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Evaluating genetically heat stress (HS) of cattle is still a major challenge, also because of the need to have access to HS sensitive phenotypes relating their reaction to temperature and humidity as described by the temperature-humidity index (THI). A solution could be to add the information of data from sensors. However, activity (ACT) and rumination (RUM) sensors reflect no “production” [i.e., milk yields (MY), milk composition, but also intake or growth] related aspects which is suboptimal. Therefore, the objectives of this study were thus to assess the possible usefulness for adding sensors data for HS genetic evaluation based on a dairy situation. But the context described can be easily enlarged and include beef cattle and pigs. Strategy was illustrated using daily SenseHub collar records that were obtained from October 2019 to July 2022 in six dairy herds in Wallonia in Southern Belgium. A total of 453 Walloon Holstein cows were followed during this period for activity time. Meteorological data and a HS sensitive “production” trait (here MY) were also obtained from 2015 to 2022 for 1,740 cows from the same herds. In total 32,154 MY, 130,867 ACT and 130,848 RUM records were available. The thresholds at which the different traits start to be affected by HS were estimated at a THI of 63, 64 and 66, respectively, for MY, ACT and RUM. A three-trait random regression reaction norm model was fitted with these different thresholds. Estimated heritability values were 0.19 ± 0.03, 0.14 ± 0.06 and 0.21 ± 0.05 respectively for MY, ACT and RUM using AIREML. Ratios of variance associated to THI and constant genetic effect were 0.002, 0.02 and 0.04 indicating a stronger reaction by RUM but also ACT than MY to THI. However, it is uncertain if the reactions of these sensor traits are reflected in the production traits. Estimated genetic correlations of regressions on THI for ACT and RUM showed values of 0.51 ± 0.70 and 0.08 ± 0.54 with MY and 0.77 ± 0.69 between ACT and RUM. The first eigenvector of the correlation matrix explained 65% of the total variability and standardized coefficients of this eigenvector were 0.42, 0.69 and 0.59 for MY, ACT and RUM. A total of 50% of the variance associated with the regression on THI for MY could be explained by the regressions on THI for ACT and RUM. Adding the constant genetic effects for ACT and RUM only marginally increased to 51% the ability to explain the effect of THI on MY. Based on these results, reactions of different indicator traits, here sensor data, may reflect a different reaction than production and therefore economically important traits. Additional research, also beyond dairy cattle, should continue to address this topic that is very important for the validation of sensor data detecting HS.

In the context of genetic research, but even more for routine genetic evaluations, a recurrent problem is the difficulty to obtain measurements for certain required phenotypes. This can be caused by limited facilities, difficulties in sampling, expensive analyses, limited staff relative to number of animals, and many other reasons. It may therefore be necessary to select which animals to phenotype while maintaining the phenotyping quality and avoiding bias or omitting some animals of interest. This implies developing strategies to do the phenotyping in a non-biased way using a priori available minimum knowledge. An example could be predicting post weaning growth of cattle based on earlier information as birth weight and weaning weight to optimize the use of feed intake or methane measurement facilities. In pigs, similar situations might occur for growth or emissions. However, another important example is boar taint (BT) which shows that not only sequential weighing, but also partial measurements of BT may require attention. BT is defined as urinary and fecal and associated with 2 major compounds, androstenone (AND) and skatole (SKA). The classical method of odor discrimination, “Human Nose Score” (HNS), is notoriously unreliable. Even if odor is known to be complex, adding AND and SKA to HNS would help reveal better the boar taint condition (i.e., phenotype). However, the measurements of AND and SKA or other compounds in fat are expensive. Therefore, it would be important to optimize these measurements by selecting relevant fat samples taken at slaughterhouses. We tested the following optimization strategy. The objective was to obtain a good distribution of a posteriori measured AND and SKA in the selected samples given initial asymmetric populations with positive HNS (3 to 10% declared positives). In a first step, the strategy pooled 2 available HNS for 997 across 22 contemporary groups (CG) and added limited chemical measurements (218/997) available for older samples. The data were obtained from a boar progeny testing scheme; therefore, CG were linked through connecting sires from artificial insemination having sons in different CG. A multi-trait BLUP model was used adjusting for metabolic weight using a 4-generation pedigree. A total of 15% of the animals in the last 6 contemporary groups were selected using first positive HNS (17/300), then keeping the highest and lowest predictions and maintaining diversity by not repeating boars from the same sire-family (i.e., when a family was already present, the next animal was taken). A posteriori chemical analyses were performed on selected samples to compare predicted and observed classifications in tainted and untainted. Research showed an accuracy of 0.73 (Table 1), leading to 58% tainted and 42% untainted compared with the expected ratio. Improvements of this strategy are currently in development, including using genomics.

