Why breed disease-resilient livestock, and how?

Decoding epigenetic markers: implications of traits and genes through DNA methylation in resilience and susceptibility to mastitis in dairy cows

Cattle farming faces challenges linked to intensive exploitation and climate change, requiring the reinforcement of animal resilience in response to these dynamic environments. Currently, genetic selection is used to enhance resilience by identifying animals resistant to specific diseases; however, certain diseases, such as mastitis, pose difficulties in genetic prediction. This study introduced the utilization of enzymatic methyl sequencing (EM-seq) of the blood genomic DNA from twelve dairy cows to identify DNA methylation biomarkers, with the aim of predicting resilience and susceptibility to mastitis. The analysis uncovered significant differences between cows resilient and susceptible to mastitis, with 196,275 differentially methylated cytosines (DMCs) and 1,227 Differentially Methylated Regions (DMRs). Key genes associated with the immune response and morphological traits, including ENOPH1, MYL10 and KIR2DL5A, were identified by our analysis. Quantitative trait loci (QTL) were also highlighted and the body weight trait was the most targeted by DMCs and DMRs. Based on our results, the risk of developing mastitis can potentially be estimated with as few as fifty methylation biomarkers, paving the way for early animal selection. This research sets the stage for improved animal health management and economic yields within the framework of agricultural sustainability through early selection based on the epigenetic status of animals.

Validating statistical properties of resilience indicators derived from simulated longitudinal performance measures of farmed animals

Resilience is commonly defined as the ability of an individual to be minimally affected or to quickly recover from a challenge. Improvement of animals' resilience is a vital component of sustainable livestock production but has so far been hampered by the lack of established quantitative resilience measures. Several studies proposed that summary statistics of the deviations of an animal's observed performance from its target performance trajectory (i.e., performance in the absence of challenge) may constitute suitable quantitative resilience indicators. However, these statistical indicators require further validation. The aim of this study was to obtain a better understanding of these resilience indicators in their ability to discriminate between different response types and their dependence on different response characteristics of animals, and data recording features. To this purpose, milk-yield trajectories of individual dairy cattle differing in resilience, without and when exposed to a short-term challenge, were simulated. Individuals were categorised into three broad response types (with individual variation within each type): Fully Resilient animals, which experience no systematic perturbation in milk yield after challenge, Non-Resilient animals whose milk yield permanently deviates from the target trajectory after challenge and Partially Resilient animals that experience temporary perturbations but recover. The following statistical resilience indicators previously suggested in the literature were validated with respect to their ability to discriminate between response types and their sensitivity to various response features and data characteristics: logarithm of mean of squares (LMS), logarithm of variance (LV), skewness (S), lag-1 autocorrelation (AC1), and area under the curve (AUC) of deviations. Furthermore, different methods for estimating unknown target trajectories were evaluated. All of the considered resilience indicators could distinguish between the Fully Resilient response type and either of the other two types when target trajectories were known or estimated using a parametric method. When the comparison was between Partially Resilient and Non-Resilient, only LMS, LV, and AUC could correctly rank the response types, provided that the observation period was at least twice as long as the perturbation period. Skewness was in general the least reliable indicator, although all indicators showed correct dependency on the amplitude and duration of the perturbations. In addition, all resilience indicators except for AC1 were robust to lower frequency of measurements. In general, parametric methods (quantile or repeated regression) combined with three resilience indicators (LMS, LV and AUC) were found the most reliable techniques for ranking animals in terms of their resilience.

Pathogen stimulations and immune cells synergistically affect the gene expression profile characteristics of porcine peripheral blood mononuclear cells

BACKGROUND

Pigs serve as a crucial source of protein in the human diet and play a fundamental role in ensuring food security. However, infectious diseases caused by bacteria or viruses are a major threat to effective global pig farming, jeopardizing human health. Peripheral blood mononuclear cells (PBMCs) are a mixture of immune cells that play crucial roles in immunity and disease resistance in pigs. Previous studies on the gene expression regulation patterns of PBMCs have concentrated on a single immune stimulus or immune cell subpopulation, which has limited our comprehensive understanding of the mechanisms of the pig immune response.

RESULTS

Here, we integrated and re-analyzed RNA-seq data published online for porcine PBMC stimulated by lipopolysaccharide (LPS), polyinosinic acid (PolyI:C), and various unknown microorganisms (EM). The results revealed that gene expression and its functional characterization are highly specific to the pathogen, identifying 603, 254, and 882 pathogen-specific genes and 38 shared genes, respectively. Notably, LPS and PolyI:C stimulation directly triggered inflammatory and immune-response pathways, while exposure to mixed microbes (EM) enhanced metabolic processes. These pathogen-specific genes were enriched in immune trait-associated quantitative trait loci (QTL) and eGenes in porcine immune tissues and were implicated in specific cell types. Furthermore, we discussed the roles of eQTLs rs3473322705 and rs1109431654 in regulating pathogen- and cell-specific genes CD300A and CD93, using cellular experiments. Additionally, by integrating genome-wide association studies datasets from 33 complex traits and diseases in humans, we found that pathogen-specific genes were significantly enriched for immune traits and metabolic diseases.

CONCLUSIONS

We systematically analyzed the gene expression profiles of the three stimulations and demonstrated pathogen-specific and cell-specific gene regulation across different stimulations in porcine PBMCs. These findings enhance our understanding of shared and distinct regulatory mechanisms of genetic variants in pig immune traits.

Expression genome-wide association study identifies key regulatory variants enriched with metabolic and immune functions in four porcine tissues

BACKGROUND

Integration of high throughput DNA genotyping and RNA-sequencing data enables the discovery of genomic regions that regulate gene expression, known as expression quantitative trait loci (eQTL). In pigs, efforts to date have been mainly focused on purebred lines for traits with commercial relevance as such growth and meat quality. However, little is known on genetic variants and mechanisms associated with the robustness of an animal, thus its overall health status. Here, the liver, lung, spleen, and muscle transcriptomes of 100 three-way crossbred female finishers were studied, with the aim of identifying novel eQTL regulatory regions and transcription factors (TFs) associated with regulation of porcine metabolism and health-related traits.

RESULTS

An expression genome-wide association study with 535,896 genotypes and the expression of 12,680 genes in liver, 13,310 genes in lung, 12,650 genes in spleen, and 12,595 genes in muscle resulted in 4,293, 10,630, 4,533, and 6,871 eQTL regions for each of these tissues, respectively. Although only a small fraction of the eQTLs were annotated as cis-eQTLs, these presented a higher number of polymorphisms per region and significantly stronger associations with their target gene compared to trans-eQTLs. Between 20 and 115 eQTL hotspots were identified across the four tissues. Interestingly, these were all enriched for immune-related biological processes. In spleen, two TFs were identified: ERF and ZNF45, with key roles in regulation of gene expression.

