Comparing genomic prediction models by means of cross validation

• Autores: Schrauf, Matías Florián; Campos, Gustavo de los; Munilla Leguizamón, Sebastián
• Año de publicación: 2021
• Idioma: inglés
• Tipo de recurso: artículo
• Fuente: Frontiers in Plant Science; Vol.12; art. 734512; http://www.frontiersin.org
• Materia: GENOMIC SELECTION; CROSS VALIDATION; PLANT BREEDING; GENOMIC MODELS; MODEL SELECTION
• Institución: Universidad de Buenos Aires. Facultad de Agronomía

Fil: Schrauf, Matías Florián. Universidad de Buenos Aires. Facultad de Agronomía. Buenos Aires, Argentina.
Fil: Schrauf, Matías Florián. Wageningen University and Research. Wageningen Livestock Research. Animal Breeding and Genomics. Wageningen, Países Bajos.
Fil: Campos, Gustavo de los. Michigan State University. Departments of Epidemiology, Biostatistics, Statistics and Probabilty. Institute for Quantitative Health Science and Engineering. East Lansing, MI, Estados Unidos.
Fil: Munilla Leguizamón, Sebastián. Universidad de Buenos Aires. Facultad de Agronomía. Buenos Aires, Argentina.
Fil: Munilla Leguizamón, Sebastián. Universidad de Buenos Aires. Facultad de Ciencias Veterinarias. Instituto de Investigaciones en Producción Animal (INPA). Buenos Aires, Argentina.
Fil: Munilla Leguizamón, Sebastián. CONICET - Universidad de Buenos Aires. Facultad de Ciencias Veterinarias. Instituto de Investigaciones en Producción Animal (INPA). Buenos Aires, Argentina.
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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