Letícia Fernanda de Oliveira

Heat stress imposes significant challenges to welfare, health, and productivity of lactating sows due to their limited thermoregulatory capacity and increased metabolic heat load. Genetic selection for improved heat tolerance is a promising yet complex approach, given the large number of biological processes involved in heat stress response. Therefore, the main objective of this study was to unravel biotypes related to thermotolerance in lactating sows using a multivariate approach. A total of 37 traits, including physiological, behavioural, and anatomical measures, as well as climatic resilience indicators derived from automatically recorded vaginal temperature data, were included in the study. Phenotypic data were collected between June and July 2021 in 1645 multiparous Landrace × Large White sows housed under commercial conditions in North Carolina (United States). The additive genetic correlation matrix among these traits was used for performing multivariate analyses (principal component analysis and factor analysis). Heritability estimates ranged from 0.04 for ear skin temperature (TES) to 0.39 for ear area (EA), whereas repeatability estimates ranged from 0.10 for TES to 0.58 for vaginal temperature measured at 12:00 h (TV12h). Genetic correlations were high and positive among surface temperature traits (0.76–0.79) and among vaginal temperature measures and their derived climatic resilience indicators (0.62–0.99), whereas correlations of these temperature‐related traits with morphological and behavioural traits were generally low to moderate. Four principal components and common factors were extracted, explaining 71.20% of the total variance of the data. However, a factor analysis using an orthogonal Varimax rotation was superior in isolating distinct and conceptually clear biotypes. The first factor, which explained 38.80% of the total variance, was named “Core body temperature” and was defined primarily by multiple vaginal temperature measures and climatic resilience indicators. The second factor (12.61% of the total variance) was labelled “Heat dissipation measures”, which grouped skin temperatures, panting scores, and respiration rate traits. The third factor (11.68% of the total variance) was interpreted as “Vaginal temperature dynamics” and showed both positive and negative loadings, contrasting vaginal temperature variability with its autocorrelation and skewness. Finally, the fourth factor (8.11% of the total variance), referred to as “Stress reactivity and morphology”, was defined by a contrast between traits with positive loadings (responsiveness scores, vocalization, and hair density) and traits with negative loadings (body size and body condition). By successfully reducing 37 traits into four interpretable latent profiles, these factors could serve as breeding objectives, steering towards more holistic selection strategies to better understand how pigs adapt to increasingly challenging climatic conditions.

Indicators of heat stress response are heritable complex traits with polygenic inheritance. Copy number variations (CNV) are important genomic structural variations that may be linked to climatic adaptation by influencing the phenotypic variability of traits related to thermal stress and disease resistance in animals. Therefore, the primary objectives of this study were to detect CNV and CNV regions (CNVR) in pigs and explore their associations with physiological and anatomical indicators of HS response in lactating sows. A total of 4184 autosomal genome CNV (4012 deletions and 172 duplications) were detected in 969 animals. CNVR were identified by merging CNV with at least 1‐bp overlap, which enabled the identification of 236 autosomal CNVR. The association analyses led to the identification of three CNVR significantly associated with ear skin temperature and one CNVR significantly associated with vaginal temperature considering all records, vaginal temperature measured at 8:00 h, and hair density. Eleven genes harboured the CNVR with significant associations. In summary, various CNV and CNVR were identified in crossbred maternal‐line pigs, including CNVR significantly associated with physiological and anatomical heat stress response indicators in lactating sows. Candidate genes involved in immune and stress responses overlapped with the significant CNVR, suggesting that they may contribute to climatic resilience in pigs. The findings of this study contribute to better understanding the genetic background of heat stress response in lactating sows.

