Interpreting Genotype ✕ Environment Interaction in Wheat by Partial Least Squares Regression

Vargas, Mateo; Crossa, José; Sayre, Ken D.; Reynolds, Matthew; Ramirez, Martha E.; Talbot, Mike

doi:10.2135/cropsci1998.0011183x003800030010x

Cited by 87 publications

(77 citation statements)

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“…The analysis and interpretation of GEI range from simple analysis of variance to more speciWc analyses of genotype performance, from the univariate linear regression analysis of Finlay and Wilkinson (1963) and Eberhart and Russell (1966) or Perkins and Jinks (1968), to the multivariate AMMI (Zobel et al 1988), GGE biplot (Yan 2001) and PLSR (Partial Least Squares Regression) models (Vargas et al 1998). The commonly used linear regression analysis typically explains only a portion of the interactions.…”

Section: Introductionmentioning

confidence: 99%

Specific adaptation of barley varieties in different locations in Ethiopia

Abay

Bjørnstad

2008

Euphytica

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Barley is one of the most important cereal crops grown for the livelihoods of the poor farmers of Tigray region in northern Ethiopia. As many low input and marginal environments it has beneWted less from the yield increases achieved by modern breeding. This has been largely attributed due to genotype £ environment intraction (GEI). To investigate the causes of GEI, ten barley varieties including local checks (two farmers developed varieties, four modern varieties and three rare local varieties) were tested over 21 environments. Participatory methods were applied to sample an adequate number of environments spanning the regional diversity. The yielding ability and stability of the varieties was graphically depicted by GGE and PLSR biplot. There were two major groups of environments, the central and northern highlands, the latter with less rainfall and poorer soils. Rainfall per month and total nitrogen level were the environmental variables that diVerentiated these two groups. In Tigray, rainfall in June and July were negatively correlated with yield, reXecting waterlogging problems. The diVerent varieties were either speciWcally or widely adapted across the two environments. The variety 'Himblil', originating in Tigray, was the highest yielding and also most stable in the region of origin. However, it was inferior to improved varieties (Shege and Dimtu) at high yield levels. The association of earliness with grain yield indicates that the trait can be eVectively manipulated within the existing materials. We recommend breeding for drought/water logging resistance based on selection in the target environment as the best strategy to provide stable and high yielding varieties for Tigray.

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Section: Introductionmentioning

confidence: 99%

Specific adaptation of barley varieties in different locations in Ethiopia

Abay

Bjørnstad

2008

Euphytica

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“…From the 1980's, the use of environmental variables and the prediction of their influence on the productivity of some species have been widely applied in the studies on the GxE interaction, and currently several authors have been inserting environmental information, whether as characterization factors and environmental stratification as covariates in the analysis models of GxE interaction (Haun, 1982;Denis, 1988;Van Eeuwijk et al, 1996;Vargas et al, 1998;Crossa et al, 1999;Van Eeuwijk et al, 2005;Voltas et al, 2005;Thomason and Phillips, 2006;Vargas et al, 2006;Boer et al, 2007;Ramburan et al, 2011;Heslot et al, 2014). Van Eeuwijk et al (1996), in a seminal study, summarizes some methods based on factor analysis for the insertion of information about environmental covariates for the explanation of the GxE interaction, and, according to the author, such models are just an extension of the most general case:…”

Section: Methodologies Using Covariatesmentioning

confidence: 99%

“…Where: H is the number of environmental covariates (Van Eeuwijk et al, 1996;Vargas et al, 1998;Crossa et al, 1999). Some studies have been using explanatory covariates in the most variable way possible.…”

Section: Methodologies Using Covariatesmentioning

confidence: 99%

Evolution of methodology for the study of adaptability and stability in cultivated species

Carvalho¹,

Silva²,

Rocha³

et al. 2016

Afr. J. Agric. Res.

