Comparison of different models for genetic analysis of clinical mastitis in Austrian Fleckvieh dual-purpose cows

Converting estimated breeding values from the observed to probability scale for health traits

Dairy cattle health traits are paramount from a welfare and economic viewpoint; therefore, modern breeding programs prioritize the genetic improvement of these traits. Estimated breeding values for health traits are published as the probability of animals staying healthy. They are obtained using threshold models, which assume that the observed binary phenotype (i.e., healthy or sick) is dictated by an underlying normally distributed liability exceeding or not a threshold. This methodology requires significant computing time and faces convergence challenges as it implies a nonlinear system of equations. Linear models have more straightforward computations and provide a robust approximation to threshold models; thus, they could be used to overcome the mentioned challenges. However, linear models yield estimated breeding values on the observed scale, requiring an approximation to the liability scale analogous to that from threshold models to later obtain the estimated breeding values on the probability scale. In addition, the robustness of the approximation of linear to threshold models depends on the amount of information and the incidence of the trait, with extreme incidence (i.e., ≤ 5%) deviating from optimal approximation. Our objective was to test a transformation from the observed to the liability and then to the probability scale in the genetic evaluation of health traits with moderate and very low (extreme) incidence. Data comprised displaced abomasum (5.1M), ketosis (3.6M), lameness (5M), and mastitis (6.3M) records from a Holstein population with a pedigree of 6M animals, of which 1.7M were genotyped. Univariate threshold and linear models were performed to predict breeding values. The agreement between estimated breeding values on the probability scale derived from threshold and linear models was assessed using Spearman rank correlations and comparison of estimated breeding values distributions. Correlations were at least 0.95, and estimated breeding value distributions almost entirely overlapped for all the traits but displaced abomasum, the trait with the lowest incidence (2%). Computing time was ∼3x longer for threshold than for linear models. In this Holstein population, the approximation was suboptimal for a trait with extreme incidence (2%). However, when the incidence was ≥6%, the approximation was robust, and its use is recommended along with linear models for analyzing categorical traits in large populations to ease the computational burden.

The impact of genotyping strategies and statistical models on accuracy of genomic prediction for survival in pigs

BACKGROUND

Survival from birth to slaughter is an important economic trait in commercial pig productions. Increasing survival can improve both economic efficiency and animal welfare. The aim of this study is to explore the impact of genotyping strategies and statistical models on the accuracy of genomic prediction for survival in pigs during the total growing period from birth to slaughter.  RESULTS: We simulated pig populations with different direct and maternal heritabilities and used a linear mixed model, a logit model, and a probit model to predict genomic breeding values of pig survival based on data of individual survival records with binary outcomes (0, 1). The results show that in the case of only alive animals having genotype data, unbiased genomic predictions can be achieved when using variances estimated from pedigree-based model. Models using genomic information achieved up to 59.2% higher accuracy of estimated breeding value compared to pedigree-based model, dependent on genotyping scenarios. The scenario of genotyping all individuals, both dead and alive individuals, obtained the highest accuracy. When an equal number of individuals (80%) were genotyped, random sample of individuals with genotypes achieved higher accuracy than only alive individuals with genotypes. The linear model, logit model and probit model achieved similar accuracy.

CONCLUSIONS

Our conclusion is that genomic prediction of pig survival is feasible in the situation that only alive pigs have genotypes, but genomic information of dead individuals can increase accuracy of genomic prediction by 2.06% to 6.04%.

