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BACKGROUND: Johne's disease is a chronic wasting disease caused by the bacterium Mycobacterium avium subspecies paratuberculosis (MAP). Johne's disease is highly contagious and MAP infection in dairy cattle can eventually lead to death. With no available treatment for Johne's disease, genetic selection and improvements in management practices could help reduce its prevalence. In a previous study, the gene coding interleukin-10 receptor subunit alpha (IL10Rα) was associated with Johne's disease in dairy cattle. Our objective was to determine how IL10Rα affects the pathogenesis of MAP by examining the effect of a live MAP challenge on a mammary epithelial cell line (MAC-T) that had IL10Rα knocked out using CRISPR/cas9. The wild type and the IL10Rα knockout MAC-T cell lines were exposed to live MAP bacteria for 72 h. Thereafter, mRNA was extracted from infected and uninfected cells. Differentially expressed genes were compared between the wild type and the IL10Rα knockout cell lines. Gene ontology was performed based on the differentially expressed genes to determine which biological pathways were involved. RESULTS: Immune system processes pathways were targeted to determine the effect of IL10Rα on the response to MAP infection. There was a difference in immune response between the wild type and IL10Rα knockout MAC-T cell lines, and less difference in immune response between infected and not infected IL10Rα knockout MAC-T cells, indicating IL10Rα plays an important role in the progression of MAP infection. Additionally, these comparisons allowed us to identify other genes involved in inflammation-mediated chemokine and cytokine signalling, interleukin signalling and toll-like receptor pathways. CONCLUSIONS: Identifying differentially expressed genes in wild type and ILR10α knockout MAC-T cells infected with live MAP bacteria provided further evidence that IL10Rα contributes to mounting an immune response to MAP infection and allowed us to identify additional potential candidate genes involved in this process. We found there was a complex immune response during MAP infection that is controlled by many genes.
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Células Epiteliais , Mycobacterium avium subsp. paratuberculosis , Paratuberculose , Mycobacterium avium subsp. paratuberculosis/imunologia , Animais , Células Epiteliais/microbiologia , Células Epiteliais/metabolismo , Células Epiteliais/imunologia , Linhagem Celular , Bovinos , Paratuberculose/imunologia , Paratuberculose/microbiologia , Paratuberculose/genética , Feminino , Subunidade alfa de Receptor de Interleucina-10/genética , Subunidade alfa de Receptor de Interleucina-10/metabolismo , Glândulas Mamárias Animais/imunologia , Glândulas Mamárias Animais/microbiologia , Glândulas Mamárias Animais/metabolismo , Glândulas Mamárias Animais/patologiaRESUMO
This study aimed to evaluate the impact of copy number variants (CNV) on 13 reproduction and 12 disease traits in Holstein cattle. Intensity signal files containing log R ratio and B allele frequency information from 13,730 Holstein animals genotyped with a 95K SNP panel, and 8,467 Holstein animals genotyped with a 50K SNP panel were used to identify the CNVs. Subsequently, the identified CNVs were validated using whole-genome sequence data from 126 animals, resulting in 870 high-confidence copy number variant regions (CNVR) on 12,131 animals. Out of these, 54 CNVR had frequencies higher than or equal to 1% in the population and were used in the genome-wide association analysis (one CNVR at a time, including the G matrix). Results revealed that 4 CNVR were significantly associated with at least one of the traits analyzed in this study. Specifically, 2 CNVR were associated with 3 reproduction traits (i.e., calf survival, first service to conception, and nonreturn rate), and 2 CNVR were associated with 2 disease traits (i.e., metritis and retained placenta). These CNVR harbored genes implicated in immune response, cellular signaling, and neuronal development, supporting their potential involvement in these traits. Further investigations to unravel the mechanistic and functional implications of these CNVR on the mentioned traits are warranted.
