An improved transmissibility model to detect transgenerational transmitted environmental effects.

BACKGROUND: Evolutionary studies have reported that non-genetic information can be inherited across generations (epigenetic marks, microbiota, cultural inheritance). Non-genetic information is considered to be a key element to explain the adaptation of wild species to environmental constraints because it lies at the root of the transgenerational transmission of environmental effects. The "transmissibility model" was proposed several years ago to better predict the transmissible potential of each animal by taking these diverse sources of inheritance into account in a global transmissible potential. We propose to improve this model to account for the influence of the environment on the global transmissible potential as well. This extension of the transmissibility model is the "transmissibility model with environment" that considers a covariance between transmissibility samplings of animals sharing the same environment. The null hypothesis of "no transmitted environmental effect" can be tested by comparing the two models using a likelihood ratio test (LRT). RESULTS: We performed simulations that mimicked an experimental design consisting of two lines of animals with one exposed to a particular environment at a given generation. This enabled us to evaluate the performances of the transmissibility model with environment so as to detect and quantify transgenerational transmitted environmental effects. The power and the realized type I error of the LRT were compared to those of a T-test comparing the phenotype of the two lines, three generations after the environmental exposure for different sets of parameters. The power of the LRT ranged from 45 to 94%, whereas that of the T-test was always lower than 26%. In addition, the realized type I error of the T-test was 15% and that of the LRT was 5%, as expected. Variances, the covariance between transmissibility samplings, and path coefficients of transmission estimated with the transmissibility model with environment were close to their true values for all sets of parameters. CONCLUSIONS: The transmissibility model with environment is effective in modeling vertical transmission of environmental effects.

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.

Quantifying heritability and estimating evolutionary potential in the wild when individuals that share genes also share environments.

Accurate heritability estimates for fitness-related traits are required to predict an organism's ability to respond to global change. Heritability estimates are theoretically expected to be inflated if, due to limited dispersal, individuals that share genes are also likely to share similar environments. However, if relatives occupy similar environments due, at least partly, to genetic variation for habitat selection, then accounting for environmental similarity in quantitative genetic models may result in diminished heritability estimates in wild populations. This potential issue has been pointed out in the literature, but has not been evaluated by empirical studies. Here, we investigate whether environmental similarity among individuals can be partly explained by genetic variation for habitat selection, and how this link potentially blurs estimates for heritability in fitness-related traits. Using intensive GPS monitoring, we quantified home-range habitat composition for 293 roe deer inhabiting a heterogeneous landscape to assess environmental similarity. To investigate if environmental similarity might harbour genetic variation, we combined genome-wide data in a quantitative genetic framework to evaluate genetic variation for home-range habitat composition, which is partly the result of habitat selection at settlement. Finally, we explored how environmental similarity affects heritability estimates for behaviours related to the risk avoidance-resource acquisition trade-off (i.e. being in open habitat and distance to roads) and proxies of individual performance (i.e. body mass and hind foot length). We found substantial heritability for home-range habitat composition, with estimates ranging from 0.40 (proportion of meadows) to 0.85 (proportion of refuge habitat). Accounting for similarity in habitat composition between relatives decreased the heritability estimates for both behavioural and morphological traits (reduction ranging from 55% to 100% and from 22% to 41% respectively). As a consequence, only half of these heritability estimates remained significantly different from zero. Our results show that similar genotypes occupy similar environments, which could lead to heritable variation being incorrectly attributed to environmental effects. To accurately distinguish the sources of phenotypic variation and predict the ability of organisms to respond to global change, it is necessary to develop quantitative genetic studies investigating the mechanisms underpinning environmental similarity among relatives.

Copula miss-specification in REML multivariate genetic animal model estimation.

