A note on mate allocation for dominance handling in genomic selection

Toro, M. Á.; Varona, L.

doi:10.1186/1297-9686-42-33

Cited by 129 publications

(192 citation statements)

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“…Nonadditive effects are generally considered a nuisance and ignored whenever possible, though it has been long recognized that it is the nonadditive effects that underlie inbreeding depression (Roff and Emerson, 2006). However, as noted by Toro and Varona (2010), estimation of nonadditive genetic effects in animal breeding is important because ignoring these effects produces less accurate estimates of breeding values and affects ranking breeding values. Toro and Varona (2010) also mentioned the greater computational complexity of these models and the inaccuracy in the estimation of variance components.…”

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“…However, as noted by Toro and Varona (2010), estimation of nonadditive genetic effects in animal breeding is important because ignoring these effects produces less accurate estimates of breeding values and affects ranking breeding values. Toro and Varona (2010) also mentioned the greater computational complexity of these models and the inaccuracy in the estimation of variance components. Dominance variance can be given as an example which is gener-ally confounded with other effects such as random litter effects.…”

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Estimation of additive and dominance variance for litter size components in rabbits

et al. 2014

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Additive, dominance, and permanent environmental variance components were estimated for the number of kits born alive, number of kits born dead, and total number of kits born of a synthetic rabbit line (called Pannon Ka). The data file consisted of 11 582 kindling records of 2620 does collected between the years 1996-2013. The total number of animals in the pedigree files was 4012. The examined traits were evaluated using single-trait and two-trait (number of kits born alive-dead) animal models containing all or part of the following effects: additive genetic effects, permanent environmental effects, dominance effects. Heritability estimates calculated using the basic single-trait and two-trait models were 0.094 ± 0.018 and 0.090 ± 0.016 for number of kits born alive, 0.037 ± 0.010 and 0.041 ± 0.012 for number of kits born dead, and 0.117 ± 0.018 for total number of kits born, respectively. The relative significance of permanent environmental effects was 0.069 ± 0.014 and 0.069 ± 0.012 for number of kits born alive, 0.025 ± 0.011 and 0.023 ± 0.010 for number of kits born dead, and 0.060 ± 0.013 for total number of kits born, respectively. Using the extended single-trait and two-trait models, the ratios of the dominance components compared to the phenotypic variances were 0.048 ± 0.008 and 0.046 ± 0.007 for number of kits born alive, 0.068 ± 0.006 and 0.065 ± 0.006 for number of kits born dead, and 0.005 ± 0.0073 for total number of kits born, respectively. Genetic correlation coefficients between number of kits born alive and number of kits born dead were 0.401 ± 0.171 and 0.521 ± 0.182, respectively. Spearman's rank correlations between the breeding values of the different single-trait models were close to unity in all traits (0.992-0.990). Much lower breeding value stability was found for two-trait models (0.384-0.898), especially for number of kits born dead. Results showed that the dominance components for number of kits born alive and number of kits born dead were not zero and affected the ranking of the animals (based on the breeding values).

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Estimation of additive and dominance variance for litter size components in rabbits

et al. 2014

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“…Recently, genomic evaluations have renewed the interest in the prediction of nonadditive genetic effects (e.g., Toro and Varona 2010;Su et al 2012;Wellmann and Bennewitz 2012). One of the reasons is that it is much easier to work with dominance, knowing for each evaluated locus which animals are heterozygotes, but also that prediction of the genotypic value of future matings is straightforward (Toro and Varona 2010).…”

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“…One of the reasons is that it is much easier to work with dominance, knowing for each evaluated locus which animals are heterozygotes, but also that prediction of the genotypic value of future matings is straightforward (Toro and Varona 2010). …”

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On the Additive and Dominant Variance and Covariance of Individuals Within the Genomic Selection Scope

Vitezica

Varona

Legarra³

2013

Genetics

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Genomic evaluation models can fit additive and dominant SNP effects. Under quantitative genetics theory, additive or "breeding" values of individuals are generated by substitution effects, which involve both "biological" additive and dominant effects of the markers. Dominance deviations include only a portion of the biological dominant effects of the markers. Additive variance includes variation due to the additive and dominant effects of the markers. We describe a matrix of dominant genomic relationships across individuals, D, which is similar to the G matrix used in genomic best linear unbiased prediction. This matrix can be used in a mixed-model context for genomic evaluations or to estimate dominant and additive variances in the population. From the "genotypic" value of individuals, an alternative parameterization defines additive and dominance as the parts attributable to the additive and dominant effect of the markers. This approach underestimates the additive genetic variance and overestimates the dominance variance. Transforming the variances from one model into the other is trivial if the distribution of allelic frequencies is known. We illustrate these results with mouse data (four traits, 1884 mice, and 10,946 markers) and simulated data (2100 individuals and 10,000 markers). Variance components were estimated correctly in the model, considering breeding values and dominance deviations. For the model considering genotypic values, the inclusion of dominant effects biased the estimate of additive variance. Genomic models were more accurate for the estimation of variance components than their pedigree-based counterparts.

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“…Minimising inbreeding is particularly important for robustness traits because inbreeding depression is greatest for traits associated with animal fitness (McParland et al, 2007). Moreover, genomic mating plans can be potentially used to exploit non-additive effects (Toro and Varona, 2010), which is particularly important, given the proliferation of crossbreeding in some populations. An important issue for difficult or expensive to measure traits is the persistence of the accuracy of genomic predictions across generations.…”

Section: Future Perspectives For Genomic Selection For Robustness Traitsmentioning

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Genomic selection: the option for new robustness traits?

Calus

Berry

Banos

et al. 2013

Advances in Animal Biosciences

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Genomic selection is rapidly becoming the state-of-the-art genetic selection methodology in dairy cattle breeding schemes around the world. The objective of this paper was to explore possibilities to apply genomic selection for traits related to dairy cow robustness. Deterministic simulations indicate that replacing progeny testing with genomic selection may favour genetic response for production traits at the expense of robustness traits, owing to a disproportional change in accuracies obtained across trait groups. Nevertheless, several options are available to improve the accuracy of genomic selection for robustness traits. Moreover, genomic selection opens up the opportunity to begin selection for new traits using specialised reference populations of limited size where phenotyping of large populations of animals is currently prohibitive. Reference populations for such traits may be nucleustype herds, research herds or pooled data from (international) research experiments or research herds. The ROBUSTMILK project has set an example for the latter approach, by collating international data for progesterone-based traits, feed intake and energy balance-related traits. Reference population design, both in terms of relatedness of the animals and variability in phenotypic performance, is important to optimise the accuracy of genomic selection. Use of indicator traits, combined with multi-trait genomic prediction models, can further contribute to improved accuracy of genomic prediction for robustness traits. Experience to date indicates that for newly recorded robustness traits that are negatively correlated with the main breeding goal, cow reference populations of >10 000 are required when genotyping is based on medium-or high-density single-nucleotide polymorphism arrays. Further genotyping advances (e.g. sequencing) combined with post-genomics technologies will enhance the opportunities for (genomic) selection to improve cow robustness.

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A note on mate allocation for dominance handling in genomic selection

Cited by 129 publications

References 13 publications

Estimation of additive and dominance variance for litter size components in rabbits

Estimation of additive and dominance variance for litter size components in rabbits

On the Additive and Dominant Variance and Covariance of Individuals Within the Genomic Selection Scope

Genomic selection: the option for new robustness traits?

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