Indirect genetic effects increase the heritable variation available to selection and are largest for behaviors: a meta-analysis

The evolutionary potential of traits is governed by the amount of heritable variation available to selection. While this is typically quantified based on genetic variation in a focal individual for its own traits (direct genetic effects, DGEs), when social interactions occur, genetic variation in interacting partners can influence a focal individual's traits (indirect genetic effects, IGEs). Theory and studies on domesticated species have suggested IGEs can greatly impact evolutionary trajectories, but whether this is true more broadly remains unclear. Here, we perform a systematic review and meta-analysis to quantify the amount of trait variance explained by IGEs and the contribution of IGEs to predictions of adaptive potential. We identified 180 effect sizes from 47 studies across 21 species and found that, on average, IGEs of a single social partner account for a small but statistically significant amount of phenotypic variation (0.03). As IGEs affect the trait values of each interacting group member and due to a typically positive-although statistically nonsignificant-correlation with DGEs (r DGE-IGE = 0.26), IGEs ultimately increase trait heritability substantially from 0.27 (narrow-sense heritability) to 0.45 (total heritable variance). This 66% average increase in heritability suggests IGEs can increase the amount of genetic variation available to selection. Furthermore, whilst showing considerable variation across studies, IGEs were most prominent for behaviors and, to a lesser extent, for reproduction and survival, in contrast to morphological, metabolic, physiological, and development traits. Our meta-analysis, therefore, shows that IGEs tend to enhance the evolutionary potential of traits, especially for those tightly related to interactions with other individuals, such as behavior and reproduction.

Genome-Wide Association Study of Body Conformation Traits in a Three-Way Crossbred Commercial Pig Population

Body conformation is the most direct production index, which can fully reflect pig growth status and is closely related to critical economic traits. In this study, we conducted a genome-wide association study (GWAS) on body conformation traits in a population of 1518 Duroc × (Landrace × Yorkshire) commercial pigs. These traits included body length (BL), body height (BH), chest circumference (CC), abdominal circumference (AC), and waist circumference (WC). Both the mixed linear model (MLM) and fixed and random model circulating probability unification (FarmCPU) approaches were employed for the analysis. Our findings revealed 60 significant single nucleotide polymorphisms (SNPs) associated with these body conformation traits in the crossbred pig population. Specifically, sixteen SNPs were significantly associated with BL, three SNPs with BH, thirteen SNPs with CC, twelve SNPs with AC, and sixteen SNPs with WC. Moreover, we identified several promising candidate genes located within the genomic regions associated with body conformation traits. These candidate genes include INTS10, KIRREL3, SOX21, BMP2, MAP4K3, SOD3, FAM160B1, ATL2, SPRED2, SEC16B, and RASAL2. Furthermore, our analysis revealed a novel significant quantitative trait locus (QTL) on SSC7 specifically associated with waist circumference, spanning an 84 kb interval. Overall, the identification of these significant SNPs and potential candidate genes in crossbred commercial pigs enhances our understanding of the genetic basis underlying body conformation traits. Additionally, these findings provide valuable genetic resources for pig breeding programs.

Adaptation of Livestock to New Diets Using Feed Components without Competition with Human Edible Protein Sources-A Review of the Possibilities and Recommendations

Simple Summary

Livestock feed contains components that can also be consumed by humans, which may become less available for livestock. Proteins are such components that may become less available for livestock feed. This review focuses on using alternative protein sources in feed. We may expect protein efficiency problems and we discuss how these could be solved using a combination of alternative protein sources and animal breeding. We make recommendations for the use and optimization of novel protein sources.

Abstract

Livestock feed encompasses both human edible and human inedible components. Human edible feed components may become less available for livestock. Especially for proteins, this calls for action. This review focuses on using alternative protein sources in feed and protein efficiency, the expected problems, and how these problems could be solved. Breeding for higher protein efficiency leading to less use of the protein sources may be one strategy. Replacing (part of) the human edible feed components with human inedible components may be another strategy, which could be combined with breeding for livestock that can efficiently digest novel protein feed sources. The potential use of novel protein sources is discussed. We discuss the present knowledge on novel protein sources, including the consequences for animal performance and production costs, and make recommendations for the use and optimization of novel protein sources (1) to improve our knowledge on the inclusion of human inedible protein into the diet of livestock, (2) because cooperation between animal breeders and nutritionists is needed to share knowledge and combine expertise, and (3) to investigate the effect of animal-specific digestibility of protein sources for selective breeding for each protein source and for precision feeding. Nutrigenetics and nutrigenomics will be important tools.

Feed intake and feeding behavior traits for gestating sows recorded using electronic sow feeders

Electronic sow feeding (ESF) systems are used to control feed delivery to individual sows that are group-housed. Feeding levels for gestating sows are typically restricted to prevent excessive body weight gain. Any alteration of intake from the allocated feeding curve or unusual feeding behavior could indicate potential health issues. The objective of this study was to use data recorded by ESF to establish and characterize novel feed intake and feeding behavior traits and to estimate their heritabilities. Raw data were available from two farms with in-house manufactured (Farm A) or commercial (Farm B) ESF. The traits derived included feed intake, time spent eating, and rate of feed consumption, averaged across or within specific time periods of gestation. Additional phenotypes included average daily number of feeding events (AFE), along with the cumulative numbers of days where sows spent longer than 30 min in the ESF (ABOVE30), missed their daily intake (MISSF), or consumed below 1 kg of feed (BELOW1). The appetite of sows was represented by averages of score (APPETITE), a binary value for allocation eaten or not (DA_bin), or the standard deviation of the difference between feed intake and allocation (SDA-I). Gilts took longer to eat than sows (15.5 ± 0.13 vs. 14.1 ± 0.11 min/d) despite a lower feed allocation (2.13 ± 0.00 vs. 2.36 ± 0.01 kg/d). The lowest heritability estimates (below 0.10) occurred for feed intake traits, due to the restriction in feed allocation, although heritabilities were slightly higher for Farm B, with restriction in the eating time. The low heritability for AFE (0.05 ± 0.02) may have reflected the lack of recording of nonfeeding visits, but repeatability was moderate (0.26 ± 0.03, Farm A). Time-related traits were moderately to highly heritable and repeatable, demonstrating genetic variation between individuals in their feeding behaviors. Heritabilities for BELOW1 (Farm A: 0.16 ± 0.04 and Farm B: 0.15 ± 0.09) and SDA-I (Farm A: 0.17 ± 0.04 and Farm B: 0.10 ± 0.08) were similar across farms. In contrast, MISSF was moderately heritable in Farm A (0.19 ± 0.04) but lowly heritable in Farm B (0.05 ± 0.07). Heritabilities for DA_bin were dissimilar between farms (Farm A: 0.02 ± 0.02 and Farm B: 0.23 ± 0.10) despite similar incidence. Individual phenotypes constructed from ESF data could be useful for genetic evaluation purposes, but equivalent capabilities to generate phenotypes were not available for both ESF systems.