BACKGROUND: In animal genetics, linear mixed models are used to deal with genetic and environmental effects. The variance and covariance terms of these models are usually estimated by restricted maximum likelihood (REML), which provides unbiased estimators. A strong hypothesis of REML estimation is the multi-normality of the response variables. However, in practice, even if the marginal distributions of each phenotype are normal, the multi-normality assumption may be violated by non-normality of the cross-sectional dependence structure, that is to say when the copula of the multivariate distribution is not Gaussian. This study uses simulations to evaluate the impact of copula miss-specification in a bivariate animal model on REML estimations of variance components. RESULT: Bivariate phenotypes were simulated for populations undergoing selection, considering different copulas for the dependence structure between the error components. Two multi-trait situations were considered: two phenotypes were measured on the selection candidates, or only one phenotype was measured on the selection candidates. Three generations with random selection and five generations with truncation selection based on estimated breeding values were simulated. When selection was performed at random, no significant differences were observed between the REML estimations of variance components and the true parameters even for the non-Gaussian distributions. For the truncation selections, when two phenotypes were measured on candidates, biases were systematically observed in the variance components for high residual dependence in the case of non-Gaussian distributions, especially in the case of a heavy-tailed or asymmetric distribution when the two traits were measured. Conversely, when only one phenotype was measured on candidates, no difference was observed between the Gaussian and non-Gaussian distributions in REML estimations. CONCLUSIONS: This study confirms that REML can be used by geneticists to evaluate breeding values in the multivariate case even if the multivariate phenotypes deviate from normality in the situation of random selection or if one trait is not measured for the candidate under selection. Nevertheless, when the two traits are measured, the violation of the normality assumption may lead to non-negligible biases in the REML estimations of the variance-covariance components.

Quality of breeding value predictions from longitudinal analyses, with application to residual feed intake in pigs.

BACKGROUND: An important goal in animal breeding is to improve longitudinal traits. The objective of this study was to explore for longitudinal residual feed intake (RFI) data, which estimated breeding value (EBV), or combination of EBV, to use in a breeding program. Linear combinations of EBV (summarized breeding values, SBV) or phenotypes (summarized phenotypes) derived from the eigenvectors of the genetic covariance matrix over time were considered, and the linear regression method (LR method) was used to facilitate the evaluation of their prediction accuracy. RESULTS: Weekly feed intake, average daily gain, metabolic body weight, and backfat thickness measured on 2435 growing French Large White pigs over a 10-week period were analysed using a random regression model. In this population, the 544 dams of the phenotyped animals were genotyped. These dams did not have own phenotypes. The quality of the predictions of SBV and breeding values from summarized phenotypes of these females was evaluated. On average, predictions of SBV at the time of selection were unbiased, slightly over-dispersed and less accurate than those obtained with additional phenotypic information. The use of genomic information did not improve the quality of predictions. The use of summarized instead of longitudinal phenotypes resulted in predictions of breeding values of similar quality. CONCLUSIONS: For practical selection on longitudinal data, the results obtained with this specific design suggest that the use of summarized phenotypes could facilitate routine genetic evaluation of longitudinal traits.

Analysis of the causal structure of traits involved in sow lactation feed efficiency.

BACKGROUND: Feed efficiency during lactation involves a set of phenotypic traits that form a complex system, with some traits exerting causal effects on the others. Information regarding such interrelationships can be used to predict the effect of external interventions on the system, and ultimately to optimize management practices and multi-trait selection strategies. Structural equation models can be used to infer the magnitude of the different causes of such interrelationships. The causal network necessary to fit structural equation models can be inferred using the inductive causation (IC) algorithm. By implementing these statistical tools, we inferred the causal association between the main energy sources and sinks involved in sow lactation feed efficiency for the first time, i.e., daily lactation feed intake (dLFI) in kg/day, daily sow weight balance (dSWB) in kg/day, daily litter weight gain (dLWG) in kg/day, daily back fat thickness balance (dBFTB) in mm/day, and sow metabolic body weight (SMBW) in kg0.75. Then, we tested several selection strategies based on selection indices, with or without dLFI records, to improve sow efficiency during lactation. RESULTS: The IC algorithm using 95% highest posterior density (HPD95%) intervals resulted in a fully directed acyclic graph, in which dLFI and dLWG affected dSWB, the posterior mean of the corresponding structural coefficients (PMλ) being 0.12 and - 0.03, respectively. In turn, dSWB influenced dBFTB and SMBW, with PMλ equal to 0.70 and - 1.22, respectively. Multiple indirect effects contributed to the variances and covariances among the analyzed traits, with the most relevant indirect effects being those involved in the association between dSWB and dBFTB and between dSWB and SMBW. Selection strategies with or without phenotypic information on dLFI, or that hold this trait constant, led to the same pattern and similar responses in dLFI, dSWB, and dLWG. CONCLUSIONS: Selection based on an index including only dBFTB and dLWG records can reduce dLFI, keep dSWB constant or increase it, and increase dLWG. However, a favorable response for all three traits is probably not achievable. Holding the amount of feed provided to the sows constant did not offer an advantage in terms of response over the other strategies.