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Variações do Número de Cópias de DNA , Estudo de Associação Genômica Ampla , Polimorfismo de Nucleotídeo Único , Reprodução , Animais , Bovinos/genética , Estudo de Associação Genômica Ampla/veterinária , Reprodução/genética , Genótipo , Doenças dos Bovinos/genética , Feminino , Fenótipo , Canadá , Frequência do GeneRESUMO
Ketosis, evidenced by hyperketonemia with elevated blood ß-hydroxybutyrate (BHB) levels, is a significant metabolic disorder of dairy cattle, typically diagnosed within the first 6 weeks post-calving when high energy levels are essential to milk production. Our study aimed to identify genetic markers linked to hyperketonemia (HYK) patterns in Holstein cows during early lactation and compare these to HYK-negative cows. We screened 964 cows for HYK using a threshold of BHB ≥1.2 mmol/L during the first 2 weeks postpartum (screening period, SP). Cows that tested negative initially were retested the following week. Cows were deemed HYK-negative (CON group) if BHB levels were below 1.2 mmol/L in both tests, while those with BHB levels exceeding this threshold at any test were treated and classified as HYK-positive (HYK+). Post-treatment, HYK+ cows were monitored for two-week follow-up period (FP) and classified based on their recovery: cured (CUR; consistently low BHB), recurrent (REC; fluctuating BHB levels), severe (SEV; high initial BHB that decreased), or chronic (CHR; persistently high BHB). Using 489 cows that were genotyped, a GWAS was conducted using GCTA software, revealing significant associations of several SNPs across different HYK patterns when compared to the CON group. These SNPs were primarily linked to genes affecting milk traits and were enriched in biological pathways relevant to protein glycosylation, inflammatory response, glucose homeostasis, and fatty acid synthesis. Our findings highlight genomic regions, potential candidate genes, and biological pathways related to ketosis, underscoring potential targets for improving health management in dairy cattle. These insights could lead to better strategies for managing ketosis through genetic selection, ultimately enhancing dairy cattle welfare and productivity. Further research with a larger number of cows is recommended to validate these findings and help confirm the implicated SNPs and genes.
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White striping (WS) is a myopathy of growing concern to the turkey industry. It is rising in prevalence and has negative consequences for consumer acceptance and the functional properties of turkey meat. The objective of this study was to conduct a genome-wide association study (GWAS) and functional analysis on WS severity. Phenotypic data consisted of white striping scored on turkey breast fillets (N = 8422) by trained observers on a 0-3 scale (none to severe). Of the phenotyped birds, 4667 genotypic records were available using a proprietary 65 K single nucleotide polymorphism (SNP) chip. The SNP effects were estimated using a linear mixed model with a 30-SNP sliding window approach used to express the percentage genetic variance explained. Positional candidate genes were those located within 50 kb of the top 1% of SNP windows explaining the most genetic variance. Of the 95 positional candidate genes, seven were further classified as functional candidate genes because of their association with both a significant gene ontology and molecular function term. The results of the GWAS emphasize the polygenic nature of the trait with no specific genomic region contributing a large portion to the overall genetic variance. Significant pathways relating to growth, muscle development, collagen formation, circulatory system development, cell response to stimulus, and cytokine production were identified. These results help to support published biological associations between WS and hypoxia and oxidative stress and provide information that may be useful for future-omics studies in understanding the biological associations with WS development in turkeys.
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Doenças Musculares , Perus , Animais , Perus/genética , Estudo de Associação Genômica Ampla , Galinhas/genética , Doenças Musculares/metabolismo , Fenótipo , Carne/análiseRESUMO
Calf management and health are essential for setting up the foundation of a productive cow. The objectives of this study were to estimate the impact of preweaning practices on milk production parameters while accounting for an animal's genetic potential in New Brunswick, Canada. A retrospective cohort study was performed on 220 heifer calves from eight herds born in 2014-2015. Preweaning practices and health data were recorded by producers and reviewed by the herd veterinarian for each calf. The herd veterinarian also visited the farms to collect serum samples from calves and frozen colostrum samples. The production outcomes assessed were milk, protein and fat yields, standardized to 305 d for the first lactation (L1) and a combined group of lactations two and three (L2â +â 3). The genomic potential was determined as genomic parent averages (GPA) for the associated production parameters. Analysis was performed with multivariable linear (L1) and linear mixed (L2â +â 3) regression models. In L1, for every 1.0 kg increase in weaning weight, milk, protein, and fat yield increased by 25.5, 0.82, and 1.01 kg, respectively (Pâ <â 0.006). Colostrum feeding time (CFT) positively impacted L1 milk and protein production, with feeding between 1-2 h of life producing the greatest estimates of 626 kg of milk and 18.2 kg of protein yield (Pâ <â 0.007), compared to earlier or later CFT. Fat yield production was decreased by 80.5 kg (Pâ <â 0.006) in L1 when evaluating animals that developed a preweaning disease and were not treated with antibiotics compared to healthy untreated animals. Impacts on L2â +â 3 were similar across all production outcomes, with a positive interaction effect of CFT and weaning weight. Compared to CFTâ <â 1 h, the later CFT groups of 1-2 h andâ >â 2 h produced greater yield outcomes of 68.2 to 72.6 kg for milk (Pâ <â 0.006), 2.06 to 2.15 kg for protein (Pâ <â 0.005), and 1.8 to 1.9 kg for fat (Pâ <â 0.045) for every 1 kg increase of weaning weight, respectively. The fit of all models was significantly improved with the inclusion of GPA. These results indicate that colostrum management and preweaning health measures impacted production parameters as adults. The inclusion of GPA significantly improved the accuracy of the models, indicating that this can be an important parameter to include in future studies.