Prospects for the Analysis and Reduction of Damaging Behaviour in Group-Housed Livestock, With Application to Pig Breeding

Innovations in the breeding and management of pigs are needed to improve the performance and welfare of animals raised in social groups, and in particular to minimise biting and damage to group mates. Depending on the context, social interactions between pigs can be frequent or infrequent, aggressive, or non-aggressive. Injuries or emotional distress may follow. The behaviours leading to damage to conspecifics include progeny savaging, tail, ear or vulva biting, and excessive aggression. In combination with changes in husbandry practices designed to improve living conditions, refined methods of genetic selection may be a solution reducing these behaviours. Knowledge gaps relating to lack of data and limits in statistical analyses have been identified. The originality of this paper lies in its proposal of several statistical methods for common use in analysing and predicting unwanted behaviours, and for genetic use in the breeding context. We focus on models of interaction reflecting the identity and behaviour of group mates which can be applied directly to damaging traits, social network analysis to define new and more integrative traits, and capture-recapture analysis to replace missing data by estimating the probability of behaviours. We provide the rationale for each method and suggest they should be combined for a more accurate estimation of the variation underlying damaging behaviours.

The predictive ability of indirect genetic models is reduced when culled animals are omitted from the data

Background

Physical removal of individuals from groups causes reductions in group sizes and changes in group composition, which may affect the predictive ability of estimates of indirect genetic effects of animals on phenotypes of group mates. We hypothesized that including indirect genetic effects of culled animals and of animals without phenotypes in the analysis affects estimates of genetic parameters, improves predictive ability, and reduces bias of predicted breeding values. We tested this by applying different editing procedures, i.e. omission of individuals or groups from the data, and genetic models, i.e. a classical and an indirect genetic model (IGM) without or with weighting of indirect genetic effects based on the relative proportion of time spent in the pen or space allowance. Data consisted of average daily gain for 123,567 pigs in 11,111 groups, from which 3% of individuals in 25% of groups were prematurely removed from the group.

Results

The estimate of total heritability was higher (0.29 to 0.34) than that of direct heritability (0.23 to 0.25) regardless of the editing procedures and IGM used. Omission of individuals or groups from the data reduced the predictive ability of estimates of indirect genetic effects by 8 to 46%, and the predictive ability of estimates of the combined direct and indirect genetic effects by up to 4%. Omission of full groups introduced bias in predicted breeding values. Weighting of indirect genetic effects reduced the predictive ability of their estimates by at least 19% and of the estimates of the combined direct and indirect genetic effects by 1%.

Conclusions

We identified significant indirect genetic effects for growth in pigs. Culled animals should neither be removed from the data nor accounted for by weighting their indirect genetic effects in the model based on the relative proportion of time spent in the pen or space allowance, because it will reduce predictive ability and increase bias of predicted breeding values. Information on culled animals is important for prediction of indirect genetic effects and must be accounted for in IGM analyses by including fixed regressions based on relative time spent within the pen or relative space allowance.

Genetic parameters for haemoglobin levels in sows and piglets as well as sow reproductive performance and piglet survival

Genetic parameters were estimated for haemoglobin (Hb) levels in sows and piglets as well as sow reproductive performance and piglet survival. Reproductive traits were available between 2005 and 2014 for 7857 litters from 1029 Large White and 858 Landrace sows. In 2012 and 2013, Hb levels, sow BW and sow back fat depth were measured on 348 sows with 529 litters 5 days prior to farrowing. In addition, Hb levels were available for 1127 one-day-old piglets from 383 litters (a maximum of three piglets per litter) of 277 sows with Hb levels. The average Hb levels in sows (sow Hb), their litters (litter Hb, based on average Hb of three piglets) and individual piglets (piglet Hb) were 112 ± 12.6 g/l, 103 ± 15.3 g/l and 105 ± 21.7 g/l, respectively. Heritabilities for Hb levels were 0.09 ± 0.07 for sow Hb, 0.19 ± 0.11 for litter Hb and 0.08 ± 0.05 for piglet Hb. Estimates for the permanent environment effect of sows were 0.09 ± 0.09 for sow Hb, 0.11 ± 0.12 for litter Hb and 0.12 ± 0.03 for piglet Hb. In comparison, heritabilities for both number of stillborn piglets and pre-weaning survival were lower (0.05 ± 0.01 and 0.04 ± 0.01). Sow BW had no significant heritability, while sow back fat depth was lowly heritable (0.10 ± 0.08). Positive genetic correlations were found between sow Hb and litter Hb (0.64 ± 0.47) and between litter Hb and sow back fat depth (0.71 ± 0.53). Higher litter Hb was genetically associated with lower number of stillborn piglets (-0.78 ± 0.35) and higher pre-weaning survival (0.28 ± 0.33). Negative genetic correlations between sow Hb and average piglet birth weight of the litter (-0.60 ± 0.34) and between piglet Hb and birth weight of individual piglets (-0.37 ± 0.32) indicate that selection for heavier piglets may reduce Hb levels in sows and piglets. Similarly, selection for larger litter size will reduce average piglet birth weight (rg: -0.40 ± 0.12) and pre-weaning survival (-0.57 ± 0.13) and may lead to lower litter Hb (-0.48 ± 0.27). This study shows promising first results for the use of Hb levels as a selection criterion in pig breeding programs, and selection for higher Hb levels may improve piglet survival and limit further reduction in Hb levels in sows and piglets due to selection for larger and heavier litters.

