Introduction to a Special Issue on Genotype by Environment Interaction

León, Natalia de; Jannink, Jean‐Luc; Edwards, Jode W.; Kaeppler, Shawn M.

doi:10.2135/cropsci2016.07.0002in

Cited by 110 publications

(88 citation statements)

References 66 publications

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“…M ultienvironment variety trials are used to investigate the performance of crop genotypes, with the goal being to identify those with a superior phenotype across a range of environmental conditions (Malosetti et al, 2013;Yan, 2014). In order for multienvironment trials (METs) to efficiently generate both precise and accurate genotype performance estimates, it is necessary to understand the magnitude and patterns of genotype ´ environment (G´E) interaction in traits of interest across a target production environment (de Leon et al, 2016;Elias et al, 2016;Fehr, 1987). From a practical standpoint, G´E interaction is most consequential when it results in changes in the relative performance ranking of different genotypes, termed crossover G´E interaction, with crossover among high-performing genotypes having more consequences for making variety recommendation decisions than among poorly performing ones (Gauch and Zobel, 1997;Elias et al, 2016).…”

Section: Researchmentioning

confidence: 99%

Quantifying Genotype × Environment Effects in Long‐Term Common Wheat Yield Trials from an Agroecologically Diverse Production Region

George

Lundy

2019

Crop Science

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Multienvironment trials (METs) are used to investigate the performance of crop genotypes. To efficiently generate reliable performance estimates, the magnitude and patterns of genotype × environment interaction (G×E) in MET data must be known. We quantified G×E in fall‐planted common wheat (Triticum aestivum L.) in California, with the goal of increasing the reliability and efficiency of statewide variety testing activities. Linear mixed models and the genotype main effects plus G×E interaction effects (GGE) biplot method were used to analyze MET data for 211 common wheat genotypes, the MET consisted of 9 locations and 14 seasons. The representativeness and discriminating power of the MET locations were tested, and estimates of the optimum number of test locations were made. The analyses did not find evidence for significant, repeatable, crossover G×E. The genotype and G×E effects were of a similar magnitude, and the G×E effects were relatively strong compared with other sources of variance but were dominated by seasonal effects, with potentially repeatable genotype‐by‐location (G×L) effects being relatively weak. The GGE analyses did not detect repeatable G×L patterns across seasons. As a result, we conclude that the cereal production regions of California consist of a single, but unstable, mega‐environment for common wheat grain yield. The test location evaluation found few significant differences between test locations in terms of how well they represent the target production environment. We estimate that the number of test locations could be reduced while maintaining trial accuracy, which would improve the resource use efficiency of statewide trial activities without sacrificing information about variety‐specific common wheat yield performance.

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Section: Researchmentioning

confidence: 99%

Quantifying Genotype × Environment Effects in Long‐Term Common Wheat Yield Trials from an Agroecologically Diverse Production Region

George

Lundy

2019

Crop Science

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“…Although G×E has been well-documented and studied in plant and animal species, it remains a significant challenge to breeding programs (Crossa 2012; de Leon et al 2016; Xu 2016; Li et al 2018). Obtaining a rigorous assessment of G×E effects requires phenotypic evaluation in a wide range of environments and breeder choices about the level of trait instability across environments that will be tolerated.…”

Section: Introductionmentioning

confidence: 99%

“…These extreme conditions or drastic environmental change often result in significant G×E (Bebber et al 2013; Trenberth et al 2013; Xu 2016), though it is not necessarily representative of the normal state of the agriculture systems that often are subject to more subtle environmental variations through the use of intensive management practices. G×E interaction studies that utilize natural environments provide a practical understanding of G×E to breeding programs (de Leon et al 2016; Elias et al 2016; Malosetti et al 2016). Data generated by multi-environment trials, which are trials or experiments carried out in multiple environments or contexts with multiple replications, can be quite useful in documenting the range of G×E effects that can be observed (Yan et al 2001; Fan et al 2007; Crossa 2012; Malosetti et al 2013; Nuvunga et al 2015; Lado et al 2016; Mohammadi and Amri 2016).…”

Section: Introductionmentioning

confidence: 99%

Characterizing allele-by-environment interactions using maize introgression lines

