2016
DOI: 10.2135/cropsci2015.05.0311
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Predicting Responses in Multiple Environments: Issues in Relation to Genotype × Environment Interactions

Abstract: Prediction of the phenotypes for a set of genotypes across multiple environments is a fundamental task in any plant breeding program. Genomic prediction (GP) can assist selection decisions by combining incomplete phenotypic information over multiple environments (MEs) with dense sets of markers. We compared a range of ME‐GP models differing in the way environment‐specific genetic effects were modeled. Information among environments was shared either implicitly via the response variable, or by the introduction … Show more

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Cited by 98 publications
(149 citation statements)
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“…In the context of genomic prediction, it makes sense to also study the expected consistency of year to year performance aiming to minimize this variability [8, 28]. This stability aspect deserves further study.…”
Section: Discussionmentioning
confidence: 99%
“…In the context of genomic prediction, it makes sense to also study the expected consistency of year to year performance aiming to minimize this variability [8, 28]. This stability aspect deserves further study.…”
Section: Discussionmentioning
confidence: 99%
“…The macro‐environmental variability is determined by differences in environmental conditions, including soil type, climate, and agronomic management practices. The genotypic response to different environments is complex, and genotype × environment interaction is present when genotypic response differences vary in relation to the environmental conditions (Malosetti et al, 2016). On the other hand, micro‐environmental variability is mainly determined by spatial variability and other local scale factors (Wu, 1997).…”
mentioning
confidence: 99%
“…interaction is present when genotypic response differences vary in relation to the environmental conditions (Malosetti et al, 2016). On the other hand, micro-environmental variability is mainly determined by spatial variability and other local scale factors (Wu, 1997).…”
mentioning
confidence: 99%
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“…In addition to epistasis, another example of interactions relating to breeding is the interaction between genes and environments, which is prominent particularly in plants. Whereas mixed effect models [41,42] and physiological model-based approaches [43,44] are often used to predict gene-by-environment interactions, several authors proposed neural networks that used both environmental covariates (i.e., environment identifiers or climate covariates) and genotype information (i.e.., genotype identifiers, marker genotypes, or decomposed genetic relationship matrices) as inputs [45][46][47]. In ref.…”
Section: Beyond Genomic Predictionmentioning
confidence: 99%