Background: Non-additive genetic effects are often ignored in livestock genetic evaluations. However, fitting them in the models could improve the accuracy of genomic breeding values. Furthermore, non-additive genetic effects contribute to heterosis, which could be optimized through mating designs. Traits related to fitness and adaptation, such as heat tolerance, tend to be more influenced by non-additive genetic effects. In this context, the primary objectives of this study were to estimate variance components and assess the predictive performance of genomic prediction of breeding values based on alternative models and two independent datasets, including performance records from a purebred pig population and heat tolerance indicators recorded in crossbred lactating sows. Results: Including non-additive genetic effects when modelling performance traits in purebred pigs had no effect on the residual variance estimates for most of the traits, but lower additive genetic variances were observed, especially when additive-by-additive epistasis was included in the models. Furthermore, including non-additive genetic effects did not improve the prediction accuracy of genomic breeding values, but there was animal re-ranking across the models. For the heat tolerance indicators recorded in a crossbred population, most traits had small non-additive genetic variance with large standard error estimates. Nevertheless, panting score and hair density presented substantial additive-by-additive epistatic variance. Panting score had an epistatic variance estimate of 0.1379, which accounted for 82.22% of the total genetic variance. For hair density, the epistatic variance estimates ranged from 0.1745 to 0.1845, which represent 64.95–69.59% of the total genetic variance. Conclusions: Including non-additive genetic effects in the models did not improve the accuracy of genomic breeding values for performance traits in purebred pigs, but there was substantial re-ranking of selection candidates depending on the model fitted. Except for panting score and hair density, low non-additive genetic variance estimates were observed for heat tolerance indicators in crossbred pigs.

This study is aimed at estimating genetic parameters, effective population size, inbreeding, and inbreeding depression for birth weight, weaning weight, and average pre-weaning daily weight gain (ADG) in Piau pigs. We used information from 3841 Piau pigs, and four linear models were fitted in single-trait analyses, including or excluding maternal genetic effect, common litter effect, or a combination. The adjustments of the models were compared using the likelihood ratio test, in which the model that presented the best fit for each trait was used to estimate the (co)variance components. The inbreeding depression effect was evaluated using a linear model that included the fixed effects of sex, parity order, contemporary group, and inbreeding coefficient as a fixed covariate. The weights at birth and weaning showed low direct heritabilities (0.08 and 0.05, respectively), while the ADG showed moderate heritability (0.20). The weight at birth showed high genetic correlations with the weight at weaning (0.90) and the ADG (0.82). The weight at weaning and the ADG also showed a high genetic correlation (0.99). There was an inbreeding increase over the generations and a reduction in the effective population size. In the last generation evaluated, all the animals were inbred, the average inbreeding coefficient was 0.07, and the effective population size was 20.8. A significant inbreeding effect on ADG was observed, where an increase of 1% in the inbreeding coefficient resulted in a decrease of 0.005 g in the ADG. Thus, increasing effective population size is mandatory for controlling inbreeding and reducing the loss of variability in this Piau pig population.

This study aimed to estimate genetic parameters, effective population size, inbreeding, and inbreeding depression for birth weight, weaning weight, and average pre-weaning daily weight gain (ADG) in Piau breed pigs. We used information from 3841 Piau pigs, and four linear models were fitted in single trait analyzes including or excluding maternal genetic effect, common litter effect, or a combination of these. The models’ adjustments were compared by likelihood ratio test, in which the model that presented the best fit for each trait was used to estimate (co)variance components. The inbreeding depression effect was evaluated using a linear model that included the fixed effects of sex, parity order, contemporary group, and inbreeding coefficient as a fixed covariate. Weight at birth and weaning showed low direct heritabilities (0.08 and 0.05, respectively), while ADG showed moderate heritability (0.20). Weight at birth showed high genetic correlations with weight at weaning (0.90) and with ADG (0.82). Weight at weaning and ADG also showed a high genetic correlation (0.99). There was an inbreeding increase over the generations and a reduction of the effective population size. In the last generation evaluated all animals were inbred, the average inbreeding coefficient was 0.07 and the effective population size was 20.8. It was observed a significant inbreeding effect on ADG, wherein an increase of 1% on the inbreeding coefficient resulted in a decrease of 0.005 grams on ADG. Thus, increase effective population size is mandatory for the inbreeding control and to reduce the loss of variability in this Piau population.