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Social behaviour traits and their impact on feed efficiency are of particular interest in pig farming. The integration of automatic feeders enables the collection of multiple phenotypes for breeding purposes. The additive genetic and social genetic effect can be estimated considering all the visits to the feeder by modelling each visit independently in a 'visit-based approach'. This study aimed to determine the impact of the social genetic effect on individual feed intake and duration per visit in Pietrain pigs and Iberian pigs separately. The dataset comprised 883,906 visits from 1608 Pietrain pigs and 775,054 visits from 856 Iberian pigs. In the Pietrain population, the social genetic effects did not explain a substantial percentage of the phenotypic variance (~1%). In contrast, the Iberian population exhibited more substantial contributions, with social genetic effects accounting for 6.2% of the variance in duration per visit and 5.5% in feed intake per visit. The correlations between additive direct genetic and additive social genetic effects were slightly positive for feed intake across all analyses, and around zero for duration per visit with most of them including the zero in the highest posterior density interval (HPD95%). These weak correlations suggest that both effects could be selected independently. The visit-based approach successfully identified social genetic effects in the studied populations. Models incorporating social genetic effects demonstrated lower residual variance, enhancing the accuracy of additive values and, consequently, the potential for an improved response to selection., (© 2025 Wiley‐VCH GmbH. Published by John Wiley & Sons Ltd.)

Crossbreeding plays a pivotal role within pig breeding programmes, aiming to maximize heterosis and improve reproductive traits in crossbred maternal lines. Nevertheless, there is evidence indicating that the performance of reciprocal crosses between two genetic lines might exhibit variability. These variations in performance can be attributed to differences in the correlations between gametic effects, acting as either sire or dam, within purebred and crossbred populations. To address this issue, we propose a multivariate gametic model that incorporates up to four correlated gametic effects for each parental population. The model is employed on a data set comprising litter size data (total number of piglets born-TNB- and number of piglets born alive-NBA-) derived from a reciprocal cross involving two Iberian pig populations: Entrepelado and Retinto. The data set comprises 6933 records from 1564 purebred Entrepelado (EE) sows, 4995 records from 1015 Entrepelado × Retinto (ER) crosses, 2977 records from 756 Retinto × Entrepelado (RE) crosses and 7497 records from 1577 purebred Retinto (RR) sows. The data set is further supplemented by a pedigree encompassing 6007 individual-sire-dam entries. The statistical model also included the order of parity (with six levels), the breed of the service sire (five levels) and the herd-year-season effects (141 levels). Additionally, the model integrates random dominant and permanent environmental sow effects. The analysis employed a Bayesian approach, and the results revealed all the posterior estimates of the gametic correlations to be positive. The range of the posterior mean estimates of the correlations varied across different gametic effects and traits, with a range between 0.04 (gametic correlation between the paternal effects for purebred and the maternal for crossbred in Retinto) and 0.53 (gametic correlation between the paternal effects for purebred and the paternal for crossbred in Entrepelado). Furthermore, the posterior mean variance estimates of the maternal gametic effects were consistently surpassed those for paternal effects within all four populations. The results suggest the possible influence of imprinting effects on the genetic control of litter size, and underscore the importance of incorporating crossbred data into the breeding value predictions for purebred individuals., (© 2023 The Authors. Journal of Animal Breeding and Genetics published by John Wiley & Sons Ltd.)

Improved feed efficiency is an essential goal for the sustainability of pig production in economic and environmental terms. Traits such as feed conversion rate (FCR), residual feed intake (RFI), residual body weight gain (RG) and feeding behaviour, such as duration (TPV) and feeding rate per visit (FR) can now be measured by automatic feeding systems. The aim of this study was to evaluate the benefits of incorporating feeding behaviour traits into a selection index to improve feed efficiency in a nucleus of purebred Pietrain pigs. Data on body weight, feed intake and duration were recorded at each visit in 1608 animals. The information contained in 843,605 visits was grouped by animal ID to obtain a set of feed efficiency and feeding behaviour traits. These traits were obtained in three periods (first, second and total period). Bayesian models were built to estimate the posterior marginal distribution of the variance components. The heritabilities were between 0.44 and 0.59 for feeding behaviour traits and between 0.31 and 0.49 for feed efficiency traits. The FCR and RFI showed a considerable genetic correlation with daily feed intake (~0.65). FCR showed a genetic correlation with feeding behaviour traits, such as feed intake per visit (FPV) (0.44) and FR (0.33). Furthermore, the fast-eating pigs were less efficient. This was due to the positive genetic correlation found between the FR and the FCR (0.33) and the RFI (0.23), and the negative correlation found with the RG (-0.28). On the other hand, the inclusion of the feeding behaviour traits into a selection index slightly increased the selection response for FCR (4%) and RFI (1.8%). However, there was an increase of up to 19% in the selection response for RG and an improvement in accuracy from 0.59 to 0.70. Therefore, we concluded that it would be interesting to include feeding behaviour traits in a selection index to improve the selection response and accuracy of feed efficiency traits., (© 2023 The Authors. Journal of Animal Breeding and Genetics published by John Wiley & Sons Ltd.)