CONCLUSIONS

This study provides a comprehensive analysis with more than 26,000 eQTL regions identified that are now publicly available. The genomic regions and their variants were mostly associated with tissue-specific regulatory roles. However, some shared regions provide new insights into the complex regulation of genes and their interactions that are involved with important traits related to metabolism and immunity.

Dare to be resilient: the key to future pesticide-free orchards?

Considering the urgent need for more sustainable fruit tree production, it is high time to find durable alternatives to the systematic use of phytosanitary products in orchards. To this end, resilience can deliver a number of benefits. Relying on a combination of tolerance, resistance, and recovery traits, disease resilience appears as a cornerstone to cope with the multiple pest and disease challenges over an orchard's lifetime. Here, we describe resilience as the capacity of a tree to be minimally affected by external disturbances or to rapidly bounce back to normal functioning after being exposed to these disturbances. Based on a literature survey largely inspired from research on livestock, we highlight different approaches for dissecting phenotypic and genotypic components of resilience. In particular, multisite experimental designs and longitudinal measures of so-called 'resilience biomarkers' are required. We identified a list of promising biomarkers relying on ecophysiological and digital measurements. Recent advances in high-throughput phenotyping and genomics tools will likely facilitate fine scale temporal monitoring of tree health, allowing identification of resilient genotypes with the calculation of specific resilience indicators. Although resilience could be considered as a 'black box' trait, we demonstrate how it could become a realistic breeding goal.

Review: Environmental enrichment builds functional capacity and improves resilience as an aspect of positive welfare in production animals

The success of the animal in coping with challenges, and in harnessing opportunities to thrive, is central to its welfare. Functional capacity describes the capacity of molecules, cells, organs, body systems, the whole animal, and its community to buffer against the impacts of environmental perturbations. This buffering capacity determines the ability of the animal to maintain or regain functions in the face of environmental perturbations, which is recognised as resilience. The accuracy of physiological regulation and the maintenance of homeostatic balance underwrite the dynamic stability of outcomes such as biorhythms, feed intake, growth, milk yield, and egg production justifying their assessment as indicators of resilience. This narrative review examines the influence of environmental enrichments, especially during developmental stages in young animals, in building functional capacity and in its subsequent expression as resilience. Experience of enriched environments can build skills and competencies across multiple functional domains including but not limited to behaviour, immunity, and metabolism thereby increasing functional capacity and facilitating resilience within the context of challenges such as husbandry practices, social change, and infection. A quantitative method for measuring the distributed property of functional capacity may improve its assessment. Methods for analysing embedded energy (emergy) in ecosystems may have utility for this goal. We suggest functional capacity provides the common thread that links environmental enrichments with an ability to express resilience and may provide a novel and useful framework for measuring and reporting resilience. We conclude that the development of functional capacity and its subsequent expression as resilience is an aspect of positive animal welfare. The emergence of resilience from system dynamics highlights a need to shift from the study of physical and mental states to the study of physical and mental dynamics to describe the positive dimension of animal welfare.

The genetics of resilience and its relationships with egg production traits and antibody traits in chickens

BACKGROUND

Resilience is the capacity of an animal to be minimally affected by disturbances or to rapidly return to its initial state before exposure to a disturbance. Resilient livestock are desired because of their improved health and increased economic profit. Genetic improvement of resilience may also lead to trade-offs with production traits. Recently, resilience indicators based on longitudinal data have been suggested, but they need further evaluation to determine whether they are indeed predictive of improved resilience, such as disease resilience. This study investigated different resilience indicators based on deviations between expected and observed egg production (EP) by exploring their genetic parameters, their possible trade-offs with production traits, and their relationships with antibody traits in chickens.

METHODS

Egg production in a nucleus breeding herd environment based on 1-week-, 2-week-, or 3-week-intervals of two purebred chicken lines, a white egg-laying (33,825 chickens) and a brown egg-laying line (34,397 chickens), were used to determine deviations between observed EP and expected average batch EP, and between observed EP and expected individual EP. These deviations were used to calculate three types of resilience indicators for two life periods of each individual: natural logarithm-transformed variance (ln(variance)), skewness, and lag-one autocorrelation (autocorrelation) of deviations from 25 to 83 weeks of age and from 83 weeks of age to end of life. Then, we estimated their genetic correlations with EP traits and with two antibody traits.

RESULTS

The most promising resilience indicators were those based on 1-week-intervals, as they had the highest heritability estimates (0.02-0.12) and high genetic correlations (above 0.60) with the same resilience indicators based on longer intervals. The three types of resilience indicators differed genetically from each other, which indicates that they possibly capture different aspects of resilience. Genetic correlations of the resilience indicator traits based on 1-week-intervals with EP traits were favorable or zero, which means that trade-off effects were marginal. The resilience indicator traits based on 1-week-intervals also showed no genetic correlations with the antibody traits, which suggests that they are not informative for improved immunity or vice versa in the nucleus environment.

CONCLUSIONS

This paper gives direction towards the evaluation and implementation of resilience indicators, i.e. to further investigate resilience indicator traits based on 1-week-intervals, in breeding programs for selecting genetically more resilient layer chickens.

Investigating the relationship between fluctuations in daily milk yield as resilience indicators and health traits in Holstein cattle

Disease-related milk losses directly affect dairy herds' profitability and the production efficiency of the dairy industry. Therefore, this study aimed to quantify phenotypic variability in milk fluctuation periods related to diseases and to explore milk fluctuation traits as indicators of disease resilience. By combining high-frequency daily milk yield data with disease records of cows that were treated and recovered from the disease, we estimated milk variability trends within a fixed period around the treatment day of each record for 5 diseases: udder health, reproductive disorders, metabolic disorders, digestive disorders, and hoof health. The average milk yield decreased rapidly from 6 to 8 d before the treatment day for all diseases, with the largest milk reduction observed on the treatment day. Additionally, we assessed the significance of milk fluctuation periods highly related to diseases by defining milk fluctuations as a period of at least 10 consecutive days in which milk yield fell below 90% of the expected milk production values at least once. We defined the development and recovery phases of milk fluctuations using 3,847 milk fluctuation periods related to disease incidences, and estimated genetic parameters of milk fluctuation traits, including milk losses, duration of the fluctuation, variation rate in daily milk yield, and standard deviation of milk deviations for each phase and their genetic correlation with several important traits. In general, the disease-related milk fluctuation periods lasted 21.19 ± 10.36 d with a milk loss of 115.54 ± 92.49 kg per lactation. Compared with the development phase, the recovery phase lasted an average of 3.3 d longer, in which cows produced 11.04 kg less milk and exhibited a slower variation rate in daily milk yield of 0.35 kg/d. There were notable differences in milk fluctuation traits depending on the disease, and greater milk losses were observed when multiple diseases occurred simultaneously. All milk fluctuation traits evaluated were heritable with heritability estimates ranging from 0.01 to 0.10, and moderate to high genetic correlations with milk yield (0.34 to 0.64), milk loss throughout the lactation (0.22 to 0.97), and resilience indicator (0.39 to 0.95). These results indicate that cows with lower milk losses and higher resilience tend to have more stable milk fluctuations, which supports the potential for breeding for more disease-resilient cows based on milk fluctuation traits. Overall, this study confirms the high effect of diseases on milk yield variability and provides insightful information about their relationship with relevant traits in Holstein cattle. Furthermore, this study shows the potential of using high-frequency automatic monitoring of milk yield to assist on breeding practices and health management in dairy cows.