Genomic evaluation for wellness traits in US Jersey cattle

The number of Jersey cows in the United States has been steadily increasing in recent years according to Council on Dairy Cattle Breeding statistics. To help producers reduce the risk of health disorders in their Jersey animals, Zoetis has developed genomic predictions for wellness traits in Jersey cattle using producer-recorded data. The traits included mastitis (MAST), metritis, retained placenta, displaced abomasum (DA), ketosis, lameness, and milk fever in cows and respiratory disease, scours, and calf livability (DEAD) in calves. Phenotypic data on health events, pedigree, and genotypes were collected directly from producers upon obtaining their permission. Each trait was defined as a binary event, having a value of 1 if an animal has been recorded with a disorder and 0 otherwise. The number of phenotypic records ranged from 216,166 for DA to 628,958 for MAST for cow traits and from 186,505 for scours to 380,429 for DEAD for calf traits. The number of genotyped animals was 41,271. All traits were analyzed using a univariate threshold animal model. The model for cow wellness traits included the fixed effect of parity and random effects of herd × year × season of calving, animal, and permanent environment. The model for calf wellness traits included the fixed effect of year of birth × calving season × region and random effects of herd × year of birth and animal. A total of 45,163 SNP were used in genomic analyses. Animals genotyped with low-density chips were imputed to the required number of markers. All analyses were based on the single-step genomic BLUP. Heritabilities ranged from 0.061 for DA to 0.120 for lameness. Predicted transmitting abilities were expressed in percentage points as deviations from the average estimated probability of a disorder in the base population. Reliabilities of genomic predicted transmitting abilities had average values between 32% (DA) and 51% (MAST and DEAD). The results indicate that a direct evaluation of cow and calf wellness traits under a genomic threshold model is feasible and offers predictions with average reliabilities comparable with other lowly heritable traits for Jersey cattle.

Genome-wide association analysis for β-hydroxybutyrate concentration in Milk in Holstein dairy cattle

BACKGROUND

Ketosis in dairy cattle has been shown to cause a high morbidity in the farm and substantial financial losses to dairy farmers. Ketosis symptoms, however, are difficult to identify, therefore, the amount of ketone bodies (mainly β-hydroxybutyric acid, BHB) is used as an indicator of subclinical ketosis in cows. It has also been shown that milk BHB concentrations have a strong correlation with ketosis in dairy cattle. Mid-infrared spectroscopy (MIR) has recently became a fast, cheap and high-throughput method for analyzing milk components. The aim of this study was to perform a genome-wide association study (GWAS) on the MIR-predicted milk BHB to identify genomic regions, genes and pathways potentially affecting subclinical ketosis in North American Holstein dairy cattle.

RESULTS

Several significant regions were identified associated with MIR-predicted milk BHB concentrations (indicator of subclinical ketosis) in the first lactation (SCK1) and second and later lactations (SCK2) in Holstein dairy cows. The strongest association was located on BTA6 for SCK1 and BTA14 on SCK2. Several SNPs on BTA6 were identified in regions and variants reported previously to be associated with susceptibility to ketosis and clinical mastitis in Jersey and Holstein dairy cattle, respectively. One highly significant SNP on BTA14 was found within the DGAT1 gene with known functions on fat metabolism and inflammatory response in dairy cattle. A region on BTA6 and three SNPs on BTA20 were found to overlap between SCK1 and SCK2. However, a novel region on BTA20 (55-63 Mb) for SCK2 was also identified, which was not reported in previous association studies. Enrichment analysis of the list of candidate genes within the identified regions for MIR-predicted milk BHB concentrations yielded molecular functions and biological processes that may be involved in the inflammatory response and lipid metabolism in dairy cattle.

CONCLUSIONS

The results of this study confirmed several SNPs and genes identified in previous studies as associated with ketosis susceptibility and immune response, and also found a novel region that can be used for further analysis to identify causal variations and key regulatory genes that affect clinical/ subclinical ketosis.

