Exploration of lagged relationships between mastitis and milk yield in dairycows using a Bayesian structural equation Gaussian-threshold model

A Gaussian-threshold model is described under the general framework of structural equation models for inferring simultaneous and recursive relationships between binary and Gaussian characters, and estimating genetic parameters. Relationships between clinical mastitis (CM) and test-day milk yield (MY) in first-lactation Norwegian Red cows were examined using a recursive Gaussian-threshold model. For comparison, the data were also analyzed using a standard Gaussian-threshold, a multivariate linear model, and a recursive multivariate linear model. The first 180 days of lactation were arbitrarily divided into three periods of equal length, in order to investigate how these relationships evolve in the course of lactation. The recursive model showed negative within-period effects from (liability to) CM to test-day MY in all three lactation periods, and positive between-period effects from test-day MY to (liability to) CM in the following period. Estimates of recursive effects and of genetic parameters were time-dependent. The results suggested unfavorable effects of production on liability to mastitis, and dynamic relationships between mastitis and test-dayMYin the course of lactation. Fitting recursive effects had little influence on the estimation of genetic parameters. However, some differences were found in the estimates of heritability, genetic, and residual correlations, using different types of models (Gaussian-threshold vs. multivariate linear)

A periodic analysis of longitudinal binary responses: a case study of clinical mastitis in Norwegian Red cows

A Bayesian procedure for analyzing longitudinal binary responses using a periodic cosine function was developed. It was assumed that, after adjustment for "seasonal" effects, the oscillation of the underlying latent variables for longitudinal binary responses was a stationary series. Based on this assumption, a single dimension sinusoidal analysis of longitudinal binary responses using the Gibbs sampling and Metropolis algorithms was implemented in a study of clinical mastitis records of Norwegian Red cows taken over five lactations

Novel interpretation of sperm stress test and morphology for maturity assessment of young Norwegian Red bulls

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Associations between insulin-like factor 3, scrotal circumference and semen characteristics in young Norwegian Red bulls

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Mixture model for inferring susceptibility to mastitis in dairy cattle: a procedure for likelihood-based inference

A Gaussian mixture model with a finite number of components and correlated random effects is described. The ultimate objective is to model somatic cell count information in dairy cattle and to develop criteria for genetic selection against mastitis, an important udder disease. Parameter estimation is by maximum likelihood or by an extension of restricted maximum likelihood. A Monte Carlo expectation-maximization algorithm is used for this purpose. The expectation step is carried out using Gibbs sampling, whereas the maximization step is deterministic. Ranking rules based on the conditional probability of membership in a putative group of uninfected animals, given the somatic cell information, are discussed. Several extensions of the model are suggested

A simple algorithm to estimate genetic variance in an animal threshold model using Bayesian inference

<p>Abstract</p> <p>Background</p> <p>In the genetic analysis of binary traits with one observation per animal, animal threshold models frequently give biased heritability estimates. In some cases, this problem can be circumvented by fitting sire- or sire-dam models. However, these models are not appropriate in cases where individual records exist on parents. Therefore, the aim of our study was to develop a new Gibbs sampling algorithm for a proper estimation of genetic (co)variance components within an animal threshold model framework.</p> <p>Methods</p> <p>In the proposed algorithm, individuals are classified as either "informative" or "non-informative" with respect to genetic (co)variance components. The "non-informative" individuals are characterized by their Mendelian sampling deviations (deviance from the mid-parent mean) being completely confounded with a single residual on the underlying liability scale. For threshold models, residual variance on the underlying scale is not identifiable. Hence, variance of fully confounded Mendelian sampling deviations cannot be identified either, but can be inferred from the between-family variation. In the new algorithm, breeding values are sampled as in a standard animal model using the full relationship matrix, but genetic (co)variance components are inferred from the sampled breeding values and relationships between "informative" individuals (usually parents) only. The latter is analogous to a sire-dam model (in cases with no individual records on the parents).</p> <p>Results</p> <p>When applied to simulated data sets, the standard animal threshold model failed to produce useful results since samples of genetic variance always drifted towards infinity, while the new algorithm produced proper parameter estimates essentially identical to the results from a sire-dam model (given the fact that no individual records exist for the parents). Furthermore, the new algorithm showed much faster Markov chain mixing properties for genetic parameters (similar to the sire-dam model).</p> <p>Conclusions</p> <p>The new algorithm to estimate genetic parameters via Gibbs sampling solves the bias problems typically occurring in animal threshold model analysis of binary traits with one observation per animal. Furthermore, the method considerably speeds up mixing properties of the Gibbs sampler with respect to genetic parameters, which would be an advantage of any linear or non-linear animal model.</p

Semen quality parameters including metabolites, sperm production traits and fertility in young Norwegian Red AI bulls

Genomic selection in cattle breeding has gradually allowed younger bulls to be recruited for semen production. In this study, sperm quality parameters, seminal plasma and sperm metabolites, semen production capacity and fertility in young Norwegian Red bulls were analysed. For in vitro analyses of sperm quality and metabolites, ejaculates were collected from the same 25 bulls at both 14 and 17 months of age. Semen production and fertility data were collected for all Norwegian Red bulls in production from December 2017 throughout 2019. Bull fertility was measured as 56 days non-return rate (NR56), for both age groups. In both fresh and frozen-thawed semen samples, the proportion of hyperactive spermatozoa, average path velocity, curvilinear velocity and amplitude of lateral head displacement were higher in samples collected at 17 months of age compared to 14 months (PSemen quality parameters including metabolites, sperm production traits and fertility in young Norwegian Red AI bullsacceptedVersio

Semen quality parameters including metabolites, sperm production traits and fertility in young Norwegian Red AI bulls

Genomic selection in cattle breeding has gradually allowed younger bulls to be recruited for semen production. In this study, sperm quality parameters, seminal plasma and sperm metabolites, semen production capacity and fertility in young Norwegian Red bulls were analysed. For in vitro analyses of sperm quality and metabolites, ejaculates were collected from the same 25 bulls at both 14 and 17 months of age. Semen production and fertility data were collected for all Norwegian Red bulls in production from December 2017 throughout 2019. Bull fertility was measured as 56 days non-return rate (NR56), for both age groups.acceptedVersionpublishedVersio

Bulletin (1945-1946)

https://red.mnstate.edu/bulletins/1026/thumbnail.jp

The effects of crossbreeding with Norwegian Red dairy cattle on common postpartum diseases, fertility and body condition score

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