Individual immunity in honey bees (Apis melliferaL.) is complemented by highly evolved social behaviors. Among them, hygienic behavior has a key role involving the detection and removal of unhealthy or dead brood. Since the 1960s, several bioassays have been developed to quantify the hygienic behavior of honey bee colonies against chalkbrood, American foulbrood, and varroa infested brood. Here, we review the five main bioassays used since the late 1960s. We describe their advantages and disadvantages, including a special focus on their inherent biases. For each assay, we also discuss whether or not their use should be restricted to quantify the hygienic behavior against chalkbrood, or American foulbrood, or varroa infested brood. Overall, the bioassays involving the removal of freeze-killed brood are recommended over the bioassays relying on the removal of pin-killed brood but only for the quantification of hygienic behavior toward chalkbrood and American foulbrood. These bioassays are not recommended to quantify the hygienic behavior toward varroa infested brood, for which an accurate assessment should rely on assays based on the removal of brood artificially infested with varroa mites. Choosing an appropriate bioassay is crucial for an accurate assessment of the hygienic behavior against a defined pathogen, depending on the research question, or the goal of the breeding program.Bioensayos para cuantificar el comportamiento higiénico en las colonias de abejas melíferas (Apis melliferaL.): una revisiónLa inmunidad individual de las abejas melíferas (Apis mellifera L.) se complementa con comportamientos sociales altamente evolucionados. Entre ellos, el comportamiento higiénico tiene un papel clave en la detección y eliminación de crías enfermas o muertas. Desde la década de 1960, se han desarrollado varios bioensayos para cuantificar el comportamiento higiénico de las colonias de abejas melíferas frente a la cría infestada de Ascosfera, loque americana y varroa. Aquí revisamos los cinco principales bioensayos utilizados desde finales de la década de 1960. Describimos sus ventajas y desventajas, incluyendo un enfoque especial en sus sesgos inherentes. Para cada ensayo, también discutimos si su uso debe ser restringido o no para cuantificar el comportamiento higiénico contra la cría de loque americana o la cría infestada de varroa. En general, los bioensayos que involucran la eliminación de la cría muerta por congelación son más recomendables que los bioensayos que dependen de la eliminación de la cría muerta por pin, pero sólo para la cuantificación del comportamiento higiénico hacia la cría infestada de Ascosfera y la loque americana. Estos bioensayos no se recomiendan para cuantificar el comportamiento higiénico de la cría infestada de varroa, para lo cual una evaluación precisa debe basarse en ensayos basados en la extracción de la cría infestada artificialmente con ácaros de la varroa. La elección de un bioensayo apropiado es crucial para una evaluación precisa del comportamiento higiénico frente a un patógeno definido, dependiendo de la pregunta de investigación o del objetivo del programa de cría.

In honey bees (Apis melliferaL.), hygienic behavior of workers against Varroa destructorprovides the colonies with some resistance to this parasite. The removal of freeze-killed brood (FKB) has often been used as a proxy to assess the removal of Varroa-infested brood. The question is whether this approximation is reliable enough to estimate the benefits induced by the removal of Varroa-infested brood in unselected stocks. For this purpose, we investigated the relation between the removal of FKB and three other variables: (1) the percentage of pupae and workers infested by V. destructor; (2) the share of mites in brood compared to phoretic mites; and (3) the reproductive success of mites. To be a reliable estimate, the removal of FKB should correlate with these three variables. Since hygienic behavior is naturally expressed and highly variable in unselected stocks, we chose to use such stocks to get a wide range of FKB removal. There was no correlation between FKB and the three other variables. We conclude that removal of FKB is not a good estimate for hygienic behavior towards Varroamites in unselected stocks.