Genetic parameters for mastitis incidence and its indicators based on somatic cell score for Holsteins in Hokkaido, Japan

The objectives of this study were to estimate the heritability of mastitis incidence and genetic correlations between the mastitis and the somatic cell score (SCS) statistics, and to compare the practicability between different models. We used test-day records with the mastitis incidence and SCS collected from Holstein cows calving from 1988 to 2015 in Hokkaido, Japan. As indicators of mastitis, the average SCS (avSCS), the standard deviation of SCS (sdSCS), and the maximum SCS (maxSCS) were calculated using test-day records up to the first 305 days in milk within a lactation. We compared a four-trait repeatability animal model (MTRP) with a four-trait multiple-lactation animal model (MTML). The heritability for mastitis was equal to or lower than 0.05 in all the models. Genetic correlations between lactations with MTML within the same trait were positive and close to 1. With MTRP, the estimated genetic correlations of the mastitis incidence with avSCS, sdSCS, and maxSCS were 0.66, 0.79, and 0.82, respectively. A joint evaluation with SCS statistics is expected to give an extra reliability for mastitis because of high and positive genetic correlations among the traits.

Genetic aspects of some reproductive, udder health and energy status traits in Holstein cows

The objective of this study was to estimate heritability as well as genetic and environmental relationships between days to first heat (DFH), days to first service (DFS), interval from calving to conception (ICC), calving interval (CI) and number of inseminations per conception (NIS) with mastitis (Mast), number of mastitis occurrence (NumMast), different measures of somatic cell count (SCC) and fat to protein ratio (F:P) in the first lactation of Holstein cows in Iran using linear and threshold animal and sire models. The 33851 first lactation records of Holstein cows from five large dairy herds with calving dates from March 2002 to September 2014 were analyzed with univariate and bivariate linear and threshold animal and sire models using Gibbs sampling methodology. Data from parity one to nine comprising 62483 records were used to conduct repeatability model analysis for reproductive traits. Heritabilities of the reproduction traits varied from 0.067 (for ICC) to 0.105 (for DFH) using linear animal models. Also, the heritabilities of udder health traits varied from 0.005 to 0.102 using different models. The repeatabilities of reproductive traits ranged from 0.110 to 0.307. In general, the genetic correlations (r_g) between reproduction traits were positive and high (with the exception of r_g between DFH-NIS). The r_g between reproduction traits with udder health traits ranged from -0.029 to 0.359 and 0.151 to 0.584 using linear-linear and threshold-linear animal models, respectively. Generally, there was favorable r_g between reproduction traits with udder health traits; therefore, selection for one set of these traits would improve the correlated traits. However, due to different (co)variance components and economic weights in each country/region, it can be recommended to investigate inclusion of both sets of these traits in breeding objectives.

Relationship between somatic cell score and longevity of Holstein cows in Brazil using a piecewise Weibull proportional-hazard model

The objective of the present work was to evaluate the effect of somatic cell score on the longevity of Holstein cows raised all over Brazil, using a piecewise Weibull proportional-hazard model. The following two longevity traits were defined: true longevity, number of days from first calving to culling; and functional longevity, approximated by correcting true longevity to within-herd-year production. Records on productive life of 131330 cows were used. The model included the time-independent effect of age at first calving. The other effects were time-dependent, and included the following: region by year of calving, variation in herd-size class, milk-production class by year of calving within herd, within-herd milk-production class by number of lactations, within-herd fat content, within-herd protein content, and somatic cell score (SCS). The overall mean of the somatic cell counts (SCC) was 322000 cells/mL. The highest SCC mean was found between 130 to 290 days of lactation. The SCC mean decreased over the years. Cows from Region 5 (Rio Grande do Sul) showed higher SCC means. The risk of culling was slightly higher for functional longevity than for true longevity. The impact of longevity was high in cows from first to fourth lactation with a high SCS, with the risk of culling varying from 0.90 (true longevity: second lactation and Class 2) to 1.2 (functional longevity: fourth lactation and Class 5). Cows at the fifth lactation with a lower SCS had a higher risk of culling (1.4). Including the effect of SCS class by stage of lactation in the models was not beneficial. The decrease in SCS, especially from the first to fourth lactation, can be used for indirect selection to improve the longevity of Holstein cows in Brazil.