Application of single-step genomic evaluation using social genetic effect model for growth in pig

Objective

Social genetic effects (SGE) are an important genetic component for growth, group productivity, and welfare in pigs. The present study was conducted to evaluate i) the feasibility of the single-step genomic best linear unbiased prediction (ssGBLUP) approach with the inclusion of SGE in the model in pigs, and ii) the changes in the contribution of heritable SGE to the phenotypic variance with different scaling ω constants for genomic relationships.

Methods

The dataset included performance tested growth rate records (average daily gain) from 13,166 and 21,762 pigs Landrace (LR) and Yorkshire (YS), respectively. A total of 1,041 (LR) and 964 (YS) pigs were genotyped using the Illumina PorcineSNP60 v2 BeadChip panel. With the BLUPF90 software package, genetic parameters were estimated using a modified animal model for competitive traits. Giving a fixed weight to pedigree relationships (τ: 1), several weights (ω_xx, 0.1 to 1.0; with a 0.1 interval) were scaled with the genomic relationship for best model fit with Akaike information criterion (AIC).

Results

The genetic variances and total heritability estimates (T²) were mostly higher with ssGBLUP than in the pedigree-based analysis. The model AIC value increased with any level of ω other than 0.6 and 0.5 in LR and YS, respectively, indicating the worse fit of those models. The theoretical accuracies of direct and social breeding value were increased by decreasing ω in both breeds, indicating the better accuracy of ω_0.1 models. Therefore, the optimal values of ω to minimize AIC and to increase theoretical accuracy were 0.6 in LR and 0.5 in YS.

Conclusion

In conclusion, single-step ssGBLUP model fitting SGE showed significant improvement in accuracy compared with the pedigree-based analysis method; therefore, it could be implemented in a pig population for genomic selection based on SGE, especially in South Korean populations, with appropriate further adjustment of tuning parameters for relationship matrices.

Genetic association between sow longevity and social genetic effects on growth in pigs

Objective

Sow longevity is important for efficient and profitable pig farming. Recently, there has been an increasing interest in social genetic effect (SGE) of pigs on stress-tolerance and behavior. The present study aimed to estimate genetic correlations among average daily gain (ADG), stayability (STAY), and number of piglets born alive at the first parity (NBA1) in Korean Yorkshire pigs, using a model including SGE.

Methods

The phenotypic records of ADG and reproductive traits of 33,120 and 11,654 pigs, respectively, were evaluated. The variances and (co) variances of the studied traits were estimated by a multi-trait animal model applying the Bayesian with linear-threshold models using Gibbs sampling.

Results

The direct and SGEs on ADG had a significantly negative (−0.30) and neutral (0.04) genetic relationship with STAY, respectively. In addition, the genetic correlation between the social effects on ADG and NBA1 tended to be positive (0.27), unlike the direct effects (−0.04). The genetic correlation of the total effect on ADG with that of STAY was negative (−0.23) but non-significant, owing to the social effect.

Conclusion

These results suggested that total genetic effect on growth in the SGE model might reduce the negative effect on sow longevity because of the growth potential of pigs. We recommend including social effects as selection criteria in breeding programs to obtain satisfactory genetic changes in both growth and longevity.

Effect of feeding cereals-alternative ingredients diets or corn-soybean meal diets on performance and carcass characteristics of growing-finishing gilts and boars

Pig-breeding businesses have resulted in global breeding programs that select pigs to perform well on high-energy high-protein diets, which are traditionally based on corn and soybean meal. Nowadays, there is a shift toward diets based on cereals and co-products, therefore, high dietary inclusion of co-products can modify the expected performance of these pigs. The objective of this study was to evaluate the effect of feeding a cereals-alternative ingredients diet (CA-diet) compared to a corn-soybean meal diet (CS-diet) on the growth performance, feed efficiency, and carcass characteristics of genetically similar growing-finishing gilts and boars. In total, 160 pigs, 80 gilts and 80 boars, coming from 18 litters were used. The pigs were blocked based on litter, to ensure no genetic differences between the 2 treatments. For the starter phase, pigs fed the CA-diet performed in terms of growth, and feed efficiency, as good as the pigs fed CS-diet (P > 0.05). For the grower phase, pigs fed the CA-diet had the same ADFI (P > 0.05), but a lower daily energy intake (ADEI) (P < 0.001), and same growth performance (P > 0.05) than pig fed the CS-diet, therefore pigs fed the CA-diet were more efficient in terms of residual energy intake (REI) (P < 0.001). For the finisher phase, interaction between diet and sex had an effect on ADFI (P < 0.001), ADEI (P < 0.001), ADG (P = 0.010), and lipid deposition (Ld) (P = 0.016). Pigs fed the CA-diet were less efficient than pigs fed the CS-diet, i.e., G:F (P < 0.001), RFI (P < 0.001), and REI (P = 0.007). In general, feeding a CA-diet to pigs showed to improve the ratio between Pd and Ld, especially for boars. Also, pigs fed the CA-diet showed thinner back fat thickness (P < 0.001), same loin depth thickness (P > 0.05), but lower dressing percentage (P < 0.001), than pigs fed the CS-diet.