Using egg production longitudinal recording to study the genetic background of resilience in purebred and crossbred laying hens.

BACKGROUND: There is growing interest in using genetic selection to obtain more resilient farm animals (i.e. that are minimally affected by disturbances or rapidly recover from them). The aims of this study were to: (i) estimate the genetic parameters of resilience indicator traits based on egg production data, (ii) assess whether these traits are genetically correlated in purebreds and crossbreds, and (iii) assess the genetic correlations of these traits with egg production (EP) as total number of eggs between 25 and 83 weeks. Purebred hens (33,825 from a White Leghorn (WA) line and 34,397 from a Rhode Island (BD) line were housed in individual cages, while crossbred hens were housed in collective cages of 6 to 8 paternal half-sibs (12,852 WA and 3898 BD crossbred groups, where the name of the group refers to the line used as the sire). Deviations of a hen's weekly egg production from the average of the corresponding batch were calculated. Resilience indicator traits investigated were the natural logarithm of the variance (LNVAR), the skewness (SKEW), and the lag-one autocorrelation (AUTO-R) of these deviations. RESULTS: In both purebred lines, EP was estimated to be lowly heritable (WA: 0.11 and BD: 0.12). Resilience indicators were also estimated to be lowly heritable in both lines (LNVAR: 0.10 and 0.12, SKEW: 0.04 and 0.02, AUTO-R: 0.06 and 0.08 in WA and BD, respectively). In both crossbred groups, EP, AUTO-R, and SKEW were estimated to be less heritable than in purebreds (EP: [Formula: see text] ≤ 0.07; and resilience indicator traits: [Formula: see text] ≤ 0.03), while LNVAR had an [Formula: see text] estimate that was similar to or higher in crossbreds ([Formula: see text] ranged from 0.13 to 0.21) than in purebreds. In both purebreds and crossbreds, resilience indicator traits were estimated to have favorable genetic correlations with EP and between each other. For all traits and in both lines, estimates of genetic correlations between purebreds and crossbreds ([Formula: see text]) differed from 1 and ranged from 0.16 to 0.63. CONCLUSIONS: These results show that selection for resilience based on EP data can be considered in breeding programs for layers. Genetic improvement of resilience in crossbreds can be achieved by using information on purebreds, but would be greatly enhanced by the integration of information on crossbreds in breeding programs.

New residual feed intake criterion for longitudinal data.

BACKGROUND: Residual feed intake (RFI) is one measure of feed efficiency, which is usually obtained by multiple regression of feed intake (FI) on measures of production, body weight gain and tissue composition. If phenotypic regression is used, the resulting RFI is generally not genetically independent of production traits, whereas if RFI is computed using genetic regression coefficients, RFI and production traits are independent at the genetic level. The corresponding regression coefficients can be easily derived from the result of a multiple trait model that includes FI and production traits. However, this approach is difficult to apply in the case of multiple repeated measurements of FI and production traits. To overcome this difficulty, we used a structured antedependence approach to account for the longitudinality of the data with a phenotypic regression model or with different genetic and environmental regression coefficients [multi- structured antedependence model (SAD) regression model]. RESULTS: After demonstrating the properties of RFI obtained by the multi-SAD regression model, we applied the two models to FI and production traits that were recorded for 2435 French Large White pigs over a 10-week period. Heritability estimates were moderate with both models. With the multi-SAD regression model, heritability estimates were quite stable over time, ranging from 0.14 ± 0.04 to 0.16 ± 0.05, while heritability estimates showed a U-shaped profile with the phenotypic regression model (ranging from 0.19 ± 0.06 to 0.28 ± 0.06). Estimates of genetic correlations between RFI at different time points followed the same pattern for the two models but higher estimates were obtained with the phenotypic regression model. Estimates of breeding values that can be used for selection were obtained by eigen-decomposition of the genetic covariance matrix. Correlations between these estimated breeding values obtained with the two models ranged from 0.66 to 0.83. CONCLUSIONS: The multi-SAD model is preferred for the genetic analysis of longitudinal RFI because, compared to the phenotypic regression model, it provides RFI that are genetically independent of production traits at all time points. Furthermore, it can be applied even when production records are missing at certain time points.

Genetic parameters of resistance to pasteurellosis using novel response traits in rabbits.

