Effect of sow history features on growth and feed intake in grow-finish pigs

The sow provides a specific environment to her offspring during gestation and lactation. Certain features in the early life of the sow (sow history features) may affect her ability to deliver and feed a healthy litter. In genetic analyses of grow-finish traits, these effects are estimated as common litter or permanent sow effects. The objective of this research was to identify sow history features that affect the growth rate (GR) and feed intake (FI) of her offspring during the grow-finish stage. Data from 17,743 grow-finish pigs, coming from 604 sires and 681 crossbred sows, were recorded between May 2001 and February 2010 at the experimental farm of the Institute for Pig Genetics (Beilen, the Netherlands). The grow-finish stage was divided into 2 phases (phase 1: 26 to 75 kg; phase 2: 75 to 115 kg). The sow history features were birth litter size, birth year and season, birth farm, weaning age, age of transfer to the experimental farm, and age at first insemination. The sow features were added to the basic model one at a time to study their effect on the grow-finish traits of the pigs. Subsequently, significant sow features (P < 0.1) were fitted simultaneously in an animal model. With every extra piglet in the birth litter of the sow, the GR of her offspring decreased by 1 g/d and the FI decreased by 4 g/d. Every extra day to the first insemination increased the GR of grow-finish pigs by 0.1 g/d. The heritability estimates for GR and FI (only in phase 2 of the grow-finish stage) decreased after adding the sow features to the model. No differences were found in estimates of the common litter effects between the basic model and the model with all significant sow features. The estimates of the permanent sow effect changed for FI from 0.03 (basic model) to 0.00 (model with sow features), and for FI in phase 1, the permanent sow effect decreased from 0.03 (basic model) to 0.01 (model with sow features). In conclusion, selected sow features do affect the grow-finish traits of the pigs, but their estimates are small and explain only a small proportion of the differences in the GR and FI of grow-finish pigs. The sow features partially explained the permanent sow effect of FI-related traits and did not explain the common litter effect. Although the sow early life features can affect piglet traits, they do not predict which sows produce better performing offspring in the grow-finish stage.     

Estimating maternal genetic effects in livestock

This study investigates the estimation of direct and maternal genetic (co)variances, accounting for environmental covariances between direct and maternal effects. Estimated genetic correlations between direct and maternal effects presented in the literature have often been strongly negative, and their validity has been questioned. Explanations of extreme estimates have focused on the existence of environmental covariances between dam and offspring. As a solution, models including a regression on dam-phenotype have been proposed, but have yielded biased estimates. The performance of models that implement the variance structure arising from the classical model of Willham, however, has not been evaluated. This study investigated the covariance structure of the parts of the residual term that arise from Willham's model. Results show that a correlation between the residual of the record of an individual and that of its dam is a direct consequence of combining Willham's model with the usual assumption that phenotypic covariances between different traits are the sum of additive genetic and environmental covariances. Stochastic simulations show that fitting this structure yields unbiased estimates of the genetic (co)variances. When correlated residuals were ignored in the cases investigated, the bias in the estimated genetic correlations was approximately equal to the value of the environmental correlation. In contrast to models including a regression on dam-phenotype, there were no difficulties with interpretation of results, and the approach was consistent with standard quantitative genetic theory. The use of Willham's model while accounting for correlated residuals is conceptually appealing and yields unbiased results, with no need for regression on dam phenotype. Inclusion of the ability to fit the residual variance structure required for maternal effects into existing software packages would be helpful to animal breeders.     

Accuracies of estimated breeding values from ordinary genetic evaluations do not reflect the correlation between true and estimated breeding values in selected populations

The accuracy of estimated breeding values (EBVs) is an important parameter in livestock genetic improvement. It is used to calculate response to selection and to express the credibility of individual EBVs. Although it is well-known that selection reduces accuracy, this effect is not well-studied and accuracies from genetic evaluations are not adjusted for selection. This work investigates the effect of selection on accuracy of EBVs estimated using best linear unbiased predictors. Results show that accuracies in a selected population may be considerably smaller than the ordinary accuracy from genetic evaluation. Accuracy of the parent average is dramatically reduced by selection, up to a factor of three. Expressions for equilibrium accuracies in selected populations are presented and depend only on the unselected accuracy and the intensity of selection. Thus, schemes with the same unselected accuracy and intensity of selection also have the same equilibrium accuracy and response to selection. At the same unselected accuracy, therefore, schemes based on between-family information do not show greater reduction in response and accuracy because of the Bulmer effect. An example shows that benefit of genomic selection may be underestimated when the effect of selection on accuracy is ignored. Finally, this work argues that the SE of an EBV and the correlation between true and EBVs are different things, and that accuracies should not be adjusted for selection when they primarily serve to indicate the SEs of EBVs.     

