Phenotypically Selective Genotyping Realizes More Genetic Gains in a Rainbow Trout Breeding Program in the Presence of Genotype-by-Environment Interactions

Chu, Thinh Tuan; Sørensen, Anders Christian; Lund, Mogens Sandø; Meier, Kristian; Nielsen, Torben; Su, Guosheng

doi:10.3389/fgene.2020.00866

Cited by 12 publications

(9 citation statements)

References 17 publications

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“…However, genotyping both surviving and dead pigs refers to the genotyping phenotypically contrasting animals and that realised more genetic gain and increased prediction accuracy in comparison to genotyping only surviving animals. The finding in this study is in agreement with other studies using continuous trait (Gowane et al, 2019;Chu et al, 2020). When animals of extreme phenotypes are genotyped, the power of detecting SNP effect increased because most of the genetic information is captured in animals of extreme phenotypes (Huang and Lin, 2007).…”

Section: Discussionsupporting

confidence: 92%

“…This has the potential to introduce prediction bias and reduce accuracy (Leite et al, 2021). In general, genotyping phenotypically contrasting animals can increase the genetic gain, accuracy of GEBV and decrease the bias of GEBV for continuous trait in comparison to genotyping only top animals because information on phenotypically extreme values results in more accurate estimates of SNP effects (Gowane et al, 2019, Chu et al, 2020. For the case of PWS, genotyping phenotypically contrasting animals refers to the scenario of genotyping both surviving and dead animals.…”

Section: Introductionmentioning

confidence: 99%

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779. Genotyping dead animals improves post-weaning survival of pigs in breeding programs

Sharif-Islam

Werf

Henryon

et al. 2022

Proceedings of 12th World Congress on Genetics Applied to Livestock Production (WCGALP)

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A premise was tested that genotyping both surviving and dead pigs will realise more genetic gain in post-weaning survival (PWS) than genotyping only surviving animals. Stochastic simulation was used to estimate the rate of true genetic gain in different genotyping scenarios that differed in varying proportions of genotyping dead animals. Selection was for only PWS that had heritability of 0.02. Mortality was assumed 10%. The trait was controlled by 7,702 biallelic quantitative trait loci distributed across a 30 Morgan genome. We used 54,218 biallelic single nucleotide polymorphisms (SNPs) that were used in genomic prediction. Genotyping both surviving and dead animals realised 12 to 24% more genetic gain than genotyping only surviving animals. The power of detecting SNP effects increased when animals of extreme phenotypes are genotyped. Therefore, genotyping both surviving and dead pigs realised more genetic gain than genotyping only surviving animals.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

779. Genotyping dead animals improves post-weaning survival of pigs in breeding programs

Sharif-Islam

Werf

Henryon

et al. 2022

Proceedings of 12th World Congress on Genetics Applied to Livestock Production (WCGALP)

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“…No difference in dispersion bias was detected between the RND and SIRE strategies, and bias was virtually absent for both strategies when 25% or more birds were genotyped. Thus, a random genotyping strategy does not have to aim for all sires to be represented, which is in agreement with results of several simulation studies that have shown that bias was smaller with random genotyping compared to selective genotyping [ 14 – 17 , 37 ]. Finally, violation of the assumption that is made here, i.e.…”

Section: Resultssupporting

confidence: 87%

Selective genotyping strategies for a sib test scheme of a broiler breeder program