BACKGROUND: Pasteurellosis (Pasteurella infection) is one of the most common bacterial infections in rabbits on commercial farms and in laboratory facilities. Curative treatments using antibiotics are only partly efficient, with frequent relapses. Breeding rabbits for improved genetic resistance to pasteurellosis is a sustainable alternative approach. In this study, we infected 964 crossbred rabbits from six sire lines experimentally with Pasteurella multocida. After post-mortem examination and bacteriological analyses, abscess, bacteria, and resistance scores were derived for each rabbit based on the extent of lesions and bacterial dissemination in the body. This is the first study to use such an experimental design and response traits to measure resistance to pasteurellosis in a rabbit population. We investigated the genetic variation of these traits in order to identify potential selection criteria. We also estimated genetic correlations of resistance to pasteurellosis in the experimental population with traits that are under selection in the breeding populations (number of kits born alive and weaning weight). RESULTS: Heritability estimates for the novel response traits, abscess, bacteria, and resistance scores, ranged from 0.08 (± 0.05) to 0.16 (± 0.06). The resistance score showed very strong negative genetic correlation estimates with abscess (- 0.99 ± 0.05) and bacteria scores (- 0.98 ± 0.07). A very high positive genetic correlation of 0.99 ± 0.16 was estimated between abscess and bacteria scores. Estimates of genetic correlations of the resistance score with average daily gain traits for the first and second week after inoculation were 0.98 (± 0.06) and 0.70 (± 0.14), respectively. Estimates of genetic correlations of the disease-related traits with average daily gain pre-inoculation were favorable but with high standard errors. Estimates of genetic and phenotypic correlations of the disease-related traits with commercial selection traits were not significantly different from zero. CONCLUSIONS: Disease response traits are heritable and are highly correlated with each other, but do not show any significant genetic correlations with commercial selection traits. Thus, the prevalence of pasteurellosis could be decreased by selecting more resistant rabbits on any one of the disease response traits with a limited impact on the selection traits, which would allow implementation of a breeding program to improve resistance to pasteurellosis in rabbits.

Inclusive inheritance for residual feed intake in pigs and rabbits.

Non-genetic information (epigenetic, microbiota, behaviour) that results in different phenotypes in animals can be transmitted from one generation to the next and thus is potentially involved in the inheritance of traits. However, in livestock species, animals are selected based on genetic inheritance only. The objective of the present study was to determine whether non-genetic inherited effects play a role in the inheritance of residual feed intake (RFI) in two species: pigs and rabbits. If so, the path coefficients of the information transmitted from sire and dam to offspring would differ from the expected transmission factor of 0.5 that occurs if inherited information is of genetic origin only. Two pigs (pig1, pig2) and two rabbits (rabbit1, rabbit2) datasets were used in this study (1,603, 3,901, 5,213 and 4,584 records, respectively). The test of the path coefficients to 0.5 was performed for each dataset using likelihood ratio tests (null model: transmissibility model with both path coefficients equal to 0.5, full model: unconstrained transmissibility model). The path coefficients differed significantly from 0.5 for one of the pig datasets (pig2). Although not significant, we observed, as a general trend, that sire path coefficients of transmission were lower than dam path coefficients in three of the datasets (0.46 vs 0.53 for pig1, 0.39 vs 0.44 for pig2 and 0.38 vs 0.50 for rabbit1). These results suggest that phenomena other than genetic sources of inheritance explain the phenotypic resemblance between relatives for RFI, with a higher transmission from the dam's side than from the sire's side.

Estimating direct genetic and maternal effects affecting rabbit growth and feed efficiency with a factorial design.

The aim of this experiment was to evaluate the significance of neonatal environment on feed efficiency. For that purpose, rabbits from a line selected for residual feed intake (RFI) during 10 generations (G10 kits) were cross-fostered with non-selected control does (i.e., G0 line), and reciprocally. In parallel, sibs were fostered by mothers from their original line. Nine hundred animals were raised in individual (N = 456) or collective (N = 320) cages. Traits analysed in this study were body weight at 32 days and at 63 days, average daily gain (ADG), feed intake between weaning and 63 days (FI), feed conversion ratio (FCR) and RFI. The maternal environment offered by does from the line selected for RFI deteriorated the FCR of the kits, independently of their line of origin, during fattening (+0.08 ± 0.02) compared to FCR of kits nursed by G0 does. The line, the type of housing and the batch were significant effects for all the measured traits: G10 kits were lighter than their G0 counterparts at 32 days (-82.9 ± 9 g, p < 0.0001) and at 63 days (-161 ± 16 g, p < 0.0001). They also had a lower ADG (-2.36 ± 0.36 g/day, p < 0.0001), RFI (-521 ± 24 g/day, p < 0.0001) and a lower FI (-855 ± 31 g, p < 0.0001), resulting in a more desirable feed efficiency (FCR: -0.35 ± 0.02). There was no significant difference in the contrast of G10 and G0 performances between collective and individual/digestive cages (p > 0.22): -2.35 g/day versus 2.94 g/day for ADG, -0.39 versus -0.40 for FCR, -577 g versus -565 g for RFI and -879 g versus -859 g for FI, respectively). Thus, no genotype-by-environment (housing) interaction is expected at the commercial level, that is, no re-ranking of the animals due to collective housing.