Genotype by feed interaction for feed efficiency and growth performance traits in pigs

A major objective of pork producers is to reduce production cost. Feeding may account for over 75% of pork production costs. Thus, selecting pigs for feed efficiency (FE) traits is a priority in pig breeding programs. While in the Americas, pigs are typically fed high-input diets, based on corn and soybean meal (CS); in Western Europe, pigs are commonly fed diets based on wheat and barley with high amounts of added protein-rich coproducts (WB), e.g., from milling and seed-oil industries. These two feeding scenarios provided a realistic setting for investigating a specific type of genotype by environment interaction; thus, we investigated the genotype by feed interaction (GxF). In the presence of a GxF, different feed compositions should be considered when selecting for FE. This study aimed to 1) verify the presence of a GxF for FE and growth performance traits in different growth phases (starter, grower, and finisher) of 3-way crossbred growing-finishing pigs fed either a CS (547 boars and 558 gilts) or WB (567 boars and 558 gilts) diet; and 2) to assess and compare the expected responses to direct selection under the 2 diets and the expected correlated responses for one diet to indirect selection under the other diet. We found that GxF did not interfere in the ranking of genotypes under both diets for growth, protein deposition, feed intake, energy intake, or feed conversion rate. Therefore, for these traits, we recommend changing the diet of growing-finishing pigs from high-input feed (i.e., CS) to feed with less valuable ingredients, as WB, to reduce production costs and the environmental impact, regardless of which diet is used in selection. We found that GxF interfered in the ranking of genotypes and caused heterogeneity of genetic variance under both diets for lipid deposition (LD), residual energy intake (REI), and residual feed intake (RFI). Thus, selecting pigs under a diet different from the diet used for growing-finishing performance could compromise the LD in all growth phases, compromise the REI and RFI during the starter phase, and severely compromise the REI during the grower phase. In particular, when pigs are required to consume a WB diet for growing-finishing performance, pigs should be selected for FE under the same diet. Breeding pigs for FE under lower-input diets should be considered, because FE traits will become more important and lower-input diets will become more widespread in the near future.

Indirect Genetic Effects for Growth in Pigs Affect Behaviour and Weight Around Weaning

Selection for indirect genetic effects (IGE), i.e. the genetic effect of an individual on a trait of another individual, is a promising avenue to increase trait values in plant and animal breeding. Studies in livestock suggest that selection for IGE for growth (IGEg) might increase animals' capacity to tolerate stress. We assessed the effect of a stressful phase (weaning) on the behaviour and performance of pigs (n = 480) divergently selected for high or low IGEg. High IGEg pigs were significantly slower to explore the feed and gained less weight than low IGEg pigs in the days after weaning. In line with previous findings, high IGEg animals may have prioritized the formation of social ranks.

Indirect genetic effect model using feeding behaviour traits to define the degree of interaction between mates: an implementation in pigs growth rate

An alternative implementation of the animal model including indirect genetic effect (IGE) is presented considering pair-mate-specific interaction degrees to improve the performance of the model. Data consisted of average daily gain (ADG) records from 663 pigs kept in groups of 10 to 14 mates during the fattening period. Three types of models were used to fit ADG data: (i) animal model (AM); (ii) AM with classical IGE (AM-IGE); and (iii) AM fitting IGE with a specific degree of interaction between each pair of mates (AM-IGE_i). Several feeding behavior phenotypes were used to define the pair-mate-specific degree of interaction in AM-IGE_i: feeding rate (g/min), feeding frequency (min/day), the time between consecutive visits to the feeder (min/day), occupation time (min/day) and an index considering all these variables. All models included systematic effects batch, initial age (covariate), final age (covariate), number of pigs per pen (covariate), plus the random effect of the pen. Estimated posterior mean (posterior SD) of heritability was 0.47 (0.15) using AM. Including social genetic effects in the model, total heritable variance expressed as a proportion of total phenotypic variance (T²) was 0.54 (0.29) using AM-IGE, whereas it ranged from 0.51 to 0.55 (0.12 to 0.14) with AM-IGE_i, depending on the behavior trait used to define social interactions. These results confirm the contribution of IGEs to the total heritable variation of ADG. Moreover, important differences between models were observed in EBV rankings. The percentage of coincidence of top 10% animals between AM and AM-IGE_i ranged from 0.44 to 0.89 and from 0.41to 0.68 between AM-IGE and AM-IGE_i. Based on the goodness of fit and predictive ability, social models are preferred for the genetic evaluation of ADG. Among models including IGEs, when the pair-specific degree of interaction was defined using feeding behavior phenotypes we obtained an increase in the accuracy of genetic parameters estimates, the better goodness of fit and higher predictive ability. We conclude that feeding behavior variables can be used to measure the interaction between pen mates and to improve the performance of models including IGEs.

Genetic correlations between feed efficiency traits, and growth performance and carcass traits in purebred and crossbred pigs

Selection for feed efficiency (FE) is a strategy to reduce the production costs per unit of animal product, which is one of the major objectives of current animal breeding programs. In pig breeding, selection for FE and other traits traditionally takes place based on purebred pig (PB) performance at the nucleus level, while pork production typically makes use of crossbred animals (CB). The success of this selection, therefore, depends on the genetic correlation between the performance of PB and CB (rpc) and on the genetic correlation (rg) between FE and the other traits that are currently under selection. Different traits are being used to account for FE, but the rpc has been reported only for feed conversion rate. Therefore, this study aimed 1) to estimate the rpc for growth performance, carcass, and FE traits; 2) to estimate rg between traits within PB and CB populations; and 3) to compare three different traits representing FE: feed conversion rate, residual energy intake (REI), and residual feed intake (RFI). Phenotypes of 194,445 PB animals from 23 nucleus farms, and 46,328 CB animals from three farms where research is conducted under near commercial production conditions were available for this study. From these, 22,984 PB and 8,657 CB presented records for feed intake. The PB population consisted of five sire and four dam lines, and the CB population consisted of terminal cross-progeny generated by crossing sires from one of the five PB sire lines with commercially available two-way maternal sow crosses. Estimates of rpc ranged from 0.61 to 0.71 for growth performance traits, from 0.75 to 0.82 for carcass traits, and from 0.62 to 0.67 for FE traits. Estimates of rg between growth performance, carcass, and FE traits differed within PB and CB. REI and RFI showed substantial positive rg estimates in PB (0.84) and CB (0.90) populations. The magnitudes of rpc estimates indicate that genetic progress is being realized in CB at the production level from selection on PB performance at nucleus level. However, including CB phenotypes recorded on production farms, when predicting breeding values, has the potential to increase genetic progress for these traits in CB. Given the genetic correlations with growth performance traits and the genetic correlation between the performance of PB and CB, REI is an attractive FE parameter for a breeding program.

