Download Introduction to a Special Issue on Genotype by Environment

Published August 30, 2016
RESEARCH
Introduction to a Special Issue
on Genotype by Environment Interaction
Natalia de Leon,* Jean-Luc Jannink, Jode W. Edwards, and Shawn M. Kaeppler
ABSTRACT
Expression of a phenotype is a function of the
genotype, the environment, and the differential sensitivity of certain genotypes to different
environments, also known as genotype by environment (G ´ E) interaction. This special issue
of Crop Science includes a collection of manuscripts that reviews the long history of G ´E
research, describes new and innovative ideas,
and outlines future challenges. Improving our
understanding of these complex interactions is
expected to accelerate plant breeding progress,
minimize risk through improved cultivar deployment, and improve the efficiency of crop production through informed agriculture. Achieving
these goals requires the integration of broad
and diverse science and technology disciplines.
N. de Leon, Dep. of Agronomy, UW-Madison, Madison, WI 53706; J.-L.
Jannink, USDA-ARS, Cornell Univ., Ithaca, NY, 14853; J.W. Edwards,
Dep. of Agronomy, Iowa State Univ., Ames, IA 50011; S.M. Kaeppler,
Dep. of Agronomy, UW-Madison, Madison, WI 53706. Received 18
July 2016. *Corresponding author ([email protected]).
Abbreviations: CGM, crop growth models; G ´ E, genotype
by environment; GGE, genotype main effects and G ´ E; QTL,
quantitative trait locus.
Overview
The ability of an individual to achieve the maximum potential
encoded in its genome is a function of the environment in which
it completes its life cycle. Evolution provides numerous examples
of exquisite adaptations that allow species and individuals within
species to excel in specific environmental contexts. Polar bears
(Ursus maritimus), as an example, are a relatively new subspecies
derived from brown bears (Ursus arctos). As a mechanism to adapt
to the harsh Arctic environments, polar bears have developed specializations such as pigment-free and hollow core fur (Miller et
al., 2012). While beneficial in some environments, these adaptations may be detrimental in others. In humans, for example, it
is hypothesized that when food is scarce, people with a taste for
sweet and fatty foods may have higher survival rates than others,
whereas when food is plenty, the reverse may be true. Humans
have noticed and managed these adaptations for agricultural production of crops and animals. For example, the terroir of wine is
a term used to describe the specific flavors that are a component of
specific vintages due to factors such as soil, plants in the vineyard,
and the specific season. These unique characteristics can result in
tremendous differences in the value of bottles of wine produced
from the same variety of grape.
Published in Crop Sci. 56:2081–2089 (2016).
doi: 10.2135/cropsci2016.07.0002in
© Crop Science Society of America | 5585 Guilford Rd., Madison, WI 53711 USA
This is an open access article distributed under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
crop science, vol. 56, september– october 2016 www.crops.org2081
Expression of a phenotype is a function of the genotype, the environment and differential phenotypic
response of genotypes to different environments, also
known as genotype by environment (G ´ E) interaction.
This term formally refers to a statistical decomposition
of variance and provides a measure of the relative performance of genotypes grown in different environments.
Plant breeders have managed and leveraged these interactions throughout the history of crop domestication,
crop improvement, and dispersal, and within recent history through the formalized procedures of plant breeding.
This issue of Crop Science contains a collection of articles
focusing on the topic of G ´ E interactions. This special
issue is spurred by the exciting emerging potential of more
efficiently adapting genotypes to current and future environmental contexts due to the confluence of extensive
genome sequence, advanced environmental monitoring,
new tools to monitor crop growth throughout the life
cycle, and unprecedented computational capacity allowing the wealth of new information to be incorporated in
predictive models of ever-increasing detail and value.
