Estimation of genetic parameters and breeding values across challenged environments to select for robust pigs

Genetic parameters for novel climatic resilience indicators derived from automatically-recorded vaginal temperature in lactating sows under heat stress conditions

BACKGROUND

Longitudinal records of automatically-recorded vaginal temperature (TV) could be a key source of data for deriving novel indicators of climatic resilience (CR) for breeding more resilient pigs, especially during lactation when sows are at an increased risk of suffering from heat stress (HS). Therefore, we derived 15 CR indicators based on the variability in TV in lactating sows and estimated their genetic parameters. We also investigated their genetic relationship with sows' key reproductive traits.

RESULTS

The heritability estimates of the CR traits ranged from 0.000 ± 0.000 for slope for decreased rate of TV (SlopeDe) to 0.291 ± 0.047 for sum of TV values below the HS threshold (HSUB). Moderate to high genetic correlations (from 0.508 ± 0.056 to 0.998 ± 0.137) and Spearman rank correlations (from 0.431 to 1.000) between genomic estimated breeding values (GEBV) were observed for five CR indicators, i.e. HS duration (HSD), the normalized median multiplied by normalized variance (Nor_medvar), the highest TV value of each measurement day for each individual (MaxTv), and the sum of the TV values above (HSUA) and below (HSUB) the HS threshold. These five CR indicators were lowly to moderately genetically correlated with shoulder skin surface temperature (from 0.139 ± 0.008 to 0.478 ± 0.048) and respiration rate (from 0.079 ± 0.011 to 0.502 ± 0.098). The genetic correlations between these five selected CR indicators and sow reproductive performance traits ranged from - 0.733 to - 0.175 for total number of piglets born alive, from - 0.733 to - 0.175 for total number of piglets born, and from - 0.434 to - 0.169 for number of pigs weaned. The individuals with the highest GEBV (most climate-sensitive) had higher mean skin surface temperature, respiration rate (RR), panting score (PS), and hair density, but had lower mean body condition scores compared to those with the lowest GEBV (most climate-resilient).

CONCLUSIONS

Most of the CR indicators evaluated are heritable with substantial additive genetic variance. Five of them, i.e. HSD, MaxTv, HSUA, HSUB, and Nor_medvar share similar underlying genetic mechanisms. In addition, individuals with higher CR indicators are more likely to exhibit better HS-related physiological responses, higher body condition scores, and improved reproductive performance under hot conditions. These findings highlight the potential benefits of genetically selecting more heat-tolerant individuals based on CR indicators.

Estimation of genotype by environmental interaction for litter traits by reaction norm model in Taiwan Landrace sows

The negative effects of heat stress on swine reproduction have been well documented and the recent global warming trend caused by climate change is leading to more days with high temperatures every year. This has caused a reduction in litter trait performance of Landrace sows in Taiwan, a country extending across tropical and subtropical oceanic zones. Therefore, this study developed a modified model to determine which stages of pregnancy, before, early, middle, and late, had the largest impacts of heat stress on litter traits. A reaction norm model (RNM) was used to identify sows with high resilience to heat stress for litter traits followed by analysis of the modified model. Data from Landrace sows were collected from 2 farms in Taiwan between 2008 and 2021. A total of 11,059 records were collected for total number born (TNB), number born alive (NBA), and stillborn rate (STBR). The results showed that the heritabilities of TNB, NBA, and STBR were 0.170, 0.115, and 0.077, respectively. These results were similar between the conventional model and the modified model. In the modified model, the before and early stages of sow pregnancy were the significant periods for TNB and NBA (P < 0.05), while the early and middle stages were significant for STBR (P < 0.05). According to the RNM results, the heritability estimates for TNB, NBA, and STBR were 0.23 to 0.11, 0.18 to 0.08, and 0.10 to 0.04, respectively, showing a decrease from low temperature-humidity index (THI) to high THI. The minimum genetic correlations between the highest and the lowest THI for TNB, NBA, and STBR were 0.85, 0.64, and 0.80, respectively. The results of the RNM for breeding value showed re-ranking across THI values. In conclusion, similar results were obtained for heritability when the model was modified for heat stress estimation. Yet re-ranking of breeding values across THI could help farmers to select not only for improved litter trait performance but also for heat stress resilience of Landrace sows in Taiwan.

Exploring the value of genomic predictions to simultaneously improve production potential and resilience of farmed animals

Sustainable livestock production requires that animals have a high production potential but are also highly resilient to environmental challenges. The first step to simultaneously improve these traits through genetic selection is to accurately predict their genetic merit. In this paper, we used simulations of sheep populations to assess the effect of genomic data, different genetic evaluation models and phenotyping strategies on prediction accuracies and bias for production potential and resilience. In addition, we also assessed the effect of different selection strategies on the improvement of these traits. Results show that estimation of both traits greatly benefits from taking repeated measurements and from using genomic information. However, the prediction accuracy for production potential is compromised, and resilience estimates tends to be upwards biased, when families are clustered in groups even when genomic information is used. The prediction accuracy was also found to be lower for both traits, resilience and production potential, when the environment challenge levels are unknown. Nevertheless, we observe that genetic gain in both traits can be achieved even in the case of unknown environmental challenge, when families are distributed across a large range of environments. Simultaneous genetic improvement in both traits however greatly benefits from the use of genomic evaluation, reaction norm models and phenotyping in a wide range of environments. Using models without the reaction norm in scenarios where there is a trade-off between resilience and production potential, and phenotypes are collected from a narrow range of environments may result in a loss for one trait. The study demonstrates that genomic selection coupled with reaction-norm models offers great opportunities to simultaneously improve productivity and resilience of farmed animals even in the case of a trade-off.