Modelling the co-evolution of indirect genetic effects and inherited variability

When individuals interact, their phenotypes may be affected not only by their own genes but also by genes in their social partners. This phenomenon is known as Indirect Genetic Effects (IGEs). In aquaculture species and some plants, however, competition not only affects trait levels of individuals, but also inflates variability of trait values among individuals. In the field of quantitative genetics, the variability of trait values has been studied as a quantitative trait in itself, and is often referred to as inherited variability. Such studies, however, consider only the genetic effect of the focal individual on trait variability and do not make a connection to competition. Although the observed phenotypic relationship between competition and variability suggests an underlying genetic relationship, the current quantitative genetic models of IGE and inherited variability do not allow for such a relationship. The lack of quantitative genetic models that connect IGEs to inherited variability limits our understanding of the potential of variability to respond to selection, both in nature and agriculture. Models of trait levels, for example, show that IGEs may considerably change heritable variation in trait values. Currently, we lack the tools to investigate whether this result extends to variability of trait values. Here we present a model that integrates IGEs and inherited variability. In this model, the target phenotype, say growth rate, is a function of the genetic and environmental effects of the focal individual and of the difference in trait value between the social partner and the focal individual, multiplied by a regression coefficient. The regression coefficient is a genetic trait, which is a measure of cooperation; a negative value indicates competition, a positive value cooperation, and an increasing value due to selection indicates the evolution of cooperation. In contrast to the existing quantitative genetic models, our model allows for co-evolution of IGEs and variability, as the regression coefficient can respond to selection. Our simulations show that the model results in increased variability of body weight with increasing competition. When competition decreases, i.e., cooperation evolves, variability becomes significantly smaller. Hence, our model facilitates quantitative genetic studies on the relationship between IGEs and inherited variability. Moreover, our findings suggest that we may have been overlooking an entire level of genetic variation in variability, the one due to IGEs.

Social genetic effects for growth in pigs differ between boars and gilts

Background

Average daily gain (ADG) in pigs is affected by the so-called social (or indirect) genetic effects (SGE). However, SGE may differ between sexes because boars grow faster than gilts and their social behaviours differ. We hypothesized that direct genetic effects (DGE) and SGE for ADG in pigs differ between boars and gilts and that accounting for these differences will improve the predictive ability of a social genetic effects model (SGM). Our data consisted of ADG from 30 to 94 kg for 32,212 uncastrated males (boars) and 48,252 gilts that were raised in sex-specific pens. Data were analyzed using a univariate model with sex as a fixed effect and a bivariate model with ADG in boars and gilts as separate traits using both a classical animal model (CM) and a SGM.

Results

With the univariate model, the heritability for ADG was 0.22 ± 0.01 for the CM, while the estimate of the total heritable variance (T²) was 0.23 ± 0.01 with the SGM. With the bivariate model, the genetic variance for SGE was higher for boars (13.8 ± 5.8) than for gilts (9.3 ± 3.9). For the bivariate model, T² was 0.32 ± 0.02 for boars and 0.27 ± 0.01 for gilts. Estimates of the genetic correlations between DGE (0.88 ± 0.02) and SGE (0.30 ± 0.30) for boars versus gilts indicated that ADG in boars and gilts are different traits. Moreover, the estimate of the genetic correlation between DGE and SGE indicated presence of genetic effects of competition among gilts but not among boars. Compared to a CM, the univariate SGM improved predictive ability significantly only for gilts and the bivariate SGM improved predictive ability significantly for both boars and gilts.

Conclusions

We found significant genetic variances of SGE for ADG. The covariance between DGE and SGE was much more negative for gilts than for boars when applying the bivariate model. Because the estimate of the genetic correlation for ADG between gilts and boars differed significantly from 1 and the predictive ability for boars and gilts was improved significantly with the bivariate model, we recommend the use of a bivariate model to estimate both SGE and DGE for ADG in pigs.

The influence of housing and feeding systems on selected fattening and slaughter parameters of finishing pigs with different genotypes

The aim of the present study was to demonstrate the influence of housing and feeding systems on selected fattening and slaughter parameters of finishers with different genotypes. The experimental population consisted of 1200 finishers in three genetic variants from the Landrace (L), Large White (LW), Duroc (D) and Pietrain (P) breeds: 400 finishers L × [D × P] genotype, 400 finishers LW × [D × P] genotype and 400 finishers [L × LW] × [D × P] genotype. Subgroups were isolated for each genetic variant with the experimental factors: housing system (slatted floor or deep litter) and feeding system (dry or liquid). Selected fattening parameters were analysed: final liveweight (kg), mortality (%), average daily gain (g/day) and feed conversion ratio (kg/kg gain). Additionally, slaughter parameters were analysed: carcass weight (kg), height of longissimus dorsi (LD) muscle (mm), backfat thickness (mm) and lean meat content (%). The housing system strongly affected the final liveweight, average daily gain and carcass weight. To a lesser degree, this factor determined the mortality, feed conversion ratio, height of LD muscle, backfat thickness and lean meat content. The feeding system substantially affected almost all fattening parameters, except for the mortality. Statistical analysis of slaughter parameters showed that the impact of the feeding system was confirmed statistically only in terms of carcass weight. Genotype largely determined the final liveweight, average daily gain, carcass weight and lean meat content. Taking into account interactions of all factors, the most favourable effect in terms of production was to fattening four-way crossbreeds [L × LW] × [D × P] on slatted floors and fed by liquid feeding.