Identification and characterization of unknown disturbances in a structured population using high-throughput phenotyping data and measurement of robustness: application to growing pigs

Improving the robustness of animals has become a priority in breeding due to climate change, new societal demands, and the agroecological transition. Components of animal robustness can be extracted from the analysis of the adaptive response of an animal to disturbance using longitudinal data. Nonetheless, this response is a function of animal robustness as well as of disturbance characteristics (intensity and duration). To correctly assess an animal's robustness potential, it is therefore useful to know the characteristics of the disturbances it faces. The UpDown method, which detects and characterizes unknown disturbances at different levels of organization of the population (e.g., individual, pen, and batch disturbances), has been proposed for this purpose. Furthermore, using the outputs of the method, it is possible to extract proxies of the robustness of animals. In this context, the objective of the study was to evaluate the performances of the UpDown method to detect and characterize disturbances and quantify the robustness of animals in a genetic framework using different sets of simulations, and to apply this method to real pig longitudinal data recorded during the fattening period (body weight, cumulative feed intake, and feeding rate). Based on the simulations, the specificity of the UpDown method was high (>0.95). Its sensitivity increased with the level of organization exposed (from 0.23 to 0.32 for individual disturbances, from 0.45 to 0.59 for pen disturbances, and from 0.77 to 0.88 for batch disturbances). The UpDown method also showed a good ability to characterize detected disturbances. The average time interval between the estimated and true start date or duration of the disturbance was lower than 3 d. The correlation between the true and estimated intensity of the disturbance increased with the hierarchical level of organization (on average, 0.41, 0.78, and 0.83 for individual, pen, and batch disturbance, respectively). The accuracy of the estimated breeding values of the proxies for robustness extracted from the analysis of individual trajectories over time were moderate (lower than 0.33). Applied to real data, the UpDown method detected different disturbances depending on the phenotype analyzed. The heritability of the proxies of robustness were low to moderate (ranging from 0.11 to 0.20).

Unravelling the genetic variability of host resilience to endo- and ectoparasites in Nellore commercial herds

BACKGROUND

Host resilience (HR) to parasites can affect the performance of animals. Therefore, the aim of this study was to present a detailed investigation of the genetic mechanisms of HR to ticks (TICK), gastrointestinal nematodes (GIN), and Eimeria spp. (EIM) in Nellore cattle that were raised under natural infestation and a prophylactic parasite control strategy. In our study, HR was defined as the slope coefficient of body weight (BW) when TICK, GIN, and EIM burdens were used as environmental gradients in random regression models. In total, 1712 animals were evaluated at five measurement events (ME) at an average age of 331, 385, 443, 498, and 555 days, which generated 7307 body weight (BW) records. Of the 1712 animals, 1075 genotyped animals were used in genome-wide association studies to identify genomic regions associated with HR.

RESULTS

Posterior means of the heritability estimates for BW ranged from 0.09 to 0.54 across parasites and ME. The single nucleotide polymorphism (SNP)-derived heritability for BW at each ME ranged from a low (0.09 at ME.331) to a moderate value (0.23 at ME.555). Those estimates show that genetic progress can be achieved for BW through selection. Both genetic and genomic associations between BW and HR to TICK, GIN, and EIM confirmed that parasite infestation impacted the performance of animals. Selection for BW under an environment with a controlled parasite burden is an alternative to improve both, BW and HR. There was no impact of age of measurement on the estimates of genetic variance for HR. Five quantitative trait loci (QTL) were associated with HR to EIM but none with HR to TICK and to GIN. These QTL contain genes that were previously shown to be associated with the production of antibody modulators and chemokines that are released in the intestinal epithelium.

CONCLUSIONS

Selection for BW under natural infestation and controlled parasite burden, via prophylactic parasite control, contributes to the identification of animals that are resilient to nematodes and Eimeria ssp. Although we verified that sufficient genetic variation existed for HR, we did not find any genes associated with mechanisms that could justify the expression of HR to TICK and GIN.

Disentangling the dynamics of energy allocation to develop a proxy for robustness of fattening pigs

BACKGROUND

There is a growing need to improve robustness of fattening pigs, but this trait is difficult to phenotype. Our first objective was to develop a proxy for robustness of fattening pigs by modelling the longitudinal energy allocation coefficient to growth, with the resulting environmental variance of this allocation coefficient considered as a proxy for robustness. The second objective was to estimate its genetic parameters and correlations with traits under selection and with phenotypes that are routinely collected. In total, 5848 pigs from a Pietrain NN paternal line were tested at the AXIOM boar testing station (Azay-sur-Indre, France) from 2015 to 2022. This farm is equipped with an automatic feeding system that records individual weight and feed intake at each visit. We used a dynamic linear regression model to characterize the evolution of the allocation coefficient between the available cumulative net energy, which was estimated from feed intake, and cumulative weight gain during the fattening period. Longitudinal energy allocation coefficients were analysed using a two-step approach to estimate both the genetic variance of the coefficients and the genetic variance in their residual variance, which will be referred to as the log-transformed squared residual (LSR).

RESULTS

The LSR trait, which could be interpreted as an indicator of the response of the animal to perturbations/stress, showed a low heritability (0.05 ± 0.01), a high favourable genetic correlation with average daily growth (- 0.71 ± 0.06), and unfavourable genetic correlations with feed conversion ratio (- 0.76 ± 0.06) and residual feed intake (- 0.83 ± 0.06). Segmentation of the population in four classes using estimated breeding values for LSR showed that animals with the lowest estimated breeding values were those with the worst values for phenotypic proxies of robustness, which were assessed using records routinely collected on farm.

CONCLUSIONS

Results of this study show that selection for robustness, based on estimated breeding values for environmental variance of the allocation coefficients to growth, can be considered in breeding programs for fattening pigs.

Creating resistance to avian influenza infection through genome editing of the ANP32 gene family

Chickens genetically resistant to avian influenza could prevent future outbreaks. In chickens, influenza A virus (IAV) relies on host protein ANP32A. Here we use CRISPR/Cas9 to generate homozygous gene edited (GE) chickens containing two ANP32A amino acid substitutions that prevent viral polymerase interaction. After IAV challenge, 9/10 edited chickens remain uninfected. Challenge with a higher dose, however, led to breakthrough infections. Breakthrough IAV virus contained IAV polymerase gene mutations that conferred adaptation to the edited chicken ANP32A. Unexpectedly, this virus also replicated in chicken embryos edited to remove the entire ANP32A gene and instead co-opted alternative ANP32 protein family members, chicken ANP32B and ANP32E. Additional genome editing for removal of ANP32B and ANP32E eliminated all viral growth in chicken cells. Our data illustrate a first proof of concept step to generate IAV-resistant chickens and show that multiple genetic modifications will be required to curtail viral escape.