Differential sensitivity of climate conditions on birth weight genetic values in mice divergently selected for birth weight residual variance

After 32 generations of a divergent selection experiment for residual variance of birth weight in mice, two divergent lines were thus obtained: the heterogeneous line (H-line) and the homogeneous line (L-line). Throughout the generations, differences were observed between the two lines in traits such as litter size, survival at weaning, and birth weight variability caused by unidentified environmental conditions. The L-line exhibited advantages in terms of higher survival rates, larger litter sizes, and less sensitivity to changes in food intake. The study is an examination of the effects of climate as an environmental factor on the performance of these animals. Climate factors including maximum, minimum, and mean temperature (T), humidity (H), and TH index; at three stages (the fecundation, a week before the parturition and the parturition), were linked to a birth weight dataset consisting of 22,614 records distributed as follows: 8,853 corresponding to the H-line, 12,649 to the L-line, and 1,112 to the initial population. Out of the 27 analyzed climatic variables, the maximum temperature 1 wk before parturition (MXTW) was identified as the most influential when comparing heteroscedastic models with the deviance information criterion. The order of Legendre polynomial to apply in the following random regression model was tested by a cross-validation using homoscedastic models. Finally, MXTW was compared on how it affected the two divergent lines by analyzing predicted breeding values (PBV) obtained from a random regression heteroscedastic model. The mean PBV of the H-line in the first generation showed a range of 0.070 g with a negative slope, which was 35 times higher than the range obtained for the L-line, which varied within 0.002 g. In the last generation of selection, the H-line exhibited greater instability of PBV across temperatures, with a difference of 0.101 g between the maximum and minimum mean PBV, compared to 0.017 g for the L-line. The standard deviations of the slopes in the H-line were more dispersed than in the L-line. Unlike the H-line, the L-line had slopes that were not significantly different from 0 throughout the generations of selection, indicating greater stability in response to MXTW variations. The H-line exhibited a higher sensitivity to changes in MXTW, particularly in birth weight, with the L-line being more stable. The selection for uniformity of birth weight could lead to less sensitive animals under environmental changes.

Genome-wide association study of disease resilience traits from a natural polymicrobial disease challenge model in pigs identifies the importance of the major histocompatibility complex region

Infectious diseases cause tremendous financial losses in the pork industry, emphasizing the importance of disease resilience, which is the ability of an animal to maintain performance under disease. Previously, a natural polymicrobial disease challenge model was established, in which pigs were challenged in the late nursery phase by multiple pathogens to maximize expression of genetic differences in disease resilience. Genetic analysis found that performance traits in this model, including growth rate, feed and water intake, and carcass traits, as well as clinical disease phenotypes, were heritable and could be selected for to increase disease resilience of pigs. The objectives of the current study were to identify genomic regions that are associated with disease resilience in this model, using genome-wide association studies and fine-mapping methods, and to use gene set enrichment analyses to determine whether genomic regions associated with disease resilience are enriched for previously published quantitative trait loci, functional pathways, and differentially expressed genes subject to physiological states. Multiple quantitative trait loci were detected for all recorded performance and clinical disease traits. The major histocompatibility complex region was found to explain substantial genetic variance for multiple traits, including for growth rate in the late nursery (12.8%) and finisher (2.7%), for several clinical disease traits (up to 2.7%), and for several feeding and drinking traits (up to 4%). Further fine mapping identified 4 quantitative trait loci in the major histocompatibility complex region for growth rate in the late nursery that spanned the subregions for class I, II, and III, with 1 single-nucleotide polymorphism in the major histocompatibility complex class I subregion capturing the largest effects, explaining 0.8–27.1% of genetic variance for growth rate and for multiple clinical disease traits. This single-nucleotide polymorphism was located in the enhancer of TRIM39 gene, which is involved in innate immune response. The major histocompatibility complex region was pleiotropic for growth rate in the late nursery and finisher, and for treatment and mortality rates. Growth rate in the late nursery showed strong negative genetic correlations in the major histocompatibility complex region with treatment or mortality rates (−0.62 to −0.85) and a strong positive genetic correlation with growth rate in the finisher (0.79). Gene set enrichment analyses found genomic regions associated with resilience phenotypes to be enriched for previously identified disease susceptibility and immune capacity quantitative trait loci, for genes that were differentially expressed following bacterial or virus infection and immune response, and for gene ontology terms related to immune and inflammatory response. In conclusion, the major histocompatibility complex and other quantitative trait loci that harbor immune-related genes were identified to be associated with disease resilience traits in a large-scale natural polymicrobial disease challenge. The major histocompatibility complex region was pleiotropic for growth rate under challenge and for clinical disease traits. Four quantitative trait loci were identified across the class I, II, and III subregions of the major histocompatibility complex for nursery growth rate under challenge, with 1 single-nucleotide polymorphism in the major histocompatibility complex class I subregion capturing the largest effects. The major histocompatibility complex and other quantitative trait loci identified play an important role in host response to infectious diseases and can be incorporated in selection to improve disease resilience, in particular the identified single-nucleotide polymorphism in the major histocompatibility complex class I subregion.