Tirado

Kadam

et al. 2019

Preprint

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Relatively small genomic introgressions containing quantitative trait loci can have significant impacts on the phenotype of an individual plant. However, the magnitude of phenotypic effects for the same introgression can vary quite substantially in different environments due to allele-by-environment interactions. To study potential patterns of allele-by-environment interactions, fifteen near-isogenic lines (NILs) with >90% B73 genetic background and multiple Mo17 introgressions were grown in 16 different environments. These environments included five geographical locations with multiple planting dates and multiple planting densities. The phenotypic impact of the introgressions was evaluated for up to 26 traits that span different growth stages in each environment to assess allele-by-environment interactions. Results from this study showed that small portions of the genome can drive significant genotype-by-environment interaction across a wide range of vegetative and reproductive traits, and the magnitude of the allele-by-environment interaction varies across traits. Some introgressed segments were more prone to genotype-by-environment interaction than others when evaluating the interaction on a whole plant basis throughout developmental time, indicating variation in phenotypic plasticity throughout the genome. Understanding the profile of allele-by-environment interaction is useful in considerations of how small introgressions of QTL or transgene containing regions might be expected to impact traits in diverse environments.Key MessageSignificant allele-by-environment interactions are observed for traits throughout development from small introgressed segments of the genome.

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“…Even so, there is a gap between the yields obtained by farmers and those obtained in experimental stations using the best management practices (Fischer & Edmeades, 2010). On the other hand, the characterization of genotype by environment interaction (GEI) is necessary to understand the adaptation of cultivars and identification of superior cultivars (van Eeuwijk et al, 1996;de León et al, 2016;Lado et al, 2016). The study of evaluation trials networks efficiency and the determination of relevant management variables that limit yield expression in sunflower are crucial to improve the selection efficiency of superior cultivars (de la Vega et al, 2001).…”

Section: Introductionmentioning

confidence: 99%

Genotype by environment interaction in sunflower (Helianthus annus L.) to optimize trial network efficiency

González-Barrios¹,

Castro²,

Pérez³

et al. 2018

Span. j. agric. res.

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Modeling genotype by environment interaction (GEI) is one of the most challenging aspects of plant breeding programs. The use of efficient trial networks is an effective way to evaluate GEI to define selection strategies. Furthermore, the experimental design and the number of locations, replications, and years are crucial aspects of multi-environment trial (MET) network optimization. The objective of this study was to evaluate the efficiency and performance of a MET network of sunflower (Helianthus annuus L.). Specifically, we evaluated GEI in the network by delineating mega-environments, estimating genotypic stability and identifying relevant environmental covariates. Additionally, we optimized the network by comparing experimental design efficiencies. We used the National Evaluation Network of Sunflower Cultivars of Uruguay (NENSU) in a period of 20 years. MET plot yield and flowering time information was used to evaluate GEI. Additionally, meteorological information was studied for each sunflower physiological stage. An optimal network under these conditions should have three replications, two years of evaluation and at least three locations. The use of incomplete randomized block experimental design showed reasonable performance. Three mega-environments were defined, explained mainly by different management of sowing dates. Late sowings dates had the worst performance in grain yield and oil production, associated with higher temperatures before anthesis and fewer days allocated to grain filling. The optimization of MET networks through the analysis of the experimental design efficiency, the presence of GEI, and appropriate management strategies have a positive impact on the expression of yield potential and selection of superior cultivars.Additional keywords: genotype by environment interaction; multi-environment trials; sunflower; network efficiency; yield stability. Abbreviations used: CPD (critical percentage difference); GEI (genotype by environment interaction); GGE (genotype plus genotype by environment); LE (La Estanzuela); MET (multi-environment trial); NENSU (National evaluation network of sunflower cultivars of Uruguay); PLS (partial least squares); RCBD (randomized complete block design); YG (Young).

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Introduction to a Special Issue on Genotype by Environment Interaction

Cited by 110 publications

References 66 publications

Quantifying Genotype × Environment Effects in Long‐Term Common Wheat Yield Trials from an Agroecologically Diverse Production Region

Quantifying Genotype × Environment Effects in Long‐Term Common Wheat Yield Trials from an Agroecologically Diverse Production Region

Characterizing allele-by-environment interactions using maize introgression lines

Genotype by environment interaction in sunflower (Helianthus annus L.) to optimize trial network efficiency

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