Uterine capacity (UC), defined as the total number of kits from unilaterally ovariectomized does at birth, has a high genetic correlation with litter size. The aim of our research was to identify genomic regions associated with litter size traits through a genomewide association study using rabbits from a divergent selection experiment for UC. A high-density SNP array (200K) was used to genotype 181 does from a control population, high and low UC lines. Traits included total number born (TNB), number born alive (NBA), number born dead, ovulation rate (OR), implanted embryos (IE) and embryo, foetal and prenatal survivals at second parity. We implemented the Bayes B method and the associations were tested by Bayes factors and the percentage of genomic variance (GV) explained by windows. Different genomic regions associated with TNB, NBA, IE and OR were found. These regions explained 7.36%, 1.27%, 15.87% and 3.95% of GV, respectively. Two consecutive windows on chromosome 17 were associated with TNB, NBA and IE. This genomic region accounted for 6.32% of GV of TNB. In this region, we found the BMP4, PTDGR, PTGER2, STYX and CDKN3 candidate genes which presented functional annotations linked to some reproductive processes. Our findings suggest that a genomic region on chromosome 17 has an important effect on litter size traits. However, further analyses are needed to validate this region in other maternal rabbit lines., (© 2019 Blackwell Verlag GmbH.)

Data from seven generations of a divergent selection experiment designed for environmental variability of birth weight were analysed to estimate genetic parameters and to explore signs of selection response. A total of 10 783 birth weight records from 638 females and 1127 litters in combination with 10 007 pedigree records were used. Each record of birth weight was assigned to the mother of the pup in a heteroscedastic model, and after seven generations of selection, evidence of success in the selection process was shown. A Bayesian analysis showed that success of the selection process started from the first generation for birth weight and from the second generation for its environmental variability. Genetic parameters were estimated across generations. However, only from the third generation onwards were the records useful to consider the results to be reliable. The results showed a consistent positive and low genetic correlation between the birth weight trait and its environmental variability, which could allow an independent selection process. This study has demonstrated that the genetic control of the birth weight environmental variability is possible in mice. Nevertheless, before the results are applied directly in farm animals, it would be worth confirming any other implications on other important traits, such as robustness, longevity and welfare., (© 2015 Blackwell Verlag GmbH.)

Data from a divergent experiment for birthweight (BrW) environmental variability were used to estimate genetic parameters for BrW trait and its environmental variability by fitting both homoscedastic (HO) and heteroscedastic (HE) models. A total of 5 475 records of BrW from animals born from inbred dams, and 7 140 pedigree records were used. The heritability of BrW using the model HO was 0.27, with the litter effect much more important, 0.43. The model HE provided a genetic correlation between the trait and its environmental variability that was very high and negative, -0.97, and a high value for the additive genetic variance for environmental variability, suggesting an artefact in the model. The residual skewness was found to be essentially null. A model considering the genetic correlation null was also fitted, and used to obtain the breeding values for the selection process. Moreover, the trait was considered as maternal resulting in similar estimates under the model HO, but more reasonable for the genetic correlation between the trait and its environmental variability of 0.48 with a value of 0.25 for the additive genetic variance regarding environmental variability under the model HE. This led to the conclusion that environmental variability of BrW in mice must be selected via dams. Estimated parameters in a reduced dataset without inbred animals did not substantially change this conclusion., (© 2012 Blackwell Verlag GmbH.)

An experiment with mice was designed to test the relative efficiency of three selection methods that help to minimize the rate of inbreeding during selection. A common house mice (Mus musculus) population was selected for 17 generations to increase the weight gain between 21 and 42 days. The population was split at random into three lines A, B and C where three selection methods were applied: individual selection and random mating, weighted selection with random mating and individual selection with minimum coancestry mating, respectively. There were three replicates for each line. Cumulated selection response was similar in the three lines, but there were differences in the level of inbreeding attained (in percentage): 31.24 (method A), 24.72 (method B) and 27.88 (method C). As consequence, lines B and C (weighted selection and minimum coancestry) showed a lower value of deterioration of fitness traits (the intrauterine mortality and the mortality at birth) than line A (random mating)., (© 2011 Blackwell Verlag GmbH.)

This note provides a description of software that allows to fit Bayesian genetically structured variance models using Markov chain Monte Carlo (MCMC). The gsevm v.2 program was written in Fortran 90. The DOS and Unix executable programs, the user's guide, and some example files are freely available for research purposes at http://www.bdporc.irta.es/estudis.jsp. The main feature of the program is to compute Monte Carlo estimates of marginal posterior distributions of parameters of interest. The program is quite flexible, allowing the user to fit a variety of linear models at the level of the mean and the logvariance.