The impact of calf management and health events have been predominately investigated during the preweaning period. However, calfhood events could also impact the animal's health and productivity as an adult. Results from this study indicate that colostrum feeding time and weaning weight were associated with production outcomes (milk, protein, and fat yields) across the first three lactations, and disease and antibiotic treatment can be detrimental to fat yield in the first lactation. By including genetic potential in the assessment of preweaning colostrum practices and health measures on production outcomes, we can more precisely identify areas to optimize calf management.
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Colostro , Indústria de Laticínios , Humanos , Gravidez , Bovinos , Animais , Feminino , Estudos Retrospectivos , Indústria de Laticínios/métodos , Leite/metabolismo , Lactação , DesmameRESUMO
The behavioral activity of laying hens in an aviary is indicative of their welfare and health. Furthermore, hens' usage of the different locations within an aviary has been shown to influence laying performance and egg quality. For example, hens that spent a longer duration of time in the nest during laying were observed to have lower laying performance. Therefore, understanding genetics of laying hens' usage of the aviary could be important for predicting egg quality, production traits and health and welfare. The objectives of this study were to estimate genetic parameters for duration of time spent at different locations within the aviary and an adjacent winter garden using a multivariate repeatability model and to compare correlations between time spent in these locations. For this study, a total of 1,106 Dekalb white laying hens (Hendrix Genetics) were genotyped using a proprietary 60K SNP array. These hens had access to 5 different zones within the aviary, which included the top level tier, nest box tier, lower level tier, floor littered area and a winter garden. Hens were in the aviary for a total of 290 d and daily records of duration were collected for each hen visit to any location in the aviary, culminating in a total of 937,740 records. Heritability estimates ranged from 0.05 (0.01) to 0.28 (0.03) for the duration of time spent in the different zones. The lowest heritability was estimated for time spent at the lower level tier, while a higher heritability was estimated for time spent in the floor littered area. A moderately high negative genetic correlation of -0.59 (0.08) was observed between time spent in the top level tier and time spent in the floor littered area, while a favorable correlation of 0.37 (0.14) was found between time spent in the lower level tier and time spent in the winter garden. The findings of this study show that the duration of time spent at different zones within an aviary has genetic basis and could be used for selecting animals for better performance and higher welfare.
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Galinhas , Jardins , Animais , Feminino , Galinhas/genética , Genótipo , Fenótipo , Estações do AnoRESUMO
The Resilient Dairy Genome Project (RDGP) is an international large-scale applied research project that aims to generate genomic tools to breed more resilient dairy cows. In this context, improving feed efficiency and reducing greenhouse gases from dairy is a high priority. The inclusion of traits related to feed efficiency (e.g., dry matter intake [DMI]) or greenhouse gases (e.g., methane emissions [CH4]) relies on available genotypes as well as high quality phenotypes. Currently, 7 countries (i.e., Australia, Canada, Denmark, Germany, Spain, Switzerland, and United States) contribute with genotypes and phenotypes including DMI and CH4. However, combining data are challenging due to differences in recording protocols, measurement technology, genotyping, and animal management across sources. In this study, we provide an overview of how the RDGP partners address these issues to advance international collaboration to generate genomic tools for resilient dairy. Specifically, we describe the current state of the RDGP database, data collection protocols in each country, and the strategies used for managing the shared data. As of February 2022, the database contains 1,289,593 DMI records from 12,687 cows and 17,403 CH4 records from 3,093 cows and continues to grow as countries upload new data over the coming years. No strong genomic differentiation between the populations was identified in this study, which may be beneficial for eventual across-country genomic predictions. Moreover, our results reinforce the need to account for the heterogeneity in the DMI and CH4 phenotypes in genomic analysis.