The topic of G ´ E has been of continued interest
throughout the history of this journal. In fact, four of
the top 30 most-cited articles in the history of the journal are related to this topic indicating its long-standing
importance in crop breeding and production (in order of
citation number: Eberhart and Russell, 1966; Allard and
Bradshaw, 1964; Lin et al., 1986; Rosielle and Hamblin,
1981). The review article by Allard and Bradshaw (1964)
stated that “aside from the satisfaction that would accompany understanding of the biochemical, physiological, or
morphological basis of the interplay between genotype
and environment, the question arises whether studies of
basic causes have anything to offer the practicing breeder
whose primary responsibility is to develop and identify
superior varieties.” Advances in the identification of loci
affecting quantitative traits (QTL) and in the use of crop
growth models (CGM) lead us to posit the answer is “yes.”
The applied breeder now has unprecedented ability to
manipulate genes identified as mechanistically involved in
G ´ E. Likewise, interactions that are understood at morphological and physiological levels can be predicted using
CGM, leading to target ideotypes defined phenotypically,
as promoted for many years (Donald, 1968), or genetically
as in new approaches (Technow et al., 2015).
Definition and Importance of G ´ E
A conceptual G ´ E interaction is commonly depicted as
the slope of the line when genotype performance is plotted
against an environmental gradient (Fig. 1). Non-parallel,
but non-intersecting lines indicate that the rank of cultivar
performance stays the same across environments. Lines that
intersect indicate that there is a change in rank of cultivars across environments, and the optimum cultivar will
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be location specific. G ´ E affects virtually every aspect
of the decision making process involved in plant breeding
programs including identification of the most relevant testing environments, allocation of resources within a breeding
program, and choice of germplasm and breeding strategy.
G ´ E can also be conceptualized as a measurement of
the relative plasticity of genotypes in terms of the expression
of specific phenotypes in the context of variable environmental influences. Although a clear divide characterizes the
long and rich history of the scientific field concerned with
phenotypic plasticity, one foundational understanding has
united researchers across disciplines: the ability of genotypes
to express different phenotypes when influenced by different environmental signals has a genetic basis. The divide
comes from the focus that different groups of researchers
have taken to study this phenomenon. On the one hand,
phenotypic plasticity has been the focus of much research
in the field of molecular and evolutionary biology studying natural populations and model species. Efforts in that
area have typically aimed at understanding the biological
and genetic fundamentals of this plasticity and its evolution.
In the early 1960s the work by Bradshaw (1965) provided
much of the foundation to this area of research. Through
the use of relevant examples, his pioneer efforts elucidated
a fundamental understanding of the genetic basis of phenotypic plasticity and the significance of this phenomenon
for adaptation of specific genotypes as a function of the
expression of specific traits in response to environmental
influences to which they are exposed (Bradshaw, 1965).
A somewhat complementary and more pragmatic view
of phenotypic plasticity emerged in the field of animal and
plant breeding (Pigliucci, 2001). Falconer (1952) introduced
the idea that G ´ E could be considered as the pleiotropic
effect of particular variants across environments. This concept implies that any given trait when evaluated across more
than one environment can be analyzed as genetically correlated traits. In this case, the magnitude of such a correlation
indicates the degree of shared genetic control and the sign
of the correlation indicates the direction of the allelic effect
for the environments being considered. This perspective
has provided an important framework for interpreting and
handling G ´ E in plant breeding programs (Des Marais et
al., 2013). As with most other areas of quantitative genetics,
this has led to the development of statistical as opposed to
biological parameters to quantify, understand, and interpret
G ´ E in plant breeding (Malosetti et al., 2013).
The study of the genetic nature of G ´ E is complicated
by the fact that such statistics have to be calculated and interpreted in the context of a specified universe of environments:
specific assumptions of what constitutes reasonable environments need to be determined before any inferences can be
made from G ´ E measurements. This fact calls for the need
to establish appropriately-sized field evaluations involving
relevant environmental conditions rather than contrasting
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Fig. 1. The G ´ E (VGE) displayed here illustrates two components, homogeneity vs. heterogeneity of the genetic variance (VG) and the
correlation between performance across environment. (A) Homogeneity of VG and no correlation between environments; (B) Heterogeneity
of VG in different environments and no correlation between environments; (C) Crossover interactions are due to imperfect correlations
between genotypic performance across environments (in this case -1) and here homogeneous VG; (D) VGE is due to a combination of
heterogeneous VG and an imperfect correlation between genotypic performance across environments.
controlled environment evaluations which by design are
expected to provide an overestimation of the variation compared to that found in natural conditions (Anderson et al.,
2014). Thus meaningful assessments of G ´ E require long
term research as responses are expected to vary significantly
from year to year (Agren and Schemske, 2012).