Genetic parameters and expected responses to selection for components of feed efficiency in a Duroc pig line

Background

Improving feed efficiency (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FE}}$$\end{document}FE) is a key factor for any pig breeding company. Although this can be achieved by selection on an index of multi-trait best linear unbiased prediction of breeding values with optimal economic weights, considering deviations of feed intake from actual needs (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{RFI}}$$\end{document}RFI) should be of value for further research on biological aspects of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FE}}$$\end{document}FE. Here, we present a random regression model that extends the classical definition of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{RFI}}$$\end{document}RFI by including animal-specific needs in the model. Using this model, we explore the genetic determinism of several \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FE}}$$\end{document}FE components: use of feed for growth (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{WG}}$$\end{document}WG), use of feed for backfat deposition (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FG}}$$\end{document}FG), use of feed for maintenance (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{MW}}$$\end{document}MW), and unspecific efficiency in the use of feed (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{RFI}}$$\end{document}RFI). Expected response to alternative selection indexes involving different components is also studied.

Results

Based on goodness-of-fit to the available feed intake (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FI}}$$\end{document}FI) data, the model that assumes individual (genetic and permanent) variation in the use of feed for maintenance, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{WG}}$$\end{document}WG and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FG}}$$\end{document}FG showed the best performance. Joint individual variation in feed allocation to maintenance, growth and backfat deposition comprised 37% of the individual variation of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FI}}$$\end{document}FI. The estimated heritabilities of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{RFI}}$$\end{document}RFI using the model that accounts for animal-specific needs and the traditional \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{RFI}}$$\end{document}RFI model were 0.12 and 0.18, respectively. The estimated heritabilities for the regression coefficients were 0.44, 0.39 and 0.55 for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{MW}}$$\end{document}MW, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{WG}}$$\end{document}WG and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FG}}$$\end{document}FG, respectively. Estimates of genetic correlations of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{RFI}}$$\end{document}RFI were positive with amount of feed used for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{WG}}$$\end{document}WG and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FG}}$$\end{document}FG but negative for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{MW}}$$\end{document}MW. Expected response in overall efficiency, reducing \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FI}}$$\end{document}FI without altering performance, was 2.5% higher when the model assumed animal-specific needs than when the traditional definition of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{RFI}}$$\end{document}RFI was considered.

Conclusions

Expected response in overall efficiency, by reducing \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FI}}$$\end{document}FI without altering performance, is slightly better with a model that assumes animal-specific needs instead of batch-specific needs to correct \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FI}}$$\end{document}FI. The relatively small difference between the traditional \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{RFI}}$$\end{document}RFI model and our model is due to random intercepts (unspecific use of feed) accounting for the majority of variability in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FI}}$$\end{document}FI. Overall, a model that accounts for animal-specific needs for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{MW}}$$\end{document}MW, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{WG}}$$\end{document}WG and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FG}}$$\end{document}FG is statistically superior and allows for the possibility to act differentially on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FE}}$$\end{document}FE components.

Estimation of genetic parameters for traits associated with reproduction, lactation, and efficiency in sows

Increased milk production due to high litter size, coupled with low feed intake, results in excessive mobilization of sow body reserves during lactation, which can have detrimental effects on future reproductive performance. A possibility to prevent this is to improve sow lactation performance genetically, along with other traits of interest. The aim of this study was to estimate breed-specific genetic parameters (by parity, between parities, and across parities) for traits associated with lactation and reproduction in Yorkshire and Landrace sows. Performance data were available for 2,107 sows with 1 to 3 parities (3,424 farrowings total). Sow back fat, loin depth and BW at farrowing, sow feed intake (SFI), and body weight loss (BWL) during lactation showed moderate heritabilities (0.21 to 0.37) in both breeds, whereas back fat loss (BFL), loin depth loss (LDL), and litter weight gain (LWG) showed low heritabilities (0.12 to 0.18). Among the efficiency traits, sow lactation efficiency showed extremely low heritability (near zero) in Yorkshire sows but a slightly higher (0.05) estimate in Landrace sows, whereas sow residual feed intake (SRFI) and energy balance traits showed moderate heritabilities in both breeds. Genetic correlations indicated that SFI during lactation had strong negative genetic correlations with body resource mobilization traits (BWL, BFL, and LDL; -0.35 to -0.70), and tissue mobilization traits in turn had strong positive genetic correlations with LWG (+0.24 to +0.54; < 0.05). However, SFI did not have a significant genetic correlation with LWG. These genetic correlations suggest that SFI during lactation is predominantly used for reducing sow body tissue losses, rather than for milk production. Estimates of genetic correlations for the same trait measured in parities 1 and 2 ranged from 0.64 to 0.98, which suggests that first and later parities should be treated as genetically different for some traits. Genetic correlations estimated between traits in parities 1 and 2 indicated that BWF and BWL measured in parity 1 can be used as indicator traits for SFI and SRFI measured in parities 1 and 2. In conclusion, traits associated with lactation in sows have a sizable genetic component and show potential for genetic improvement.

Can a super sow be a robust sow? Consequences of litter investment in purebred and crossbred sows of different parities

The aim of this project was to study the consequences of litter investment on physical characteristics in primiparous and multiparous sows in 3 Norwegian breeds (Norsvin Duroc [ = 12], Norsvin Landrace [ = 12], and crossbreeds [Norsvin Landrace and Swedish Yorkshire { = 15}]). We predicted that the maternal sow line (Norsvin Landrace) would invest more in their litter in term of higher weight at birth, resulting in a higher litter weight of weaned piglets but with the consequence of greater loss in body condition and a higher prevalence of shoulder lesions. It was predicted that this should be more pronounced in primiparous sows than in multiparous sows. As predicted, the maternal pure line (Norsvin Landrace) had higher litter investment in terms of litter weight at birth ( = 0.003) and litter weight at weaning ( = 0.050) as well as higher total litter investment (litter weight at weaning plus weight of dead piglets [stillborn and mummified piglets and weight of piglets that died after farrowing but before weaning]; = 0.050) and suffered larger losses of body condition ( = 0.016) and had a higher prevalence of shoulder lesions ( = 0.008) during lactation than other breeds. Moreover, only in Norsvin Landrace was development of shoulder lesions related to inadequate feed consumption ( = 0.006). This has become a major welfare concern of modern pig breeding. Although primiparous and multiparous sows had similar litter sizes, primiparous sows had lower litter investment in terms of litter weight at birth ( = 0.032) and litter weight at weaning ( = 0.007) as well as total litter investment ( = 0.008). Primiparous sows suffered greater losses in body condition ( = 0.012) and developed more shoulder lesions ( = 0.026) due to lower total feed consumption ( < 0.001) during lactation than multiparous sows. Especially in the highly productive maternal line (Norsvin Landrace), development of shoulder lesions during the lactation period was more pronounced in primiparous sows than in multiparous sows ( < 0.001). The selection program has shifted the balance to greater investments in earlier life, when sows still need resources for their own growth and development. This has resulted in a larger number of weaned piglets but at a higher sow welfare cost in terms of higher losses in body condition and a higher prevalence of shoulder lesions. Our results pinpoint the importance of improving the balance between economic traits and traits that improve welfare and longevity of the sows.

