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Estimation of genetic parameters and prediction of breeding values for multivariate threshold and continuous data in a simulated horse population using Gibbs sampling and residual maximum likelihood
Author(s) -
Stock K.F.,
Hoeschele I.,
Distl O.
Publication year - 2007
Publication title -
journal of animal breeding and genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.689
H-Index - 51
eISSN - 1439-0388
pISSN - 0931-2668
DOI - 10.1111/j.1439-0388.2007.00666.x
Subject(s) - restricted maximum likelihood , best linear unbiased prediction , heritability , statistics , quantitative trait locus , genetic correlation , biology , mixed model , population , generalized linear mixed model , mathematics , selection (genetic algorithm) , genetics , genetic variation , maximum likelihood , computer science , gene , artificial intelligence , demography , sociology
Summary Simulated horse data were used to compare multivariate estimation of genetic parameters and prediction of breeding values (BV) for categorical, continuous and molecular genetic data using linear animal models via residual maximum likelihood (REML) and best linear unbiased prediction (BLUP) and mixed linear‐threshold animal models via Gibbs sampling (GS). Simulation included additive genetic values, residuals and fixed effects for one continuous trait, liabilities of four binary traits, and quantitative trait locus (QTL) effects and genetic markers with different recombination rates and polymorphism information content for one of the liabilities. Analysed data sets differed in the number of animals with trait records and availability of genetic marker information. Consideration of genetic marker information in the model resulted in marked overestimation of the heritability of the QTL trait. If information on 10 000 or 5000 animals was used, bias of heritabilities and additive genetic correlations was mostly smaller, correlation between true and predicted BV was always higher and identification of genetically superior and inferior animals was – with regard to the moderately heritable traits, in many cases – more reliable with GS than with REML/BLUP. If information on only 1000 animals was used, neither GS nor REML/BLUP produced genetic parameter estimates with relative bias ≤25% and BV correlation >50% for all traits. Selection decisions for binary traits should rather be based on GS than on REML/BLUP breeding values.