differences among scenarios. Most of scenarios of based on AWM provide overestimations for skatole in both SL and ML populations. On the other hand, the skatole and indole prediction were no biased in the ssGBLUP (S1) in both SL and ML populations. Most of scenarios based on AWM provide no biased predictions for indole in both SL and ML populations. In summary, using biological information through AWM matrix and gene networks to derive weights for genomic prediction resulted in no increase in predictive ability for boar taint compounds. In addition, this approach increased the number of analyses steps. Thus, we can conclude that ssGBLUP is most appropriate for the analysis of boar taint compounds in comparison with the weighted strategies used in the present work.