Longitudinal analysis of direct and indirect effects on average daily gain in rabbits using a structured antedependence model.

BACKGROUND: Indirect genetic effects (IGE) are important components of various traits in several species. Although the intensity of social interactions between partners likely vary over time, very few genetic studies have investigated how IGE vary over time for traits under selection in livestock species. To overcome this issue, our aim was: (1) to analyze longitudinal records of average daily gain (ADG) in rabbits subjected to a 5-week period of feed restriction using a structured antedependence (SAD) model that includes IGE and (2) to evaluate, by simulation, the response to selection when IGE are present and genetic evaluation is based on a SAD model that includes IGE or not. RESULTS: The direct genetic variance for ADG (g/d) increased from week 1 to 3 [from 8.03 to 13.47 (g/d)2] and then decreased [6.20 (g/d)2 at week 5], while the indirect genetic variance decreased from week 1 to 4 [from 0.43 to 0.22 (g/d)2]. The correlation between the direct genetic effects of different weeks was moderate to high (ranging from 0.46 to 0.86) and tended to decrease with time interval between measurements. The same trend was observed for IGE for weeks 2 to 5 (correlations ranging from 0.62 to 0.91). Estimates of the correlation between IGE of week 1 and IGE of the other weeks did not follow the same pattern and correlations were lower. Estimates of correlations between direct and indirect effects were negative at all times. After seven generations of simulated selection, the increase in ADG from selection on EBV from a SAD model that included IGE was higher (~ 30%) than when those effects were omitted. CONCLUSIONS: Indirect genetic effects are larger just after mixing animals at weaning than later in the fattening period, probably because of the establishment of social hierarchy that is generally observed at that time. Accounting for IGE in the selection criterion maximizes genetic progress.

Genome-wide association mapping for type and mammary health traits in French dairy goats identifies a pleiotropic region on chromosome 19 in the Saanen breed.

Type traits and mammary health traits are important to dairy ruminant breeding because they influence animal health, milking ability, and longevity, as well as the economic sustainability of farms. The availability of the genomic sequence and a single nucleotide polymorphism chip in goats has opened up new fields of investigation to better understand the genes and mechanisms that underlie such complex traits and to be able to select them. Our objective was to perform a genome-wide association study in dairy goats for 11 type traits and somatic cell count (SCC) as proxies for mastitis resistance. A genome-wide association study was implemented using a daughter design composed of 1,941 Alpine and Saanen goats sired by 20 artificial insemination bucks, genotyped with the Illumina GoatSNP50 BeadChip (Illumina Inc., San Diego, CA). This association study was based on both linkage analyses and linkage disequilibrium using QTLmap software (http://dga7.jouy.inra.fr/qtlmap/) interval mapping was performed with the likelihood ratio test using linear regressions. Breeds were analyzed together and separately. The study highlighted 37 chromosome-wide significant quantitative trait loci (QTL) with linkage analyses and 222 genome-wide significant QTL for linkage disequilibrium, for type and SCC traits in dairy goats. Genomic control of those traits was mostly polygenic and breed-specific, suggesting that within-breed selection would be favored for those traits. Of note, Capra hircus autosome (CHI) 19 appeared to be highly enriched in single nucleotide polymorphisms associated with type and SCC, with 2 highly significant regions in the Saanen breed. One region (33-42 Mb) was significantly associated with SCC and includes candidate genes associated with response to intramammary infections (RARA, STAT3, STAT5A, and STAT5B). Another region of the CHI 19 (24.5-27 Mb) exhibited an adverse pleiotropic effect on milk production (milk, fat yield, and protein yield) and udder traits (udder floor position and rear udder attachment) that agreed with the negative genetic correlations that exist between those 2 groups of traits. These QTL were not found in the Alpine breed. In Alpine, the 2 most significant regions were associated with chest depth on CHI 6 (45.8-46.0 Mb) and CHI 8 (80.7-81.1 Mb). These results will be helpful for goat selection in the future and could lead to identification of causal mutations.

