Genotype-by-Environment Interactions Emerge from Simple Assemblages of Mathematical Functions in Ecological Models

Génard, Michel; Lescourret, Françoise; Bevacqua, Danièle; Boivin, Thomas

doi:10.3389/fevo.2017.00013

Cited by 7 publications

(8 citation statements)

References 29 publications

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“…However, it is G × E in fitness, rather than G × E for any particular trait, that can result in the adaptive maintenance of variation. Indeed, G × E at the level of fitness can be manifest without G × E in any of the component traits (Génard, Lescourret, Bevacqua, & Boivin, ). The critical requirement for the adaptive maintenance of G × E is the absence of any segregating genotype that is favored in all environments; if such a genotype existed, it would sweep to fixation, producing a monomorphic population.…”

Section: Why Does G × E Vary? Underlying Themes Uniting Hypotheses Abmentioning

confidence: 99%

“…Preference–performance correlations fundamentally rely on G × E, because it is only adaptive for genotypes to choose a particular environment if they have lower fitness in other environments. (One caveat is that G × E may exist at the level of fitness but not be evident in any individual traits (Génard et al., ).) For example, if genotype A has high fitness in environment A and low fitness in environment B, it should choose A.…”

Section: Why Does G × E Vary? Underlying Themes Uniting Hypotheses Abmentioning

confidence: 99%

“…(One caveat is that G × E may exist at the level of fitness but not be evident in any individual traits (Génard et al, 2017…”

Section: Hypothesis 4: Preference-performance Correlations Derive Fmentioning

confidence: 99%

See 2 more Smart Citations

Why does the magnitude of genotype‐by‐environment interaction vary?

Saltz

Bell

Flint

et al. 2018

Ecology and Evolution

108

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Genotype‐by‐environment interaction (G × E), that is, genetic variation in phenotypic plasticity, is a central concept in ecology and evolutionary biology. G×E has wide‐ranging implications for trait development and for understanding how organisms will respond to environmental change. Although G × E has been extensively documented, its presence and magnitude vary dramatically across populations and traits. Despite this, we still know little about why G × E is so evident in some traits and populations, but minimal or absent in others. To encourage synthetic research in this area, we review diverse hypotheses for the underlying biological causes of variation in G × E. We extract common themes from these hypotheses to develop a more synthetic understanding of variation in G × E and suggest some important next steps.

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Section: Why Does G × E Vary? Underlying Themes Uniting Hypotheses Abmentioning

confidence: 99%

Section: Why Does G × E Vary? Underlying Themes Uniting Hypotheses Abmentioning

confidence: 99%

See 1 more Smart Citation

Why does the magnitude of genotype‐by‐environment interaction vary?

Saltz

Bell

Flint

et al. 2018

Ecology and Evolution

108

View full text Add to dashboard Cite

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“…Although genotypic sensitivities to environmental conditions are hard to phenotype, their estimates facilitate predictions along the environmental gradient, provided that the relevant environmental variables are measured (Tardieu and Tuberosa, 2010b; van Eeuwijk et al, 2019). As traits along the trait hierarchy might interact in non-linear ways, G×E for the target trait can be observed, even if its underlying traits do not show G×E (Génard et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

From QTLs to Adaptation Landscapes: Using Genotype-To-Phenotype Models to Characterize G×E Over Time