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The GxE interaction only became widely discussed from evolutionary studies and evaluations of the causes of behavioral changes of species cultivated in environments. In the last 60 years, several methodologies for the study of adaptability and stability of genotypes in multiple environments trials were developed in order to assist the breeder's choice regarding which genotypes are more stable and which are the most suitable for the crops in the most diverse environments. The methods that use linear regression analysis were the first to be used in a general way by breeders, followed by multivariate analysis methods and mixed models. The need to identify the genetic and environmental causes that are behind the GxE interaction led to the development of new models that include the use of covariates and which can also include both multivariate methods and mixed modeling. However, further studies are needed to identify the causes of GxE interaction as well as for the more accurate measurement of its effects on phenotypic expression of varieties in competition trials carried out in genetic breeding programs.

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“…More recent and comprehensive analysis (Reynolds et al 1998). Cluster analysis was based on cross-over interaction of genotypes as described by Vargas et al (1998) using CIMMYT international nursery yield data indicated that main genotype clusters correspond to three main types of environment, viz., temperate, continuous heat stress and terminal heat stress, and confirmed relative humidity as an important factor determining G×E within some of these clusters (Lillemo et al 2005).…”

Section: Abiotic-stress Environmentsmentioning

confidence: 99%

Physiological Interventions in Breeding for Adaptation to Abiotic Stress

Reynolds¹,

Trethowan²

Wageningen UR Frontis Series

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Abstract. The physiological-trait-based breeding approach has merit over breeding for yield per se because it increases the probability of crosses resulting in additive gene action. While considerable investment in germplasm characterization is required, conceptual models of crop genotypes can be employed as research tools to quantify likely genetic gains associated with specific trait or in defining traits that may have generic value across different stresses. For example, deeper root growth that permits better access to soil water has obvious benefit under drought, while under hot, irrigated conditions permits leaf canopies to match the high evaporative demand associated with hot, low-relative-humidity environments, resulting in higher leaf gas-exchange rates and heat escape through evaporative cooling. Although improvement in adaptation to abiotic stress may occur as a result of transgressive segregation, exotic parents can be used to increase total allelic diversity for such traits. The bread-wheat-breeding programme at CIMMYT is exploiting new genetic diversity using inter-specific hybridization of the ancestral genomes of bread wheat. Novel genetic diversity is also being accessed more directly by crossing adapted germplasm with landrace accessions originating in abiotically stressed environments that have become isolated from mainstream gene pools. Through studying these genetic resources it has been possible to calculate the theoretical impact of combining their best values of trait expression into the check cultivar to gain some insight into which traits may hold most promise in terms of genetic enhancement. It was apparent that the genetic diversity found for water use efficiency offers the greatest and most consistent opportunity for increasing yield, while increasing stem carbohydrates and access to water at depth also shows some potential. Direct physiological interventions in breeding include (i) characterization of potential parents for more strategic crossing; (ii) early-generation selection; and (iii) evaluation of promising genetic resources in pre-breeding. The early-generation selection trait 'canopy temperature' (measured with an infrared thermometer) has been readily adopted since measurement is quick, easy and inexpensive. Although genetic markers are not currently used in selection for complex traits, as technology advances and combines with gene discovery approaches, more quantitative trait loci (QTLs) associated with adaptation to complex environments will emerge. A multi-staged approach to identifying molecular markers may be the best approach where QTLs for generic traits -i.e., valid across a range of environments -are identified in well controlled field environments and used to optimize germplasm. Subsequently, environment-specific models would be used to factor in additional traits commonly found in a specific region that may not be directly related to moisture stress, factors such as nematodes or microelement deficiency or toxicity that are exacerbated under drought. 130M.P. REYNO...

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Interpreting Genotype ✕ Environment Interaction in Wheat by Partial Least Squares Regression

Cited by 87 publications

References 0 publications

Specific adaptation of barley varieties in different locations in Ethiopia

Specific adaptation of barley varieties in different locations in Ethiopia

Evolution of methodology for the study of adaptability and stability in cultivated species

Physiological Interventions in Breeding for Adaptation to Abiotic Stress

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