Animal board invited review: Quantification of resilience in farm animals

Resilience, when defined as the capacity of an animal to respond to short-term environmental challenges and to return to the prechallenge status, is a dynamic and complex trait. Resilient animals can reinforce the capacity of the herd to cope with often fluctuating and unpredictable environmental conditions. The ability of modern technologies to simultaneously record multiple performance measures of individual animals over time is a huge step forward to evaluate the resilience of farm animals. However, resilience is not directly measurable and requires mathematical models with biologically meaningful parameters to obtain quantitative resilience indicators. Furthermore, interpretive models may also be needed to determine the periods of perturbation as perceived by the animal. These applications do not require explicit knowledge of the origin of the perturbations and are developed based on real-time information obtained in the data during and outside the perturbation period. The main objective of this paper was to review and illustrate with examples, different modelling approaches applied to this new generation of data (i.e., with high-frequency recording) to detect and quantify animal responses to perturbations. Case studies were developed to illustrate alternative approaches to real-time and post-treatment of data. In addition, perspectives on the use of hybrid models for better understanding and predicting animal resilience are presented. Quantification of resilience at the individual level makes possible the inclusion of this trait into future breeding programmes. This would allow improvement of the capacity of animals to adapt to a changing environment, and therefore potentially reduce the impact of disease and other environmental stressors on animal welfare. Moreover, such quantification allows the farmer to tailor the management strategy to help individual animals to cope with the perturbation, hence reducing the use of pharmaceuticals, and decreasing the level of pain of the animal.

Porcine Reproductive and Respiratory Syndrome (PRRS) and CD163 Resistance Polymorphic Markers: What Is the Scenario in Naturally Infected Pig Livestock in Central Italy?

Porcine Reproductive and Respiratory Syndrome (PRRS) caused by the PRRS virus affects farmed pigs worldwide, causing direct and indirect losses. The most severe manifestations of PRRS infection are observed in piglets and pregnant sows. The clinical outcome of the infection depends on the PRRSV strain's virulence, the pregnancy state of the female, environmental factors, the presence of protective antibodies due to previous infections, and the host's genetic susceptibility. The latter aspect was investigated in this study, in particular, evaluating the most significant polymorphisms (SNPs) of the CD163 gene in slaughtered pigs reared in Central Italy. Total RNAs were extracted from 377 swine samples and subjected to RT-PCR targeted to the CD163 gene, followed by sequencing analysis. Contextually, the viral RNA was detected by RT-qPCR in order to phenotypically categorize animals into infected and not infected. In particular, 36 haplotypes were found, and their frequencies ranged from 0.13% to 35.15%. There were 62 resulting genotypes, three of which were associated with a putative resistance to the disease. Both the haplotypes and genotypes were inferred by PHASE v.2.1 software. To the best of our knowledge, this type of investigation was conducted for the first time on pig livestock distributed in different regions of Central Italy. Thus, the obtained findings may be considered very important since they add useful information about swine genetic background in relation to PRRS infection, from the perspective of adopting Marker-Assisted Selection (MAS) as a possible and alternative strategy to control this still widespread disease.

The WUR0000125 PRRS resilience SNP had no apparent effect on pigs' infectivity and susceptibility in a novel transmission trial

BACKGROUND

Porcine reproductive and respiratory syndrome (PRRS) remains one of the most important infectious diseases for the pig industry. A novel small-scale transmission experiment was designed to assess whether the WUR0000125 (WUR for Wageningen University and Research) PRRS resilience single nucleotide polymorphism (SNP) confers lower susceptibility and infectivity to pigs under natural porcine reproductive and respiratory syndrome virus (PRRSV-2) transmission.

METHODS

Commercial full- and half-sib piglets (n = 164) were assigned as either Inoculation, Shedder, or Contact pigs. Pigs were grouped according to their relatedness structure and WUR genotype, with R- and R+ referring to pigs with zero and one copy of the dominant WUR resilience allele, respectively. Barcoding of the PRRSV-2 strain (SD09-200) was applied to track pig genotype-specific transmission. Blood and nasal swab samples were collected and concentrations of PRRSV-2 were determined by quantitative (q)-PCR and cell culture and expressed in units of median tissue culture infectious dose (TCID₅₀). The Log₁₀TCID₅₀ at each sampling event, derived infection status, and area under the curve (AUC) were response variables in linear and generalized linear mixed models to infer WUR genotype differences in Contact pig susceptibility and Shedder pig infectivity.

RESULTS

All Shedder and Contact pigs, except one, became infected through natural transmission. There was no significant (p > 0.05) effect of Contact pig genotype on any virus measures that would indicate WUR genotype differences in susceptibility. Contact pigs tended to have higher serum AUC (p = 0.017) and log₁₀TCID₅₀ (p = 0.034) when infected by an R+ shedder, potentially due to more infectious R+ shedders at the early stages of the transmission trial. However, no significant Shedder genotype effect was found in serum (p = 0.274) or nasal secretion (p = 0.951) that would indicate genotype differences in infectivity.

CONCLUSIONS

The novel design demonstrated that it is possible to estimate genotype effects on Shedder pig infectivity and Contact pig susceptibility that are not confounded by family effects. The study, however, provided no supportive evidence that genetic selection on WUR genotype would affect PRRSV-2 transmission. The results of this study need to be independently validated in a larger trial using different PRRSV strains before dismissing the effects of the WUR marker or the previously detected GBP5 gene on PRRSV transmission.

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.

Prevalence and Risk Factors Associated with Theileria annulata Infection in Two Bovine Portuguese Autochthonous Breeds

Tropical Bovine Theileriosis is an important tick-borne disease. This study aims to assess the occurrence of Theileria annulata infection in two indigenous Portuguese cattle breeds. A total of 843 blood samples collected from animals of Alentejana (n = 420) and Mertolenga (n = 423) breeds were analyzed. The detection of Theileria annulata was determined by amplification of a fragment of the merozoite-pyroplasm surface antigen gene with 319 base pairs (bp). The prevalence found (10.8%) is lower than that reported in previous studies (21.3%). A statistically significant difference was found for positivity between breeds (p < 0.05). There is also a higher probability of older animals being positive compared to younger ones (p < 0.05). The region where Mertolenga animals are located is shown to have a significant impact on positivity (p < 0.05). Thus, the development of sustainable T. annulata control strategies and their implementation, adapted to the epidemiological conditions of higher risk, will be extremely important.

