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Malosetti M.,Wageningen University | Ribaut J.-M.,Consultative Group on International Agricultural Research Generation Challenge Programme | van Eeuwijk F.A.,Wageningen University
Frontiers in Physiology | Year: 2013

Genotype-by-environment interaction (GEI) is an important phenomenon in plant breeding. This paper presents a series of models for describing, exploring, understanding, and predicting GEI. All models depart from a two-way table of genotype by environment means. First, a series of descriptive and explorative models/approaches are presented: Finlay-Wilkinson model, AMMI model, GGE biplot. All of these approaches have in common that they merely try to group genotypes and environments and do not use other information than the two-way table of means. Next, factorial regression is introduced as an approach to explicitly introduce genotypic and environmental covariates for describing and explaining GEI. Finally, QTL modeling is presented as a natural extension of factorial regression, where marker information is translated into genetic predictors. Tests for regression coefficients corresponding to these genetic predictors are tests for main effect QTL expression and QTL by environment interaction (QEI). QTL models for which QEI depends on environmental covariables form an interesting model class for predicting GEI for new genotypes and new environments. For realistic modeling of genotypic differences across multiple environments, sophisticated mixed models are necessary to allow for heterogeneity of genetic variances and correlations across environments. The use and interpretation of all models is illustrated by an example data set from the CIMMYT maize breeding program, containing environments differing in drought and nitrogen stress. To help readers to carry out the statistical analyses, GenStat® programs, 15th Edition and Discovery® version, are presented as "Appendix." © 2013 Malosetti, Ribaut and van Eeuwijk. Source


Delannay X.,Consultative Group on International Agricultural Research Generation Challenge Programme | McLaren G.,Consultative Group on International Agricultural Research Generation Challenge Programme | Ribaut J.,Consultative Group on International Agricultural Research Generation Challenge Programme
Molecular Breeding | Year: 2012

Molecular breeding (MB) increases genetic gain per crop cycle, stacks favourable alleles at target loci and reduces the number of selection cycles. In the last decade, the private sector has benefitted immensely from MB, which demonstrates its efficacy. In contrast, MB adoption is still limited in the public sector, and it is hardly used in developing countries. Major bottlenecks in these countries include shortage of well-trained personnel, inadequate high-throughput capacity, poor phenotyping infrastructure, lack of information systems or adapted analysis tools or simply resource-limited breeding programmes. The emerging virtual platforms aided by the information and communication technology revolution will help to overcome some of these limitations by providing breeders with better access to genomic resources, advanced laboratory services and robust analytical and data management tools. Apart from some advanced national agricultural research systems (NARS), the implementation of large-scale molecular breeding programmes in developing countries will take time. However, the exponential development of genomic resources, including for less-studied crops, the ever-decreasing cost of marker technologies and the emergence of platforms for accessing MB tools and support services, plus the increasing public-private partnerships and needs-driven demand for improved varieties to counter the global food crisis, are all grounds to predict that MB will have a significant impact on crop breeding in developing countries. These predictions are supported by some preliminary successful examples presented in this paper. © 2011 Springer Science+Business Media B.V. Source

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