Genetic analysis of disease resilience of wean-to-finish pigs under a natural disease challenge model using reaction norms

Background

Disease resilience is the ability to maintain performance across environments with different disease challenge loads (CL). A reaction norm describes the phenotypes that a genotype can produce across a range of environments and can be implemented using random regression models. The objectives of this study were to: (1) develop measures of CL using growth rate and clinical disease data recorded under a natural polymicrobial disease challenge model; and (2) quantify genetic variation in disease resilience using reaction norm models.

Methods

Different CL were derived from contemporary group effect estimates for average daily gain (ADG) and clinical disease phenotypes, including medical treatment rate (TRT), mortality rate, and subjective health scores. Resulting CL were then used as environmental covariates in reaction norm analyses of ADG and TRT in the challenge nursery and finisher, and compared using model loglikelihoods and estimates of genetic variance associated with CL. Linear and cubic spline reaction norm models were compared based on goodness-of-fit and with multi-variate analyses, for which phenotypes were separated into three traits based on low, medium, or high CL.

Results

Based on model likelihoods and estimates of genetic variance explained by the reaction norm, the best CL for ADG in the nursery was based on early ADG in the finisher, while the CL derived from clinical disease traits across the nursery and finisher was best for ADG in the finisher and for TRT in the nursery and across the nursery and finisher. With increasing CL, estimates of heritability for nursery and finisher ADG initially decreased, then increased, while estimates for TRT generally increased with CL. Genetic correlations for ADG and TRT were low between high versus low CL, but high for close CL. Linear reaction norm models fitted the data significantly better than the standard genetic model without genetic slopes, while the cubic spline model fitted the data significantly better than the linear reaction norm model for most traits. Reaction norm models also fitted the data better than multi-variate models.

Conclusions

Reaction norm models identified genotype-by-environment interactions related to disease CL. Results can be used to select more resilient animals across different levels of CL, high-performance animals at a given CL, or a combination of these.

Unraveling the actual background of second litter syndrome in pigs: based on Large White data

Second litter syndrome (SLS) in sows is when fertility performance is lower in the second parity than in the first parity. The causes of SLS have been associated with lactation weight loss, premature first insemination, short lactation length, short weaning to insemination interval, season, and farm of farrowing. There is little known about the genetic background of SLS or if it is a real biological problem or just a statistical issue. Thus, we aimed to evaluate risk factors, investigate genetic background of SLS, and estimate the probability of SLS existing due to the statistical properties of the trait. The records of 246 799 litters (total number born, TNB) from 46 218 Large White sows were used. A total of 15 398 sows had SLS. Two traits were defined: first a binominal trait if a sow had SLS or not (biSLS) and second a continuous trait (Range) created by subtracting the total number of piglets born in the first parity (TNB1) from the piglets born in the second parity (TNB2). Lactation length, farm, and season of the farrowing had significant effects on SLS traits when tested as fixed effects in the genetic model. These effects are farm management-related factors. The age at first insemination and weaning to insemination interval were significant only for other reproduction traits (e.g., TNB1, TNB2, litter weight in parity 1 and 2). The heritability of biSLS was 0.05 (on observed scale), whereas heritability of Range was 0.03. To verify the existence of SLS data with records of 50 000 sows and 9 parities was simulated. The simulations showed that the average expected frequency of SLS across all the parities was 0.49 (±0.05) while the observed frequency in the actual data was 0.46 (±0.04). We compared this to SLS frequencies in 67 farms and only 2 farms had more piglets born in the first parity compared to the second. Therefore, on the individual sow level SLS is likely due to statistical properties of the trait, whereas on the farm level SLS is likely due to farm management. Thus, SLS should not be considered an abnormality nor a syndrome if on average the herd litter size in parity 2 is larger than in parity 1.

Genetics and genomics of uniformity and resilience in livestock and aquaculture species: A review

Genetic control of residual variance offers opportunities to increase uniformity and resilience of livestock and aquaculture species. Improving uniformity and resilience of animals will improve health and welfare of animals and lead to more homogenous products. Our aims in this review were to summarize the current models and methods to study genetic control of residual variance, genetic parameters and genomic results for residual variance and discuss future research directions. Typically, the genetic coefficient of variation is high (median = 0.27; range 0-0.86) and the heritability of residual variance is low (median = 0.01; range 0-0.10). Higher heritabilities can be achieved when increasing the number of records per animal. Divergent selection experiments have supported the feasibility of selecting for high or low residual variance. Genomic studies have revealed associations in regions related to stress, including those from the heat shock protein family. Although the number of studies is growing, genetic control of residual variance is still poorly understood, but big data and genomics offer great opportunities.