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Gases de Efeito Estufa , Feminino , Animais , Bovinos , Genômica , Genótipo , Austrália , MetanoRESUMO
Genetic selection can be a feasible method to help mitigate enteric methane emissions from dairy cattle, as methane emission-related traits are heritable and genetic gains are persistent and cumulative over time. The objective of this study was to estimate heritability of methane emission phenotypes and the genetic and phenotypic correlations between them in Holstein cattle. We used 1765 individual records of methane emission obtained from 330 Holstein cattle from two Canadian herds. Methane emissions were measured using the GreenFeed system, and three methane traits were analyzed: the amount of daily methane produced (g/d), methane yield (g methane/kg dry matter intake), and methane intensity (g methane/kg milk). Genetic parameters were estimated using univariate and bivariate repeatability animal models. Heritability estimates (±SE) of 0.16 (±0.10), 0.27 (±0.12), and 0.21 (±0.14) were obtained for daily methane production, methane yield, and methane intensity, respectively. A high genetic correlation (rg = 0.94 ± 0.23) between daily methane production and methane intensity indicates that selecting for daily methane production would result in lower methane per unit of milk produced. This study provides preliminary estimates of genetic parameters for methane emission traits, suggesting that there is potential to mitigate methane emission in Holstein cattle through genetic selection.
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Robustness can refer to an animal's ability to overcome perturbations. Intense selection for production traits in livestock has resulted in reduced robustness which has negative implications for livability as well as production. There is increasing emphasis on improving robustness through poultry breeding, which may involve identifying novel phenotypes that could be used in selection strategies. The hypothalamic-pituitary-adrenal (HPA) axis and associated hormones (e.g., corticosterone) participate in many metabolic processes that are related to robustness. Corticosterone can be measured non-invasively in feathers (FCORT) and reflects the average HPA axis activity over the feather growing period, however measurement is expensive and time consuming. Fault bars are visible feather deformities that may be related to HPA axis activity and may be a more feasible indicator trait. In this study, we estimated variance components for FCORT and fault bars in a population of purebred turkeys as well as their genetic and partial phenotypic correlations with other economically relevant traits including growth and efficiency, carcass yield, and meat quality. The estimated heritability for FCORT was 0.21 ± 0.07 and for the fault bar traits (presence, incidence, severity, and index) estimates ranged from 0.09 to 0.24. The genetic correlation of FCORT with breast weight, breast meat yield, fillet weight, and ultimate pH were estimated at -0.34 ± 0.21, -0.45 ± 0.23, -0.33 ± 0.24, and 0.32 ± 0.24, respectively. The phenotypic correlations of FCORT with breast weight, breast meat yield, fillet weight, drum weight, and walking ability were -0.16, -0.23, -0.18, 0.17, and 0.21, respectively. Some fault bar traits showed similar genetic correlations with breast weight, breast meat yield, and walking ability but the magnitude was lower than those with FCORT. While the dataset is limited and results should be interpreted with caution, this study indicates that selection for traits related to HPA axis activity is possible in domestic turkeys. Further research should focus on investigating the association of these traits with other robustness-related traits and how to potentially implement these traits in turkey breeding.
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Plumas , Perus , Animais , Perus/genética , Corticosterona , Sistema Hipotálamo-Hipofisário , Sistema Hipófise-Suprarrenal , FenótipoRESUMO
Increasing the productivity of Canadian dairy goats is critical to the competitiveness of the sector; however, little is known about the underlying genetic architecture of economically important traits in these populations. Consequently, the objectives of this study were as follows: (1) to perform a single-step GWAS for milk production traits (milk, protein, and fat yields, and protein and fat percentages in first and later lactations) and conformation traits (body capacity, dairy character, feet and legs, fore udder, general appearance, rear udder, suspensory ligament, and teats) in the Canadian Alpine and Saanen breeds; and (2) to identify positional and functional candidate genes related to these traits. The data available for analysis included 305-d milk production records for 6,409 Alpine and 3,434 Saanen does in first lactation and 5,827 Alpine and 2,632 Saanen does in later lactations; as well as linear type conformation records for 5,158 Alpine and 2,342 Saanen does. Genotypes were available for 833 Alpine and 874 Saanen animals. Both single-breed and multiple-breed GWAS were performed using single-trait animal models. Positional and functional candidate genes were then identified in downstream analyses. The GWAS identified 189 unique SNP that were significant at the chromosomal level, corresponding to 271 unique positional candidate genes within 50 kb up- and downstream, across breeds and traits. This study provides evidence for the economic importance of several candidate genes (e.g., CSN1S1, CSN2, CSN1S2, CSN3, DGAT1, and ZNF16) in the Canadian Alpine and Saanen populations that have been previously reported in other dairy goat populations. Moreover, several novel positional and functional candidate genes (e.g., RPL8, DCK, and MOB1B) were also identified. Overall, the results of this study have provided greater insight into the genetic architecture of milk production and conformation traits in the Canadian Alpine and Saanen populations. Greater understanding of these traits will help to improve dairy goat breeding programs.
