Estimation of Additive, Dominance, and Imprinting Genetic Variance Using Genomic Data

Lopes, M.S.; Bastiaansen, J.W.M.; Janss, Luc; Knol, E.F.; Bovenhuis, H.

doi:10.1534/g3.115.019513

Cited by 41 publications

(61 citation statements)

References 47 publications

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“…A large contribution of dominance effects to the total genetic variance in DG has been reported in a previous study, performed on a smaller number of records from the same populations (Lopes et al . ). In our previous study, however, a genotypic model instead of a breeding model (as used in the current study) was applied, and the influence of dominance effects on the prediction accuracy of phenotypes was not evaluated.…”

Section: Discussionmentioning

confidence: 97%

See 1 more Smart Citation

Genomic prediction of growth in pigs based on a model including additive and dominance effects

Lopes¹,

Bastiaansen

Janss

et al. 2015

J Animal Breeding Genetics

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Independent of whether prediction is based on pedigree or genomic information, the focus of animal breeders has been on additive genetic effects or 'breeding values'. However, when predicting phenotypes rather than breeding values of an animal, models that account for both additive and dominance effects might be more accurate. Our aim with this study was to compare the accuracy of predicting phenotypes using a model that accounts for only additive effects (MA) and a model that accounts for both additive and dominance effects simultaneously (MAD). Lifetime daily gain (DG) was evaluated in three pig populations (1424 Pietrain, 2023 Landrace, and 2157 Large White). Animals were genotyped using the Illumina SNP60K Beadchip and assigned to either a training data set to estimate the genetic parameters and SNP effects, or to a validation data set to assess the prediction accuracy. Models MA and MAD applied random regression on SNP genotypes and were implemented in the program Bayz. The additive heritability of DG across the three populations and the two models was very similar at approximately 0.26. The proportion of phenotypic variance explained by dominance effects ranged from 0.04 (Large White) to 0.11 (Pietrain), indicating that importance of dominance might be breed-specific. Prediction accuracies were higher when predicting phenotypes using total genetic values (sum of breeding values and dominance deviations) from the MAD model compared to using breeding values from both MA and MAD models. The highest increase in accuracy (from 0.195 to 0.222) was observed in the Pietrain, and the lowest in Large White (from 0.354 to 0.359). Predicting phenotypes using total genetic values instead of breeding values in purebred data improved prediction accuracy and reduced the bias of genomic predictions. Additional benefit of the method is expected when applied to predict crossbred phenotypes, where dominance levels are expected to be higher.

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Section: Discussionmentioning

confidence: 97%

“…However, in a previous study (Lopes et al . ), we estimated additive, dominance, and imprinting variance of the trait backfat and observed that the largest dominance variation was found in the Large White population, and the smallest in the Pietrain population. This is the opposite of the results for DG in this study.…”

Section: Discussionmentioning

confidence: 99%

Genomic prediction of growth in pigs based on a model including additive and dominance effects

Lopes¹,

Bastiaansen

Janss

et al. 2015

J Animal Breeding Genetics

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“…For average daily gain in Duroc pigs, Su et al [9] estimated a ratio of 15%, but their results were based on genotypic variance components and cannot be directly compared to genetic variance components [32]. For average daily weight gain in Yorkshire and Landrace pigs, Lopes et al [44] reported ratios of 13.8 and 28%, respectively by including genomic information. For Fleckvieh cattle, Ertl et al [12] calculated ratios that ranged from 3.4% for stature to 69% for protein yield by using a univariate SNP-BLUP model.…”

