Considering dominance in reduced single‐step genomic evaluations

Ertl, Johann; Edel, Christian; Pimentel, E.C.G.; Emmerling, Reiner; Götz, Kay-Uwe

doi:10.1111/jbg.12323

Cited by 5 publications

(9 citation statements)

References 24 publications

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“…Details of the derivation are given in the Appendix 1. This leads to the same results as reported by Ertl et al (), with

c \sim N (0, σ_{c}^{2} C^{*}),

C^{*} = [\begin{matrix} boldC & {boldCD}_{g}^{- 1} {boldD}_{g, n g} \\ D_{n g, g} D_{g}^{- 1} boldC & {boldD}_{n g, g} {boldD}_{g}^{- 1} {boldCD}_{g}^{- 1} {boldD}_{g, n g} + {boldD}_{ng} - {boldD}_{n g, g} {boldD}_{g}^{- 1} {boldD}_{g, n g} \end{matrix}],

where

C = false(1 - α false) G + α {boldD}_{g} .

The matrix D was previously described, and the terms ng and g represent non‐genotyped and genotyped animals, respectively. The parameter α can be interpreted as the relative weight on polygenic dominance effects as described by Christensen and Lund ().…”

Section: Methodssupporting

confidence: 86%

“…As recently reported by Ertl, Edel, Pimentel, Emmerling, and Götz (), many recent studies have emphasized the importance of accounting for dominance (e.g., Lopes, Bastiaansen, Janss, Knol, & Bovenhuis, ). Over the past few years, as in many countries and production systems, a substantial number of dairy cows and pigs have been genotyped in Belgium.…”

Section: Introductionmentioning

confidence: 84%

“…Over the past few years, as in many countries and production systems, a substantial number of dairy cows and pigs have been genotyped in Belgium. This has created a need to, but also allows for, better accounting for dominance in genomic evaluations (Ertl et al, ). Many different methods, such as the genomic best linear unbiased prediction (GBLUP), were developed to capture additive variance directly or indirectly by regressing phenotypes on marker information (e.g., de los Campos, Sorensen, & Gianola, ).…”

Section: Introductionmentioning

confidence: 99%

“…However, it was recently shown, at least empirically, that this basic strategy can be extended with reasonable quality of prediction (Ertl et al, ). Our study will now address two additional issues.…”

Section: Introductionmentioning

confidence: 99%

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Additional considerations to the use of single‐step genomic predictions in a dominance setting

Mota

Vanderick

Colinet

et al. 2019

J Animal Breeding Genetics

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Recent publications indicate that single-step models are suitable to estimate breeding values, dominance deviations and total genetic values with acceptable quality.Additive single-step methods implicitly extend known number of allele information from genotyped to non-genotyped animals. This theory is well derived in an additive setting. It was recently shown, at least empirically, that this basic strategy can be extended to dominance with reasonable prediction quality. Our study addressed two additional issues. It illustrated the theoretical basis for extension and validated genomic predictions to dominance based on single-step genomic best linear unbiased prediction theory. This development was then extended to include inbreeding into dominance relationships, which is a currently not yet solved issue. Different parametrizations of dominance relationship matrices were proposed. Five dominance single-step inverse matrices were tested and described as C 1 , C 2 , C 3 , C 4 and C 5 . Genotypes were simulated for a real pig population (n = 11,943 animals). In order to avoid any confounding issues with additive effects, pseudo-records including only dominance deviations and residuals were simulated. SNP effects of heterozygous genotypes were summed up to generate true dominance deviations. We added random noise to those values and used them as phenotypes. Accuracy was defined as correlation between true and predicted dominance deviations. We conducted five replicates and estimated accuracies in three sets: between all (S 1 ), non-genotyped (S 2 ) and inbred non-genotyped (S 3 ) animals. Potential bias was assessed by regressing true dominance deviations on predicted values. Matrices accounting for inbreeding (C 3 , C 4 and C 5 ) best fit. Accuracies were on average 0.77, 0.40 and 0.46 in S 1 , S 2 and S 3 , respectively. In addition, C 3 , C 4 and C 5 scenarios have shown better accuracies than C 1 and C 2 , and dominance deviations were less biased. Better matrix compatibility (accuracy and bias) was observed by re-scaling diagonal elements to 1 minus the inbreeding coefficient (C 5 ). K E Y W O R D Sbias, dominance relationships, matrix compatibility, prediction accuracy | 431 MOTA eT Al.