et al. 2019

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Genotype by environment interaction (G×E) for the target trait, e.g. yield, is an emerging property of agricultural systems and results from the interplay between a hierarchy of secondary traits involving the capture and allocation of environmental resources during the growing season. This hierarchy of secondary traits ranges from basic traits that correspond to response mechanisms/sensitivities, to intermediate traits that integrate a larger number of processes over time and therefore show a larger amount of G×E. Traits underlying yield differ in their contribution to adaptation across environmental conditions and have different levels of G×E. Here, we provide a framework to study the performance of genotype to phenotype (G2P) modeling approaches. We generate and analyze response surfaces, or adaptation landscapes, for yield and yield related traits, emphasizing the organization of the traits in a hierarchy and their development and interactions over time. We use the crop growth model APSIM-wheat with genotype-dependent parameters as a tool to simulate non-linear trait responses over time with complex trait dependencies and apply it to wheat crops in Australia. For biological realism, APSIM parameters were given a genetic basis of 300 QTLs sampled from a gamma distribution whose shape and rate parameters were estimated from real wheat data. In the simulations, the hierarchical organization of the traits and their interactions over time cause G×E for yield even when underlying traits do not show G×E. Insight into how G×E arises during growth and development helps to improve the accuracy of phenotype predictions within and across environments and to optimize trial networks. We produced a tangible simulated adaptation landscape for yield that we first investigated for its biological credibility by statistical models for G×E that incorporate genotypic and environmental covariables. Subsequently, the simulated trait data were used to evaluate statistical genotype-to-phenotype models for multiple traits and environments and to characterize relationships between traits over time and across environments, as a way to identify traits that could be useful to select for specific adaptation. Designed appropriately, these types of simulated landscapes might also serve as a basis to train other, more deep learning methodologies in order to transfer such network models to real-world situations.

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“…It is a generally accepted concept that the environment in which an organism is placed affects the expression and function of genes responsible for a trait (Allard and Bradshaw, 1964; Baye et al, 2011). The magnitude of phenotypic plasticity to adapt to different environments is genotype-dependent, hence the environment can interact with a genotype to shape the phenotypic traits, leading to genotype-by-environment (GE) interaction (Genard et al, 2017). In plant breeding, GE interaction is expressed as either genotypic rank-change among genotypes due to varied responses to changing environments or as absolute change in trait values without a rank change (Crossa, 2012).…”

Section: Introductionmentioning

confidence: 99%

When a phenotype is not the genotype: Implications of phenotype misclassification and pedigree errors in genomics-assisted breeding of sweetpotato [Ipomoea batatas(L.) Lam.]

Gemenet¹,

Boeck²,

Pereira

et al. 2019

Preprint

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Experimental error, especially through genotype misclassification and pedigree errors, negatively affects breeding decisions by creating ‘noise’ that compounds the genetic signals for selection. Unlike genotype-by-environment interactions, for which different methods have been proposed to address, the effect of ‘noise’ due to pedigree errors and misclassification has not received much attention in most crops. We used two case studies in sweetpotato, based on data from the International Potato Center’s breeding program to estimate the level of phenotype misclassification and pedigree error and to demonstrate the consequences of such errors when combining phenotypes with the respective genotypes. In the first case study, 27.7% phenotype misclassification was observed when moving genotypes from a diversity panel throughin-vitro, screenhouse and field trialing. Additionally, 22.7% pedigree error was observed from misclassification between and within families. The second case study involving multi-environment testing of a full-sib population and quantitative trait loci (QTL) mapping showed reduced genetic correlations among pairs of environments in mega-environments with higher phenotype misclassification errors when compared to the mega-environments with lower phenotype misclassification errors. Additionally, no QTL could be identified in the low genetic correlation mega-environments. Simulation analysis indicated that phenotype misclassification was more detrimental to QTL detection when compared to missingness in data. The current information is important to inform current and future breeding activities involving genomic-assisted breeding decisions in sweetpotato, and to facilitate putting in place improved workflows that minimize phenotype misclassification and pedigree errors.

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Genotype-by-Environment Interactions Emerge from Simple Assemblages of Mathematical Functions in Ecological Models

Cited by 7 publications

References 29 publications

Why does the magnitude of genotype‐by‐environment interaction vary?

Why does the magnitude of genotype‐by‐environment interaction vary?

From QTLs to Adaptation Landscapes: Using Genotype-To-Phenotype Models to Characterize G×E Over Time

When a phenotype is not the genotype: Implications of phenotype misclassification and pedigree errors in genomics-assisted breeding of sweetpotato [Ipomoea batatas(L.) Lam.]

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