Título

Using an incomplete block design to allocate lines to environments improves sparse genome-based prediction in plant breeding

Autor

Osval Antonio Montesinos-Lopez

ABELARDO MONTESINOS LOPEZ

RICARDO ACOSTA DIAZ

Rajeev Varshney

Jose Crossa

ALISON BENTLEY

Nivel de Acceso

Acceso Abierto

Licencia

http://creativecommons.org/licenses/by/4.0

Identificador alterno

isni/0000 0000 8990 8592

isni/0000 0004 0633 1134

isni/0000 0000 9323 1772

isni/0000 0004 7864 4340

issn/0000 0004 0478 8931

Materias

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA - (CTI) Bayes Theorem - (MESH) Genome - (MESH) Inflammatory Bowel Diseases - (MESH) Models, Genetic - (MESH) Plant Breeding - (MESH)

Resumen o descripción

Genomic selection (GS) is a predictive methodology that trains statistical machine-learning models with a reference population that is used to perform genome-enabled predictions of new lines. In plant breeding, it has the potential to increase the speed and reduce the cost of selection. However, to optimize resources, sparse testing methods have been proposed. A common approach is to guarantee a proportion of nonoverlapping and overlapping lines allocated randomly in locations, that is, lines appearing in some locations but not in all. In this study we propose using incomplete block designs (IBD), principally, for the allocation of lines to locations in such a way that not all lines are observed in all locations. We compare this allocation with a random allocation of lines to locations guaranteeing that the lines are allocated to

the same number of locations as under the IBD design. We implemented this benchmarking on several crop data sets under the Bayesian genomic best linear unbiased predictor (GBLUP) model, finding that allocation under the principle of IBD outperformed random allocation by between 1.4% and 26.5% across locations, traits, and data sets in terms of mean square error. Although a wide range of performance improvements were observed, our results provide evidence that using IBD for the allocation of lines to locations can help improve predictive performance compared with random allocation. This has the potential to be applied to large-scale plant breeding programs.

Editor

Plant Genome

Fecha de publicación

14 de febrero de 2022

Tipo de publicación

Artículo

Recurso de información

http://biblos.ucol.mx//handle/123456789/128

Idioma

Inglés

Repositorio Orígen

Repositorio institucional de la Universidad de Colima

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