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“…Details of the derivation are given in the Appendix 1. This leads to the same results as reported by Ertl et al (), with

c \sim N (0, σ_{c}^{2} C^{*}),

C^{*} = [\begin{matrix} boldC & {boldCD}_{g}^{- 1} {boldD}_{g, n g} \\ D_{n g, g} D_{g}^{- 1} boldC & {boldD}_{n g, g} {boldD}_{g}^{- 1} {boldCD}_{g}^{- 1} {boldD}_{g, n g} + {boldD}_{ng} - {boldD}_{n g, g} {boldD}_{g}^{- 1} {boldD}_{g, n g} \end{matrix}],

where

C = false(1 - α false) G + α {boldD}_{g} .

Section: Methodssupporting

confidence: 86%

Section: Introductionmentioning

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Additional considerations to the use of single‐step genomic predictions in a dominance setting

Mota

Vanderick

Colinet

et al. 2019

J Animal Breeding Genetics

View full text Add to dashboard Cite

show abstract

“…With the availability of methods to calculate genomic dominance relationship matrices it is straight-forward to include dominance effects in the ssGBLUP models. However, the main problem for using dominance effects in single-step analyses is that it is computationally not feasible to invert genomic dominance relationship matrices from large numbers of samples (Ertl et al 2018). The convergence of single-step methods is one of the main problems encountered when a large number of samples are used.…”

Section: Introductionmentioning

confidence: 99%

Genomic Studies Reveal Substantial Dominant Effects and Improved Genomic Predictions in an Open-Pollinated Breeding Population of Eucalyptus pellita

Thavamanikumar¹,

Arnold

Luo

et al. 2020

G3 Genes|Genomes|Genetics

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Most of the genomic studies in plants and animals have used additive models for studying genetic parameters and prediction accuracies. In this study, we used genomic models with additive and nonadditive effects to analyse the genetic architecture of growth and wood traits in an open-pollinated (OP) population of Eucalyptus pellita. We used two progeny trials consisting of 5742 trees from 244 OP families to estimate genetic parameters and to test genomic prediction accuracies of three growth traits (diameter at breast height - DBH, total height - Ht and tree volume - Vol) and kraft pulp yield (KPY). From 5742 trees, 468 trees from 28 families were genotyped with 2023 pre-selected markers from candidate genes. We used the pedigree-based additive best linear unbiased prediction (ABLUP) model and two marker-based models (single-step genomic BLUP - ssGBLUP and genomic BLUP - GBLUP) to estimate the genetic parameters and compare the prediction accuracies. Analyses with the two genomic models revealed large dominant effects influencing the growth traits but not KPY. Theoretical breeding value accuracies were higher with the dominance effect in ssGBLUP model for the three growth traits. Accuracies of cross-validation with random folding in the genotyped trees have ranged from 0.60 to 0.82 in different models. Accuracies of ABLUP were lower than the genomic models. Accuracies ranging from 0.50 to 0.76 were observed for within family cross-validation predictions with low relationships between training and validation populations indicating part of the functional variation is captured by the markers through short-range linkage disequilibrium (LD). Within-family phenotype predictive abilities and prediction accuracies of genetic values with dominance effects are higher than the additive models for growth traits indicating the importance of dominance effects in predicting phenotypes and genetic values. This study demonstrates the importance of genomic approaches in OP families to study nonadditive effects. To capture the LD between markers and the quantitative trait loci (QTL) it may be important to use informative markers from candidate genes.

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