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

Genetics Selection Evolution. 2018;50(1):1-8 DOI 10.1186/s12711-018-0437-3


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Journal Title: Genetics Selection Evolution

ISSN: 0999-193X (Print); 1297-9686 (Online)

Publisher: BMC

Society/Institution: French National Institute for Agricultural Research

LCC Subject Category: Agriculture: Animal culture | Science: Biology (General): Genetics

Country of publisher: United Kingdom

Language of fulltext: German, French, English

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Zulma G. Vitezica (INP ENSAT, UMR 1388 GenPhySE)
Antonio Reverter (CSIRO Agriculture and Food)
William Herring (PIC North America)
Andres Legarra (INRA, UMR 1388 GenPhySE)


Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 26 weeks


Abstract | Full Text

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.