Bayesian Inference for Categorical Traits with an Application to Variance Component Estimation

Luo, Michael; Boêttcher, P.; Schaeffer, L.R.; Dekkers, Jack C. M.

doi:10.3168/jds.s0022-0302(01)74524-9

Cited by 81 publications

(80 citation statements)

References 21 publications

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“…Application of threshold animal models to categorical traits was often problematic because of the 'extreme category problems' where all observations for some subclasses are in the same category (Misztal et al, 1989). Luo et al (2001) showed also that threshold animal models had problems with convergence, and yielded biased estimates when convergence was reached. The threshold model gave biased variances for herd-year and additive genetic effects.…”

Section: Methodsmentioning

confidence: 99%

Genetic relationship between milk urea nitrogen and reproductive performance in Holstein dairy cows

Hossein-Zadeh

Ardalan

2011

Animal

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The objective of this study was to describe the genetic and phenotypic relationship between milk urea nitrogen (MUN) and reproductive traits in Iranian Holstein dairy cows. Test-day MUN data obtained from 57 301 dairy cows on 20 large dairy herds in Iran between January 2005 and June 2009. Genetic parameters for MUN and reproductive traits were estimated with a five-trait model using ASREML program. Random regression test-day models were used to estimate heritabilities separately for MUN from first, second and third lactations. Regression curves were modeled using Legendre polynomials of order 3. Herd-year-season along with age at calving was included as fixed effects in all models for reproductive traits. Heritabilities for MUN and reproductive traits were estimated separately for first lactation, second lactation and third lactation. The estimated heritabilities for MUN varied from 0.18 to 0.22. The heritability estimate was low for reproductive traits, which ranged from 0.02 to 0.06 for different traits and across parities. Except for days open, phenotypic and genetic correlations of MUN with reproductive performance traits were close to zero. Genetic correlations between MUN and days open were 0.23, 0.35 and 0.45 in first, second and third lactation, respectively. However, the phenotypic correlation between MUN at different parities was moderate (0.28 to 0.35), but the genetic correlation between MUN at different parities was high and ranged from 0.84 to 0.97. This study shows a limited application of MUN for use in selection programs to improve reproductive performance.

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Section: Methodsmentioning

confidence: 99%

Genetic relationship between milk urea nitrogen and reproductive performance in Holstein dairy cows

Hossein-Zadeh

Ardalan

2011

Animal

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“…Effects of data structure on accuracy of genetic parameter estimates have been discussed elsewhere (Stock et al, 2007). In addition to the four binary traits of interest, one continuous trait was included in order to improve mixing and convergence of the Gibbs sampler (Luo et al, 2001) and accuracy of genetic evaluation (Janss and Foulley, 1993;Varona et al, 1999). However, inclusion of a correlated continuous trait does not warrant unbiased estimates of genetic parameters in a complex multiple-trait setting as used for this simulation (Stock et al, 2007).…”

Section: Discussionmentioning

confidence: 99%

Bayesian prediction of breeding values for multivariate binary and continuous traits in simulated horse populations using threshold–linear models with Gibbs sampling

Stock

Distl

Hoeschele

2008

Animal

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Simulated data were used to determine the properties of multivariate prediction of breeding values for categorical and continuous traits using phenotypic, molecular genetic and pedigree information by mixed linear-threshold animal models via Gibbs sampling. Simulation parameters were chosen such that the data resembled situations encountered in Warmblood horse populations. Genetic evaluation was performed in the context of the radiographic findings in the equine limbs. The simulated pedigree comprised seven generations and 40 000 animals per generation. The simulated data included additive genetic values, residuals and fixed effects for one continuous trait and liabilities of four binary traits. For one of the binary traits, quantitative trait locus (QTL) effects and genetic markers were simulated, with three different scenarios with respect to recombination rate (r) between genetic markers and QTL and polymorphism information content (PIC) of genetic markers being studied: r 5 0.00 and PIC 5 0.90 (r0p9), r 5 0.01 and PIC 5 0.90 (r1p9), and r 5 0.00 and PIC 5 0.70 (r0p7). For each scenario, 10 replicates were sampled from the simulated horse population, and six different data sets were generated per replicate. Data sets differed in number and distribution of animals with trait records and the availability of genetic marker information. Breeding values were predicted via Gibbs sampling using a Bayesian mixed linear-threshold animal model with residual covariances fixed to zero and a proper prior for the genetic covariance matrix. Relative breeding values were used to investigate expected response to multiand single-trait selection. In the sires with 10 or more offspring with trait information, correlations between true and predicted breeding values ranged between 0.89 and 0.94 for the continuous traits and between 0.39 and 0.77 for the binary traits. Proportions of successful identification of sires of average, favourable and unfavourable genetic value were 81% to 86% for the continuous trait and 57% to 74% for the binary traits in these sires. Expected decrease of prevalence of the QTL trait was 3% to 12% after multi-trait selection for all binary traits and 9% to 17% after single-trait selection for the QTL trait. The combined use of phenotype and genotype data was superior to the use of phenotype data alone. It was concluded that information on phenotypes and highly informative genetic markers should be used for prediction of breeding values in mixed linear-threshold animal models via Gibbs sampling to achieve maximum reduction in prevalences of binary traits.

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“…Previous studies that used animal models for analyses with categorical data (Luo, Boettcher, & Schaeffer, 2001;Phocas & Laloe, 2003) reported difficulty to achieve Gibbs chain convergence chain , which was not observable in this study as verified in Figures 2, 3 and 4.…”

Section: Resultsmentioning

confidence: 47%

Categorical Visual Score Traits of a Nellore Beef Cattle Population

Júnior¹,

Petrini²,

Mourão³

et al. 2017

JAS

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Variance components and genetic parameters of economically relevant traits in livestock, whether continuous or categorical, can be estimated by methods computationally available providing support for the selection and mating of animals in breeding programs. The objectives of this paper were to obtain and compare the variance components estimates for visual traits under continuous or categorical distribution in single-trait analysis and their correlations with continuous productive traits in two-trait analysis. Data of conformation (CONF), precocity of fat deposition (PREC) and muscling (MUSC) visual scores evaluated at 18 months of age as well as the weight at 18 months of age (YW) were collected from animals born from 2000 to 2012, in Nellore cattle herds raised in Southeastern and Central Western tropical regions of Brazil. Methods III of Henderson, Restricted Maximum Likelihood (REML), Bayesian Inference and generalized linear mixed model (GLMM) were tested. Variance components obtained from single-trait analysis were similar to those obtained from two-trait analysis. The estimates of heritability (h 2 ) for the visual scores ranged from 0.1081 to 0.2190. Heritability estimates for traits evaluated by visual scores have moderate to high magnitude justifying the inclusion of visual scores as selection criteria in animal breeding and the selection of animals with higher scores for mating. High genetic correlations between yearling weight and morphological traits were verified. For visual scores of conformation, precocity and muscling, the most suitable model based on one-trait or two-trait analyses considered an animal model, a linear distribution of the data and the estimation method of the components of (co)variance based on Bayesian methodology.

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Bayesian Inference for Categorical Traits with an Application to Variance Component Estimation

Cited by 81 publications

References 21 publications

Genetic relationship between milk urea nitrogen and reproductive performance in Holstein dairy cows

Genetic relationship between milk urea nitrogen and reproductive performance in Holstein dairy cows

Bayesian prediction of breeding values for multivariate binary and continuous traits in simulated horse populations using threshold–linear models with Gibbs sampling

Categorical Visual Score Traits of a Nellore Beef Cattle Population

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