ArticleFrontiers in plant science2021
Comparing Genomic Prediction Models by Means of Cross Validation.
Article in Frontiers in plant science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Genomic prediction of wild-derived powdery mildew resistance for strawberry (Fragaria × ananassa) pre-breeding.Heredity · 2026Article
- Large scale wheat data integration improves genomic prediction accuracy with the potential to facilitate international breeding collaborations.Communications biology · 2026Article
- Lipid Metabolism and Actin Cytoskeleton Regulation Underlie Yield and Disease Resistance in TwoPlants (Basel, Switzerland) · 2025Article
- EasyGeSe - a resource for benchmarking genomic prediction methods.BMC genomics · 2025Article
- Incorporating transcriptomic data into genomic prediction models to improve the prediction accuracy of phenotypes of efficiency traits.Genetics, selection, evolution : GSE · 2025Article
- Improving genomic prediction accuracy for methane emission and feed efficiency in sheep: integrating rumen microbial PCA with host genomic variation using neural network GBLUP (NN-GBLUP).Genetics, selection, evolution : GSE · 2025Article
- Environmental genomic selection to leverage polygenic local adaptation in barley landraces.Communications biology · 2025Article
- WheatGP, a genomic prediction method based on CNN and LSTM.Briefings in bioinformatics · 2025Article
- Multi-trait ridge regression BLUP withFrontiers in plant science · 2025Article
- Genome-wide association analysis of resistance to bacterial cold-water disease in an important rainbow trout aquaculture breeding population.Frontiers in genetics · 2025Article
- Assessment of the potential for genomic selection to improve resistance to fusarium stalk rot in maize.Frontiers in plant science · 2025Article
- On the ability of the LR method to detect bias when there is pedigree misspecification and lack of connectedness.Genetics, selection, evolution : GSE · 2024Article
- Implications of accounting for marker-based population structure in the quantitative genetic evaluation of genetic parameters related to growth and wood properties in Norway spruce.BMC genomic data · 2024Article
- Ensemble learning for integrative prediction of genetic values with genomic variants.BMC bioinformatics · 2024Article
- Stacked generalization as a computational method for the genomic selection.Frontiers in genetics · 2024Article
- Predicting risk of obesity in overweight adults using interpretable machine learning algorithms.Frontiers in endocrinology · 2023Article
- An application based on bioinformatics and machine learning for risk prediction of sepsis at first clinical presentation using transcriptomic data.Frontiers in genetics · 2022Article
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
In the two decades of continuous development of genomic selection, a great variety of models have been proposed to make predictions from the information available in dense marker panels. Besides deciding which particular model to use, practitioners also need to make many minor choices for those parameters in the model which are not typically estimated by the data (so called "hyper-parameters"). When the focus is placed on predictions, most of these decisions are made in a direction sought to optimize predictive accuracy. Here we discuss and illustrate using publicly available crop datasets the use of cross validation to make many such decisions. In particular, we emphasize the importance of paired comparisons to achieve high power in the comparison between candidate models, as well as the need to define notions of relevance in the difference between their performances. Regarding the latter, we borrow the idea of equivalence margins from clinical research and introduce new statistical tests. We conclude that most hyper-parameters can be learnt from the data by either minimizing REML or by using weakly-informative priors, with good predictive results. In particular, the default options in a popular software are generally competitive with the optimal values. With regard to the performance assessments themselves, we conclude that the paired k-fold cross validation is a generally applicable and statistically powerful methodology to assess differences in model accuracies. Coupled with the definition of equivalence margins based on expected genetic gain, it becomes a useful tool for breeders.
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