Suitability of traits related to aggression and handleability for integration into pig breeding programmes: Genetic parameters and comparison between Gaussian and binary trait specifications

Changes in husbandry systems as well as consumers' increasing demands for animal welfare lead to increasing importance of traits such as handleability and aggressiveness in pigs. However, before using such novel traits for selection decisions, information on genetic parameters for these traits for the specific population is required. Therefore, weight gain and behaviour-related traits were recorded in 1004 pigs (814 Pietrain x German Landrace crossbred, 190 German Landrace purebred) at different ages. Behaviour indicators and tests were assessed and conducted, respectively under commercial farm conditions and included scoring of skin lesions (twice) and behaviour during backtests (twice), injections (once), handling (twice) and weighing (three times). Since behaviour scores often exhibit suboptimal statistical properties for parametric analyses, variance components were estimated using an animal model assuming a normal (Gaussian, GA; all traits) and additionally a binary distribution of variables (BI; using a logit-link function for all behaviour traits). Heritabilities for behavioural traits ranged from 0.02 ± 0.04 (finishing pig handling test; BI) to 0.36 ± 0.08 (backtest 2; GA) suggesting that some of the traits are potentially useful for genetic selection (e.g. finishing pig weighing test: h2 (GA) = 0.20 ± 0.07). Only minor differences were observed for results from binary and Gaussian analyses of the same traits suggesting that either approach might yield valid results. However, four-fold cross-validation using correlations between breeding values of a sub-set of animals for the sample trait finishing pig weighing score indicated slight superiority of the logit model (r = 0.85 ± 0.04 vs. r = 0.77 ± 0.03). Generally, only weak to moderate associations were found between behavioural reactions to the same test at different ages (rp ≤ 0.11 for weighing at different ages; rp = 0.30 but rg (GA) = 0.84 ± 0.11 for the backtests) as well as between reactions to different tests. Therefore, for inclusion of behaviour traits into breeding programmes, and considering high labour input required for some tests such as the backtest, it is recommended to assess behaviour during situations that are relevant and identical to practical conditions, while the use of indicator traits generally does not appear to be a very promising alternative.

Alternative somatic cell count traits exploitable in genetic selection for mastitis resistance in Italian Holsteins

The aim of the present study was to characterize alternative somatic cell count (SCC) traits that could be exploited in genetic selection for mastitis resistance. Data were from 66,407 first-parity Holsteins in 404 herds. Novel SCC traits included average somatic cell score (SCS, log-transformation of SCC) in early lactation (SCS_150), standard deviation of SCS of the entire lactation (SCS_SD), the presence of at least one test-day (TD) SCC >400,000 cells/mL in the lactation, and the ratio of number of TD SCC >400,000 cells/mL to total number of TD in the lactation. Novel traits and lactation-mean SCS (SCS_LM) were analyzed using linear mixed or logistic regression models, including month of calving, year of calving, number of TD, and milk yield as fixed effects, and herd and residual as random terms. A multitrait linear animal model was applied to a random subset of 152 herds (n = 22,695 cows) to assess heritability of and genetic correlations between SCC traits. Alternative SCC traits were affected by the environmental factors included in the model; in particular, results suggested a seasonal effect and a tendency toward an improvement of the udder health status in the last years. Association was also found between novel SCC traits and milk production. Alternative SCC traits exhibited coefficients of additive genetic variation that were similar to or larger than that of traditional SCS_LM. Heritability of novel SCC traits was smaller than heritability of SCS_LM (0.126 ± 0.014), ranging from 0.044 ± 0.008 (SCS_SD) to 0.087 ± 0.010 (SCS_150). Genetic correlations between SCC traits ranged from 0.217 ± 0.096 (SCS_150 and SCS_SD) to 0.969 ± 0.010 (SCS_LM and SCS_150). Alternative SCC traits exhibited additive genetic variation that is potentially exploitable in breeding programs of Italian Holstein population to improve resistance to mastitis.