Methods of Measuring G ´ E
Historically breeders have recognized the potentially negative
implications of G ´ E in selection and cultivar deployment
and have focused on developing tools and resources to quantify it as a first step toward minimizing its detrimental effect
and, whenever possible, taking advantage of positive interactions (Freeman, 1973; Cooper, 1999; Cooper et al., 2014;
Sadras and Richards, 2014). Commonly, target cultivars are
identified for deployment to specific sets of environments
(Cooper et al., 1997; Chapman et al., 1997). That identification ideally proceeds from the interpretation of analyses that
measure the differential sensitivity of genotypes to environments and that connect that variation to particular biological
mechanisms (Sadras and Richards, 2014).
A proportion of the G ´ E studies have used managed
stress trials to emphasize the effect of particular sources of,
generally, abiotic stresses on the performance of diverse
crop science, vol. 56, september– october 2016 genotypes. Although this approach has shown promise
for understanding the effects of particular environmental
disruptions on phenotypes, they are usually difficult (and
therefore expensive) to implement. Most of the evaluations
of the effect of the environment on performance in plant
breeding have relied on multi-environmental field testing
that represent target production environments and those are
used to identify and develop cultivars (Comstock, 1977).
These multi-location studies provide two-way tables of
means for diverse genotypes across different environments.
Data from such two-way tables can be initially analyzed
using models that incorporate the effect of the genotype, the
environment, and also that partition the remaining variation
into the effect of the interaction between environments and
genotypes and the residual experimental error. This provides
a good indication of the proportion of the variance that refers
to the main effect of genotype compared to G ´ E, but it
is limited in terms of providing insight into the nature of
the interaction. Much of that descriptive information, in the
context of plant breeding, has been founded on the work by
Finlay and Wilkinson (1963) and modified by others (Eberhart and Russell, 1966) which qualified G ´ E based on
the slope of the regression of the performance of particular
genotypes across an environmental gradient. The most basic
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models determine this quality gradient based on the average performance of all genotypes in that environment. This
methodology permits to interpolate the performance of the
specific genotypes being investigated across untested environments, as long as the environments are within the range
of the gradient in tested locations. This traditional concept
of stability is useful for the study of phenotypic plasticity as
it provides a single measurement of the slope of the regression line of genotypes along environmental gradients which
can be used as the entry phenotype for genotypic-phenotypic associations, for example, to understand the genetic
architecture of plasticity itself. Other complementary and
well recognized methods developed to assess environmental
stability include mean-CV analysis from Francis and Kannenberg (1978) and Shukla’s (1972) stability variance.
A significant expansion of traditional methodologies to
describe G ´ E involves the incorporation of multidimensional environmental characterizations to statistical models.
The additive main effects and multiplicative interaction
(AMMI) model was one of the initial implementations of this
strategy (Gollob, 1968; Gauch, 1988). In this context, G ´ E is
modeled as the product of the effect of the specific sensitivity
of a genotype to a latent (unobservable) environmental variable. A principal components strategy maximizes the variation
explained by the products of the resulting genotype sensitivities
by environmental variables (Gabriel, 1978). Another variant to
this overall strategy came through the development of modeling strategies that incorporated not only the G ´ E variation
but also the combined effect of the genotypic main effect and
the G ´ E as a sum of the multiplicative terms. This general
set of methods are called “genotype main effects and G ´ E”
or GGE model (Yan et al., 2000). These multiplicative strategies are particularly useful because they provide meaningful
graphical displays of performance which allows direct interpretation of the relationship between specific environments and
between particular genotypes, and, in the case of GGE, direct
interpretation of the effect of specific genotypes in particular
environments. From the standpoint of improving the interpretation of the effect of particular environmental effects on
performance, another advancement came from the incorporation of explicit quantification of environmental components to
statistical models as explanatory variables. These so-called factorial regression models connect the differential sensitivity of
genotypes to observed environmental variables (e.g., rainfall in
May) which could be chosen based on what is needed for crop
growth (van Eeuwijk et al., 1996). Since overall performance
is what breeders are interested in, this type of analysis facilitates
direct biological interpretation of performance and therefore
has direct utility for practical breeding programs.