The hygienic behavior of honey bee workers contributes to the social immunity of colonies. The ability of workers to detect and remove unhealthy or dead brood prevents the transmission of brood diseases inside the colony. Over the last five decades, this trait has been extensively studied and improved in several research and breeding programs. Given the strong interest for hygienic behavior, we here review the costs and benefits associated with this trait, extending preceding reviews on this subject from the late 1990s. Since the 1990s, there have been no major new insights on the efficiency of this behavior against American foulbrood and chalkbrood. However, the number of publications on hygienic behavior against the mite Varroa destructorhas considerably increased, fueling the debate regarding the efficiency of hygienic behavior against this parasite. Breeding programs have shown that selection for a specific trait might also impact other traits. Thus, we also review the cost of trade-offs between hygienic behavior and other economically important traits for bee breeders. Overall, the benefits of hygienic behavior seem to largely outweigh its costs for both colonies and bee breeders.

To develop a breed assignment model, three main steps are generally followed: 1) The selection of breed informative single nucleotide polymorphism (SNP); 2) The training of a model, based on a reference population, that allows to classify animals to their breed of origin; and 3) The validation of the developed model on external animals i.e., that were not used in previous steps. However, there is no consensus in the literature about which methodology to follow for the first step, nor about the number of SNP to be selected. This can raise many questions when developing the model and lead to the use of sophisticated methodologies for selecting SNP (e.g., with iterative algorithms, partitions of SNP, or combination of several methods). Therefore, it may be of interest to avoid the first step by the use of all the available SNP. For this purpose, we propose the use of a genomic relationship matrix (GRM), combined or not with a machine learning method, for breed assignment. We compared it with a previously developed model based on selected informative SNP. Four methodologies were investigated: 1) The PLS_NSC methodology: selection of SNP based on a partial least square-discriminant analysis (PLS-DA) and breed assignment by classification based on the nearest shrunken centroids (NSC) method; 2) Breed assignment based on the highest mean relatedness of an animal to the reference populations of each breed (referred to mean_GRM); 3) Breed assignment based on the highest SD of the relatedness of an animal to the reference populations of each breed (referred to SD_GRM) and 4) The GRM_SVM methodology: the use of means and SD of the relatedness defined in mean_GRM and SD_GRM methodologies combined with the linear support vector machine (SVM), a machine learning method used for classification. Regarding mean global accuracies, results showed that the use of mean_GRM or GRM_SVM was not significantly different (Bonferroni corrected P> 0.0083) than the model based on a reduced SNP panel (PLS_NSC). Moreover, the mean_GRM and GRM_SVM methodology were more efficient than PLS_NSC as it was faster to compute. Therefore, it is possible to bypass the selection of SNP and, by the use of a GRM, to develop an efficient breed assignment model. In routine, we recommend the use of GRM_SVM over mean_GRM as it gave a slightly increased global accuracy, which can help endangered breeds to be maintained. The script to execute the different methodologies can be accessed on: https://github.com/hwilmot675/Breed_assignment.The genomic relationship matrix can reliably be used, potentially combined with a machine learning method, to assign animals to their breed. With this methodology, the step of selection of a single nucleotide polymorphism panel can be avoided.Breed assignment models generally rely on three main steps: 1) Selection of markers that allow to distinguish the breeds under study; 2) Development of a classification model that assigns each animal to its breed of origin; and 3) Validation of the developed model with new animals, to verify that the developed model is not overfitted. The first step often raises several questions about the methodology to select the best markers or about the number of markers to select. That is why it can be interesting to avoid this first step and to use an appropriate methodology that performs similarly without the need for single nucleotide polymorphism (SNP) selection. In this study, we developed different methodologies based on the genomic relationship matrix (GRM), combined or not with a machine learning method, to assign animals to their breed of origin. The results showed that the model based on a GRM combined with a machine learning method showed equivalent percentage of correct assignment to a previously developed model relying on SNP selection while being substantially faster to compute. It is therefore possible to assign animals to their breed by the use of a GRM and to bypass the first step of selection of SNP.