Multi-environment multi-QTL association mapping identifies disease resistance QTL in barley germplasm from Latin America

Gutiérrez, Lucı́a; Germán, Silvia; Pereyra, Silvia; Hayes, Patrick; Pérez, Carlos A.; Capettini, Flávio; Locatelli, Andrés; Berberian, Natalia; Falconí, Esteban; Estrada, Rigoberto; Fros, Darío; Gonza, Víctor; Altamirano, Hernan; Espino, Julio Huerta; Neyra, Edgar; Orjeda, Gisella; Sandoval-Islas, Sergio; Singh, Ravi P.; Turkington, Kelly; Castro, Ariel J.

doi:10.1007/s00122-014-2448-y

Cited by 67 publications

(62 citation statements)

References 84 publications

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“…Genotype ´ environment interaction has been exploited in genomic studies to map quantitative trait loci in balanced populations (Piepho, 2000;Malosetti et al, 2004;van Eeuwijk et al, 2005;Boer et al, 2007;Mathews et al, 2008) and genome-wide association studies (Gutiérrez et al, 2015) using mixed models. Additionally, GS studies have included GEI information by performing overall predictions across environments (Heffner et al, 2011;Resende et al, 2011), within environments or groups of environments (Burgueño et al, 2012;Ly et al, 2013;Dawson et al, 2013;Heslot et al, 2014), or using marker-by-environment predictions (Jarquín et al, 2014;Lopez-Cruz et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Modeling Genotype × Environment Interaction for Genomic Selection with Unbalanced Data from a Wheat Breeding Program

et al. 2016

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Genomic selection (GS) has successfully been used in plant breeding to improve selection efficiency and reduce breeding time and cost. However, there is not a clear strategy on how to incorporate genotype × environment interaction (GEI) to GS models. Increased prediction accuracy could be achieved using mixed models to exploit GEI by borrowing information from other environments. The objective of this work was to compare strategies to exploit GEI in GS using mixed models. Specifically, we compared strategies to predict new genotypes by borrowing information from other environments modeling the correlation matrix across environments and to design sets of environments aiming for low GEI to predict genomic performance in new environments. We evaluated 1477 advanced wheat (Triticum aestivum L.) lines for yield in 35 location–year combinations genotyped with genotyping‐by‐sequencing (GBS). Mixed models were used to obtain either overall or by‐environment predictions for different sets of environments. Overall accuracy was high (0.5). Borrowing information from relatives evaluated in multiple environments and modeling the correlation matrix across environments was the best strategy to predict new genotypes. On the other hand, the best strategy for predicting the performance of genotypes in new environments was either to predict across locations for single years or to predict within defined mega‐environments (MEs) for any year or location. In summary, higher predictive ability was obtained by characterizing and by modeling GEI in the GS context.

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Section: Introductionmentioning

confidence: 99%

Modeling Genotype × Environment Interaction for Genomic Selection with Unbalanced Data from a Wheat Breeding Program

et al. 2016

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“…Different approaches have been proposed to study GEI such as linear-bilinear models (Finlay and Wilkinson, 1963;Gauch, 1992;Yan et al, 2001;Crossa and Cornelius, 2002), mixed models (Piepho, 1998;Burgueño et al, 2008;Cullis et al, 2010), and crop growth models (Chapman, 2008;Technow et al, 2015;Malosetti et al, 2016). These methods have been extensively used for different breeding strategies including traditional phenotypic selection and testing (Yan et al, 2007;González-Barrios et al, 2017), quantitative trait loci mapping (Piepho, 2000;Malosetti et al, 2004;Mathews et al, 2008;Quero et al, 2014), genome-wide association mapping (van Eeuwijk et al, 2010;Locatelli et al, 2013;Gutiérrez et al, 2015;Racedo et al, 2016;Monteverde et al, 2018), and genomic selection (Burgueño et al, 2012;Lopez-Cruz et al, 2015;Lado et al, 2016). Environments can then be grouped in sets that produce a similar ranking of the genotypes (mega-environments [MEs]; Braun et al, 1996).…”

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confidence: 99%

Mega‐Environmental Design: Using Genotype × Environment Interaction to Optimize Resources for Cultivar Testing