Several mixed model applications have also been
proposed to analyze and interpret G ´ E primarily for
multi-environment analysis that involves a large number
of genotypes (Smith et al., 2005). In this context, genotypes can be modeled as random effects and their potential
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heterogeneity of variances (and co-variances) can be interpreted as an indication of differential genotypic sensitivity
to certain environmental cues. A more in-depth description of this and other similar and related methodological
approaches can be found in Malosetti et al. (2013).
Genetic and Physiological Mechanisms
of G ´ E
The differential expression of certain genotypes in the presence
of distinctive environmental factors has been assessed using
condition-dependent mutants such as those presenting specific
sensitivity to light, water, and hormones. The primary goal of
such studies is to identify genetic models that could describe
pathways directly affected by those environmental interactions
(Kim et al., 2009; Cutler et al., 2010). More recently, genomewide transcriptional studies have also assessed the responses
of contrasting genotypes when exposed to various types of
environmental stresses. In those studies, numerous transcripts
appear to be up or downregulated and the combination of that
information has proven useful in terms of identifying relevant
regulatory networks associated with that response (e.g., Zou et
al., 2011). However, specific characteristics of the natural variants regulating those pathways is still largely unknown (Des
Marais and Juenger, 2010; Juenger, 2013). In a recent study,
DesMarais et al. (2013) have attempted to gain insight about
the most important genomic regions associated with G ´ E
through a meta-analysis of available studies that have evaluated
the association of phenotypes and genotypes across multiple
environmental conditions. This meta-analysis included crop
species and natural populations and assessed a variety of traits.
A number of interesting patterns and mechanisms associated
with G ´ E were identified across the diversity of traits, species
and environmental treatments assessed. Overall, the control of
G ´ E appears to involve multiple genetic regions. Although
the summary provided in this review is very enlightening, the
majority of the studies involved natural populations or model
species evaluated in contrasting conditions within controlled
environments. An expansion to include studies involving crop
species in natural production conditions will provide an additional perspective on these findings.
Several potential mechanisms have been proposed to
explain the genetic basis of G ´ E (reviewed by El-Soda et
al., 2014). These interactions have been associated with pleiotropic effects that can range from antagonistic effects to, what
is most commonly observed, a differential sensitivity of alleles
across environments (Scheiner, 1993; DeWitt et al., 1998;
Agrawal, 2001; Pigliucci, 2005). Several examples of these
types of relationships are available in the literature for naturally and artificially selected populations (Hall et al., 2010;
Hancock et al., 2011, Brekke et al., 2011a, 2011b; Anderson
et al., 2013). A complementary mechanism invoked to help
explain G ´ E involves the effect of the genetic background,
which can differentially affect a trait in distinct environments. Although it is intuitively easy to imagine that this
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crop science, vol. 56, september– october 2016
epistatic relationship should frequently occur, the ability to
empirically demonstrate this relationship has been challenging, due to the limited power most studies have to detect
statistically significant interaction effects (Alcazar et al.,
2009) in the context of multi-environment testing. Additionally, the complex relationship of alleles within a locus has
been suggested to affect G ´ E: the presence of more than
one allele at a locus is directly associated with phenotypic
plasticity in outbred species or in the comparison of inbreds
and hybrids involving self-pollinated species. This property
is especially relevant for transient environmental conditions
to which different alleles confer adaptation.