Symposium review: Novel strategies to genetically improve mastitis resistance in dairy cattle

Mastitis is a disease of major economic importance to the dairy cattle sector because of the high incidence of clinical mastitis and prevalence of subclinical mastitis and, consequently, the costs associated with treatment, production losses, and reduced animal welfare. Disease-recording systems compiling data from a large number of farms are still not widely implemented around the world; thus, selection for mastitis resistance is often based on genetically correlated indicator traits such as somatic cell count (SCC), udder depth, and fore udder attachment. However, in the past years, several countries have initiated collection systems of clinical mastitis, based on producers recording data in most cases. The large data sets generated have enabled researchers to assess incidence of this disease and to investigate the genetic background of clinical mastitis itself, as well as its relationships with other traits of interest to the dairy industry. The genetic correlations between clinical mastitis and its previous proxies were estimated more accurately and confirmed the strong relationship of clinical mastitis with SCC and udder depth. New traits deriving from SCC were also studied, with the most relevant findings being associated with mean somatic cell score (SCS) in early lactation, standard deviation of SCS, and excessive test-day SCC pattern. Genetic correlations between clinical mastitis and other economically important traits indicated that selection for mastitis resistance would also improve resistance against other diseases and enhance both fertility and longevity. However, milk yield remains negatively correlated with clinical mastitis, emphasizing the importance of including health traits in the breeding objectives to achieve genetic progress for all important traits. These studies enabled the establishment of new genetic and genomic evaluation models, which are more efficient for selection to mastitis resistance. Further studies that are potential keys for future improvement of mastitis resistance are deep investigation of the bacteriology of mastitis, identification of novel indicator traits and tools for selection, and development of a larger female reference population to improve reliability of genomic evaluations. These cutting-edge studies will result in a better understanding of the genetic background of mastitis resistance and enable a more accurate phenotyping and genetic selection to improve mastitis resistance, and consequently, animal welfare and industry profitability.

Genetic Characterization of Dog Personality Traits

The genetic architecture of behavioral traits in dogs is of great interest to owners, breeders, and professionals involved in animal welfare, as well as to scientists studying the genetics of animal (including human) behavior. The genetic component of dog behavior is supported by between-breed differences and some evidence of within-breed variation. However, it is a challenge to gather sufficiently large datasets to dissect the genetic basis of complex traits such as behavior, which are both time-consuming and logistically difficult to measure, and known to be influenced by nongenetic factors. In this study, we exploited the knowledge that owners have of their dogs to generate a large dataset of personality traits in Labrador Retrievers. While accounting for key environmental factors, we demonstrate that genetic variance can be detected for dog personality traits assessed using questionnaire data. We identified substantial genetic variance for several traits, including fetching tendency and fear of loud noises, while other traits revealed negligibly small heritabilities. Genetic correlations were also estimated between traits; however, due to fairly large SEs, only a handful of trait pairs yielded statistically significant estimates. Genomic analyses indicated that these traits are mainly polygenic, such that individual genomic regions have small effects, and suggested chromosomal associations for six of the traits. The polygenic nature of these traits is consistent with previous behavioral genetics studies in other species, for example in mouse, and confirms that large datasets are required to quantify the genetic variance and to identify the individual genes that influence behavioral traits.