2019

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The efficient use of testing resources is one of the key factors for successful plant breeding programs. Controlling micro‐ and macro‐environmental variability is an effective way of improving the testing efficiency and the selection of superior genotypes. Common experimental designs in genotypic testing usually use replicated or augmented experiments at each location, but they are balanced across locations. Some studies suggest that the increase in population size even at the expense of balanced experiments might be beneficial if genotype × environment interaction (GEI) is modeled. The objective of this study was to compare strategies for micro and macro‐environmental variability control that include GEI information to optimize resource allocation in multi‐environment trials (METs). Six experimental designs combined with four spatial correction models were compared for efficiency under three experimental sizes using simulations under a real yield variability map. Additionally, six resource allocation strategies were evaluated in terms of accuracy and the expected response to selection. The α‐lattice (ALPHA) experimental design was the best one at controlling micro‐environmental variability. The moderate mega‐environmental design (MED) strategy had the largest response to selection. This strategy uses historical mega‐environments (MEs) to unbalance genotypic testing within MEs while modeling GEI. The MED was the best resource allocation strategy and could potentially increase selection response up to 43% in breeding programs when genotypes are evaluated in METs.

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“…However, both approaches seem to be complementary in the sense that their ultimate goal is the detection of the genes underlying the quantitative complex trait for further cloning. Several examples of the use of the association mapping approach in QDR studies for the more limiting diseases can be found in recent literature (Benson et al, 2015;Gutiérrez et al, 2015;Arruda et al, 2016;Iquira et al, 2015;Olukolu et al, 2016;Turuspekov et al, 2016). However, despite the broad use of this approach, no genes detected by AM for plant QDR have been cloned so far.…”

Section: How To Study Complex Traits and Qdrsmentioning

confidence: 99%

Unraveling the molecules hidden in the gray shadows of quantitative disease resistance to pathogens

2018

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One of the most challenging questions in plant breeding and molecular plant pathology research is what are the genetic and molecular bases of quantitative disease resistance (QDR)?. The scarce knowledge of how this type of resistance works has hindered plant breeders to fully take advantage of it. To overcome these obstacles new methodologies for the study of quantitative traits have been developed. Approaches such as genetic mapping, identification of quantitative trait loci (QTL) and association mapping, including candidate gene approach and genome wide association studies, have been historically undertaken to dissect quantitative traits and therefore to study QDR. Additionally, great advances in quantitative phenotypic data collection have been provided to improve these analyses. Recently, genes associated to QDR have been cloned, leading to new hypothesis concerning the molecular bases of this type of resistance. In this review we present the more recent advances about QDR and corresponding application, which have allowed postulating new ideas that can help to construct new QDR models. Some of the hypotheses presented here as possible explanations for QDR are related to the expression level and alternative splicing of some defense-related genes expression, the action of "weak alleles" of R genes, the presence of allelic variants in genes involved in the defense response and a central role of kinases or pseudokinases. With the information recapitulated in this review it is possible to conclude that the conceptual distinction between qualitative and quantitative resistance may be questioned since both share important components. Keywords: breeding, complex traits, genome, gene expression, plant immunity, quantitative disease resistance (QDR), quantitative trait loci (QTL). RESUMENUna de las preguntas más desafiantes del fitomejoramiento y de la fitopatología molecular es ¿cuáles son las bases genéticas y moleculares de la resistencia cuantitativa a enfermedades?. El escaso conocimiento de cómo este tipo de resistencia funciona ha obstaculizado que los fitomejoradores la aprovecharlo plenamente. Para superar estos obstáculos se han desarrollado nuevas metodologías para el estudio de rasgos cuantitativos. Los enfoques como el mapeo genético, la identificación de loci de rasgos cuantitativos (QTL) y el mapeo por asociaciones, incluyendo el enfoque de genes candidatos y los estudios de asociación amplia del genoma, se han llevado a cabo históricamente para describir rasgos cuantitativos y por lo tanto para estudiar QDR. Además, se han proporcionado grandes avances en la obtención de datos fenotípicos cuantitativos para mejorar estos análisis. Recientemente, algunos genes asociados a QDR han sido clonados, lo que conduce a nuevas hipótesis sobre las bases moleculares de este tipo de resistencia. En esta revisión presentamos los avances más recientes sobre QDR y la correspondiente aplicación, que han permitido postular nuevas ideas que pueden ayudar a construir nuevos modelos. Algunas de las hipótesis presen...

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Multi-environment multi-QTL association mapping identifies disease resistance QTL in barley germplasm from Latin America

Cited by 67 publications

References 84 publications

Modeling Genotype × Environment Interaction for Genomic Selection with Unbalanced Data from a Wheat Breeding Program

Modeling Genotype × Environment Interaction for Genomic Selection with Unbalanced Data from a Wheat Breeding Program

Mega‐Environmental Design: Using Genotype × Environment Interaction to Optimize Resources for Cultivar Testing

Unraveling the molecules hidden in the gray shadows of quantitative disease resistance to pathogens

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