It is also well recognized that environmental factors can
influence levels of DNA methylation and alter chromatin
states. Such epigenetic modulation has been proposed as a
mechanism affecting plasticity levels of specific plant genotypes across environments (Reinders et al., 2009; Bossdorf
et al., 2010; Mirouze and Paszkowski, 2011; Pecinka and
Mittelsten-Scheid, 2012; Zhang et al., 2013). The relative
contribution of the diverse set of mechanisms just described is
an active field of study in evolutionary science. It is important
to highlight, however, that most of the molecular characterizations conducted to date focused on plant model species
or combinations of naturally occurring populations evaluated in controlled conditions and frequently involved pairs
of discrete and contrasting environments rather than a more
realistic continuous array of complex and interactive climatic
factors such as those present in production environments.
Content of this Special Issue
Greater gain from selection will come from greater ability to
predict the performance of specific genotype-environment
combinations. Prediction of future combinations in turn
depends on a deeper understanding of performance of past
combinations, as determined by detailed data on each component and the ability to meaningfully analyze that data.
Plant breeders and geneticists are now poised to collect that
detailed data in ways unimagined even 5 yr ago. Three types
of information are needed to train prediction models: genotypic, phenotypic, and “envirotypic,” the latter neologism,
with its cognate “envirotyping,” meaning comprehensive
measurement of environmental characteristics (e.g., weather
and soil characteristics). The cost of collecting all three types
continues to decline, thanks to innovations in sequencing,
and automated field recording of phenotypes and weather.
Woven through both the collection and analysis of these
data are new computer technologies that, through power
and miniaturization, enable automated data collection (e.g.,
highly sensitive detectors in small drones to regularly scan
fields) and large scale analyses (e.g., statistical models incorporating millions of genotypic data points).
Naturally, having the data does not equate with knowing how best to analyze and interpret it. Contributions
on G ´ E analysis in this issue are gathered to assess what
tools plant breeders have, what opportunities are becoming
available, some analysis ideas, and some initial attempts at
applying the wealth of new data becoming available. The
issue starts with four reviews. The first reflects on the history of G ´ E analyses providing references to the giants on
whose shoulders we now stand (Elias et al., 2016, this issue).
The second gives an excellent overview and perspective
for junior researchers about G ´ E analyses, giving them
a learning program of practical approaches using modern
but by now well-tested tools (van Eeuwijk et al., 2016, this
issue). For practicing breeders, Yan et al. (2016, this issue)
provide a worked example of a useful selection tool in GGE
biplot analysis. Finally, Hayes et al. (2016, this issue) give an
animal breeding perspective and suggest that animal and
plant breeders are in a process of converging on common
solutions to the analysis of G ´ E.
From there, articles in the issue address development
of new methods to design and analyze multi-environment
trials. Regardless of new technologies, running such trials is
expensive, and therefore they should be optimally designed.
Kleinknecht et al. (2016, this issue) provide a simulation
approach, conditioning on parameters such as the number
of entries and the variance components to be expected in
the trials. In the analysis of trials, separating signal from
noise is the initial goal, one for which the AMMI model has
shown good properties. Paderewski et al. (2016, this issue)
extend that analysis to four-way factorials including entries,
locations, years, and management practices.
We then come to the focal task of performance estimation or prediction. The issue collects several reports on
method development for this purpose. A useful taxonomy
of prediction challenges has developed in the context of G
´ E research that we describe here in an attempt to categorize methods. Given a matrix of individuals observed
in environments, different types of information may be
available for prediction (Table 1).
Table 1. Taxonomy of prediction challenges.
Validation environments
Validation individuals
Tested for some individuals
Not tested
Tested in some environments
CV2†/(G ;E )§
CV3‡/(Gt;Eu)§
Not tested
CV1†/(Gu;Et)§
CV4/(Gu;Eu)§
t
t
† Defined in Burgueno et al. (2012).
‡ Used in Heslot et al. (2014).
§ Defined in Malosetti et al. (2016, this issue), where the superscript t and u indicate tested and untested, respectively.
crop science, vol. 56, september– october 2016 www.crops.org2085
Among these cross-validation schemes, CV2 is clearly
the “easiest” in the sense that cross-validation accuracies are
most likely to be high. From there, CV1 and CV3 present
different challenges: as the difference between validation and
training individuals increases, CV1 accuracies will decrease.