Multiple-trait structured antedependence model to study the relationship between litter size and birth weight in pigs and rabbits.

BACKGROUND: Some genetic studies need to take into account correlations between traits that are repeatedly measured over time. Multiple-trait random regression models are commonly used to analyze repeated traits but suffer from several major drawbacks. In the present study, we developed a multiple-trait extension of the structured antedependence model (SAD) to overcome this issue and validated its usefulness by modeling the association between litter size (LS) and average birth weight (ABW) over parities in pigs and rabbits. METHODS: The single-trait SAD model assumes that a random effect at time [Formula: see text] can be explained by the previous values of the random effect (i.e. at previous times). The proposed multiple-trait extension of the SAD model consists in adding a cross-antedependence parameter to the single-trait SAD model. This model can be easily fitted using ASReml and the OWN Fortran program that we have developed. In comparison with the random regression model, we used our multiple-trait SAD model to analyze the LS and ABW of 4345 litters from 1817 Large White sows and 8706 litters from 2286 L-1777 does over a maximum of five successive parities. RESULTS: For both species, the multiple-trait SAD fitted the data better than the random regression model. The difference between AIC of the two models (AIC_random regression-AIC_SAD) were equal to 7 and 227 for pigs and rabbits, respectively. A similar pattern of heritability and correlation estimates was obtained for both species. Heritabilities were lower for LS (ranging from 0.09 to 0.29) than for ABW (ranging from 0.23 to 0.39). The general trend was a decrease of the genetic correlation for a given trait between more distant parities. Estimates of genetic correlations between LS and ABW were negative and ranged from -0.03 to -0.52 across parities. No correlation was observed between the permanent environmental effects, except between the permanent environmental effects of LS and ABW of the same parity, for which the estimate of the correlation was strongly negative (ranging from -0.57 to -0.67). CONCLUSIONS: We demonstrated that application of our multiple-trait SAD model is feasible for studying several traits with repeated measurements and showed that it provided a better fit to the data than the random regression model.

Interaction of direct and social genetic effects with feeding regime in growing rabbits.

BACKGROUND: Most rabbit production farms apply feed restriction at fattening because of its protective effect against digestive diseases that affect growing rabbits. However, it leads to competitive behaviour between cage mates, which is not observed when animals are fed ad libitum. Our aim was to estimate the contribution of direct ([Formula: see text]) and social ([Formula: see text]) genetic effects (also known as indirect genetic effects) to total heritable variance of average daily gain ([Formula: see text]) in rabbits on different feeding regimens (FR), and the magnitude of the interaction between genotype and FR (G × FR). METHODS: A total of 6264 contemporary kits were housed in cages of eight individuals and raised on full ([Formula: see text]) or restricted ([Formula: see text]) feeding to 75% of the ad libitum intake. A Bayesian analysis of weekly records of [Formula: see text] (from 32 to 60 days of age) in rabbits on [Formula: see text] and [Formula: see text] was performed with a two-trait model including [Formula: see text] and [Formula: see text]. RESULTS: The ratio between total heritable variance and phenotypic variance ([Formula: see text]) was low (<0.10) and did not differ significantly between FR. However, the ratio between [Formula: see text] (i.e. variance of [Formula: see text] relative to phenotypic variance) and [Formula: see text] was ~0.52 and 0.86 for animals on [Formula: see text] and [Formula: see text], respectively, thus [Formula: see text] contributed more to the heritable variance of animals on [Formula: see text] than on [Formula: see text]. Feeding regimen also affected the sign and magnitude of the correlation between [Formula: see text] and [Formula: see text], i.e. -0.5 and ~0 for animals on [Formula: see text] and [Formula: see text], respectively. The posterior mean (posterior sd) of the correlation between estimated total breeding values (ETBV) of animals on [Formula: see text] and [Formula: see text] was 0.26 (0.20), indicating very strong G × FR interactions. The correlations between [Formula: see text] and [Formula: see text] in rabbits on [Formula: see text] and [Formula: see text] ranged from -0.47 ([Formula: see text] on [Formula: see text] and [Formula: see text] on [Formula: see text]) to 0.64. CONCLUSIONS: Our results suggest that selection of rabbits for [Formula: see text] under [Formula: see text] may completely fail to improve [Formula: see text] in rabbits on [Formula: see text]. Social genetic effects contribute substantially to ETBV of rabbits on [Formula: see text] but not on [Formula: see text]. Selection for [Formula: see text] should be performed under production conditions regarding the FR, by accounting for [Formula: see text] if the amount of food is limited.