Genetic correlations between growth performance and carcass traits of purebred and crossbred pigs raised in tropical and temperate climates1

In pig breeding, selection commonly takes place in purebred (PB) pigs raised mainly in temperate climates (TEMP) under optimal environmental conditions in nucleus farms. However, pork production typically makes use of crossbred (CB) animals raised in nonstandardized commercial farms, which are located not only in TEMP regions but also in tropical and subtropical regions (TROP). Besides the differences in the genetic background of PB and CB, differences in climate conditions, and differences between nucleus and commercial farms can lower the genetic correlation between the performance of PB in the TEMP (PBTEMP) and CB in the TROP (CBTROP). Genetic correlations (rg) between the performance of PB and CB growing-finishing pigs in TROP and TEMP environments have not been reported yet, due to the scarcity of data in both CB and TROP. Therefore, the present study aimed 1) to verify the presence of genotype × environment interaction (G × E) and 2) to estimate the rg for carcass and growth performance traits when PB and 3-way CB pigs are raised in 2 different climatic environments (TROP and TEMP). Phenotypic records of 217,332 PB and 195,978 CB, representing 2 climatic environments: TROP (Brazil) and TEMP (Canada, France, and the Netherlands) were available for this study. The PB population consisted of 2 sire lines, and the CB population consisted of terminal 3-way cross progeny generated by crossing sires from one of the PB sire lines with commercially available 2-way maternal sow crosses. G × E appears to be present for average daily gain, protein deposition, and muscle depth given the rg estimates between PB in both environments (0.64 to 0.79). With the presence of G × E, phenotypes should be collected in TROP when the objective is to improve the performance of CB in the TROP. Also, based on the rg estimates between PBTEMP and CBTROP (0.22 to 0.25), and on the expected responses to selection, selecting based only on the performance of PBTEMP would give limited genetic progress in the CBTROP. The rg estimates between PBTROP and CBTROP are high (0.80 to 0.99), suggesting that combined crossbred-purebred selection schemes would probably not be necessary to increase genetic progress in CBTROP. However, the calculated responses to selection show that when the objective is the improvement of CBTROP, direct selection based on the performance of CBTROP has the potential to lead to the higher genetic progress compared with indirect selection on the performance of PBTROP.

Quantifying the health challenges in an Australian piggery using medication records for the definition of disease resilience1

Disease resilience is the ability to maintain performance and health, despite infection challenges in the environment. The evaluation of disease resilience requires measures of environment infection challenges, along with other environmental challenges. The overall objective of this study was to define disease resilience using pedigree, production, and medication records from an Australian herd of Large White pigs. The extent to which the infection challenges were captured by environmental descriptors based on contemporary group (CG) estimates of growth was assessed (n = 8,835). There were moderately negative linear relationships (r = -0.29, p = 0.08) between CG estimates (39 CGs) of growth and the frequency of medicated pigs (n = 812 medicated pigs). This suggests that CG estimates of growth partly capture health challenges. However, because the health challenges were not of the pathogenic nature for this herd, these environmental descriptors may not be appropriate for the evaluation of disease resilience. Subsequently, an alternative approach to select for health was provided, where health was defined as a binary outcome of medication status, fitted in a generalized linear mixed sire model. Two health-trait definitions were explored, which differed in the number of control (nonmedicated) pigs per litter. The 'reduced-control' health trait had a representative sample of littermates with available performance records, and the 'full-control' health trait included all piglets weaned per litter (i.e., performance-tested and non-performance-tested pigs). All 812 medicated pigs had performance records available. The remaining 8,023 pigs in the reduced-control and 21,352 pigs in the full-control health traits were assumed to have not been medicated (controls). Male pigs from litters with a higher number of postweaning deaths were more likely to be medicated for both health traits. Heritability was consistent for both trait definitions, at 0.06 ± 0.04 (± SE) (reduced-control) and 0.04 ± 0.03 (full-control). While results may be specific for individual herds depending on health status, these estimates align with those presented in literature for other health traits. Together, these results demonstrate that routinely collected medication records may be useful for pig breeding programs and their economic importance and genetic background should be explored further.

Reaction norm analysis of pig growth using environmental descriptors based on alternative traits

Contemporary group (CG) estimates of different phenotypes have not been widely explored for pigs. The objective of this study was to extend the traits used to derive environmental descriptors of the growing pig, to include CG estimates of early growth between birth and start of feed intake test (EADG), growth during feed intake test (TADG), lifetime growth (ADG), daily feed intake (DFI), backfat (BF) and muscle depth (MD). Pedigree and performance records (n = 7,746) from a commercial Australian piggery were used to derive environmental descriptors based on CG estimates of these six traits. The CG estimates of growth traits described different aspects of the environment from the CG estimates of carcass traits (r < 0.10). These definitions of the environment then were used in reaction norm analysis of growth, to evaluate sire-by-environment interaction (Sire × E) for growth. The most appropriate reaction norm model to evaluate Sire × E for growth was dependent on the environmental descriptor used. If the trait used to derive an environmental descriptor was distinctly different from growth (e.g., BF and MD), CG as an additional random effect was required in the model. If not included, inflated common litter effect and sire intercept variance suggest there was unaccounted environmental variability. There was no significant Sire × E using any of the definitions of the environment, with estimated variance in sire slopes largest when environments were defined by BF ( σ ^ bi 2  = 97 ± 83 (g/day)² ), followed by environments defined by DFI ( σ ^ bi 2  = 39 ± 101 (g/day)² ). While there appears to be differences in ability to detect Sire × E, improved data structure is required to better assess these environmental descriptors based on alternative traits. The ideal trait, or combination of traits, used to derive environmental descriptors may be unique for individual herds. Therefore, multiple phenotypes should be further explored for the evaluation of Sire × E for growth in the pig.