Invited review: Opportunities for genetic improvement of metabolic diseases

Metabolic disorders are disturbances to one or more of the metabolic processes in dairy cattle. Dysfunction of any of these processes is associated with the manifestation of metabolic diseases or disorders. In this review, data recording, incidences, genetic parameters, predictors, and status of genetic evaluations were examined for (1) ketosis, (2) displaced abomasum, (3) milk fever, and (4) tetany, as these are the most prevalent metabolic diseases where published genetic parameters are available. The reported incidences of clinical cases of metabolic disorders are generally low (less than 10% of cows are recorded as having a metabolic disease per herd per year or parity/lactation). Heritability estimates are also low and are typically less than 5%. Genetic correlations between metabolic traits are mainly positive, indicating that selection to improve one of these diseases is likely to have a positive effect on the others. Furthermore, there may also be opportunities to select for general disease resistance in terms of metabolic stability. Although there is inconsistency in published genetic correlation estimates between milk yield and metabolic traits, selection for milk yield may be expected to lead to a deterioration in metabolic disorders. Under-recording and difficulty in diagnosing subclinical cases are among the reasons why interest is growing in using easily measurable predictors of metabolic diseases, either recorded on-farm by using sensors and milk tests or off-farm using data collected from routine milk recording. Some countries have already initiated genetic evaluations of metabolic disease traits and currently most of these use clinical observations of disease. However, there are opportunities to use clinical diseases in addition to predictor traits and genomic information to strengthen genetic evaluations for metabolic health in the future.

Farmer-observed health data around calving--Genetic parameters and association with veterinarian diagnoses in Austrian Fleckvieh cows

The objective of this study was to investigate if farmer-observed health data around calving can be used together with veterinarian diagnoses for genetic evaluations. Four diseases are recorded by farmers: retained placenta, downer cow syndrome, mastitis, and lameness. Mean disease frequencies were 4.7, 3.8, and 1.8% for retained placenta, downer cow syndrome, and mastitis, respectively. Lameness had a very low frequency (0.7%) and a preliminary analysis revealed a heritability close to zero for this trait. Therefore, lameness was not considered in the analysis. For genetic analyses, univariate and bivariate linear animal models were fitted. Heritabilities for retained placenta, downer cow syndrome, and mastitis were 0.01, 0.03, and 0.003, respectively. Genetic correlations among the investigated disease traits were low to moderate and not significantly different from zero. Pearson correlations between estimated breeding values for disease traits and other routinely evaluated traits were computed, which revealed mostly favorable relationships to fertility, maternal calving ease, muscling, and longevity. In addition, a moderate favorable association was found between mastitis and somatic cell score. Heritability estimates of farmer-observed health traits were comparable to estimates based on veterinarian diagnoses. Genetic correlations between the investigated diseases based on farmer observations and veterinarian diagnoses were almost 1, with estimates ranging from 0.98 to 0.99. These results suggest that farmer recorded health data could be used together with veterinarian diagnoses for genetic evaluations.

Genetic analysis of fertility-related diseases and disorders in Norwegian Red cows

Heritability of and genetic correlations among silent heat (SH), cystic ovaries (CO), metritis (MET), and retained placenta (RP) were inferred. These traits were chosen because they are the 4 most frequent fertility-related diseases and disorders among first-lactation cows in Norway. Records of 503,683 first-lactation daughters of 1,058 Norwegian Red sires with first calving from 2000 through 2006 were analyzed with a 4-variate threshold sire model. Presence or absence of each of the 4 diseases was scored as 1 or 0 based on whether or not the cow had at least 1 veterinary treatment for the disease. The mean frequency was 3.1% for SH, 0.9% for MET, 0.5% for CO, and 1.5% for RP. The model for liability had effects of age at calving and of month-year of calving, herd, sire of the cow, and a residual. Posterior mean (SD) of heritability of liability was 0.06 (0.01) for SH, 0.03 (0.01) for MET, 0.07 (0.01) for CO, and 0.06 (0.01) for RP. The genetic correlation between MET and RP was strong, with posterior mean (SD) 0.64 (0.10). A negative genetic correlation (-0.26) was found between RP and CO. The posterior distributions of the other genetic correlations included zero with high density, and could not be considered different from zero. The frequency of fertility-related diseases and disorders is very low in the Norwegian Red population at present, so there is limited scope for genetic improvement. However, this study indicates that reasonably precise genetic evaluation of sires is feasible for these traits given information from large daughter groups.