CV-α: designing validations sets to increase the precision and enable multiple comparison tests in genomic prediction

Yassue, Rafael Massahiro; Sabadin, Felipe; Galli, Giovanni; Alves, Filipe Couto; Fritsche‐Neto, Roberto

doi:10.1007/s10681-021-02831-x

“…To evaluate the model performance, we used the CV-a crossvalidation with five folds and four replicates (Yassue et al, 2021b). The predictive ability was estimated by Pearson's correlation between predicted genotypic and observed values from the validation set.…”

Section: Obtaining Single-cross Combinations and Genomic Predictionmentioning

confidence: 99%

On the usefulness of mock genomes to define heterotic pools, testers, and hybrid predictions in orphan crops

Machado

¹

,

DoVale

²

,

Sabadin

³

et al. 2023

Self Cite

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The advances in genomics in recent years have increased the accuracy and efficiency of breeding programs for many crops. Nevertheless, the adoption of genomic enhancement for several other crops essential in developing countries is still limited, especially for those that do not have a reference genome. These crops are more often called orphans. This is the first report to show how the results provided by different platforms, including the use of a simulated genome, called the mock genome, can generate in population structure and genetic diversity studies, especially when the intention is to use this information to support the formation of heterotic groups, choice of testers, and genomic prediction of single crosses. For that, we used a method to assemble a reference genome to perform the single-nucleotide polymorphism (SNP) calling without needing an external genome. Thus, we compared the analysis results using the mock genome with the standard approaches (array and genotyping-by-sequencing (GBS)). The results showed that the GBS-Mock presented similar results to the standard methods of genetic diversity studies, division of heterotic groups, the definition of testers, and genomic prediction. These results showed that a mock genome constructed from the population’s intrinsic polymorphisms to perform the SNP calling is an effective alternative for conducting genomic studies of this nature in orphan crops, especially those that do not have a reference genome.

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“…The predictive ability of the genomic prediction models was estimated as the correlation between the predicted and the observed genotypic values via a cross-validated, alpha-based design (CV-α, Yassue et al, 2020), which is an extension of the methodology presented by Shao (1993). This model validation procedure consists of assigning observations to train or validation folds (groups) in each replication using alpha-lattice sorting premises and evaluating the model's prediction in each assignment iteration performed.…”

mentioning

confidence: 99%

Genotyping marker density and prediction models effects in long-term breeding schemes of cross-pollinated crops

DoVale

¹

,

Carvalho

²

,

Sabadin

³

et al. 2022

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Reductions of genotyping marker density have been extensively evaluated as potential strategies to reduce the genotyping costs of genomic selection (GS). Low-density marker panels are appealing in GS because they entail lower multicollinearity and computing time and allow more individuals to be genotyped for the same cost. However, statistical models used in GS are usually evaluated with empirical data, using "static" training sets and populations. This may be adequate for making predictions during a breeding program's initial cycles but not for the long-term. Moreover, to the best of our knowledge, no GS models consider the effect of dominance, which is particularly important for breeding outcomes in cross-pollinated crops. Hence, dominance effects are important and unexplored in GS for long-term programs involving allogamous species. To address it, we employed two approaches: analysis of empirical maize datasets and simulations of long-term breeding applying phenotypic and genomic recurrent selection (intrapopulation and reciprocal schemes). In both schemes, we simulated twenty breeding cycles and assessed the effect of marker density reduction on the population mean, the best crosses, additive variance, selective accuracy, and response to selection with models (additive, additive-dominant, general (GCA), and specific combining ability (SCA)). Our results indicate that marker reduction based on linkage disequilibrium levels provides useful predictions only within a cycle, as accuracy significantly decreases over cycles. In the long-term, without training set updating, high-marker density provides the best responses to selection. The model to be used depends on the breeding scheme: additive for intrapopulation and additivedominant or SCA for reciprocal.

show abstract

“…The predictive ability of the genomic prediction models was estimated as the correlation between the predicted and the observed genotypic values via a cross-validated, alpha-based design (CV-α, Yassue et al, 2020), which is an extension of the methodology presented by Shao (1993). This model validation procedure consists of assigning observations to train or validation folds (groups) in each replication using alpha-lattice sorting premises and evaluating the model's prediction in each assignment iteration performed.…”

mentioning

confidence: 99%

Genotyping marker density and prediction models effects in long-term breeding schemes of cross-pollinated crops

DoVale

¹

,

Carvalho

²

,

Sabadin

³

et al. 2022

Preprint

Self Cite

0

View full text Add to dashboard Cite

Reductions of genotyping marker density have been extensively evaluated as potential strategies to reduce the genotyping costs of genomic selection (GS). Low-density marker panels are appealing in GS because they entail lower multicollinearity and computing time and allow more individuals to be genotyped for the same cost. However, statistical models used in GS are usually evaluated with empirical data, using "static" training sets and populations. This may be adequate for making predictions during a breeding program's initial cycles but not for the long-term. Moreover, to the best of our knowledge, no GS models consider the effect of dominance, which is particularly important for breeding outcomes in cross-pollinated crops. Hence, dominance effects are important and unexplored in GS for long-term programs involving allogamous species. To address it, we employed two approaches: analysis of empirical maize datasets and simulations of long-term breeding applying phenotypic and genomic recurrent selection (intrapopulation and reciprocal schemes). In both schemes, we simulated twenty breeding cycles and assessed the effect of marker density reduction on the population mean, the best crosses, additive variance, selective accuracy, and response to selection with models (additive, additive-dominant, general (GCA), and specific combining ability (SCA)). Our results indicate that marker reduction based on linkage disequilibrium levels provides useful predictions only within a cycle, as accuracy significantly decreases over cycles. In the long-term, without training set updating, high-marker density provides the best responses to selection. The model to be used depends on the breeding scheme: additive for intrapopulation and additive-dominant or SCA for reciprocal.

show abstract

CV-α: designing validations sets to increase the precision and enable multiple comparison tests in genomic prediction

Cited by 5 publications

References 47 publications

On the usefulness of mock genomes to define heterotic pools, testers, and hybrid predictions in orphan crops

On the usefulness of mock genomes to define heterotic pools, testers, and hybrid predictions in orphan crops

Genotyping marker density and prediction models effects in long-term breeding schemes of cross-pollinated crops

Genotyping marker density and prediction models effects in long-term breeding schemes of cross-pollinated crops

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