Linear model vs. survival analysis for genetic evaluation of sires for longevity in Chianina beef cattle

The objective of this study was to compare sire EBVs for longevity in Chianina beef cattle estimated with linear models and survival analysis. Two datasets were created, one considered all data (SURVall), the other only uncensored records (SURVun). The linear models were used to analyze longevity measured as three correlated dichotomous (yes/no) measures of survival in the first three parities (LIN-S3) and as an overall measure of lifespan in months (LIN-LPL). Correlation between sire EBVs from the two survival analyses were 0.85. For LIN-S3 the correlations of EBVs across parities were between 0.69 to 0.93. Medium correlations (from 0.50 to 0.62) were found when only uncensored data (SURVun) were compared to the linear model (LIN-S3). Higher correlations (from 0.71 to 0.93) were found when EBV based on both censored and uncensored data (SURVall) were compared to LIN-S3. Heritability was estimated at 0.11, 0.09 and 0.08 for SURVall, SURVun and LIN-LPL, respectively; and 0.05, 0.02 and 0.02, respectively, for survival in the first three parities according to LIN-S3. Linear and non-linear models differed in many aspects; the most precise EBV were obtained when all data was used in the evaluation.     

Prevalence of various radiographic manifestations of osteochondrosis and their correlations between and within joints in Dutch Warmblood horses

Reasons for performing study: Osteochondrosis (OC) is the most important orthopaedic developmental disorder in horses and may manifest in several different forms. No detailed study on the prevalence and/or interrelation of these forms is available, even though these data are a prerequisite for conclusive genetic studies. Objectives: To assess the prevalence of the various manifestations of OC as detected radiographically and to evaluate possible relationships between their occurrence within the same joint and between different joints. Methods: The FP (femoropatellar), TC (tarsocrural) and MCP/MTP (metacarpophalangeal/metatarsophalangeal) joints of 811 yearlings selected randomly, descending from 32 representative stallions, were radiographed and scored for the presence and grade of osteochondrotic lesions. Results were compared at the sire, animal, joint and predilection site levels. Results: In the FP joint, the percentage of animals showing normal joint contours in all sites was 60.7%. For the TC joint and the combined MCP/MTP joints, these figures were 68.6 and 64.6%, respectively. For all joints combined, the percentage dropped to 30.5%. Sedation improved detection of OC lesions in the FP joint. There was a high correlation between the right and left joints. The correlation between flattened bone contours and fragments was considerably less. Conclusions: Scoring on a detailed scale is necessary to achieve good insight into the prevalence of OC. Observations on the right and left joints can be combined in further analyses, whereas flattened bone contours and fragments should be evaluated as statistically different disorders. Potential relevance: This study provides insight into the prevalences of various manifestations of OC and their relationships, within and between joints. These results form the basis for detailed quantitative and/or molecular genetic studies that should lead to the establishment of breeding indices and/or genetic marker sets for OC.     

The relationship between growth and osteochondrosis in specific joints in pigs

Osteochondrosis (OC) is a disturbance of the process of endochondral ossification during skeletal growth. Osteochondrosis is considered the main reason for leg weakness in pigs, which is the second reason for culling in sows, after fertility problems. Previous studies suggest there is a relationship between OC and growth. However, little is known about this relationship, in particular about the timeframe in which growth influences ossification. The aim of the study was to identify the age at which growth and OC are related, and whether this relationship differs between joints. To understand the relationship between the growth pattern and the onset of OC, repeated body weight (BW) measurements and OC scores of 345 pigs were collected. An average of 17 body weight measurements from birth until slaughter at 6 months of age was used. OC was scored macroscopically after slaughter in 24 locations of five joints. Pigs were divided in three defined groups based on the severity of OC; no OC, minor, or severe OC.Until weaning at day 21 no differences in weight and gain were found between the three defined groups. From weaning onwards, pigs diagnosed with minor or severe OC showed higher BWs than pigs diagnosed without OC. The higher weights were due to increased growth before the age of three months. This period might coincide with the window of susceptibility for OC in pigs. The relationship with growth seems to be joint-dependent. Pigs with OC in the elbow joints or with OC in two joints had high BWs, whereas pigs with OC in the femoropatellar joints had low BWs compared to mean BW. Determination of the window of susceptibility and of the relationship between weight gain and OC may help in developing strategies to reduce OC in pig populations.     

