Mixed Model Methodology for Farm and Ranch Beef Cattle Testing Programs

Quaas, R. L.; Pollak, Edward

doi:10.2527/jas1981.5161277x

Cited by 282 publications

(151 citation statements)

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“…Residual and maternal effects were assumed uncorrelated with each other and with animal effects. In order to reduce computational requirements, the model was fitted using an equivalent reduced animal model (Quaas and Pollak, 1980) and a single-trait pseudoexpectation approach to estimate variance components (Schaeffer, 1986). This is an iterative procedure based on quadratic forms similar to those used in the Residual Maximum Likelihood (REML) procedure (Patterson and Thompson, 1971 (Falconer, 1989).…”

mentioning

confidence: 99%

Selection response of growth rate in rabbits for meat production

Estany¹,

Camacho²,

Baselga³

et al. 1992

Genet. Sel. Evol.

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mentioning

confidence: 99%

Selection response of growth rate in rabbits for meat production

Estany¹,

Camacho²,

Baselga³

et al. 1992

Genet. Sel. Evol.

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“…Given pedigree information, recursive formulae have been widely used to compute IBD probabilities [2,4,6,9,18,[22][23][24][25]. These formulae are based on the principle that a priori the allele transmitted from a parent to an offspring is equally likely to be the parent's maternal or paternal allele.…”

Section: Ibd Probabilities At the Mqtlmentioning

confidence: 99%

The effect of using approximate gametic variance covariance matrices on marker assisted selection by BLUP

et al. 2004

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-Under additive inheritance, the Henderson mixed model equations (HMME) provide an efficient approach to obtaining genetic evaluations by marker assisted best linear unbiased prediction (MABLUP) given pedigree relationships, trait and marker data. For large pedigrees with many missing markers, however, it is not feasible to calculate the exact gametic variance covariance matrix required to construct HMME. The objective of this study was to investigate the consequences of using approximate gametic variance covariance matrices on response to selection by MABLUP. Two methods were used to generate approximate variance covariance matrices. The first method (Method A) completely discards the marker information for individuals with an unknown linkage phase between two flanking markers. The second method (Method B) makes use of the marker information at only the most polymorphic marker locus for individuals with an unknown linkage phase. Data sets were simulated with and without missing marker data for flanking markers with 2, 4, 6, 8 or 12 alleles. Several missing marker data patterns were considered. The genetic variability explained by marked quantitative trait loci (MQTL) was modeled with one or two MQTL of equal effect. Response to selection by MABLUP using Method A or Method B were compared with that obtained by MABLUP using the exact genetic variance covariance matrix, which was estimated using 15 000 samples from the conditional distribution of genotypic values given the observed marker data. For the simulated conditions, the superiority of MABLUP over BLUP based only on pedigree relationships and trait data varied between 0.1% and 13.5% for Method A, between 1.7% and 23.8% for Method B, and between 7.6% and 28.9% for the exact method. The relative performance of the methods under investigation was not affected by the number of MQTL in the model.

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“…We allow for the possibility of any animal j having no record; nevertheless, a genetic evaluation on that same animal may be desired if it is related to other animals having data. In the reduced animal model (RAM) of Quaas and Pollak [14], y is partitioned into two major subsets:…”

Section: The Reduced Animal Model With Maternal Effectsmentioning

confidence: 99%

Bayesian inference on genetic merit under uncertain paternity

Cardoso

Tempelman

2003

Genet. Sel. Evol.

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-A hierarchical animal model was developed for inference on genetic merit of livestock with uncertain paternity. Fully conditional posterior distributions for fixed and genetic effects, variance components, sire assignments and their probabilities are derived to facilitate a Bayesian inference strategy using MCMC methods. We compared this model to a model based on the Henderson average numerator relationship (ANRM) in a simulation study with 10 replicated datasets generated for each of two traits. Trait 1 had a medium heritability (h 2 ) for each of direct and maternal genetic effects whereas Trait 2 had a high h 2 attributable only to direct effects. The average posterior probabilities inferred on the true sire were between 1 and 10% larger than the corresponding priors (the inverse of the number of candidate sires in a mating pasture) for Trait 1 and between 4 and 13% larger than the corresponding priors for Trait 2. The predicted additive and maternal genetic effects were very similar using both models; however, model choice criteria (Pseudo Bayes Factor and Deviance Information Criterion) decisively favored the proposed hierarchical model over the ANRM model. uncertain paternity / multiple-sire / genetic merit / Bayesian inference / reduced animal model

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Mixed Model Methodology for Farm and Ranch Beef Cattle Testing Programs

Cited by 282 publications

References 0 publications

Selection response of growth rate in rabbits for meat production

Selection response of growth rate in rabbits for meat production

The effect of using approximate gametic variance covariance matrices on marker assisted selection by BLUP

Bayesian inference on genetic merit under uncertain paternity

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