Modelling the Genetic Basis of Response Curves Underlying Genotype × Environment Interaction

Eeuwijk, F.A. van; Malosetti, Marcos; Boer, Martin P.

doi:10.1007/1-4020-5906-x_10

Cited by 13 publications

(16 citation statements)

References 23 publications

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“…Upon the collection of additional genotypic information in the form of measurements describing plant development or information relating to gene and metabolic expression, a next step in modeling could be the fitting of statistical models containing increased biological realism. Such models would immediately become nonlinear (Ma et al 2002;Malosetti et al 2006;Van Eeuwijk et al 2007). Of course, from a biological point of view, nonlinear QTL models are still simplified representations of the interacting biological and environmental components of the dynamic plant system (Hammer et al 2006), but for most applied prediction purposes, like marker-assisted breeding, such nonlinear models would represent an improvement over the present linear models.…”

Section: Discussionmentioning

confidence: 99%

“…More generally, the phenotypic behavior can be modeled in the form of QTL-dependent response curves to the environmental characterizations (Hammer et al 2006;Malosetti et al 2006;Van Eeuwijk et al 2007). These response curves are expected to have nonlinear forms, but limited environmental information will typically allow only linear approximations to these curves.…”

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confidence: 99%

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A Mixed-Model Quantitative Trait Loci (QTL) Analysis for Multiple-Environment Trial Data Using Environmental Covariables for QTL-by-Environment Interactions, With an Example in Maize

et al. 2007

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Complex quantitative traits of plants as measured on collections of genotypes across multiple environments are the outcome of processes that depend in intricate ways on genotype and environment simultaneously. For a better understanding of the genetic architecture of such traits as observed across environments, genotype-by-environment interaction should be modeled with statistical models that use explicit information on genotypes and environments. The modeling approach we propose explains genotype-by-environment interaction by differential quantitative trait locus (QTL) expression in relation to environmental variables. We analyzed grain yield and grain moisture for an experimental data set composed of 976 F 5 maize testcross progenies evaluated across 12 environments in the U.S. corn belt during 1994 and 1995. The strategy we used was based on mixed models and started with a phenotypic analysis of multienvironment data, modeling genotype-by-environment interactions and associated genetic correlations between environments, while taking into account intraenvironmental error structures. The phenotypic mixed models were then extended to QTL models via the incorporation of marker information as genotypic covariables. A majority of the detected QTL showed significant QTL-by-environment interactions (QEI). The QEI were further analyzed by including environmental covariates into the mixed model. Most QEI could be understood as differential QTL expression conditional on longitude or year, both consequences of temperature differences during critical stages of the growth.T HE incidence of genotype-by-environment interactions (GEI) for quantitative traits has important implications for any attempts to understand the genetic architecture of these traits by mapping quantitative trait loci (QTL) and also for the effectiveness of attempts to improve these traits by both conventional and markerassisted selection (MAS) breeding strategies. The literature on GEI and QTL-by-environment interactions (QEI) for quantitative traits in maize is ambiguous, with evidence in favor (Moreau et al. 2004) and against (Ledeaux et al. 2006) their importance. The diversity of the results for the importance of QEI for quantitative traits in crop plants observed in the literature strongly suggests that explicit testing for their presence, magnitude, and form is a critical step in any attempt to understand the genetic architecture of these traits. Further, theoretical considerations of the impact of different forms of QEI on the outcomes of MAS in plant breeding (Podlich et al. 2004;Cooper et al. 2002Cooper et al. , 2005Cooper et al. , 2006 motivate the development of methods for explicitly studying the importance of QEI as a component of the genetic architecture of quantitative traits.When QEI occurs and environmental covariables derived from geographical and weather information are available, QTL effects across environments can be tested for dependence on particular environmental covariables (Crossa et al. 1999;Malosetti et al. 2004;Varga...

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

A Mixed-Model Quantitative Trait Loci (QTL) Analysis for Multiple-Environment Trial Data Using Environmental Covariables for QTL-by-Environment Interactions, With an Example in Maize

et al. 2007

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“…Those QTLs with their closest markers placed within 10 cM from each other and their support intervals showing partial or complete overlap were considered to be present in a QTL cluster. The additive effects (AE) and the IPCA1 and IPCA2 values (I1 and I2, respectively) computed from the AMMI analysis were also included in QTL mapping (Van Eeuwijk et al, 2007) in order to evaluate the presence of additive and environmentally modulated loci.…”

Section: Qtl Analysesmentioning

confidence: 99%

Genotype × environment interactions and QTL clusters underlying dough rheology traits in Triticum aestivum L.

Prashant

Mani

Rai

et al. 2015

Journal of Cereal Science

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“…One of the most important applications of this type of study is the possibility to incorporate architecture-derived information into breeding programs to make them more effective. It also allows a better understanding of the genetic correlation among traits (Jiang and Zeng 1995;Mackay 2001), the interaction between genotypes and environments (Malosetti et al 2004van Eeuwijk et al 2005van Eeuwijk et al , 2007van Eeuwijk et al , 2009Boer et al 2007;Mathews et al 2008;Messmer et al 2009;Pastina et al 2012), and the determination of the breeding value of individuals for marker-assisted selection Zeng et al 1999;Dekkers and Hospital 2002;Hospital 2009).…”

Section: Introductionmentioning

confidence: 99%

A model for quantitative trait loci mapping, linkage phase, and segregation pattern estimation for a full-sib progeny

Gazaffi

Margarido

Pastina

et al. 2014

Tree Genetics & Genomes

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Quantitative trait loci (QTL) mapping is an important approach for the study of the genetic architecture of quantitative traits. For perennial species, inbred lines cannot be obtained due to inbreed depression and a long juvenile period. Instead, linkage mapping can be performed by using a full-sib progeny. This creates a complex scenario because both markers and QTL alleles can have different segregation patterns as well as different linkage phases between them. We present a two-step method for QTL mapping using full-sib progeny based on composite interval mapping (i.e., interval mapping with cofactors), considering an integrated genetic map with markers with different segregation patterns and conditional probabilities obtained by a multipoint approach. The model is based on three orthogonal contrasts to estimate the additive effect (one in each parent) and dominance effect. These estimatives are obtained using the EM algorithm. In the first step, the genome is scanned to detect QTL. After, segregation pattern and linkage phases between QTL and markers are estimated. A simulated example is presented to validate the methodology. In general, the new model is more effective than existing approaches, because it can reveal QTL present in a full-sib progeny that segregates in any pattern present and can also identify dominance effects. Also, the inclusion of cofactors provided more statistical power for QTL mapping.

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Modelling the Genetic Basis of Response Curves Underlying Genotype × Environment Interaction

Cited by 13 publications

References 23 publications

A Mixed-Model Quantitative Trait Loci (QTL) Analysis for Multiple-Environment Trial Data Using Environmental Covariables for QTL-by-Environment Interactions, With an Example in Maize

A Mixed-Model Quantitative Trait Loci (QTL) Analysis for Multiple-Environment Trial Data Using Environmental Covariables for QTL-by-Environment Interactions, With an Example in Maize

Genotype × environment interactions and QTL clusters underlying dough rheology traits in Triticum aestivum L.

A model for quantitative trait loci mapping, linkage phase, and segregation pattern estimation for a full-sib progeny

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