Mixed models including environmental covariables for studying QTL by environment interaction

Malosetti, Marcos; Voltas, Jordi; Romagosa, I.; Ullrich, S. E.; Eeuwijk, F.A. van

doi:10.1023/b:euph.0000040511.46388.ef

Cited by 147 publications

(157 citation statements)

References 30 publications

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“…A simulation study showed that modelling the variance-covariance matrix within a mixed model framework was more powerful in detecting fixed QTL and QEI effects than when fixed models were used (Piepho 2005). The methodology presented here extends the models described by Malosetti et al (2004) to incorporate individual plot variability and illustrates the flexibility of the mixed model approach to accommodate additional genotype and experiment factors. It is easily implemented in any statistical software that accommodates mixed models.…”

Section: Introductionmentioning

confidence: 94%

“…For each of the three traits, the strategy for modelling multienvironment QTL combined mixed model methodology with regression using the two-way table of means and comprises variations of Malosetti et al (2004) and Boer et al (2007). A genome-wide scan for significant QTL expression was performed using a SIM procedure.…”

Section: Multi-environment Qtl Mixed Modelmentioning

confidence: 99%

“…In recent years, mixed model frameworks have been used to detect QTL-by-environment (QEI) effects while modelling the variance-covariance matrix (Piepho 2000;Verbyla et al 2003;Malosetti et al 2004;van Eeuwijk et al 2005;Boer et al 2007). Verbyla et al (2003) fitted QEI effects as random, while others considered these effects as fixed.…”

Section: Introductionmentioning

confidence: 99%

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Multi-environment QTL mixed models for drought stress adaptation in wheat

et al. 2008

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Many quantitative trait loci (QTL) detection methods ignore QTL-by-environment interaction (QEI) and are limited in accommodation of error and environment-specific variance. This paper outlines a mixed model approach using a recombinant inbred spring wheat population grown in six drought stress trials. Genotype estimates for yield, anthesis date and height were calculated using the best design and spatial effects model for each trial. Parsimonious factor analytic models best captured the variance-covariance structure, including genetic correlations, among environments. The 1RS.1BL rye chromosome translocation (from one parent) which decreased progeny yield by 13.8 g m -2 was explicitly included in the QTL model. Simple interval mapping (SIM) was used in a genome-wide scan for significant QTL, where QTL effects were fitted as fixed environmentspecific effects. All significant environment-specific QTL were subsequently included in a multi-QTL model and evaluated for main and QEI effects with non-significant QEI effects being dropped. QTL effects (either consistent or environment-specific) included eight yield, four anthesis, and six height QTL. One yield QTL co-located (or was linked) to an anthesis QTL, while another co-located with a height QTL. In the final multi-QTL model, only one QTL for yield (6 g m -2 ) was consistent across environments (no QEI), while the remaining QTL had significant QEI effects (average size per environment of 5.1 g m -2 ). Compared to single trial analyses, the described framework allowed explicit modelling and detection of QEI effects and incorporation of additional classification information about genotypes.

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Section: Introductionmentioning

confidence: 94%

Section: Multi-environment Qtl Mixed Modelmentioning

confidence: 99%

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Multi-environment QTL mixed models for drought stress adaptation in wheat

et al. 2008

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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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“…The variancecovariance matrix for the residuals δ ij should be flexible enough to allow for heterogeneity of variance across environments and heterogeneity of correlations between environments due to genetic effects not modelled by the QTL part of the model (Piepho 2000;Verbyla et al 2003;Malosetti et al 2004;Piepho and Pillen 2004).…”

Section: Modelling Genotypic Responses As Functions Of Qtlsmentioning

confidence: 99%

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

Eeuwijk

Malosetti²,

Boer³

Scale and Complexity in Plant Systems Research

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Abstract.To increase tolerance to abiotic stresses in breeding programmes, typically families and collections of genotypes are evaluated in series of trials (environments) representing different levels of stress. The statistical analysis of the data from such trials concentrates on modelling the phenotypic behaviour of the genotypes across the set of environments. This phenotypic behaviour can be modelled in the form of genotype-specific linear and non-linear response curves in relation to environmental characterizations. Non-parallelism of the response curves indicates genotype × environment interaction. Identification of the genetic basis of the parameters determining the response curves will help in the development of breeding programmes for improving abiotic stress tolerance and understanding genotype × environment interaction. In this paper we present two strategies for locating quantitative trait loci for response-curve parameters and estimation of their allele effects. The procedures are illustrated by an application to drought stress in maize.

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Mixed models including environmental covariables for studying QTL by environment interaction

Cited by 147 publications

References 30 publications

Multi-environment QTL mixed models for drought stress adaptation in wheat

Multi-environment QTL mixed models for drought stress adaptation in wheat

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

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

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