Genetic analysis of reproductive performance in sows during porcine reproductive and respiratory syndrome (PRRS) and porcine epidemic diarrhea (PED) outbreaks

Background

Porcine reproductive and respiratory syndrome (PRRS) is one of the most infectious swine diseases in the world, resulting in over 600 million dollars of economic loss in the USA alone. More recently, the USA swine industry has been having additional major economic losses due to the spread of porcine epidemic diarrhea (PED). However, information regarding the amount of genetic variation for response to diseases in reproductive sows is still very limited. The objectives of this study were to identify periods of infection with of PRRS virus (PRRSV) and/or PED virus (PEDV), and to estimate the impact their impact on the phenotypic and genetic reproductive performance of commercial sows.

Results

Disease (PRRS or PED) was significant (P < 0.05) for all traits analyzed except for total piglets born. Heritability estimates for traits during Clean (without any disease), PRRS, and PED ranged from 0.01 (number of mummies; Clean and PED) to 0.41 (abortion; PED). Genetic correlations between traits within disease statuses ranged from −0.99 (proportion born dead with number weaned; PRRS) to 0.99 (number born dead with born alive; Clean). Within trait, between disease statuses, estimates ranged from − 0.17 (number weaned between PRRS and PED) to 0.99 (abortion between Clean and PRRS).

Conclusion

Results indicate that selection for improved performance during PRRS and PED in commercial sows is possible and would not negatively impact performance in Clean environments.

The effect of a porcine reproductive and respiratory syndrome outbreak on genetic parameters and reaction norms for reproductive performance in pigs1

The objective of this study was to estimate genetic parameters of antibody response and reproductive traits after exposure to porcine reproductive and respiratory syndrome virus. Blood samples were taken approximately 60 d after the outbreak. Antibody levels were quantified as the sample-to-positive ratio (S/P ratio) using a fluorescent microsphere assay. Reproductive traits included total number born (TNB), number born alive (NBA), number stillborn (NSB), number mummified (NBM), and number born dead (NBD). Mortality traits were log transformed for genetic analyses. Data were split into prior, during, and after the disease outbreak phases using visual appraisal of the estimates of farm-year-week effects for each reproductive trait. For NBA, data from all phases were combined into a reaction norm analysis with regression on estimates of farm-year-week effects for NBA. Heritability for S/P ratio was estimated at 0.17 ± 0.05. Heritability estimates for reproduction traits were all low and were lower during the outbreak for NBA but greater for mortality traits. TNB was not greatly affected during the outbreak, as many sows that farrowed during the outbreak were mated prior to the outbreak. Heritability for TNB decreased from 0.13 (prior) to 0.08 (after). Genetic correlation estimates between prior to and during the outbreak were high for TNB (0.86 ± 0.23) and NBA (0.98 ± 0.38) but lower for mortality traits: 0.65 ± 0.43, -0.42 ± 0.55, and 0.29 ± 1.39 for LNSB, LNBM, and LNBD, respectively. TNB prior to and after the outbreak had a lower genetic correlation (0.32 ± 0.33). In general, genetic correlation estimates of S/P ratio with reproductive performance during the outbreak were below 0.20 in absolute value, except for LNSB (-0.73 ± 0.29). Based on the reaction norm model, estimates of genetic correlations between the intercept and slope terms ranged from 0.24 ± 0.50 to 0.54 ± 0.35 depending on the parameterization used, indicating that selection for the intercept may result in indirect selection for steeper slopes, and thus, less resilient animals. In general, estimates of genetic correlations between farm-year-week effect classes based on the reaction norm model resembled estimates of genetic correlations from the multivariate analysis. Overall, compared to previous studies, antibody S/P ratios showed a lower heritability (0.17 ± 0.05) and low genetic correlations with reproductive performance during a porcine reproductive and respiratory syndrome outbreak, except for the LNSB.