Direct and social genetic effects on body weight at 270 days and carcass and ham quality traits in heavy pigs

The aims of this study were to estimate covariance components for BW at 270 d (BW270) and carcass and ham quality traits in heavy pigs using models accounting for social effects and to compare the ability of such models to fit the data relative to models ignoring social interactions. Phenotypic records were from 9,871 pigs sired by 293 purebred boars mated to 456 crossbred sows. Piglets were born and reared at the same farm and randomly assigned at 60 d of age to groups (6.1 pigs per group on average) housed in finishing pens, each having an area of 6 m(2). The average additive genetic relationship among group mates was 0.11. Pigs were slaughtered at 277 ± 3 d of age and 169.7 ± 13.9 kg BW in groups of nearly 70 animals each. Four univariate animal models were compared: a basic model (M1) including only direct additive genetic effects, a model (M2) with nonheritable social group (pen) effects in addition to effects in M1, a model (M3) accounting for litter effects in addition to M2, and a model (M4) accounting for social genetic effects in addition to effects in M3. Restricted maximum likelihood estimates of covariance components were obtained for BW270; carcass backfat depth; carcass lean meat content (CLM); iodine number (IOD); and linoleic acid content (LIA) of raw ham subcutaneous fat; subcutaneous fat depth in the proximity of semimembranosus muscle (SFD1) and quadriceps femoris muscle (SFD2); and linear scores for ham round shape (RS), subcutaneous fat (SF), and marbling. Likelihood ratio tests indicated that, for all traits, M2 fit the data better than M1 and that M3 was superior to M2 except for SFD1 and SFD2. Model M4 was significantly better than M3 for BW270 (P < 0.001) and CLM, IOD, RS, and SF (P < 0.05). The contribution of social genetic effects to the total heritable variance was large for CLM and BW270, ranging from 33.2 to 35%, whereas the one for ham quality traits ranged from 6.8 (RS) to 11.2% (SF). Direct and social genetic effects on BW270 were uncorrelated, whereas there was a negative genetic covariance between direct and social effects on CLM, IOD, RS, and SF, which reduced the total heritable variance. This variance, measured relative to phenotypic variance, ranged from 21 (CLM) to 54% (BW270). Results indicate that social genetic effects affect variation in traits relevant for heavy pigs used in dry-cured hams manufacturing. Such effects should be exploited and taken into account in design of breeding programs for heavy pigs.

The prospects of selection for social genetic effects to improve welfare and productivity in livestock

Social interactions between individuals living in a group can have both positive and negative effects on welfare, productivity, and health of these individuals. Negative effects of social interactions in livestock are easier to observe than positive effects. For example, laying hens may develop feather pecking, which can cause mortality due to cannibalism, and pigs may develop tail biting or excessive aggression. Several studies have shown that social interactions affect the genetic variation in a trait. Genetic improvement of socially-affected traits, however, has proven to be difficult until relatively recently. The use of classical selection methods, like individual selection, may result in selection responses opposite to expected, because these methods neglect the effect of an individual on its group mates (social genetic effects). It has become clear that improvement of socially-affected traits requires selection methods that take into account not only the direct effect of an individual on its own phenotype but also the social genetic effects, also known as indirect genetic effects, of an individual on the phenotypes of its group mates. Here, we review the theoretical and empirical work on social genetic effects, with a focus on livestock. First, we present the theory of social genetic effects. Subsequently, we evaluate the evidence for social genetic effects in livestock and other species, by reviewing estimates of genetic parameters for direct and social genetic effects. Then we describe the results of different selection experiments. Finally, we discuss issues concerning the implementation of social genetic effects in livestock breeding programs. This review demonstrates that selection for socially-affected traits, using methods that target both the direct and social genetic effects, is a promising, but sometimes difficult to use in practice, tool to simultaneously improve production and welfare in livestock.

Selection based on indirect genetic effects for growth, environmental enrichment and coping style affect the immune status of pigs

Pigs living in intensive husbandry systems may experience both acute and chronic stress through standard management procedures and limitations in their physical and social environment, which may have implications for their immune status. Here, the effect of a new breeding method where pigs were selected on their heritable influence on their pen mates' growth, and environmental enrichment on the immune status of pigs was investigated. Hereto, 240 pigs with a relatively positive genetic effect on the growth of their pen mates (+SBV) and 240 pigs with a relatively negative genetic effect on the growth of their pen mates (-SBV) were housed in barren or straw-enriched pens from 4 to 23 weeks of age (n  =  80 pens in total). A blood sample was taken from the pigs before, three days after a 24 h regrouping test, and at week 22. In addition, effects of coping style, as assessed in a backtest, and gender were also investigated. Mainly, +SBV were found to have lower leukocyte, lymphocyte and haptoglobin concentrations than -SBV pigs. Enriched housed pigs had a lower neutrophil to lymphocyte (N:L) ratio and lower haptoglobin concentrations, but had higher antibody titers specific for Keyhole Limpet Hemocyanin (KLH) than barren housed pigs. No interactions were found between SBV class and housing. Furthermore, pigs with a proactive coping style had higher alternative complement activity and, in the enriched pens, higher antibody titers specific for KLH than pigs with a reactive coping style. Lastly, females tended to have lower leukocyte, but higher haptoglobin concentrations than castrated males. Overall, these results suggest that +SBV pigs and enriched housed pigs were less affected by stress than -SBV and barren housed pigs, respectively. Moreover, immune activation might be differently organized in individuals with different coping styles and to a lesser extent in individuals of opposite genders.