Genetic parameters for resistance to gastrointestinal nematodes in sheep: a meta-analysis

Gastrointestinal nematodes (GINs) are damaging parasites of global sheep populations. The key weapons in fighting GINs have been anthelmintic drugs, but the emergence of drug-resistant parasites has meant that alternative control methods are needed. One of these alternatives is to breed for enhanced host resistance to GINs, and decades of research have estimated the genetic contribution to different measures of resistance to GINs and their genetic correlations with other desirable performance traits. It is clear that parasite resistance is a heritable trait that can be selected for. Despite this consensus, estimates of both heritability of resistance and genetic correlations with other traits vary widely between studies, and the reasons for this variation have not been examined. This study provides a comprehensive and quantitative meta-analysis of genetic parameters for resistance to GINs in sheep, including measures of worm burden (faecal egg counts, FECs), anti-parasite immunity (GIN-specific antibodies), and parasite-induced pathology (FAMACHA© scores). Analysis of 591 heritability estimates from 121 studies revealed a global heritability estimate for resistance to GINs of 0.25 (95% confidence interval (CI) = 0.22-0.27) that was stable across breeds, ages, geographical location and analytical methods. Meanwhile, analysis of 559 genetic correlations from 54 studies revealed that resistance to GINs overall has a positive genetic correlation of +0.10 (95% CI = 0.02-0.19) with performance traits, and that this was consistent across breeds, ages, sexes and analytical methods. Importantly, the direction of the genetic correlation varied with the resistance trait measured: while FECs and FAMACHA© scores were favourably correlated with performance traits, adaptive immune markers were unfavourably correlated, suggesting that selection for enhanced immune responses to GINs could reduce animal performance. Overall, the results suggest that breeding for resistance to GINs should continue to form part of integrated management programs to reduce the impact of parasites on health and performance, but that selection for enhanced immune responses should be avoided.

Genetic and phenotypic correlations between Aleutian disease tests with body weight, growth, and feed efficiency traits in mink

The ineffectiveness of vaccination, medicine, and culling strategy leads mink farmers to control Aleutian disease (AD) by selecting AD-resilient mink based on AD tests. However, the genetic background of AD tests and their correlations with economically important or AD-resilient traits are limited. This study estimated the genetic and phenotypic correlations between four AD tests and seven body weight (BW) traits, six growth parameters from the Richards growth model, and eight feed-related traits. Univariate models were used to test the significance (P < 0.05) of fixed effects (sex, color type, AD test year, birth year, and row-by-year), random effects (additive genetic, maternal genetic, and permanent environmental), and a covariate of age using ASReml 4.1. Likewise, pairwise bivariate analyses were conducted to estimate the phenotypic and genetic correlations among the studied traits. Both antigen- and virus capsid protein-based enzyme-linked immunosorbent assay tests (ELISA-G and ELISA-P) showed significant (P < 0.05) moderate positive genetic correlations (±SE) with maturation rate (from 0.36 ± 0.18 to 0.38 ± 0.19). ELISA-G showed a significant negative genetic correlation (±SE) with average daily gain (ADG, -0.37 ± 0.16). ELISA-P showed a significant positive moderate genetic correlation (±SE) with off-feed days (DOF, 0.42 ± 0.17). These findings indicated that selection for low ELISA scores would reduce the maturation rate, increase ADG (by ELISA-G), and minimize DOF (by ELISA-P). The iodine agglutination test (IAT) showed significant genetic correlations with DOF (0.73 ± 0.16), BW at 16 weeks of age (BW16, 0.45 ± 0.23), and BW at harvest (HW, -0.47 ± 0.20), indicating that selection for lower IAT scores would lead to lower DOF and BW16, and higher HW. These estimated genetic correlations suggested that the selection of AD tests would not cause adverse effects on the growth, feed efficiency, and feed intake of mink. The estimates from this study might strengthen the previous finding that ELISA-G could be applied as a reliable and practical indicator trait in the genetic selection of AD-resilient mink in AD-positive farms.

Can breeders prevent pathogen adaptation when selecting for increased resistance to infectious diseases?

Background

Recent research shows that genetic selection has high potential to reduce the prevalence of infectious diseases in livestock. However, like all interventions that target infectious diseases, genetic selection of livestock can exert selection pressure on pathogen populations. Such selection on the pathogen may lead to escape strategies and reduce the effect of selection of livestock for disease resistance. Thus, to successfully breed livestock for lower disease prevalence, it is essential to develop strategies that prevent the invasion of pathogen mutants that escape host resistance. Here we investigate the conditions under which such “escape mutants” can replace wild-type pathogens in a closed livestock population using a mathematical model of disease transmission.

Results

Assuming a single gene that confers sufficient resistance, results show that genetic selection for resistance in livestock typically leads to an “invasion window” within which an escape mutant of the pathogen can invade. The bounds of the invasion window are determined by the frequency of resistant hosts in the population. The lower bound occurs when the escape mutant has an advantage over the wild-type pathogen in the population. The upper bound occurs when local eradication of the pathogen is expected. The invasion window is smallest when host resistance is strong and when infection with the wild-type pathogen provides cross immunity to infection with the escape mutant.

Conclusions

To minimise opportunities for pathogens to adapt, under the assumptions of our model, the aim of disease control through genetic selection should be to achieve herd-level eradication of the infection faster than the rate of emergence of escape mutants of the pathogen. Especially for microparasitic infections, this could be achieved by placing animals into herds according to their genetic resistance, such that these herds stay completely out of the invasion window. In contrast to classical breeding theory, our model suggests that multi-trait selection with gradual improvement of each trait of the breeding goal might not be the best strategy when resistance to infectious disease is part of the breeding goal. Temporally, combining genetic selection with other interventions helps to make the invasion window smaller, and thereby reduces the risk of invasion of escape mutants.

Genetic Resistance of Bovines to Theileriosis

Diseases caused by ticks have a high impact on the health, welfare, and productivity of livestock species. They are also an important cause of economic losses in farms worldwide. An example of such diseases is theileriosis, which can be controlled by drugs or vaccines, although these are not fully efficient. Therefore, there is a need to develop alternative and more sustainable and efficient complementary strategies. These may involve the identification and selection of animals more resistant to the disease. Several previous studies have identified significant differences in resistance between different breeds, with resistant breeds typically identified as those native to the region where they are being studied, and susceptible as those from exotic breeds. These studies have indicated that resistance traits are intrinsically related to the modulation of the immune response to infection. This review aims to systematize the general knowledge about theileriosis, emphasize resistance to this disease as a sustainable control strategy, and identify which traits of resistance to the disease are already known in cattle.

