Calculating cow and daughter yield deviations and partitioning of genetic evaluations under a random regression model

Mrode, Raphael; Swanson, G. J. T.

doi:10.1016/j.livprodsci.2003.09.001

Cited by 27 publications

(28 citation statements)

References 4 publications

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“…Within each biological trait, two different traits were defined, one for first lactation observations and another one for all later lactation observations. On the basis of this model, daily DYDs were calculated for all sires and all six traits, applying the method of Mrode and Swanson (2004). Daily DYDs from lactation day 8 up to day 312 were summed to obtain a DYD on a 305-day basis.…”

Section: Methodsmentioning

confidence: 99%

The Role of the Bovine Growth Hormone Receptor and Prolactin Receptor Genes in Milk, Fat and Protein Production in Finnish Ayrshire Dairy Cattle

Szyda²,

et al. 2006

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We herein report new evidence that the QTL effect on chromosome 20 in Finnish Ayrshire can be explained by variation in two distinct genes, growth hormone receptor (GHR) and prolactin receptor (PRLR). In a previous study in Holstein-Friesian dairy cattle an F279Y polymorphism in the transmembrane domain of GHR was found to be associated with an effect on milk yield and composition. The result of our multimarker regression analysis suggests that in Finnish Ayrshire two QTL segregate on the chromosomal region including GHR and PRLR. By sequencing the coding sequences of GHR and PRLR and the sequence of three GHR promoters from the pooled samples of individuals of known QTL genotype, we identified two substitutions that were associated with milk production traits: the previously reported F-to-Y substitution in the transmembrane domain of GHR and an S-to-N substitution in the signal peptide of PRLR. The results provide strong evidence that the effect of PRLR S18N polymorphism is distinct from the GHR F279Y effect. In particular, the GHR F279Y has the highest influence on protein percentage and fat percentage while PRLR S18N markedly influences protein and fat yield. Furthermore, an interaction between the two loci is suggested.

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Section: Methodsmentioning

confidence: 99%

The Role of the Bovine Growth Hormone Receptor and Prolactin Receptor Genes in Milk, Fat and Protein Production in Finnish Ayrshire Dairy Cattle

Szyda²,

et al. 2006

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“…By definition, the IDD are cow performances adjusted for fixed effects, nongenetic random effects, and genetic effects of the cow's dam (Mrode and Swanson, 2004). Here, however, IDD were computed by using animal model deregression from 305-d combined EBV (Mäntysaari et al, 2011).…”

Section: Datamentioning

confidence: 99%

The estimation of genomic relationships using breedwise allele frequencies among animals in multibreed populations

Makgahlela

Strandén

Nielsen

et al. 2013

Journal of Dairy Science

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Different approaches of calculating genomic measures of relationship were explored and compared with pedigree relationships (A) within and across base breeds in a crossbreed population, using genotypes for 38,194 loci of 4,106 Nordic Red dairy cattle. Four genomic relationship matrices (G) were calculated using either observed allele frequencies (AF) across breeds or within-breed AF. The G matrices were compared separately when the AF were estimated in the observed and in the base population. Breedwise AF in the current and base population were estimated using linear regression models of individual genotypes on breed composition. Different G matrices were further used to predict direct estimated genomic values using a genomic BLUP model. Higher variability existed in the diagonal elements of G across breeds (standard deviation=0.06, on average) compared with A (0.01). The use of simple observed AF across base breeds to compute G increased coefficients for individuals in distantly related populations. Estimated breedwise AF reduced differences in coefficients similarly within and across populations. The variability of the current adjusted G matrix decreased from 0.055 to 0.035 when breedwise AF were estimated from the base breed population. The direct estimated genomic values and their validation reliabilities were, however, unaffected by AF used to compute G when estimated with a genomic BLUP model, due to inclusion of breed means in the model. In multibreed populations, G adjusted with breedwise AF from the founder population may provide more consistency among relationship coefficients between genotyped and ungenotyped individuals in an across-breed single-step evaluation.

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“…Phenotypes used for this study were daughter yield deviations (DYD) corresponding to the average performance of a sire's daughters, adjusted for fixed and non-genetic random effects and for the additive genetic value of their dam (Mrode & Swanson, 2004). To account for the varying accuracy of the DYD, they were weighted by their error variance, which is proportional to the sire's effective daughters ' contribution (EDC) (Fikse & Banos, 2001).…”

Section: (I) Datamentioning

confidence: 99%

Fine tuning genomic evaluations in dairy cattle through SNP pre-selection with the Elastic-Net algorithm

Croiseau¹,

Legarra²,

Guillaume³

et al. 2011

Genet. Res.

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For genomic selection methods, the statistical challenge is to estimate the effect of each of the available single-nucleotide polymorphism (SNP). In a context where the number of SNPs (p) is much higher than the number of bulls (n), this task may lead to a poor estimation of these SNP effects if, as for genomic BLUP (gBLUP), all SNPs have a non-null effect. An alternative is to use approaches that have been developed specifically to solve the 'p >> n' problem. This is the case of variable selection methods and among them, we focus on the Elastic-Net (EN) algorithm that is a penalized regression approach. Performances of EN, gBLUP and pedigree-based BLUP were compared with data from three French dairy cattle breeds, giving very encouraging results for EN. We tried to push further the idea of improving SNP effect estimates by considering fewer of them. This variable selection strategy was considered both in the case of gBLUP and EN by adding an SNP pre-selection step based on quantitative trait locus (QTL) detection. Similar results were observed with or without a pre-selection step, in terms of correlations between direct genomic value (DGV) and observed daughter yield deviation in a validation data set. However, when applied to the EN algorithm, this strategy led to a substantial reduction of the number of SNPs included in the prediction equation. In a context where the number of genotyped animals and the number of SNPs gets larger and larger, SNP pre-selection strongly alleviates computing requirements and ensures that national evaluations can be completed within a reasonable time frame.

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Calculating cow and daughter yield deviations and partitioning of genetic evaluations under a random regression model

Cited by 27 publications

References 4 publications

The Role of the Bovine Growth Hormone Receptor and Prolactin Receptor Genes in Milk, Fat and Protein Production in Finnish Ayrshire Dairy Cattle

The Role of the Bovine Growth Hormone Receptor and Prolactin Receptor Genes in Milk, Fat and Protein Production in Finnish Ayrshire Dairy Cattle

The estimation of genomic relationships using breedwise allele frequencies among animals in multibreed populations

Fine tuning genomic evaluations in dairy cattle through SNP pre-selection with the Elastic-Net algorithm

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