Genetic heteroscedastic models for ordinal traits: application to sheep litter size.

BACKGROUND: Classical genetic canalization models, which accommodate the mean and variance of a trait separately, provide a flexible approach to take heteroscedasticity for continuous traits into account. However, this model is not appropriate for discrete traits. The aim of this work was to propose heteroscedastic threshold models suitable for the genetic analysis of ordinal data. METHODS: In order to first fit the mean and variance of ordinal traits separately, we extended the classical threshold model (TM) for discrete data by introducing non-genetic and genetic factors of heterogeneity on the variance of its underlying variable, which leads to a homothetic threshold model HTM and its alternative parameterization HTM' in which the thresholds of different individuals are linked by a homothetic-translation. Relaxing the constraint between the thresholds led us to propose an independent threshold model ITM that was more flexible than HTM' but required the estimation of more parameters. TM, HTM and ITM models were applied to study 19,671 records on litter size in Romane sheep. RESULTS: Both HTM and ITM were able to disentangle the link between the mean and variance that holds in the classical homoscedastic threshold model. The results obtained for the litter size of Romane ewes showed that the data was best fitted with HTM compared to ITM and TM. The correlations between the observed and predicted variances were equal to 0.6 and 0.2 for HTM and TM, respectively. These analyses showed the existence of a genetic component for the heterogeneity of litter size in sheep that was taken into account in HTM. CONCLUSIONS: HTM is the most suitable model to study the variability of litter size in sheep. It accommodates both the mean and variance separately while requiring the estimation of only a few parameters.

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).

Improving the response of animals to environmental disturbances in terms of robustness is a key element to face the new breeding constraints related to climate change and the agroecological transition. Characterizing the disturbances that an animal experiences is a necessary first step to correctly evaluate its robustness. We propose a new method to do so based on the analysis of high-throughput phenotyping data. Using simulated data, we demonstrate that this method is effective for detecting and characterizing unknown disturbances and is thus helpful in correctly evaluating an animal's robustness. Applied to real growing pig data, it allowed us to obtain new measurements of robustness and to estimate their heritability in order to consider including these new traits for selection.

Integrating computer vision algorithms and RFID system for identification and tracking of group-housed animals: an example with pigs.

Precision livestock farming aims to individually and automatically monitor animal activity to ensure their health, well-being, and productivity. Computer vision has emerged as a promising tool for this purpose. However, accurately tracking individuals using imaging remains challenging, especially in group housing where animals may have similar appearances. Close interaction or crowding among animals can lead to the loss or swapping of animal IDs, compromising tracking accuracy. To address this challenge, we implemented a framework combining a tracking-by-detection method with a radio frequency identification (RFID) system. We tested this approach using twelve pigs in a single pen as an illustrative example. Three of the pigs had distinctive natural coat markings, enabling their visual identification within the group. The remaining pigs either shared similar coat color patterns or were entirely white, making them visually indistinguishable from each other. We employed the latest version of the You Only Look Once (YOLOv8) and BoT-SORT algorithms for detection and tracking, respectively. YOLOv8 was fine-tuned with a dataset of 3,600 images to detect and classify different pig classes, achieving a mean average precision of all the classes of 99%. The fine-tuned YOLOv8 model and the tracker BoT-SORT were then applied to a 166.7-min video comprising 100,018 frames. Results showed that pigs with distinguishable coat color markings could be tracked 91% of the time on average. For pigs with similar coat color, the RFID system was used to identify individual animals when they entered the feeding station, and this RFID identification was linked to the image trajectory of each pig, both backward and forward. The two pigs with similar markings could be tracked for an average of 48.6 min, while the seven white pigs could be tracked for an average of 59.1 min. In all cases, the tracking time assigned to each pig matched the ground truth 90% of the time or more. Thus, our proposed framework enabled reliable tracking of group-housed pigs for extended periods, offering a promising alternative to the independent use of image or RFID approaches alone. This approach represents a significant step forward in combining multiple devices for animal identification, tracking, and traceability, particularly when homogeneous animals are kept in groups.

