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Genomic analysis of dominance effects on milk production and conformation traits in Fleckvieh cattle.

Ertl J, Legarra A, Vitezica ZG, Varona L, Edel C, Emmerling R, Götz KU - Genet. Sel. Evol. (2014)

Bottom Line: Inclusion of dominance effects did not increase the accuracy of the predicted breeding and total genetic values.Selection on total genetic value instead of breeding value would result in a larger expected total genetic superiority in progeny, i.e. 14.8% for milk yield and 27.8% for protein yield and reduce the expected additive genetic gain only by 4.5% for milk yield and 2.6% for protein yield.Exploitation of dominance variance in assortative matings was promising and did not appear to severely compromise additive genetic gain.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Animal Breeding, Bavarian State Research Centre for Agriculture, Prof,-Dürrwaechter-Platz 1, Poing-Grub 85586, Germany. johann.ertl@lfl.bayern.de.

ABSTRACT

Background: Estimates of dominance variance in dairy cattle based on pedigree data vary considerably across traits and amount to up to 50% of the total genetic variance for conformation traits and up to 43% for milk production traits. Using bovine SNP (single nucleotide polymorphism) genotypes, dominance variance can be estimated both at the marker level and at the animal level using genomic dominance effect relationship matrices. Yield deviations of high-density genotyped Fleckvieh cows were used to assess cross-validation accuracy of genomic predictions with additive and dominance models. The potential use of dominance variance in planned matings was also investigated.

Results: Variance components of nine milk production and conformation traits were estimated with additive and dominance models using yield deviations of 1996 Fleckvieh cows and ranged from 3.3% to 50.5% of the total genetic variance. REML and Gibbs sampling estimates showed good concordance. Although standard errors of estimates of dominance variance were rather large, estimates of dominance variance for milk, fat and protein yields, somatic cell score and milkability were significantly different from 0. Cross-validation accuracy of predicted breeding values was higher with genomic models than with the pedigree model. Inclusion of dominance effects did not increase the accuracy of the predicted breeding and total genetic values. Additive and dominance SNP effects for milk yield and protein yield were estimated with a BLUP (best linear unbiased prediction) model and used to calculate expectations of breeding values and total genetic values for putative offspring. Selection on total genetic value instead of breeding value would result in a larger expected total genetic superiority in progeny, i.e. 14.8% for milk yield and 27.8% for protein yield and reduce the expected additive genetic gain only by 4.5% for milk yield and 2.6% for protein yield.

Conclusions: Estimated dominance variance was substantial for most of the analyzed traits. Due to small dominance effect relationships between cows, predictions of individual dominance deviations were very inaccurate and including dominance in the model did not improve prediction accuracy in the cross-validation study. Exploitation of dominance variance in assortative matings was promising and did not appear to severely compromise additive genetic gain.

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Histograms of off-diagonal elements of relationship matrices G (unscaled) (a) and D (b).
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Figure 1: Histograms of off-diagonal elements of relationship matrices G (unscaled) (a) and D (b).

Mentions: Figure 1 shows the histograms of off-diagonal elements of the additive and dominance genomic relationship matrices. Means of off-diagonals of G (before scaling) and D were equal to 0, which implies that the population was in Hardy-Weinberg equilibrium. The standard deviation of off-diagonals of G was equal to 0.036, which is five times larger than the standard deviation of off-diagonals of D, i.e. 0.007. The proportion of off-diagonals that were smaller than -0.05 or larger than 0.05 was 6.27% for G but only 0.02% for D. Therefore, matrix D was less informative than G.


Genomic analysis of dominance effects on milk production and conformation traits in Fleckvieh cattle.

Ertl J, Legarra A, Vitezica ZG, Varona L, Edel C, Emmerling R, Götz KU - Genet. Sel. Evol. (2014)

Histograms of off-diagonal elements of relationship matrices G (unscaled) (a) and D (b).
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4230028&req=5

Figure 1: Histograms of off-diagonal elements of relationship matrices G (unscaled) (a) and D (b).
Mentions: Figure 1 shows the histograms of off-diagonal elements of the additive and dominance genomic relationship matrices. Means of off-diagonals of G (before scaling) and D were equal to 0, which implies that the population was in Hardy-Weinberg equilibrium. The standard deviation of off-diagonals of G was equal to 0.036, which is five times larger than the standard deviation of off-diagonals of D, i.e. 0.007. The proportion of off-diagonals that were smaller than -0.05 or larger than 0.05 was 6.27% for G but only 0.02% for D. Therefore, matrix D was less informative than G.

Bottom Line: Inclusion of dominance effects did not increase the accuracy of the predicted breeding and total genetic values.Selection on total genetic value instead of breeding value would result in a larger expected total genetic superiority in progeny, i.e. 14.8% for milk yield and 27.8% for protein yield and reduce the expected additive genetic gain only by 4.5% for milk yield and 2.6% for protein yield.Exploitation of dominance variance in assortative matings was promising and did not appear to severely compromise additive genetic gain.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Animal Breeding, Bavarian State Research Centre for Agriculture, Prof,-Dürrwaechter-Platz 1, Poing-Grub 85586, Germany. johann.ertl@lfl.bayern.de.

ABSTRACT

Background: Estimates of dominance variance in dairy cattle based on pedigree data vary considerably across traits and amount to up to 50% of the total genetic variance for conformation traits and up to 43% for milk production traits. Using bovine SNP (single nucleotide polymorphism) genotypes, dominance variance can be estimated both at the marker level and at the animal level using genomic dominance effect relationship matrices. Yield deviations of high-density genotyped Fleckvieh cows were used to assess cross-validation accuracy of genomic predictions with additive and dominance models. The potential use of dominance variance in planned matings was also investigated.

Results: Variance components of nine milk production and conformation traits were estimated with additive and dominance models using yield deviations of 1996 Fleckvieh cows and ranged from 3.3% to 50.5% of the total genetic variance. REML and Gibbs sampling estimates showed good concordance. Although standard errors of estimates of dominance variance were rather large, estimates of dominance variance for milk, fat and protein yields, somatic cell score and milkability were significantly different from 0. Cross-validation accuracy of predicted breeding values was higher with genomic models than with the pedigree model. Inclusion of dominance effects did not increase the accuracy of the predicted breeding and total genetic values. Additive and dominance SNP effects for milk yield and protein yield were estimated with a BLUP (best linear unbiased prediction) model and used to calculate expectations of breeding values and total genetic values for putative offspring. Selection on total genetic value instead of breeding value would result in a larger expected total genetic superiority in progeny, i.e. 14.8% for milk yield and 27.8% for protein yield and reduce the expected additive genetic gain only by 4.5% for milk yield and 2.6% for protein yield.

Conclusions: Estimated dominance variance was substantial for most of the analyzed traits. Due to small dominance effect relationships between cows, predictions of individual dominance deviations were very inaccurate and including dominance in the model did not improve prediction accuracy in the cross-validation study. Exploitation of dominance variance in assortative matings was promising and did not appear to severely compromise additive genetic gain.

Show MeSH