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Estudo de Associação Genômica Ampla , Leite , Feminino , Animais , Estudo de Associação Genômica Ampla/veterinária , Canadá , Fenótipo , Lactação/genética , Cabras/genéticaRESUMO
In the dairy industry, mate allocation is dependent on the producer's breeding goals and the parents' breeding values. The probability of pregnancy differs among sire-dam combinations, and the compatibility of a pair may vary due to the combination of gametic haplotypes. Under the hypothesis that incomplete incompatibility would reduce the odds of fertilization, and complete incompatibility would lead to a non-fertilizing or lethal combination, deviation from Mendelian inheritance expectations would be observed for incompatible pairs. By adding an interaction to a transmission ratio distortion (TRD) model, which detects departure from the Mendelian expectations, genomic regions linked to gametic incompatibility can be identified. This study aimed to determine the genetic background of gametic incompatibility in Holstein cattle. A total of 283,817 genotyped Holstein trios were used in a TRD analysis, resulting in 422 significant regions, which contained 2075 positional genes further investigated for network, overrepresentation, and guilt-by-association analyses. The identified biological pathways were associated with immunology and cellular communication and a total of 16 functional candidate genes were identified. Further investigation of gametic incompatibility will provide opportunities to improve mate allocation for the dairy cattle industry.
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Genoma , Células Germinativas , Gravidez , Feminino , Animais , Bovinos , Genótipo , Haplótipos , Fertilização/genéticaRESUMO
Understanding how cows respond to heat stress has helped to provide effective herd management practices to tackle this environmental challenge. The possibility of selecting animals that are genetically more heat tolerant may provide additional means to maintain or even improve the productivity of the Canadian dairy industry, which is facing a shifting environment due to climate changes. The objective of this study was to estimate the genetic parameters for heat tolerance of milk, fat, and protein yields in Canadian Holstein cows. A total of 1.3 million test-day records from 195,448 first-parity cows were available. A repeatability test-day model fitting a reaction norm on the temperature-humidity index (THI) was used to estimate the genetic parameters. The estimated genetic correlations between additive genetic effect for production and for heat tolerance ranged from -0.13 to -0.21, indicating an antagonistic relationship between the level of production and heat tolerance. Heritability increased marginally as THI increased above its threshold for milk yield (0.20 to 0.23) and protein yield (0.14 to 0.16) and remained constant for fat yield (0.17). A Spearman rank correlation between the estimated breeding values under thermal comfort and under heat stress showed a potential genotype by environmental interaction. The existence of a genetic variability for heat tolerance allows for the selection of more heat tolerant cows.
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Fertility and hatchability are economically important traits due to their effect on poult output coming from the turkey hatchery. Traditionally, fertility is recorded as the number of fertile eggs set in the incubator (FERT), defined at a time point during incubation by the identification of a developing embryo. Hatchability is recorded as either the number of fertile eggs that hatched (hatch of fertile, HOF) or the number hatched from all the eggs set (hatch of set, HOS). These traits are collected throughout the productive life of the bird and are conventionally cumulated, resulting in each bird having a single record per trait. Genetic evaluations of these traits have been estimated using pedigree relationships. However, the longitudinal nature of the traits and the availability of genomic information have renewed interest in using random regression (RR) to capture the differences in repeatedly recorded traits, as well as in the incorporation of genomic relationships. Therefore, the objectives of this study were: 1) to compare the applicability of a RR model with a cumulative model (CUM) using both pedigree and genomic information for genetic evaluation of FERT, HOF, and HOS and 2) to estimate and compare predictability from the models. For this study, a total of 63,935 biweekly FERT, HOF, and HOS records from 7,211 hens mated to 1,524 toms were available for a maternal turkey line. In total, 4,832 animals had genotypic records, and pedigree information on 11,191 animals was available. Estimated heritability from the CUM model using pedigree information was 0.11 ± 0.02, 0.24 ± 0.02, and 0.24 ± 0.02 for FERT, HOF, and HOS, respectively. With random regression using pedigree relationships, heritability estimates were in the range of 0.04-0.09, 0.11-0.17, and 0.09-0.18 for FERT, HOF, and HOS, respectively. The incorporation of genomic information increased the heritability by an average of 28 and 23% for CUM and RR models, respectively. In addition, the incorporation of genomic information caused predictability to increase by approximately 11 and 7% for HOF and HOS, respectively; however, a decrease in predictability of about 12% was observed for FERT. Our findings suggest that RR models using pedigree and genomic relationships simultaneously will achieve a higher predictability than the traditional CUM model.