Observational study on associations between resilience indicators based on daily milk yield in first lactation and lifetime profitability

Resilience is the ability of cows to be minimally affected by disturbances, such as pathogens, heat waves, and changes in feed quality, or to quickly recover. Obvious advantages of resilience are good animal welfare and easy and pleasant management for farmers. Furthermore, economic effects are also expected, but these remain to be determined. The goal of this study was to investigate the association between resilience and lifetime gross margin, using indicators of resilience calculated from fluctuations in daily milk yield using an observational study. Resilience indicators and lifetime gross margin were calculated for 1,325 cows from 21 herds. These cows were not alive anymore and, therefore, had complete lifetime data available for many traits. The resilience indicators were the natural log-transformed variance (LnVar) and the lag-1 autocorrelation (r_auto) of daily milk yield deviations from cow-specific lactation curves in parity 1. Good resilience is indicated by low LnVar (small yield response to disturbances) and low r_auto (quick yield recovery to baseline). Lifetime gross margin was calculated as the sum of all revenues minus the sum of all costs throughout life. Included revenues were from milk, calf value, and slaughter of the cow. Included costs were from feed, rearing, insemination, management around calving, disease treatments, and destruction in case of death on farm. Feed intake was unknown and, therefore, lifetime feed costs had to be estimated based on milk yield records. The association of each resilience indicator with lifetime gross margin, and also with the underlying revenues and costs, was investigated using analysis of covariance (ANCOVA) models. Mean daily milk yield in first lactation, herd, and year of birth were included as covariates and factors. Natural log-transformed variance had a significantly negative association with lifetime gross margin, which means that cows with stable milk yield (low LnVar, good resilience) in parity 1 generated on average a higher lifetime gross margin than cows that had the same milk yield level but with more fluctuations. The association with lifetime gross margin could be mainly attributed to higher lifetime milk revenues for cows with low LnVar, due to a longer lifespan. Unlike LnVar, r_auto was not significantly associated with lifetime gross margin or any of the underlying lifetime costs and revenues. However, it was significantly associated with yearly treatment costs, which is important for ease of management. In conclusion, the importance of resilience for total profit generated by a cow at the end of life was confirmed by the significant association of LnVar with lifetime gross margin, although effects of differences in feed efficiency between resilient and less resilient cows remain to be studied. The economic advantage can be mainly ascribed to benefits of long lifespan.

Invited review: strategic adoption of antibiotic-free pork production: the importance of a holistic approach

The discovery of the use of antibiotics to enhance growth in the 1950s proved to be one of the most dramatic and influential in the history of animal agriculture. Antibiotics have served animal agriculture, as well as human and animal medicine, well for more than seven decades, but emerging from this tremendous success has been the phenomenon of antimicrobial resistance. Consequently, human medicine and animal agriculture are being called upon, through legislation and/or marketplace demands, to reduce or eliminate antibiotics as growth promotants and even as therapeutics. As explained in this review, adoption of antibiotic-free (ABF) pork production would represent a sea change. By identifying key areas requiring attention, the clear message of this review is that success with ABF production, also referred to as "no antibiotics ever," demands a multifaceted and multidisciplinary approach. Too frequently, the topic has been approached in a piecemeal fashion by considering only one aspect of production, such as the use of certain feed additives or the adjustment in health management. Based on the literature and on practical experience, a more holistic approach is essential. It will require the modification of diet formulations to not only provide essential nutrients and energy, but to also maximize the effectiveness of normal immunological and physiological capabilities that support good health. It must also include the selection of effective non-antibiotic feed additives along with functional ingredients that have been shown to improve the utility and architecture of the gastrointestinal tract, to improve the microbiome, and to support the immune system. This holistic approach will require refining animal management strategies, including selection for more robust genetics, greater focus on care during the particularly sensitive perinatal and post-weaning periods, and practices that minimize social and environmental stressors. A clear strategy is needed to reduce pathogen load in the barn, such as greater emphasis on hygiene and biosecurity, adoption of a strategic vaccine program and the universal adoption of all-in-all-out housing. Of course, overall health management of the herd, as well as the details of animal flows, cannot be ignored. These management areas will support the basic biology of the pig in avoiding or, where necessary, overcoming pathogen challenges without the need for antibiotics, or at least with reduced usage.

A high-throughput study of visceral organs in CT-scanned pigs

It has been debated whether intensive selection for growth and carcass yield in pig breeding programmes can affect the size of internal organs, and thereby reduce the animal’s ability to handle stress and increase the risk of sudden deaths. To explore the respiratory and circulatory system in pigs, a deep learning based computational pipeline was built to extract the size of lungs and hearts from CT-scan images. This pipeline was applied on CT images from 11,000 boar selection candidates acquired during the last decade. Further, heart and lung volumes were analysed genetically and correlated with production traits. Both heart and lung volumes were heritable, with h² estimated to 0.35 and 0.34, respectively, in Landrace, and 0.28 and 0.4 in Duroc. Both volumes were positively correlated with lean meat percentage, and lung volume was negatively genetically correlated with growth (r_g = − 0.48 ± 0.07 for Landrace and r_g = − 0.44 ± 0.07 for Duroc). The main findings suggest that the current pig breeding programs could, as an indirect response to selection, affect the size of hearts- and lungs. The presented methods can be used to monitor the development of internal organs in the future.

Research Progress in the Early Warning of Chicken Diseases by Monitoring Clinical Symptoms

Global animal protein consumption has been steadily increasing as a result of population growth and the increasing demand for nutritious diets. The poultry industry provides a large portion of meat and eggs for human consumption. The early detection and warning of poultry infectious diseases play a critical role in the poultry breeding and production systems, improving animal welfare and reducing losses. However, inadequate methods for the early detection and prevention of infectious diseases in poultry farms sometimes fail to prevent decreased productivity and even widespread mortality. The health status of poultry is often reflected by its individual physiological, physical and behavioral clinical symptoms, such as higher body temperature resulting from fever, abnormal vocalization caused by respiratory disease and abnormal behaviors due to pathogenic infection. Therefore, the use of technologies for symptom detection can monitor the health status of broilers and laying hens in a continuous, noninvasive and automated way, and potentially assist in the early warning decision-making process. This review summarized recent literature on poultry disease detection and highlighted clinical symptom-monitoring technologies for sick poultry. The review concluded that current technologies are already showing their superiority to manual inspection, but the clinical symptom-based monitoring systems have not been fully utilized for on-farm early detection.

Robustness scores in fattening pigs based on routinely collected phenotypes: determination and genetic parameters

The objective was to determine operational proxies for robustness based on data collected routinely on farm that allow phenotyping of these traits in fattening pigs, and to estimate their genetic parameters. A total of 7,256 pigs, from two Piétrain paternal lines (Pie and Pie NN), were tested at the AXIOM boar testing station (Azay-sur-Indre, France) from 2019 to 2021. During the fattening period (from 75 to 150 d of age), individual performance indicators were recorded (growth, backfat, loin depth, feed intake, and feed conversion ratio [FCR]) together with indicators such as insufficient growth, observable defect, symptoms of diseases, and antibiotic and anti-inflammatory injections. These indicators were combined into three categorical robustness scores: R1, R2, and R3. Genetic parameters were estimated using an animal linear model. The robustness score R2 (selectable or not selectable animal) that combined information from status at testing and mortality had the highest heritability estimates of 0.08 ± 0.03 for Pie NN line and a value of 0.09 ± 0.02 for Pie line, compared with traits R1 and R3. The score R3 that combines information from the score R2 with antibiotic and anti-inflammatory injections presented slightly lower heritability estimates (0.05 ± 0.02 to 0.07 ± 0.03). Genetic correlations between R2 and R3 were high and favorable (0.93 ± 0.04 to 0.95 ± 0.03) and R2 and R3 can be considered identical with regard to the confidence interval. These two robustness scores were also highly and favorably genetically correlated with initial body weight and average daily gain, and unfavorably correlated with daily feed intake (ranging from 0.73 ± 0.06 to 0.90 ± 0.08). Estimates of genetic correlations of R2 and R3 with backfat depth and raw FCR (not standardized between starting and finishing weights) were moderate and unfavorable (0.20 ± 0.13 to 0.46 ± 0.20). A part of these genetic correlations, that are of low precision due to the number of data available, have to be confirmed on larger datasets. The results showed the interest of using routine phenotypes collected on farm to build simple robustness indicators that can be applied in breeding.