Indirect genetic effects for growth rate in domestic pigs alter aggressive and manipulative biting behaviour

Indirect genetic effects (IGEs) are heritable effects of an individual on phenotypic values of others, and may result from social interactions. We determined the behavioural consequences of selection for IGEs for growth (IGEg) in pigs in a G × E treatment design. Pigs (n = 480) were selected for high versus low IGEg with a contrast of 14 g average daily gain and were housed in either barren or straw-enriched pens (n = 80). High IGEg pigs showed from 8 to 23 weeks age 40 % less aggressive biting (P = 0.006), 27 % less ear biting (P = 0.03), and 40 % less biting on enrichment material (P = 0.005). High IGEg pigs had a lower tail damage score (high 2.0; low 2.2; P = 0.004), and consumed 30 % less jute sacks (P = 0.002). Selection on high IGEg reduced biting behaviours additive to the, generally much larger, effects of straw-bedding (P < 0.01), with no G × E interactions. These results show opportunities to reduce harmful biting behaviours in pigs.

The quantitative genetics of indirect genetic effects: a selective review of modelling issues

Indirect genetic effects (IGE) occur when the genotype of an individual affects the phenotypic trait value of another conspecific individual. IGEs can have profound effects on both the magnitude and the direction of response to selection. Models of inheritance and response to selection in traits subject to IGEs have been developed within two frameworks; a trait-based framework in which IGEs are specified as a direct consequence of individual trait values, and a variance-component framework in which phenotypic variance is decomposed into a direct and an indirect additive genetic component. This work is a selective review of the quantitative genetics of traits affected by IGEs, with a focus on modelling, estimation and interpretation issues. It includes a discussion on variance-component vs trait-based models of IGEs, a review of issues related to the estimation of IGEs from field data, including the estimation of the interaction coefficient Ψ (psi), and a discussion on the relevance of IGEs for response to selection in cases where the strength of interaction varies among pairs of individuals. An investigation of the trait-based model shows that the interaction coefficient Ψ may deviate considerably from the corresponding regression coefficient when feedback occurs. The increasing research effort devoted to IGEs suggests that they are a widespread phenomenon, probably particularly in natural populations and plants. Further work in this field should considerably broaden our understanding of the quantitative genetics of inheritance and response to selection in relation to the social organisation of populations.

Genetic parameters for feed intake, litter weight, body condition and rebreeding success in primiparous Norwegian Landrace sows

The aim of this study was to estimate genetic parameters for feed intake recorded as farmers' perception of young sows' appetite for the first 3 weeks of lactation (APP) and feed intake recorded for one day in the 3rd week of lactation (FEED), litter weight (LW) at 3 weeks, sow body condition at weaning (BC) and the following five reproduction traits: weaning-to-service interval of 1 to 7 days (WSI7), weaning-to-service interval of 1 to 50 days (WSI50), delayed service or not (DELAYED), pregnant on first service or not (PREGNANT) and litter size in 2nd parity (NBT2). The analyses included data on 4606 Norwegian Landrace 1st-parity sows and their litters. The Gibbs sampling method was used. The traits DELAYED and PREGNANT were analysed as threshold traits and APP, FEED, LW, BC, WSI7, WSI50 and NBT2 were analysed as linear traits. The heritability estimates for APP and FEED were low (<0.1), whereas the estimates for DELAYED and PREGNANT were rather high (0.4 and 0.3). The heritability estimate for BC was 0.2. The genetic correlations confirmed the complexity of breeding for sow performance; selection for heavy 1st litters may lead to lower body condition at weaning, which in turn leads to lower reproductive performance and smaller litters in 2nd parity. Selection for higher sow feed intake would improve body condition, but the simple way of measuring feed intake tested in this study (APP and FEED) cannot be recommended because of the low heritability obtained for these traits.

Backtest and novelty behavior of female and castrated male piglets, with diverging social breeding values for growth

Pigs housed together in a group influence each other's growth. Part of this effect is genetic and can be represented in a social breeding value. It is unknown, however, which traits are associated with social breeding values. The aim of this study was, therefore, to investigate whether personality and response to novelty could be associated with social breeding values for growth in piglets. Female and castrated male piglets from 80 litters, with either an estimated relative positive or negative social breeding value (+SBV or -SBV) for growth, were individually tested in a backtest and novel environment test, and group-wise in a novel object (i.e., a feeder with feed) test and human approach test. All tests were performed during the suckling period. No differences between +SBV and -SBV piglets were found for the frequency and latency of struggling and vocalizing in the backtest (at least, P > 0.30). In the novel object test, piglets with a +SBV for growth touched the feeder faster than piglets with -SBV for growth (P = 0.01) and were more frequently present near the person in the human approach test (P < 0.01). No behavioral differences between +SBV and -SBV piglets were found in the novel environment test (at least, P > 0.40), but piglets that struggled more in the backtest walked more in this test (P = 0.02). Behavior was affected by gender in each test. Female piglets were faster than castrated male piglets to start struggling in the backtest (P = 0.047). In the novel object test, females were faster than males to touch the feeder and sample the feed. In the human approach test, they were also faster than male piglets to touch a person (all, P < 0.001). Females were also more frequently present near the feeder (P < 0.001) and person (P = 0.03). In the novel environment test, female piglets explored the floor more (P = 0.046), produced less low- (P = 0.04) and high-pitched vocalizations (P = 0.02), and defecated (P = 0.08) and urinated less than male piglets (P < 0.01). It was concluded that +SBV and -SBV piglets do not differ in their response to the backtest, and only subtle differences were found in their response to novelty. More research is warranted to identify the traits underlying SBV for growth in pigs. Moreover, castrated male piglets seemed to react more fearfully to each test than female piglets.