Hollander

Breen²,

Henshall³

et al. 2023

Genet Sel Evol

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Background In broiler breeding, genotype-by-environment interaction is known to result in a genetic correlation between body weight measured in bio-secure and commercial environments that is substantially less than 1. Thus, measuring body weights on sibs of selection candidates in a commercial environment and genotyping them could increase genetic progress. Using real data, the aim of this study was to evaluate which genotyping strategy and which proportion of sibs placed in the commercial environment should be genotyped to optimize a sib-testing breeding program in broilers. Phenotypic body weight and genomic information were collected on all sibs raised in a commercial environment, which allowed to retrospectively analyze different sampling strategies and genotyping proportions. Results Accuracies of genomic estimated breeding values (GEBV) obtained with the different genotyping strategies were assessed by computing their correlation with GEBV obtained when all sibs in the commercial environment were genotyped. Results showed that, compared to random sampling (RND), genotyping sibs with extreme phenotypes (EXT) resulted in higher GEBV accuracy across all genotyping proportions, especially for genotyping proportions of 12.5% or 25%, which resulted in correlations of 0.91 vs 0.88 for 12.5% and 0.94 vs 0.91 for 25% genotyped. Including pedigree on birds with phenotype in the commercial environment that were not genotyped increased accuracy at lower genotyping proportions, especially for the RND strategy (correlations of 0.88 vs 0.65 at 12.5% and 0.91 vs 0.80 at 25%), and a smaller but still substantial increase in accuracy for the EXT strategy (0.91 vs 0.79 for 12.5% and 0.94 vs 0.88 for 25% genotyped). Dispersion bias was virtually absent for RND if 25% or more birds were genotyped. However, GEBV were considerably inflated for EXT, especially when the proportion genotyped was low, which was further exacerbated if the pedigree of non-genotyped sibs was excluded. Conclusions When less than 75% of all animals placed in a commercial environment are genotyped, it is recommended to use the EXT strategy, because it yields the highest accuracy. However, caution should be taken when interpreting the resulting GEBV because they will be over-dispersed. When 75% or more of the animals are genotyped, random sampling is recommended because it yields virtually no bias of GEBV and results in similar accuracies as the EXT strategy.

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“…In honey bees, the dams of DPQ and dams of BQ that produce candidates for GPS could be selected based on pedigree-based EBV [54]. However, genotyping queens with contrasting phenotypes should be considered to maintain prediction accuracy [55,56].…”

Section: Optimal Breeding Schemementioning

confidence: 99%

Simulation studies to optimize genomic selection in honey bees

Bernstein

Hoppe

et al. 2021

Genet Sel Evol

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Background With the completion of a single nucleotide polymorphism (SNP) chip for honey bees, the technical basis of genomic selection is laid. However, for its application in practice, methods to estimate genomic breeding values need to be adapted to the specificities of the genetics and breeding infrastructure of this species. Drone-producing queens (DPQ) are used for mating control, and usually, they head non-phenotyped colonies that will be placed on mating stations. Breeding queens (BQ) head colonies that are intended to be phenotyped and used to produce new queens. Our aim was to evaluate different breeding program designs for the initiation of genomic selection in honey bees. Methods Stochastic simulations were conducted to evaluate the quality of the estimated breeding values. We developed a variation of the genomic relationship matrix to include genotypes of DPQ and tested different sizes of the reference population. The results were used to estimate genetic gain in the initial selection cycle of a genomic breeding program. This program was run over six years, and different numbers of genotyped queens per year were considered. Resources could be allocated to increase the reference population, or to perform genomic preselection of BQ and/or DPQ. Results Including the genotypes of 5000 phenotyped BQ increased the accuracy of predictions of breeding values by up to 173%, depending on the size of the reference population and the trait considered. To initiate a breeding program, genotyping a minimum number of 1000 queens per year is required. In this case, genetic gain was highest when genomic preselection of DPQ was coupled with the genotyping of 10–20% of the phenotyped BQ. For maximum genetic gain per used genotype, more than 2500 genotyped queens per year and preselection of all BQ and DPQ are required. Conclusions This study shows that the first priority in a breeding program is to genotype phenotyped BQ to obtain a sufficiently large reference population, which allows successful genomic preselection of queens. To maximize genetic gain, DPQ should be preselected, and their genotypes included in the genomic relationship matrix. We suggest, that the developed methods for genomic prediction are suitable for implementation in genomic honey bee breeding programs.

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Phenotypically Selective Genotyping Realizes More Genetic Gains in a Rainbow Trout Breeding Program in the Presence of Genotype-by-Environment Interactions

Cited by 12 publications

References 17 publications

779. Genotyping dead animals improves post-weaning survival of pigs in breeding programs

779. Genotyping dead animals improves post-weaning survival of pigs in breeding programs

Selective genotyping strategies for a sib test scheme of a broiler breeder program

Simulation studies to optimize genomic selection in honey bees

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