Dominance and epistatic genetic variances for litter size in pigs using genomic models

Abstract Background Epistatic genomic relationship matrices for interactions of any-order can be constructed using the Hadamard products of orthogonal additive and dominance genomic relationship matrices and standardization based on the trace of the resulting matrices. Variance components for litter...

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Main Authors: Zulma G. Vitezica, Antonio Reverter, William Herring, Andres Legarra
Format: Article
Language:deu
Published: BMC 2018-12-01
Series:Genetics Selection Evolution
Online Access:http://link.springer.com/article/10.1186/s12711-018-0437-3
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author Zulma G. Vitezica
Antonio Reverter
William Herring
Andres Legarra
author_facet Zulma G. Vitezica
Antonio Reverter
William Herring
Andres Legarra
author_sort Zulma G. Vitezica
collection DOAJ
description Abstract Background Epistatic genomic relationship matrices for interactions of any-order can be constructed using the Hadamard products of orthogonal additive and dominance genomic relationship matrices and standardization based on the trace of the resulting matrices. Variance components for litter size in pigs were estimated by Bayesian methods for five nested models with additive, dominance, and pairwise epistatic effects in a pig dataset, and including genomic inbreeding as a covariate. Results Estimates of additive and non-additive (dominance and epistatic) variance components were obtained for litter size. The variance component estimates were empirically orthogonal, i.e. they did not change when fitting increasingly complex models. Most of the genetic variance was captured by non-epistatic effects, as expected. In the full model, estimates of dominance and total epistatic variances (additive-by-additive plus additive-by-dominance plus dominance-by-dominance), expressed as a proportion of the total phenotypic variance, were equal to 0.02 and 0.04, respectively. The estimate of broad-sense heritability for litter size (0.15) was almost twice that of the narrow-sense heritability (0.09). Ignoring inbreeding depression yielded upward biased estimates of dominance variance, while estimates of epistatic variances were only slightly affected. Conclusions Epistatic variance components can be easily computed using genomic relationship matrices. Correct orthogonal definition of the relationship matrices resulted in orthogonal partition of genetic variance into additive, dominance, and epistatic components, but obtaining accurate variance component estimates remains an issue. Genomic models that include non-additive effects must also consider inbreeding depression in order to avoid upward bias of estimates of dominance variance. Inclusion of epistasis did not improve the accuracy of prediction of breeding values.
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spelling doaj.art-44aada122a764c18821da2d319a6fbd82022-12-22T03:57:17ZdeuBMCGenetics Selection Evolution1297-96862018-12-015011810.1186/s12711-018-0437-3Dominance and epistatic genetic variances for litter size in pigs using genomic modelsZulma G. Vitezica0Antonio Reverter1William Herring2Andres Legarra3INP ENSAT, UMR 1388 GenPhySECSIRO Agriculture and FoodPIC North AmericaINRA, UMR 1388 GenPhySEAbstract Background Epistatic genomic relationship matrices for interactions of any-order can be constructed using the Hadamard products of orthogonal additive and dominance genomic relationship matrices and standardization based on the trace of the resulting matrices. Variance components for litter size in pigs were estimated by Bayesian methods for five nested models with additive, dominance, and pairwise epistatic effects in a pig dataset, and including genomic inbreeding as a covariate. Results Estimates of additive and non-additive (dominance and epistatic) variance components were obtained for litter size. The variance component estimates were empirically orthogonal, i.e. they did not change when fitting increasingly complex models. Most of the genetic variance was captured by non-epistatic effects, as expected. In the full model, estimates of dominance and total epistatic variances (additive-by-additive plus additive-by-dominance plus dominance-by-dominance), expressed as a proportion of the total phenotypic variance, were equal to 0.02 and 0.04, respectively. The estimate of broad-sense heritability for litter size (0.15) was almost twice that of the narrow-sense heritability (0.09). Ignoring inbreeding depression yielded upward biased estimates of dominance variance, while estimates of epistatic variances were only slightly affected. Conclusions Epistatic variance components can be easily computed using genomic relationship matrices. Correct orthogonal definition of the relationship matrices resulted in orthogonal partition of genetic variance into additive, dominance, and epistatic components, but obtaining accurate variance component estimates remains an issue. Genomic models that include non-additive effects must also consider inbreeding depression in order to avoid upward bias of estimates of dominance variance. Inclusion of epistasis did not improve the accuracy of prediction of breeding values.http://link.springer.com/article/10.1186/s12711-018-0437-3
spellingShingle Zulma G. Vitezica
Antonio Reverter
William Herring
Andres Legarra
Dominance and epistatic genetic variances for litter size in pigs using genomic models
Genetics Selection Evolution
title Dominance and epistatic genetic variances for litter size in pigs using genomic models
title_full Dominance and epistatic genetic variances for litter size in pigs using genomic models
title_fullStr Dominance and epistatic genetic variances for litter size in pigs using genomic models
title_full_unstemmed Dominance and epistatic genetic variances for litter size in pigs using genomic models
title_short Dominance and epistatic genetic variances for litter size in pigs using genomic models
title_sort dominance and epistatic genetic variances for litter size in pigs using genomic models
url http://link.springer.com/article/10.1186/s12711-018-0437-3
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AT williamherring dominanceandepistaticgeneticvariancesforlittersizeinpigsusinggenomicmodels
AT andreslegarra dominanceandepistaticgeneticvariancesforlittersizeinpigsusinggenomicmodels