Opportunities to Improve Resilience in Animal Breeding Programs

Resilience is the capacity of an animal to be minimally affected by disturbances or to rapidly return to the state pertained before exposure to a disturbance. However, indicators for general resilience to environmental disturbances have not yet been defined, and perhaps therefore resilience is not yet included in breeding goals. The current developments on big data collection give opportunities to determine new resilience indicators based on longitudinal data, which can aid to incorporate resilience in animal breeding goals. The objectives of this paper were: (1) to define resilience indicator traits based on big data, (2) to define economic values for resilience, and (3) to show the potential to improve resilience of livestock through inclusion of resilience in breeding goals. Resilience might be measured based on deviations from expected production levels over a period of time. Suitable resilience indicators could be the variance of deviations, the autocorrelation of deviations, the skewness of deviations, and the slope of a reaction norm. These (new) resilience indicators provide opportunity to include resilience in breeding programs. Economic values of resilience indicators in the selection index can be calculated based on reduced costs due to labor and treatments. For example, when labor time is restricted, the economic value of resilience increases with an increasing number of animals per farm, and can become as large as the economic value of production. This shows the importance of including resilience in breeding goals. Two scenarios were described to show the additional benefit of including resilience in breeding programs. These examples showed that it is hard to improve resilience with only production traits in the selection index, but that it is possible to greatly improve resilience by including resilience indicators in the selection index. However, when health-related traits are already present in the selection index, the effect is smaller. Nevertheless, inclusion of resilience indicators in the selection index increases the response in the breeding goal and resilience, which results in less labor-demanding, and thus easier-to-manage livestock. Current developments on massive collection of data, and new phenotypes based on these data, offer exciting opportunities to breed for improved resilience of livestock.

Genetic Parameters for Resistance to Non-specific Diseases and Production Traits Measured in Challenging and Selection Environments; Application to a Rabbit Case

Breeding for disease resistance is a challenging but increasingly necessary objective to overcome the issues with the reduced use of antibiotics and growing concern for animal welfare while limiting economic losses. However, implementing such strategies is a complex process because animals face numerous diseases, and the environments on selection farms differ from those on commercial farms. We evaluated whether selection for resistance to non-specific diseases based on a single visual record in selection (S) and challenging (Ch) environments is possible. Records from 23,773 purebred rabbits born between 2012 and 2016 were used in this study. After weaning (at 32 days of age), 17,712 rabbits were raised in the S environment and 6,061 sibs were raised in the Ch environment. Clinical signs of disease were recorded for all animals at the end of the test, at a single time point, at 70 or 80 days of age. The causes of mortality occurring before the end of the test were also recorded. Three disease traits were analyzed: signs of respiratory disease, signs of digestive disease, and a composite trait (Resist) taking into account signs of digestive, respiratory and various infectious diseases. This latter composite trait is proposed to capture the global resistance to disease. All disease traits were binary, with 0 being the absence of symptoms. Two production traits were also recorded: the number of kits born alive (4,121 litters) and the weaning weight (13,090 rabbits). Disease traits were analyzed with animal threshold models, assuming that traits are different in the two environments. Bivariate analyses were carried out using linear animal models. The heritabilities of the disease traits ranged from 0.04 ± 0.01 to 0.11 ± 0.03. The genetic correlations between disease traits in both environments were below unity (≤ 0.84), indicating genotype by environment interactions. Most of the genetic correlations between disease and production traits were not significantly different from zero, except between the weaning weight and Resist_S, with a favorable correlation of -0.34 ± 0.12. Given these genetic parameters, for the same level of exposure of rabbits to pathogens, the expected response to selection is a reduction of disease incidence of 4-6% per generation.

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.

Genetic parameters of in vivo primal cuts and body composition (PigAtlas) in pigs measured by computed tomography (CT)

Genetic parameters of in vivo primal cuts in breeding pigs using computed tomography were estimated. A total of 2,439 Duroc and 1998 Landrace boars from the Topigs Norsvin boar testing station in Norway were CT scanned as part of the genetic program. In vivo primal cuts were derived from the CT images using atlas segmentation; the method called the Pig Atlas. The (co)variance estimates were obtained from univariate (heritabilities) and multivariate (correlations) animal genetic models using DMU software. The heritabilities for all primal cuts proportions (%) were intermediate to large for both breeds, h² ranging from 0.15 to 0.50. Negative genetic correlations were found between most of the other primal cuts, and the strongest correlation was between belly and ham. Carcass lean meat percentage showed a positive correlation to shoulder and ham, but was negatively correlated to belly. In this study, in vivo primal cuts from atlas segmentation are used for genetic parameter calculations for the first time. Computed Tomography (CT) makes it possible to measure in vivo body or carcass composition. This will aid the selection response by measuring on the candidates themselves instead of using relatives. Primal cut proportion and composition measured in vivo by computed tomography and atlas segmentation show heritable variation comparable to previous post mortem studies.