Section: Discussionmentioning

confidence: 99%

Genomic evaluation by including dominance effects and inbreeding depression for purebred and crossbred performance with an application in pigs

et al. 2016

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BackgroundImproved performance of crossbred animals is partly due to heterosis. One of the major genetic bases of heterosis is dominance, but it is seldom used in pedigree-based genetic evaluation of livestock. Recently, a trivariate genomic best linear unbiased prediction (GBLUP) model including dominance was developed, which can distinguish purebreds from crossbred animals explicitly. The objectives of this study were: (1) methodological, to show that inclusion of marker-based inbreeding accounts for directional dominance and inbreeding depression in purebred and crossbred animals, to revisit variance components of additive and dominance genetic effects using this model, and to develop marker-based estimators of genetic correlations between purebred and crossbred animals and of correlations of allele substitution effects between breeds; (2) to evaluate the impact of accounting for dominance effects and inbreeding depression on predictive ability for total number of piglets born (TNB) in a pig dataset composed of two purebred populations and their crossbreds. We also developed an equivalent model that makes the estimation of variance components tractable.ResultsFor TNB in Danish Landrace and Yorkshire populations and their reciprocal crosses, the estimated proportions of dominance genetic variance to additive genetic variance ranged from 5 to 11%. Genetic correlations between breeding values for purebred and crossbred performances for TNB ranged from 0.79 to 0.95 for Landrace and from 0.43 to 0.54 for Yorkshire across models. The estimated correlation of allele substitution effects between Landrace and Yorkshire was low for purebred performances, but high for crossbred performances. Predictive ability for crossbred animals was similar with or without dominance. The inbreeding depression effect increased predictive ability and the estimated inbreeding depression parameter was more negative for Landrace than for Yorkshire animals and was in between for crossbred animals.ConclusionsMethodological developments led to closed-form estimators of inbreeding depression, variance components and correlations that can be easily interpreted in a quantitative genetics context. Our results confirm that genetic correlations of breeding values between purebred and crossbred performances within breed are positive and moderate. Inclusion of dominance in the GBLUP model does not improve predictive ability for crossbred animals, whereas inclusion of inbreeding depression does.

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“…Heritability estimated from a genomebased model was slightly less than the pedigree-based estimates for most traits using the same data (Table 4). Reduced estimates of heritability from a genome-based model as opposed to a pedigree-based model have been previously observed (Lopes et al 2015). This phenomenon has been referred to as the "missing heritability problem" (Lee et al 2011).…”

Section: Prediction Of Genomic Breed Compositionmentioning

confidence: 95%

Genomic prediction of breed composition and heterosis effects in Angus, Charolais and Hereford crosses using 50K genotypes

Akanno¹,

Chen²,

Abo-Ismail

et al. 2017

Can. J. Anim. Sci.

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This study examined the feasibility and accuracy of using Illumina BovineSNP50 genotypes to estimate individual cattle breed composition and heterosis relative to estimate from pedigree. First, pedigree was used to compute breed fractions for 1124 crossbred cattle. Given the breed composition of sires and dams, retained heterosis and retained heterozygosity were computed for all individuals. Second, all animals' genotypes were used to compute individual's genomic breed fractions by applying a cross-validation method. Average genome-wide heterozygosity and retained heterozygosity based on genomic breed fraction were computed. Lastly, accuracies of breed composition, retained heterozygosity and retained heterosis were assessed as Pearson's correlation between pedigree-and genome-based predictions. The average breed compositions observed were 0.52 Angus, 0.23 Charolais, and 0.25 Hereford for pedigree-based prediction and 0.46, 0.26, and 0.28 for genome-based prediction, respectively. Correlations of predicted breed composition ranged from 0.94 to 0.96. Genome-based retained heterozygosity and retained heterosis from pedigree were also highly correlated (0.96). A positive association of nonadditive genetic effects was observed for growth traits reflecting the importance of heterosis for these traits. Genomic prediction can aid analyses that depend on knowledge of breed composition and serve as a reliable method to predict heterosis to improve the efficiency of commercial crossbreeding schemes.

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Estimation of Additive, Dominance, and Imprinting Genetic Variance Using Genomic Data

Cited by 41 publications

References 47 publications

Genomic prediction of growth in pigs based on a model including additive and dominance effects

Genomic prediction of growth in pigs based on a model including additive and dominance effects

Genomic evaluation by including dominance effects and inbreeding depression for purebred and crossbred performance with an application in pigs

Genomic prediction of breed composition and heterosis effects in Angus, Charolais and Hereford crosses using 50K genotypes

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