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Dry matter intake (DMI) is a fundamental component of the animal's feed efficiency, but measuring DMI of individual cows is expensive. Mid-infrared reflectance spectroscopy (MIRS) on milk samples could be an inexpensive alternative to predict DMI. The objectives of this study were (1) to assess if milk MIRS data could improve DMI predictions of Canadian Holstein cows using artificial neural networks (ANN); (2) to investigate the ability of different ANN architectures to predict unobserved DMI; and (3) to validate the robustness of developed prediction models. A total of 7,398 milk samples from 509 dairy cows distributed over Canada, Denmark, and the United States were analyzed. Data from Denmark and the United States were used to increase the training data size and variability to improve the generalization of the prediction models over the lactation. For each milk spectra record, the corresponding weekly average DMI (kg/d), test-day milk yield (MY, kg/d), fat yield (FY, g/d), and protein yield (PY, g/d), metabolic body weight (MBW), age at calving, year of calving, season of calving, days in milk, lactation number, country, and herd were available. The weekly average DMI was predicted with various ANN architectures using 7 predictor sets, which were created by different combinations MY, FY, PY, MBW, and MIRS data. All predictor sets also included age of calving and days in milk. In addition, the classification effects of season of calving, country, and lactation number were included in all models. The explored ANN architectures consisted of 3 training algorithms (Bayesian regularization, Levenberg-Marquardt, and scaled conjugate gradient), 2 types of activation functions (hyperbolic tangent and linear), and from 1 to 10 neurons in hidden layers). In addition, partial least squares regression was also applied to predict the DMI. Models were compared using cross-validation based on leaving out 10% of records (validation A) and leaving out 10% of cows (validation B). Superior fitting statistics of models comprising MIRS information compared with the models fitting milk, fat and protein yields suggest that other unknown milk components may help explain variation in weekly average DMI. For instance, using MY, FY, PY, and MBW as predictor variables produced a predictive accuracy (r) ranging from 0.510 to 0.652 across ANN models and validation sets. Using MIRS together with MY, FY, PY, and MBW as predictors resulted in improved fitting (r = 0.679-0.777). Including MIRS data improved the weekly average DMI prediction of Canadian Holstein cows, but it seems that MIRS predicts DMI mostly through its association with milk production traits and its utility to estimate a measure of feed efficiency that accounts for the level of production, such as residual feed intake, might be limited and needs further investigation. The better predictive ability of nonlinear ANN compared with linear ANN and partial least squares regression indicated possible nonlinear relationships between weekly average DMI and the predictor variables. In general, ANN using Bayesian regularization and scaled conjugate gradient training algorithms yielded slightly better weekly average DMI predictions compared with ANN using the Levenberg-Marquardt training algorithm.
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Lactação , Leite , Animais , Teorema de Bayes , Peso Corporal , Canadá , Bovinos , Dieta/veterinária , Feminino , Leite/química , Redes Neurais de Computação , Espectrofotometria Infravermelho/veterináriaRESUMO
Interest in reducing eructed CH4 is growing, but measuring CH4 emissions is expensive and difficult in large populations. In this study, we investigated the effectiveness of milk mid-infrared spectroscopy (MIRS) data to predict CH4 emission in lactating Canadian Holstein cows. A total of 181 weekly average CH4 records from 158 Canadian cows and 217 records from 44 Danish cows were used. For each milk spectra record, the corresponding weekly average CH4 emission (g/d), test-day milk yield (MY, kg/d), fat yield (FY, g/d), and protein yield (PY, g/d) were available. The weekly average CH4 emission was predicted using various artificial neural networks (ANN), partial least squares regression, and different sets of predictors. The ANN architectures consisted of 3 training algorithms, 1 to 10 neurons with hyperbolic tangent activation function in the hidden layer, and 1 neuron with linear (purine) activation function in the hidden layer. Random cross-validation was used to compared the predictor sets: MY (set 1); FY (set 2); PY (set 3); MY and FY (set 4); MY and PY (set 5); MY, FY, and PY (set 6); MIRS (set 7); and MY, FY, PY, and MIRS (set 8). All predictor sets also included age at calving and days in milk, in addition to country, season of calving, and lactation number as categorical effects. Using only MY (set 1), the predictive accuracy (r) ranged from 0.245 to 0.457 and the root mean square error (RMSE) ranged from 87.28 to 99.39 across all prediction models and validation sets. Replacing MY with FY (set 2; r = 0.288-0.491; RMSE = 85.94-98.04) improved the predictive accuracy, but using PY (set 3; r = 0.260-0.468; RMSE = 86.95-98.47) did not. Adding FY to MY (set 4; r = 0.272-0.469; RMSE = 87.21-100.76) led to a negligible improvement compared with sets 1 and 3, but it slightly decreased accuracy compared with set 2. Adding PY to MY (set 5; r = 0.250-0.451; RMSE = 87.66-100.94) did not improve prediction