Contemporary group estimates adjusted for climatic effects provide a finer definition of the unknown environmental challenges experienced by growing pigs

Environmental descriptors derived from mean performances of contemporary groups (CGs) are assumed to capture any known and unknown environmental challenges. The objective of this paper was to obtain a finer definition of the unknown challenges, by adjusting CG estimates for the known climatic effects of monthly maximum air temperature (MaxT), minimum air temperature (MinT) and monthly rainfall (Rain). As the unknown component could include infection challenges, these refined descriptors may help to better model varying responses of sire progeny to environmental infection challenges for the definition of disease resilience. Data were recorded from 1999 to 2013 at a piggery in south-east Queensland, Australia (n = 31,230). Firstly, CG estimates of average daily gain (ADG) and backfat (BF) were adjusted for MaxT, MinT and Rain, which were fitted as splines. In the models used to derive CG estimates for ADG, MaxT and MinT were significant variables. The models that contained these significant climatic variables had CG estimates with a lower variance compared to models without significant climatic variables. Variance component estimates were similar across all models, suggesting that these significant climatic variables accounted for some known environmental variation captured in CG estimates. No climatic variables were significant in the models used to derive the CG estimates for BF. These CG estimates were used to categorize environments. There was no observable sire by environment interaction (Sire×E) for ADG when using the environmental descriptors based on CG estimates on BF. For the environmental descriptors based on CG estimates of ADG, there was significant Sire×E only when MinT was included in the model (p = .01). Therefore, this new definition of the environment, preadjusted by MinT, increased the ability to detect Sire×E. While the unknown challenges captured in refined CG estimates need verification for infection challenges, this may provide a practical approach for the genetic improvement of disease resilience.

Use of multi-trait and random regression models to identify genetic variation in tolerance to porcine reproductive and respiratory syndrome virus

BACKGROUND

A host can adopt two response strategies to infection: resistance (reduce pathogen load) and tolerance (minimize impact of infection on performance). Both strategies may be under genetic control and could thus be targeted for genetic improvement. Although there is evidence that supports a genetic basis for resistance to porcine reproductive and respiratory syndrome (PRRS), it is not known whether pigs also differ genetically in tolerance. We determined to what extent pigs that have been shown to vary genetically in resistance to PRRS also exhibit genetic variation in tolerance. Multi-trait linear mixed models and random regression sire models were fitted to PRRS Host Genetics Consortium data from 1320 weaned pigs (offspring of 54 sires) that were experimentally infected with a virulent strain of PRRS virus to obtain genetic parameter estimates for resistance and tolerance. Resistance was defined as the inverse of within-host viral load (VL) from 0 to 21 (VL21) or 0 to 42 (VL42) days post-infection and tolerance as the slope of the reaction-norm of average daily gain (ADG21, ADG42) on VL21 or VL42.

RESULTS

Multi-trait analysis of ADG associated with either low or high VL was not indicative of genetic variation in tolerance. Similarly, random regression models for ADG21 and ADG42 with a tolerance slope fitted for each sire did not result in a better fit to the data than a model without genetic variation in tolerance. However, the distribution of data around average VL suggested possible confounding between level and slope estimates of the regression lines. Augmenting the data with simulated growth rates of non-infected half-sibs (ADG0) helped resolve this statistical confounding and indicated that genetic variation in tolerance to PRRS may exist if genetic correlations between ADG0 and ADG21 or ADG42 are low to moderate.

CONCLUSIONS

Evidence for genetic variation in tolerance of pigs to PRRS was weak when based on data from infected piglets only. However, simulations indicated that genetic variance in tolerance may exist and could be detected if comparable data on uninfected relatives were available. In conclusion, of the two defense strategies, genetics of tolerance is more difficult to elucidate than genetics of resistance.

Thermal sensitivity of growth indicates heritable variation in 1-year-old rainbow trout (Oncorhynchus mykiss)

BACKGROUND

Rainbow trout is an important aquaculture species, which has a worldwide distribution across various production environments. The diverse locations of trout farms involve remarkable variation in environmental factors such as water temperature, which is of major importance for the performance of fish. Thus, robust fish that could thrive under different and suboptimal thermal conditions is a desirable goal for trout breeding. Using a split-family experimental design (40 full-/half-sib groups) for a rainbow trout population derived from the Finnish national breeding program, we studied how two different rearing temperatures (14 and 20 °C) affect feed intake, growth rate and feed conversion ratio in 1-year-old fish. Furthermore, we quantified the additive genetic (co-)variation for daily growth coefficient (DGC) and its thermal sensitivity (TS), defined as the slope of the growth reaction norm between the two temperatures.

RESULTS

The fish showed consistently lower feed intake, faster growth and better feed conversion ratio at the lower temperature. Heritability of TS of DGC was moderate ([Formula: see text]). The co-heritability parameter derived from selection index theory, which describes the heritable variance of TS, was negative when the intercept was placed at the lower temperature (-0.28). This resulted in moderate accuracy of selection. At the higher temperature, co-heritability of TS was positive (0.20). The genetic correlation between DGC and its TS was strongly negative (-0.64) when the intercept was at the lower temperature and positive (0.38) but not significantly different from zero at the higher temperature.

CONCLUSIONS

The considerable amount of genetic variation in TS of growth indicates a potential for selection response and thus for targeted genetic improvement in TS. The negative genetic correlation between DGC and its TS suggests that selection for high growth rate at the lower temperature will result in more temperature-sensitive fish. Instead, the correlated response of TS is less pronounced if the selection for a higher DGC occurred at the higher temperature. It seems possible to control the correlated genetic change of TS while selecting for fast growth across environments, especially if measurements from both environments are available and breeding values for reaction norm slope are directly included in the selection index.

