Genomic estimation of additive and dominance effects and impact of accounting for dominance on accuracy of genomic evaluation in sheep populations

Moghaddar, Nasir; Werf, J. H. J. van der

doi:10.1111/jbg.12287

Cited by 22 publications

(17 citation statements)

References 28 publications

(50 reference statements)

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“…Ratios of dominance deviation variance for traits related to carcass composition ranged from null to moderate to low values (0.14 and 0.12 for BFT in PB and CB, respectively). The ratio of dominance variance ranged from 0.00 to 0.07 across different BW and scanned body composition traits in a purebred population, and from 0.07 to 0.19 in a combined crossbred population in sheep (Moghaddar and van der Werf, 2017). This ratio decreased to 0.03 and 0.09 after accounting for heterosis effects.…”

Section: Variance Of Dominance Deviation Effectsmentioning

confidence: 93%

“…Although not relevantly different, estimated dominance values for lifetime daily gain in the Piétrain population in Lopes et al (2015) are slightly higher than ours, possibly because they used a model that did not account for inbreeding. Estimates of dominance variance can be inflated when average heterozygosity or inbreeding is not accounted for in the model (Aliloo et al, 2017;Moghaddar and van der Werf, 2017). Su et al (2012) reported that dominance variance represented about 5% of the phenotypic expression of ADG in Duroc pigs.…”

Section: Variance Of Dominance Deviation Effectsmentioning

confidence: 99%

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Dissecting total genetic variance into additive and dominance components of purebred and crossbred pig traits

Tusell

Gilbert

Vitezica

et al. 2019

Animal

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The partition of the total genetic variance into its additive and non-additive components can differ from trait to trait, and between purebred and crossbred populations. A quantification of these genetic variance components will determine the extent to which it would be of interest to account for dominance in genomic evaluations or to establish mate allocation strategies along different populations and traits. This study aims at assessing the contribution of the additive and dominance genomic variances to the phenotype expression of several purebred Piétrain and crossbred (Piétrain × Large White) pig performances. A total of 636 purebred and 720 crossbred male piglets were phenotyped for 22 traits that can be classified into six groups of traits: growth rate and feed efficiency, carcass composition, meat quality, behaviour, boar taint and puberty. Additive and dominance variances estimated in univariate genotypic models, including additive and dominance genotypic effects, and a genomic inbreeding covariate allowed to retrieve the additive and dominance single nucleotide polymorphism variances for purebred and crossbred performances. These estimated variances were used, together with the allelic frequencies of the parental populations, to obtain additive and dominance variances in terms of genetic breeding values and dominance deviations. Estimates of the Piétrain and Large White allelic contributions to the crossbred variance were of about the same magnitude in all the traits. Estimates of additive genetic variances were similar regardless of the inclusion of dominance. Some traits showed relevant amount of dominance genetic variance with respect to phenotypic variance in both populations (i.e. growth rate 8%, feed conversion ratio 9% to 12%, backfat thickness 14% to 12%, purebreds-crossbreds). Other traits showed higher amount in crossbreds (i.e. ham cut 8% to 13%, loin 7% to 16%, pH semimembranosus 13% to 18%, pH longissimus dorsi 9% to 14%, androstenone 5% to 13% and estradiol 6% to 11%, purebreds-crossbreds). It was not encountered a clear common pattern of dominance expression between groups of analysed traits and between populations. These estimates give initial hints regarding which traits could benefit from accounting for dominance for example to improve genomic estimated breeding value accuracy in genetic evaluations or to boost the total genetic value of progeny by means of assortative mating.