ability. Combining MY, FY, and PY (set 6; r = 0.252-0.455; RMSE = 87.74-101.93) yielded accuracy slightly lower than sets 2 and 3. Using only MIRS data (set 7; r = 0.586-0.717; RMSE = 69.09-96.20) resulted in superior accuracy compared with all previous sets. Finally, the combination of MIRS data with MY, FY, and PY (set 8; r = 0.590-0.727; RMSE = 68.02-87.78) yielded similar accuracy to set 7. Overall, sets including the MIRS data yielded significantly better predictions than the other sets. To assess the predictive ability in a new unseen herd, a limited block cross-validation was performed using 20 cows in the same Canadian herd, which yielded r = 0.229 and RMSE = 154.44, which were clearly much worse than the average r = 0.704 and RMSE = 70.83 when predictions were made by random cross-validation. These results warrant further investigation when more data become available to allow for a more comprehensive block cross-validation before applying the calibrated models for large-scale prediction of CH4 emissions.
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Lactação , Leite , Animais , Canadá , Bovinos , Feminino , Lactação/metabolismo , Metano/metabolismo , Leite/química , Redes Neurais de Computação , Purinas , Espectrofotometria Infravermelho/veterináriaRESUMO
The present study investigated the prevalence and co-occurrence of integument injuries in Canadian turkeys. Participating farmers scored 30 birds in their flock for integument injuries to the head/neck (HN), back/tail (BT), and footpad (FP) using a simplified scoring system (0: no sign of injury, 1: mild injury, 2: severe injury). Information from 62 flocks was used to calculate the prevalence of any (score ≥1) and severe (score 2) injuries on a flock- and individual-level. Chi-square analyses were performed to determine the likelihood of integument injury co-occurrence. The prevalence of each type of injury varied between flocks. While the majority of flocks reported injuries, the within-flock prevalence was relatively low and largely comprised of mild cases (score 1). Given their higher prevalence, the data indicate that FP injuries are overall more widespread and more severe among Canadian turkey flocks than HN and BT injuries. Co-occurrence of different integument injuries was observed in 7% of birds and 58.1% of flocks reported at least one bird with co-occurring injury types. Despite the low prevalence of multiple injury types, birds with one type of injury were more likely to present with other injury types. Indeed, birds with HN injuries were 4 times more likely to have BT injuries, and birds with FP injuries were 1.5 times more likely to have BT injuries compared to birds that do not have these respective injuries. The data increase our understanding of the co-occurrence of these common integument injuries which can help inform a holistic management approach to rear turkeys with healthy skin and feather cover.
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Doenças das Aves Domésticas , Animais , Doenças das Aves Domésticas/epidemiologia , Galinhas , Canadá , Perus , PlumasRESUMO
BACKGROUND: Sharing individual phenotype and genotype data between countries is complex and fraught with potential errors, while sharing summary statistics of genome-wide association studies (GWAS) is relatively straightforward, and thus would be especially useful for traits that are expensive or difficult-to-measure, such as feed efficiency. Here we examined: (1) the sharing of individual cow data from international partners; and (2) the use of sequence variants selected from GWAS of international cow data to evaluate the accuracy of genomic estimated breeding values (GEBV) for residual feed intake (RFI) in Australian cows. RESULTS: GEBV for RFI were estimated using genomic best linear unbiased prediction (GBLUP) with 50k or high-density single nucleotide polymorphisms (SNPs), from a training population of 3797 individuals in univariate to trivariate analyses where the three traits were RFI phenotypes calculated using 584 Australian lactating cows (AUSc), 824 growing heifers (AUSh), and 2526 international lactating cows (OVE). Accuracies of GEBV in AUSc were evaluated by either cohort-by-birth-year or fourfold random cross-validations. GEBV of AUSc were also predicted using only the AUS training population with a weighted genomic relationship matrix constructed with SNPs from the 50k array and sequence variants selected from a meta-GWAS that included only international datasets. The genomic heritabilities estimated using the AUSc, OVE and AUSh datasets were moderate, ranging from 0.20 to 0.36. The genetic correlations (rg) of traits between heifers and cows ranged from 0.30 to 0.95 but were associated with large standard errors. The mean accuracies of GEBV in Australian cows were up to 0.32 and almost doubled when either overseas cows, or both overseas cows and AUS heifers were included in the training population. They also increased when selected sequence variants were combined with 50k SNPs, but with a smaller relative increase. CONCLUSIONS: The accuracy of RFI GEBV increased when international data were used or when selected sequence variants were combined with 50k SNP array data. This suggests that if direct sharing of data is not feasible, a meta-analysis of summary GWAS statistics could provide selected SNPs for custom panels to use in genomic selection programs. However, since this finding is based on a small cross-validation study, confirmation through a larger study is recommended.