Genomic Selection Improves Response to Selection in Resilience by Exploiting Genotype by Environment Interactions

Genotype by environment interactions (GxE) are very common in livestock and hamper genetic improvement. On the other hand, GxE is a source of genetic variation: genetic variation in response to environment, e.g., environmental perturbations such as heat stress or disease. In livestock breeding, there is tendency to ignore GxE because of increased complexity of models for genetic evaluations and lack of accuracy in extreme environments. GxE, however, creates opportunities to increase resilience of animals toward environmental perturbations. The main aim of the paper is to investigate to which extent GxE can be exploited with traditional and genomic selection methods. Furthermore, we investigated the benefit of reaction norm (RN) models compared to conventional methods ignoring GxE. The questions were addressed with selection index theory. GxE was modeled according to a linear RN model in which the environmental gradient is the contemporary group mean. Economic values were based on linear and non-linear profit equations. Accuracies of environment-specific (G)EBV were highest in intermediate environments and lowest in extreme environments. RN models had higher accuracies of (G)EBV in extreme environments than conventional models ignoring GxE. Genomic selection always resulted in higher response to selection in all environments than sib or progeny testing schemes. The increase in response was with genomic selection between 9 and 140% compared to sib testing and between 11 and 114% compared to progeny testing when the reference population consisted of 1 million animals across all environments. When the aim was to decrease environmental sensitivity, the response in slope of the RN model with genomic selection was between 1.09 and 319 times larger than with sib or progeny testing and in the right direction in contrast to sib and progeny testing that still increased environmental sensitivity. This shows that genomic selection with large reference populations offers great opportunities to exploit GxE to increase resilience of animals.

Revealing new candidate genes for reproductive traits in pigs: combining Bayesian GWAS and functional pathways

Background

Reproductive traits such as number of stillborn piglets (SB) and number of teats (NT) have been evaluated in many genome-wide association studies (GWAS). Most of these GWAS were performed under the assumption that these traits were normally distributed. However, both SB and NT are discrete (e.g. count) variables. Therefore, it is necessary to test for better fit of other appropriate statistical models based on discrete distributions. In addition, although many GWAS have been performed, the biological meaning of the identified candidate genes, as well as their functional relationships still need to be better understood. Here, we performed and tested a Bayesian treatment of a GWAS model assuming a Poisson distribution for SB and NT in a commercial pig line. To explore the biological role of the genes that underlie SB and NT and identify the most likely candidate genes, we used the most significant single nucleotide polymorphisms (SNPs), to collect related genes and generated gene-transcription factor (TF) networks.

Results

Comparisons of the Poisson and Gaussian distributions showed that the Poisson model was appropriate for SB, while the Gaussian was appropriate for NT. The fitted GWAS models indicated 18 and 65 significant SNPs with one and nine quantitative trait locus (QTL) regions within which 18 and 57 related genes were identified for SB and NT, respectively. Based on the related TF, we selected the most representative TF for each trait and constructed a gene-TF network of gene-gene interactions and identified new candidate genes.

Conclusions

Our comparative analyses showed that the Poisson model presented the best fit for SB. Thus, to increase the accuracy of GWAS, counting models should be considered for this kind of trait. We identified multiple candidate genes (e.g. PTP4A2, NPHP1, and CYP24A1 for SB and YLPM1, SYNDIG1L, TGFB3, and VRTN for NT) and TF (e.g. NF-κB and KLF4 for SB and SOX9 and ELF5 for NT), which were consistent with known newborn survival traits (e.g. congenital heart disease in fetuses and kidney diseases and diabetes in the mother) and mammary gland biology (e.g. mammary gland development and body length).

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-016-0189-x) contains supplementary material, which is available to authorized users.

Selection for productivity and robustness traits in pigs

Pig breeding programs worldwide continue to focus on both productivity and robustness. This selection emphasis has to be accompanied by provision of better-quality environments to pigs to improve performance and to enhance health and welfare of pigs. Definition of broader breeding objectives that include robustness traits in addition to production traits is the first step in the development of selection strategies for productivity and robustness. An approach has been presented which facilitates extension of breeding objectives. Post-weaning survival, maternal genetic effects for growth as an indicator of health status and sow mature weight are examples of robustness traits. Further, breeding objectives should be defined for commercial environments and selection indexes should account for genotype by environment interactions (GxE). Average performances of groups of pigs have been used to quantify the additive effects of multiple environmental factors on performance of pigs. For growth, GxE existed when environments differed by 60 g/day between groups of pigs. This environmental variation was observed even on well managed farms. Selection for improved health of pigs should focus on disease resistance to indirectly reduce pathogen loads on farms and on disease resilience to improve the ability of pigs to cope with infection challenges. Traits defining disease resilience may be based on performance and immune measures, disease incidence or survival rates of pigs. Residual feed intake is a trait that quantifies feed efficiency. The responses of divergent selection lines for residual feed intake to various environmental challenges were often similar or even favourable for the more efficient, low residual feed intake line. These somewhat unexpected results highlight the need to gain a better understanding of the metabolic differences between more or less productive pigs. These physiological differences lead to interactions between the genetic potential of pigs for productivity and robustness and the prevalence of specific environmental conditions.