Título

A reaction norm model for genomic selection using high-dimensional genomic and environmental data

Autor

DIEGO JARQUIN

Jose Crossa

Paulino Pérez-Rodríguez

Mario Calus

Juan Burgueño

Gustavo de los Campos

Nivel de Acceso

Acceso Abierto

Licencia

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

Materias

Prediction Accuracy - (AGROVOC) Covariance Function - (AGROVOC) Covariance Structure - (AGROVOC) Prediction Problem - (AGROVOC) Multiplicative Operator - (AGROVOC) CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA - (CTI)

Resumen o descripción

In most agricultural crops the effects of genes on traits are modulated by environmental conditions, leading to genetic by environmental interaction (g × e). Modern genotyping technologies allow characterizing genomes in great detail and modern information systems can generate large volumes of environmental data. In principle, g × e can be accounted for using interactions between markers and environmental covariates (ecs). However, when genotypic and environmental information is high dimensional, modeling all possible interactions explicitly becomes infeasible. In this article we show how to model interactions between high-dimensional sets of markers and ecs using covariance functions. The model presented here consists of (random) reaction norm where the genetic and environmental gradients are described as linear functions of markers and of ecs, respectively. We assessed the proposed method using data from arvalis, consisting of 139 wheat lines genotyped with 2,395 snps and evaluated for grain yield over 8 years and various locations within northern france. A total of 68 ecs, defined based on five phases of the phenology of the crop, were used in the analysis. Interaction terms accounted for a sizable proportion (16 %) of the within-environment yield variance, and the prediction accuracy of models including interaction terms was substantially higher (17–34 %) than that of models based on main effects only. Breeding for target environmental conditions has become a central priority of most breeding programs. Methods, like the one presented here, that can capitalize upon the wealth of genomic and environmental information available, will become increasingly important.

Fecha de publicación

2013

Tipo de publicación

Artículo

Recurso de información

https://hdl.handle.net/10883/19773

Formato

application/pdf

Idioma

Inglés

Audiencia

Investigadores

Repositorio Orígen

Repositorio Institucional de Publicaciones Multimedia del CIMMYT

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