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Section: Variance Of Dominance Deviation Effectsmentioning

confidence: 93%

Section: Variance Of Dominance Deviation Effectsmentioning

confidence: 99%

Dissecting total genetic variance into additive and dominance components of purebred and crossbred pig traits

Tusell

Gilbert

Vitezica

et al. 2019

Animal

View full text Add to dashboard Cite

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“…Genomic selection models with dominance have been tested in several populations, including dairy cattle (Ertl et al, 2014 ; Aliloo et al, 2016 ; Jiang et al, 2017 ), pigs (Esfandyari et al, 2016 ; Xiang et al, 2016 ), sheep (Moghaddar and van der Werf, 2017 ), and layers (Heidaritabar et al, 2016 ) with ambiguous results. Jiang et al ( 2017 ) found a negligible percentage of variation explained by dominance effects for productive life in a Holstein cattle population, although Ertl et al ( 2014 ) suggested that dominance may suppose up to 39% of the total genetic variation for Somatic Cell Score in a population of Fleckvieh cattle.…”

Section: Genomic Selection Models With Dominancementioning

confidence: 99%

Non-additive Effects in Genomic Selection

et al. 2018

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In the last decade, genomic selection has become a standard in the genetic evaluation of livestock populations. However, most procedures for the implementation of genomic selection only consider the additive effects associated with SNP (Single Nucleotide Polymorphism) markers used to calculate the prediction of the breeding values of candidates for selection. Nevertheless, the availability of estimates of non-additive effects is of interest because: (i) they contribute to an increase in the accuracy of the prediction of breeding values and the genetic response; (ii) they allow the definition of mate allocation procedures between candidates for selection; and (iii) they can be used to enhance non-additive genetic variation through the definition of appropriate crossbreeding or purebred breeding schemes. This study presents a review of methods for the incorporation of non-additive genetic effects into genomic selection procedures and their potential applications in the prediction of future performance, mate allocation, crossbreeding, and purebred selection. The work concludes with a brief outline of some ideas for future lines of that may help the standard inclusion of non-additive effects in genomic selection.

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“…However, results of this approach showed no improvement in hybrids predictions or in hybrids ranking (data not shown), although some exciting results have been reported in the literature (Dias, Gezan, Guimarães, Nazarian, et al., 2018; dos Santos, Vasconcellos, Pires, Balestre, & Von Pinho, 2016). Some bottlenecks associated with this result could be that the estimation of dominance effects requires specific mating designs (Nazarian & Gezan, 2016), and confounding between additive and nonadditive genetic components (Lee, Goddard, Visscher, & Van Der Werf, 2010; Moghaddar & van der Werf, 2017; Muñoz et al., 2014; Nazarian & Gezan, 2016). An increase in the number of evaluated hybrids could overcome this issue.…”

Section: Discussionmentioning

confidence: 99%

Boosting predictive ability of tropical maize hybrids via genotype‐by‐environment interaction under multivariate GBLUP models

et al. 2020

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Genomic selection has been implemented in several plant and animal breeding programs and it has proven to improve efficiency and maximize genetic gains. Phenotypic data of grain yield was measured in 147 maize (Zea mays L.) single‐cross hybrids at 12 environments. Single‐cross hybrids genotypes were inferred based on their parents (inbred lines) via single nucleotide polymorphism (SNP) markers obtained from genotyping‐by‐sequencing (GBS). Factor analytic multiplicative genomic best linear unbiased prediction (GBLUP) models, in the framework of multienvironment trials, were used to predict grain yield performance of unobserved tropical maize single‐cross hybrids. Predictions were performed for two situations: untested hybrids (CV1), and hybrids evaluated in some environments but missing in others (CV2). Models that borrowed information across individuals through genomic relationships and within individuals across environments presented higher predictive accuracy than those models that ignored it. For these models, predictive accuracies were up to 0.4 until eight environments were considered as missing for the validation set, which represents 67% of missing data for a given hybrid. These results highlight the importance of including genotype‐by‐environment interactions and genomic relationship information for boosting predictions of tropical maize single‐cross hybrids for grain yield.

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Genomic estimation of additive and dominance effects and impact of accounting for dominance on accuracy of genomic evaluation in sheep populations

Cited by 22 publications

References 28 publications

Dissecting total genetic variance into additive and dominance components of purebred and crossbred pig traits

Dissecting total genetic variance into additive and dominance components of purebred and crossbred pig traits

Non-additive Effects in Genomic Selection

Boosting predictive ability of tropical maize hybrids via genotype‐by‐environment interaction under multivariate GBLUP models

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