Inferring Relationships Between Somatic Cell Score and Milk Yield Using Simultaneous and Recursive Models

Wu, Xiaolin; Heringstad, B.; Chang, YM; Campos, Gustavo de los; Gianola, Daniel

doi:10.3168/jds.2006-762

Cited by 51 publications

(80 citation statements)

References 27 publications

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“…Wu et al [26] found that estimates of some genetic and residual correlations from SIR models could differ considerably from those obtained using standard mixed models. In the present analysis, however, similar estimates of genetic and residual correlations were found regardless of the presence of recursive effects in the models.…”

Section: Estimation Of Genetic Parameters Under Recursive Relationshipsmentioning

confidence: 99%

“…A detailed description of the convergence analysis can be found in Wu et al [26]. Based on the convergence diagnostics results, it was decided that a single chain of 100 000 iterations would be used.…”

Section: Analysis Of Posterior Samplesmentioning

confidence: 99%

“…Because it does not have a recognizable form, a Metropolis-Hastings algorithm is used to sample k, centering the proposal at their current values [26]. In recursive models (i.e., K is an upper-or lower-diagonal matrix), K j j ¼ 1.…”

Section: Structural Coefficients and Dispersion Parametersmentioning

confidence: 99%

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

Heringstad

Gianola

2008

Genet. Sel. Evol.

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-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-day MY in 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).Bayesian inference / mastitis / milk yield / structural equation model / threshold model

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Section: Estimation Of Genetic Parameters Under Recursive Relationshipsmentioning

confidence: 99%

Section: Analysis Of Posterior Samplesmentioning

confidence: 99%

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

Heringstad

Gianola

2008

Genet. Sel. Evol.

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“…The authors noted the huge number of possible causal structures, even in studies with only a few traits. Wu et al (2007) stated that prior knowledge could be used to reduce the number of causal structures under consideration, and this is what has been done in most of the applications of SEMs in animal breeding. However, this strategy is only as good as the preexistent theory leading to the prior knowledge (Shipley 2002).…”

Section: Discussionmentioning

confidence: 99%

“…Many authors have used SEMs in the aforementioned context by applying prior biological knowledge for defining causal structures (De los Campos et al 2006a,b;Wu et al 2007;Konig et al 2008;De Maturana et al 2009;Wu et al 2010). Typically, one model or a limited set of models (i.e., causal structures) is preselected, and members of the set are fit and compared on the basis of some model comparison criteria such as Akaike information criteria (AIC) (Akaike 1973) or Bayesian information criteria (BIC) (Schwarz 1978).…”

mentioning

confidence: 99%

Searching for Recursive Causal Structures in Multivariate Quantitative Genetics Mixed Models

Valente

Rosa

Campos³

et al. 2010

Genetics

150

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Biology is characterized by complex interactions between phenotypes, such as recursive and simultaneous relationships between substrates and enzymes in biochemical systems. Structural equation models (SEMs) can be used to study such relationships in multivariate analyses, e.g., with multiple traits in a quantitative genetics context. Nonetheless, the number of different recursive causal structures that can be used for fitting a SEM to multivariate data can be huge, even when only a few traits are considered. In recent applications of SEMs in mixed-model quantitative genetics settings, causal structures were preselected on the basis of prior biological knowledge alone. Therefore, the wide range of possible causal structures has not been properly explored. Alternatively, causal structure spaces can be explored using algorithms that, using data-driven evidence, can search for structures that are compatible with the joint distribution of the variables under study. However, the search cannot be performed directly on the joint distribution of the phenotypes as it is possibly confounded by genetic covariance among traits. In this article we propose to search for recursive causal structures among phenotypes using the inductive causation (IC) algorithm after adjusting the data for genetic effects. A standard multiple-trait model is fitted using Bayesian methods to obtain a posterior covariance matrix of phenotypes conditional to unobservable additive genetic effects, which is then used as input for the IC algorithm. As an illustrative example, the proposed methodology was applied to simulated data related to multiple traits measured on a set of inbred lines.

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Applications of Graphical Models in Quantitative Genetics and Genomics

Rosa¹,

Felipe²,

Peñagaricano

2016

Systems Biology in Animal Production and Health, Vol. 1

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Inferring Relationships Between Somatic Cell Score and Milk Yield Using Simultaneous and Recursive Models

Cited by 51 publications

References 27 publications

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

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

Searching for Recursive Causal Structures in Multivariate Quantitative Genetics Mixed Models

Applications of Graphical Models in Quantitative Genetics and Genomics

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