Animal Board Invited Review: Meta-analysis of genetic parameters for resilience and efficiency traits in goats and sheep

Genetic selection focused purely on production traits has proven very successful in improving the productive performance of livestock. However, heightened environmental and infectious disease challenges have raised the need to also improve the resilience of animals to such external stressors, as well as their efficiency in utilising available resources. A better understanding of the relationship between efficiency and production and health traits is needed to properly account for it in breeding programmes and to produce animals that can maintain high production performance in a range of environmental conditions with minimal environmental footprint. The aim of this study was to perform a meta-analysis of genetic parameters for production, efficiency and health traits in sheep and goats. The dataset comprised 963 estimates of heritability and 572 genetic correlations collated from 162 published studies. A threelevel meta-analysis model was fitted. Pooled heritability estimates for milk production traits ranged between 0.27 ± 0.03 and 0.48 ± 0.13 in dairy goats and between 0.21 ± 0.06 and 0.33 ± 0.07 in dairy sheep. In meat sheep, the heritability of efficiency traits ranged from 0.09 ± 0.02 (prolificacy) up to 0.32 ± 0.14 (residual feed intake). For health traits, pooled heritability was 0.07 ± 0.01 (faecal egg count) and 0.21 ± 0.01 (somatic cell score) in dairy goats and 0.14 ± 0.04 (faecal egg count) and 0.13 ± 0.02 (somatic cell score) in dairy sheep. In meat sheep, the heritability of disease resistance and survival traits ranged between 0.07 ± 0.02 (mastitis) and 0.50 ± 0.10 (breech strike). Pooled estimates of genetic correlations between resilience and efficiency traits in dairy goats were not significantly different from zero with the exception of somatic cell score and fat content (-0.19 ± 0.01). In dairy sheep, only the unfavourable genetic correlation between somatic cell score and protein content (0.12 ± 0.03) was statistically significant. In meat sheep only, the correlations between growth and faecal egg count (-0.28 ± 0.11) as well as between growth and dagginess (-0.33 ± 0.13) were statistically significant and favourable. Results of this meta-analysis provide evidence of genetic antagonism between production and health in dairy sheep and goats. This was not observed in meat sheep where most of the pooled estimates had high standard errors and were non-significant. Based on the obtained results, it seems feasible to simultaneously improve efficiency and health in addition to production by including the different types of traits in the breeding goal. However, a better understanding of potential trade-offs between these traits would be beneficial. Particularly, more studies focused on reproduction and resilience traits linked to the animal's multi-trait response to challenges are required.

Breeding for disease resilience: opportunities to manage polymicrobial challenge and improve commercial performance in the pig industry

Disease resilience, defined as an animal's ability to maintain productive performance in the face of infection, provides opportunities to manage the polymicrobial challenge common in pig production. Disease resilience can deliver a number of benefits, including more sustainable production as well as improved animal health and the potential for reduced antimicrobial use. However, little progress has been made to date in the application of disease resilience in breeding programs due to a number of factors, including (1) confusion around definitions of disease resilience and its component traits disease resistance and tolerance, and (2) the difficulty in characterizing such a complex trait consisting of multiple biological functions and dynamic elements of rates of response and recovery from infection. Accordingly, this review refines the definitions of disease resistance, tolerance, and resilience based on previous studies to help improve the understanding and application of these breeding goals and traits under different scenarios. We also describe and summarize results from a "natural disease challenge model" designed to provide inputs for selection of disease resilience. The next steps for managing polymicrobial challenges faced by the pig industry will include the development of large-scale multi-omics data, new phenotyping technologies, and mathematical and statistical methods adapted to these data. Genome editing to produce pigs resistant to major diseases may complement selection for disease resilience along with continued efforts in the more traditional areas of biosecurity, vaccination and treatment. Altogether genomic approaches provide exciting opportunities for the pig industry to overcome the challenges provided by hard-to-manage diseases as well as new environmental challenges associated with climate change.

The quantitative genetics of the prevalence of infectious diseases: hidden genetic variation due to indirect genetic effects dominates heritable variation and response to selection

Infectious diseases have profound effects on life, both in nature and agriculture. However, a quantitative genetic theory of the host population for the endemic prevalence of infectious diseases is almost entirely lacking. While several studies have demonstrated the relevance of transmission of infections for heritable variation and response to selection, current quantitative genetics ignores transmission. Thus, we lack concepts of breeding value and heritable variation for endemic prevalence, and poorly understand response of endemic prevalence to selection. Here, we integrate quantitative genetics and epidemiology, and propose a quantitative genetic theory for the basic reproduction number R0 and for the endemic prevalence of an infection. We first identify the genetic factors that determine the prevalence. Subsequently, we investigate the population-level consequences of individual genetic variation, for both R0 and the endemic prevalence. Next, we present expressions for the breeding value and heritable variation, for endemic prevalence and individual binary disease status, and show that these depend strongly on the prevalence. Results show that heritable variation for endemic prevalence is substantially greater than currently believed, and increases strongly when prevalence decreases, while heritability of disease status approaches zero. As a consequence, response of the endemic prevalence to selection for lower disease status accelerates considerably when prevalence decreases, in contrast to classical predictions. Finally, we show that most heritable variation for the endemic prevalence is hidden in indirect genetic effects, suggesting a key role for kin-group selection in the evolutionary history of current populations and for genetic improvement in animals and plants.