In precision livestock farming, monitoring animal activity is crucial to ensure their health, well-being, and productivity. While digital cameras and computer vision algorithms offer a promising solution for this task, tracking individual animals of similar appearance when housed in groups can be challenging. Close interaction among animals can lead to a loss of individual identity, which affects tracking accuracy. To overcome this problem, we developed a framework that combines camera images with radio frequency identification (RFID) ear tags. This methodology was applied to a pen housing 12 pigs, with an RFID reader located inside the feeder. Among the pigs, three had unique coat markings, enabling them to be tracked most of the time without losing their identity (87% of the time). The remaining pigs could not be visually distinguished from each other, so information from the RFID system was used to recover lost IDs every time pigs entered the feeder. The framework achieves 97% accuracy in tracking, offering a reliable solution for monitoring group-housed pigs.

No direct by maternal effects interaction detected for pre-weaning growth in Romane sheep using a reaction norm model.

BACKGROUND: The pre-weaning growth of lambs, an important component of meat production, depends on maternal and direct effects. These effects cannot be observed directly and models used to study pre-weaning growth assume that they are additive. However, it is reasonable to suggest that the influence of direct effects on growth may differ depending on the value of maternal effects i.e. an interaction may exist between the two components. METHODS: To test this hypothesis, an experiment was carried out in Romane sheep in order to obtain observations of maternal phenotypic effects (milk yield and milk quality) and pre-weaning growth of the lambs. The experiment consisted of mating ewes that had markedly different maternal genetic effects with rams that contributed very different genetic effects in four replicates of a 3 × 2 factorial plan. Milk yield was measured using the lamb suckling weight differential technique and milk composition (fat and protein contents) was determined by infrared spectroscopy at 15, 21 and 35 days after lambing. Lambs were weighed at birth and then at 15, 21 and 35 days. An interaction between genotype (of the lamb) and environment (milk yield and quality) for average daily gain was tested using a restricted likelihood ratio test, comparing a linear reaction norm model (interaction model) to a classical additive model (no interaction model). RESULTS: A total of 1284 weights of 442 lambs born from 166 different ewes were analysed. On average, the ewes produced 2.3 ± 0.8 L milk per day. The average protein and fat contents were 50 ± 4 g/L and 60 ± 18 g/L, respectively. The mean 0-35 day average daily gain was 207 ± 46 g/d. Results of the restricted likelihood ratio tests did not highlight any significant interactions between the genotype of the lambs and milk production of the ewe. CONCLUSIONS: Our results support the hypothesis of additivity of maternal and direct effects on growth that is currently applied in genetic evaluation models.

Impact of environmental disturbances on estimated genetic parameters and breeding values for growth traits in pigs.

Due to the diversification of farming systems and climate change, farm animals are exposed to environmental disturbances to which they respond differently depending on their robustness. Disturbances such as heat stress or sanitary challenges (not always recorded, especially when they are of short duration and low intensity) have a transitory impact on animals, resulting in changes in phenotypes of production (feed intake, BW, etc.). The aim of this study was to evaluate the impact of such unknown disturbances on the estimated genetic parameters and breeding values (BV) for production traits. A population of 6 120 individuals over five generations divided into eight batches of 10 pens was generated, each individual underwent an ≃100-day test period. A longitudinal phenotype mimicking piglet weight during the fattening period was simulated for each individual in two situations: disturbed and non-disturbed. The disturbed phenotype was modified according to the robustness of the animal and the intensity and duration of the disturbance that the animal was subjected to. Various sets of simulations (1 000 replicates per set) were considered depending on the type of disturbance (at the level of the batch, pen, or individual), the genetic correlation (negative, neutral, or positive) between the two components of the robustness (resistance and resilience), the genetic correlation (negative, neutral, or positive) between growth and the components of robustness, and the heritability of the components of robustness (weak or moderate). An animal model was used to estimate the genetic parameters and BV for two production traits: the BW at 100 days of age (BW100) and average daily gain (ADG). The estimated heritability of the production traits was lower in the disturbed situation compared to the non-disturbed one (reduction of 0.08 and 0.05 points respectively for BW100 and ADG). The correlations between estimated breeding values of the observed phenotypes (EBV) and BV for production traits in absence of disturbance were lower in the disturbed situation (reduction of 0.04 and 0.06 points for BW100 and ADG respectively) while the partial correlation between EBV and BV for robustness was not significantly different from 0 in the two situations. These results suggest that selection in a well-controlled environment with random disturbances of low intensities does not allow to improve animal robustness while it is less effective for improving production traits than selection under no environmental disturbances.