Assuntos
Bovinos , Lactação , Animais , Austrália , Bovinos/genética , Feminino , Estudo de Associação Genômica Ampla , Genômica , Genótipo , Fenótipo , Polimorfismo de Nucleotídeo ÚnicoRESUMO
The presence of meat quality defects is increasing in the turkey industry. While the main strategy for mitigating these issues is through improved housing, management, and slaughter conditions, it may be possible to incorporate meat quality into a turkey breeding strategy with the intent to improve meat quality. Before this can occur, it is important to describe the current state of turkey meat quality as well as the correlations among the different meat quality traits and important production traits. The main objective of the present study was to provide a descriptive analysis of 8 different meat quality traits for turkey breast meat from 3 different purebred lines (A, B, and C), and their correlation with a selection of production traits. Using a total of 7,781 images, the breast meat (N = 590-3,892 birds depending on trait) was evaluated at 24 h postmortem for color (L*, a*, b*), pH, and physiochemical characteristics (drip loss, cooking loss, shear force). Descriptive statistics (mean and standard deviation) and Pearson correlations were computed to describe the relationships among traits within each genetic line. A one-factor ANOVA and post hoc t-test were conducted for each trait and between each of the genetic lines. We found significant differences between genetic lines for some color traits (L* and a*), pHinitial, drip loss, and cooking loss. The lightest line in weight (line B) had meat that was the lightest (L*) in color. The heaviest line (line C) had meat that was less red (a*) with a higher pHinitial and greater cooking loss. Unfavorable correlations between production traits and meat quality were also found for each of the genetic lines where increases in production (e.g., body weight, growth rate) resulted in meat that was lighter and redder in color and in some cases (line B and C), with an increased moisture loss. The results of this study provide an important benchmark for turkey meat quality in purebred lines and provide an updated account of the relationships between key production traits and meat quality. Although the magnitude of these correlations is low, their cumulative effect on meat quality can be more significant especially with continued selection pressure on growth and yield.
Assuntos
Galinhas , Carne , Animais , Culinária , Fenótipo , Perus/genéticaRESUMO
The dairy industry is moving toward selecting animals with better fertility to decrease the economic losses linked to reproductive issues. The reproductive tract size and position score (SPS) was recently developed in physiological studies as an indicator of pregnancy rate and the number of services to conception. Cows are scored as SPS 1, 2, or 3 based on the size of their reproductive tract and its position in the pelvis, as determined by transrectal palpation. The objective of this study was to estimate genetic parameters for SPS to assess its potential as a novel fertility trait. Phenotypes were collected at the University of British Columbia's research herd from 2017 to 2020, consisting of 3,247 within- and across-lactation SPS records from 490 Holstein cows. A univariate animal model was used to estimate the variance components for SPS. Both threshold and linear models were fit under a Bayesian approach and the results were compared using the Spearman rank correlation (r) between the estimated breeding values. The 2 models ranked the animals very similarly (r = 0.99), and the linear model was selected for further analysis. Genetic correlations with other currently evaluated traits were estimated using a bivariate animal model. The posterior means (± posterior standard deviation) for heritability and repeatability within- and across-lactation were 0.113 (± 0.013), 0.242 (± 0.012), and 0.134 (± 0.014), respectively. The SPS showed null correlations with production traits and favorable correlations with traditional fertility traits, varying from -0.730 (nonreturn rate) to 0.931 (number of services). Although preliminary, these results are encouraging because SPS seems to be more heritable than and strongly genetically correlated with number of services, nonreturn rate, and first service to conception, indicating potential for effective indirect selection response on these traits from SPS genetic selection. Therefore, further studies with larger data sets to validate these findings are warranted.