Review: Livestock disease resilience: from individual to herd level

Infectious diseases are a major threat to the sustainable production of high-producing animals. Control efforts, such as vaccination or breeding approaches often target improvements to individual resilience to infections, i.e., they strengthen an animal's ability to cope with infection, rather than preventing infection per se. There is increasing evidence for the contribution of non-clinical carriers (animals that become infected and are infectious but do not develop clinical signs) to the overall health and production of livestock populations for a wide range of infectious diseases. Therefore, we strongly advocate a shift of focus from increasing the disease resilience of individual animals to herd disease resilience as the appropriate target for sustainable disease control in livestock. Herd disease resilience not only captures the direct effects of vaccination or host genetics on the health and production performance of individuals but also the indirect effects on the environmental pathogen load that herd members are exposed to. For diseases primarily caused by infectious pathogens shed by herd members, these indirect effects on herd resilience are mediated both by individual susceptibility to infection and by characteristics (magnitude of infectiousness, duration of infectious period) that influence pathogen shedding from infected individuals. We review what is currently known about how vaccination and selective breeding affect herd disease resilience and its underlying components, and outline the changes required for improvement. To this purpose, we also seek to clarify and harmonise the terminology used in the different animal science disciplines to facilitate future collaborative approaches to infectious disease control in livestock.

Breeding Strategies for Weather Resilience in Small Ruminants in Atlantic and Mediterranean Climates

Many efforts are being made to cope with negative consequences of climate change (CC) on livestock. Among them, selective breeding of resilient animals to CC is presented as an opportunity to maintain high levels of performance regardless of variation in weather. In the present work, we proposed a set of breeding strategies to improve weather resilience in dairy goats raised in north-western European Atlantic conditions and dairy sheep raised in Mediterranean conditions while improving production efficiency at the same time. Breeding strategies differed in the selection emphasis placed on resilience traits, ranging from 0 to 40% in the index. Simulations were carried out mimicking real breeding programs including: milk yield, length of productive life, age at first kidding and mastitis incidence in dairy goats and milk, fat and protein yields, and fertility for dairy sheep. Considering the particular climatic conditions in the two regions, the predicted future climate scenarios, and genetic correlations among breeding objectives, resilience was defined as stability to weather changes for dairy goats and as the ability to improve performance under heat stress for dairy sheep. A strategy giving a selection weight of 10 and 20% for goat and sheep resilience, respectively, resulted in the best overall genetic response in terms of both, production and resilience ability. Not considering resilience in breeding programs could lead to a major production loss in future climate scenarios, whereas putting too much emphasis on resilience would result in a limited progress in milk production.

Digital Phenotyping in Livestock Farming

Currently, large volumes of data are being collected on farms using multimodal sensor technologies. These sensors measure the activity, housing conditions, feed intake, and health of farm animals. With traditional methods, the data from farm animals and their environment can be collected intermittently. However, with the advancement of wearable and non-invasive sensing tools, these measurements can be made in real-time for continuous quantitation relating to clinical biomarkers, resilience indicators, and behavioral predictors. The digital phenotyping of humans has drawn enormous attention recently due to its medical significance, but much research is still needed for the digital phenotyping of farm animals. Implications from human studies show great promise for the application of digital phenotyping technology in modern livestock farming, but these technologies must be directly applied to animals to understand their true capacities. Due to species-specific traits, certain technologies required to assess phenotypes need to be tailored efficiently and accurately. Such devices allow for the collection of information that can better inform farmers on aspects of animal welfare and production that need improvement. By explicitly addressing farm animals' individual physiological and mental (affective states) needs, sensor-based digital phenotyping has the potential to serve as an effective intervention platform. Future research is warranted for the design and development of digital phenotyping technology platforms that create shared data standards, metrics, and repositories.

Validation of resilience indicators by estimating genetic correlations among daughter groups and with yield responses to a heat wave and disturbances at herd level

Resilient cows are minimally affected in their functioning by disturbances, and if affected, they quickly recover. Previously, the variance and autocorrelation of daily deviations from a lactation curve were proposed as resilience indicators. These traits were heritable and genetically associated with good health and longevity. However, it was unknown if selection for these indicators would lead to desired changes in the phenotype. The first aim of this study was to investigate if forward prediction of the resilience indicators in another environment was possible. Therefore, the resilience indicator records were split into 2 subsets, each containing half of the daughters of each sire, split within sire into cows that calved in early year-seasons and cows that calved in more recent year-seasons. Genetic correlations between the subsets were then estimated for each resilience indicator. The second aim was to estimate genetic correlations between the resilience indicators and traits describing production responses to actual disturbances. The disturbances were a heat wave in July 2015 and yield disturbances at herd level. The latter were selected by decreases in mean yield of all primiparous cows in a herd, indicating that a disturbance occurred. The data set used for calculation of the resilience indicators and the traits describing yield responses contained 62,932,794 daily milk yield records on 199,104 primiparous cows. Genetic correlations (r_g) between recent and earlier daughter groups were 1 for both resilience indicators, which suggests that selection will result in changes in the phenotype in the next generation. Furthermore, low variance was genetically correlated with weak response in milk yield to both the heat wave and herd disturbances (r_g 0.47 to 0.97). Low autocorrelation was genetically correlated with reduced perturbation length and quick recovery after the heat wave and herd disturbances (0.28 to 0.97). These results suggest that variance and autocorrelation cover different aspects of resilience, and should be combined in a resilience index. In conclusion, genetic selection for the resilience indicators will likely result in favorable changes in the traits themselves, and in response and recovery to actual disturbances, which confirms that they are useful resilience indicators.

Detection of unrecorded environmental challenges in high-frequency recorded traits, and genetic determinism of resilience to challenge, with an application on feed intake in lambs

BACKGROUND

Resilient animals can remain productive under different environmental conditions. Rearing in increasingly heterogeneous environmental conditions increases the need of selecting resilient animals. Detection of environmental challenges that affect an entire population can provide a unique opportunity to select animals that are more resilient to these events. The objective of this study was two-fold: (1) to present a simple and practical data-driven approach to estimate the probability that, at a given date, an unrecorded environmental challenge occurred; and (2) to evaluate the genetic determinism of resilience to such events.

METHODS

Our method consists of inferring the existence of highly variable days (indicator of environmental challenges) via mixture models applied to frequently recorded phenotypic measures and then using the inferred probabilities of the occurrence of an environmental challenge in a reaction norm model to evaluate the genetic determinism of resilience to these events. These probabilities are estimated for each day (or other time frame). We illustrate the method by using an ovine dataset with daily feed intake (DFI) records.

RESULTS

Using the proposed method, we estimated the probability of the occurrence of an unrecorded environmental challenge, which proved to be informative and useful for inclusion as a covariate in a reaction norm animal model. We estimated the breeding values for sensitivity of the genetic potential for DFI of animals to environmental challenges. The level and slope of the reaction norm were negatively correlated (- 0.46 ± 0.21).

CONCLUSIONS

Our method is promising and appears to be viable to identify unrecorded events of environmental challenges, which is useful when selecting resilient animals and only productive data are available. It can be generalized to a wide variety of phenotypic records from different species and used with large datasets. The negative correlation between level and slope indicates that a hypothetical selection for increased DFI may not be optimal depending on the presence or absence of stress. We observed a reranking of individuals along the environmental gradient and low genetic correlations between extreme environmental conditions. These results confirm the existence of a G [Formula: see text] E interaction and show that the best animals in one environmental condition are not the best in another one.