Direct and associative effects for androstenone and genetic correlations with backfat and growth in entire male pigs

In the pig industry, male piglets are surgically castrated early in life to prevent boar taint. Boar taint is mainly caused by androstenone and skatole. Androstenone is a pheromone that can be released from the salivary glands when the boar is sexually aroused. Boars are housed in groups and as a consequence boars can influence and be influenced by the phenotype of other boars by (non-)heritable social interactions. The influence of these social interactions on androstenone is not well understood. The objective of this study is to investigate whether androstenone concentrations are affected by (non-)heritable social interactions and estimate their genetic correlation with growth rate and backfat. The dataset contained 6,245 boars, of which 4,455 had androstenone observations (68%). The average number of animals per pen was 7 and boars were housed in 899 unique pen-groups (boars within a single pen) and 344 unique compartment-groups (boars within a unique 'room' within a barn during time). Four models including different random effects, were compared for androstenone. Direct genetic, associative (also known as social genetic or indirect genetic effects), group, compartment, common environment and residual effects were included as random effects in the full model (M3). Including random pen and compartment effects (non-heritable social effects) significantly improved the model (M2) compared with including only direct, common environment and residual as random effects (M1, P < 0.001), and including associative effects even more (M3, P < 0.001). The sum of the direct and associative variance components determines the total genetic variance of the trait. The associative effect explained 11.7% of the total genetic variance. Backfat thickness was analysed using M2 and growth using M3. The genetic correlation between backfat (direct genetic variance) and total genetic variance for androstenone was close to 0. Backfat and the direct and associative effects for androstenone had genetic correlations of 0.14 ± 0.08 and -0.25 ± 0.18, respectively. The genetic correlation between total genetic variances for growth rate and androstenone was 0.33 ± 0.18. The genetic correlation between direct effects was 0.11 ± 0.09 and between associative effects was 0.42 ± 0.31. The genetic correlations and current selection towards lower backfat and greater growth rate suggest that no major change in androstenone is expected when breeding goals are not changed. For selection against boar taint and therefore also against androstenone , results recommend that at least the social environment of the boars should be considered.     

Sensitivity of the breeding values for growth rate and worm egg count to environmental worm burden in Australian Merino sheep

The objective of this study was to explore the sensitivity of breeding values for growth rate and worm egg count (WEC, cube root transformed) to environmental worm burden, measured as the average WEC for each contemporary group (CGWEC). Growth rate and WEC were measured on 7,818 naturally infected Merino lambs in eight flocks across Australia, linked through common use of AI sires. Through bivariate analysis, genetic correlations of 0.55 ± 0.23 and 0.30 ± 0.16 were found for growth rate and WEC between low and high CGWEC, respectively. In a second analysis, breeding values for growth rate and WEC were regressed on CGWEC with a random regression model. The heritability for growth rate varied from 0.23 to 0.16 from low to high CGWEC, and the heritability for WEC varied from 0.25 to 0.36. Results suggest that breeding values for both growth rate and WEC are sensitive to environmental worm burden. Animals expressed less genetic variation for growth rate and more genetic variation for WEC in high CGWEC than in low CGWEC. This form of genotype‐by‐environment interaction should therefore be considered in genetic evaluation of both growth rate and WEC, to increase the efficiency of selection for animals that are more parasite resistant and more resilient to environmental worm challenge.     

Estimation of indirect genetic effects in group‐housed mink (Neovison vison) should account for systematic interactions either due to kin or sex

Social interactions among individuals are abundant, both in wild and in domestic populations. With social interactions, the genes of an individual may affect the trait values of other individuals, a phenomenon known as indirect genetic effects (IGEs). IGEs can be estimated using linear mixed models. Most IGE models assume that individuals interact equally to all group mates irrespective of relatedness. Kin selection theory, however, predicts that an individual will interact differently with family members versus non‐family members. Here, we investigate kin‐ and sex‐specific non‐genetic social interactions in group‐housed mink. Furthermore, we investigated whether systematic non‐genetic interactions between kin or individuals of the same sex influence the estimates of genetic parameters. As a second objective, we clarify the relationship between estimates of the traditional IGE model and a family‐based IGE model proposed in a previous study. Our results indicate that male siblings in mink show different non‐genetic interactions than female siblings in mink and that this may impact the estimation of genetic parameters. Moreover, we have shown how estimates from a family‐based IGE model can be translated to the ordinary direct–indirect model and vice versa. We find no evidence for genetic differences in interactions among related versus unrelated mink.