A frailty modeling approach for parental effects in animal breeding

Giolo, Suely Ruiz; Demétrio, Clarice Garcia Borges

doi:10.1080/02664760903521492

Cited by 7 publications

(16 citation statements)

References 31 publications

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“…Furthermore, in the most general case where λ 0 is only assumed to be smooth (the Cox frailty model) it might be argued that λ 0 could be point wisely approximated (almost every where) by a sequence of step functions obtained even from distributions in a model with dispersion parameter, which would have a proper decomposition of the phenotypic variance. However, in this case it would not be possible to identify the dispersion parameter φ (as essentially argued in [13]); therefore, one would still have the representation issues referred above.…”

Section: Discussionmentioning

confidence: 99%

“…Note that according to (13) the variance of Y(t) is additively decomposed in three components: one depending on σ 2 g , one depending on σ 2 e and one depending on φ. This is a situation analogous to the classic gaussian linear mixed models.…”

Section: Decomposition Of the Phenotypic Variance And Heritabilitymentioning

confidence: 99%

“…Variants of the so called Cox proportional model [8] were used for univariate traits describing longevity [9,13,10]. These techniques succeeded to implement models of approximations that were operational for some of the purposes of animal breeding.…”

Section: Introductionmentioning

confidence: 99%

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Multivariate survival mixed models for genetic analysis of longevity traits

Maia

Madsen

Labouriau

2013

Journal of Applied Statistics

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A class of multivariate mixed survival models for continuous and discrete time with a complex covariance structure is introduced in a context of quantitative genetic applications. The methods introduced can be used in many applications in quantitative genetics although the discussion presented concentrates on longevity studies. The framework presented allows to combine models based on continuous time with models based on discrete time in a joint analysis. The continuous time models are approximations of the frailty model in which the hazard function will be assumed to be piece-wise constant. The discrete time models used are multivariate variants of the discrete relative risk models. These models allow for regular parametric likelihood-based inference by exploring a coincidence of their likelihood functions and the likelihood functions of suitably defined multivariate generalized linear mixed models. The models include a dispersion parameter, which is essential for obtaining a decomposition of the variance of the trait of interest as a sum of parcels representing the additive genetic effects, environmental effects and unspecified sources of variability; as required in quantitative genetic applications. The methods presented are implemented in such a way that large and complex quantitative genetic data can be analyzed.

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Section: Discussionmentioning

confidence: 99%

Section: Decomposition Of the Phenotypic Variance And Heritabilitymentioning

confidence: 99%

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Multivariate survival mixed models for genetic analysis of longevity traits

Maia

Madsen

Labouriau

2013

Journal of Applied Statistics

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“…One way of analyzing such data is by using survival analysis models with correlated frailty (Giolo and Demétrio, 2011). In the context of genome-wide selection (GWS), the correlation matrix associated with frailty is a relationship matrix that is not based on pedigree but on markers, and is known as genomic best linear unbiased prediction (GBLUP) (Meuwissen et al, 2001;VanRaden, 2008).…”

Section: Introductionmentioning

confidence: 99%

“…In dairy cattle for example, Buenger et al (2001) and Ojango et al (2005) assessed the lifetime productivity of cows, and in Nellore beef cattle, Giolo and Demétrio (2011) investigated the number of days required to achieve a standard weight gain. In pig breeding, Mészáros et al (2010) and Tarres et al (2006) evaluated the number of days between first farrowing and culling of Large White and Landrace breeds using the Weibull model.…”

Section: Introductionmentioning

confidence: 99%

Genomic selection for slaughter age in pigs using the Cox frailty model

Vs¹,

Filho²,

Resende³

et al. 2015

Genet. Mol. Res.

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ABSTRACT. The aim of this study was to compare genomic selection methodologies using a linear mixed model and the Cox survival model. We used data from an F2 population of pigs, in which the response variable was the time in days from birth to the culling of the animal and the covariates were 238 markers [237 single nucleotide polymorphism (SNP) plus the halothane gene]. The data were corrected for fixed effects, and the accuracy of the method was determined based on the correlation of the ranks of predicted genomic breeding values (GBVs) in both models with the corrected phenotypic values. The analysis was repeated with a subset of SNP markers with largest absolute effects. The results were in agreement with the GBV prediction and the estimation of marker effects for both models for uncensored data and for normality. However, when considering censored data, the Cox model with a normal random effect (S1) was more appropriate. Since there was no agreement between the linear mixed model and the imputed data (2015) (L2) for the prediction of genomic values and the estimation of marker effects, the model S1 was considered superior as it took into account the latent variable and the censored data. Marker selection increased correlations between the ranks of predicted GBVs by the linear and Cox frailty models and the corrected phenotypic values, and 120 markers were required to increase the predictive ability for the characteristic analyzed.

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Quantifying intraclass correlations for count and time‐to‐event data

Oliveira

Molenberghs

Demétrio³

et al. 2016

Biometrical J

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The intraclass correlation is commonly used with clustered data. It is often estimated based on fitting a model to hierarchical data and it leads, in turn, to several concepts such as reliability, heritability, inter-rater agreement, etc. For data where linear models can be used, such measures can be defined as ratios of variance components. Matters are more difficult for non-Gaussian outcomes. The focus here is on count and time-to-event outcomes where so-called combined models are used, extending generalized linear mixed models, to describe the data. These models combine normal and gamma random effects to allow for both correlation due to data hierarchies as well as for overdispersion. Furthermore, because the models admit closed-form expressions for the means, variances, higher moments, and even the joint marginal distribution, it is demonstrated that closed forms of intraclass correlations exist. The proposed methodology is illustrated using data from agricultural and livestock studies.

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A frailty modeling approach for parental effects in animal breeding

Cited by 7 publications

References 31 publications

Multivariate survival mixed models for genetic analysis of longevity traits

Multivariate survival mixed models for genetic analysis of longevity traits

Genomic selection for slaughter age in pigs using the Cox frailty model

Quantifying intraclass correlations for count and time‐to‐event data

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