Likewise, as the difference between validation and training
environments increases, CV3 accuracies will decrease. Logically, CV4 should prove to be the most challenging.
Three papers in this issue dissect the overall G ´ E
to the marker level, applying models that estimate marker
by environment interaction. Crossa et al. (2016, this issue)
work through a durum wheat (Triticum turgidum ssp. durum)
dataset using this approach to improve accuracies in environments that have been observed for some individuals. Jarquín
et al. (2016, this issue) develop a Bayesian implementation
of the problem. Lado et al. (2016, this issue) address the
issue of unbalanced datasets common in practical breeding
programs showing how estimating performance covariance
across environments can improve accuracy.
To move from prediction for past to future environments, that is, to go from the first to the second column in
the Table above, it is necessary to consider the nature of the
information available about environments. Environments
are characterized by many biotic (e.g., pathogens, insects,
weeds, beneficial microbes) and abiotic factors (e.g, temperature, rainfall, insolation, soil quality). Each of these
factors can, in turn, be split myriad ways (e.g., pathogen
variants or timing or rainfall quantities in different time
slices). The challenge is to discover, among all these quantities, which are important. As suggested by Heslot et al.
(2013), the problem has some parallels to that of quantitative trait locus (QTL) identification or prediction in that
we need phenotypes to sort through potentially numerous
predictors. In effect, these phenotypes represent the translation of environmental inputs into observable traits, that
is, actual plants process the inputs from “the plant’s eye
view.” The clever device of using probe genotype performance to characterize environments also recognizes the
value of allowing plants to “interpret” the effects of environmental inputs (Cooper and Fox, 1996). The task of
crop physiology is to enable scientists to do something
similar, and the sum total of its lessons are instantiated
in CGM. Two further method development articles take
up the use of CGM in predicting G ´ E. In Cooper et
al. (2016, this issue) the CGM is the actual prediction
model, run with physiological parameters determined by
iterations of random sampling of marker effects on the
parameters and retaining only iterations with model outputs close to observed data. A challenge of this approach is
that the CGM needs multiple parameter values, but only
one trait is observed: final yield. Extrapolating from one
trait to multiple parameters seems akin to locating a point
in a high-dimensional space based solely on its projection
in a low-dimensional space. We might envision data from
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a time series generated by high-throughput phenotyping to overcome the data (though not the computational)
shortfall of this approach. Malosetti et al. (2016, this issue),
in contrast, retain linear mixed models for prediction as is
standard. They emphasize the need to estimate the covariance between measurements of the same trait across
environments. When trials have been performed in the
environments, the translation of environmental inputs to
phenotypes has been observed and such covariances can
be estimated from the data. Statistical expertise is required
to strike a balance between model parsimony and model
fit. In the absence of such trials (i.e., for future environments), Malosetti et al. (2016, this issue) suggest estimating
the covariances directly from measures or predictions of
environmental inputs themselves, as also proposed by Jarquín et al. (2014). Crop growth models, however, are also
discussed and, reading between the lines, we again might
envision the use of CGM to predict covariances among
environments of varying similarity.
The issue is completed by three empirical studies of
G ´ E that show the breadth of approaches available even
under classical analysis methods. Grogan et al. (2016, this
issue) analyze the variation of maturity and yield across
environments (i.e., the plasticity of these traits) relative to
the trait means in wheat. They find that over time selection decreased responsiveness of maturity to environment
but increased that of yield: even as wheat varieties are
becoming more predictable in their harvest times, their
yields are becoming more responsive to favorable environmental opportunities. While a known response to
breeding in maize has been an increase in its tolerance to
high-density planting, interactions between response to
density and environment have not been studied. Edwards
(2016) shows that there is indeed G ´ E for response to
density such that different hybrids will be at different
distances from their optimal densities across environments. This poses an unexplored methodological problem
for breeders. Finally, Lee et al. (2016, this issue) dissect
the environmental determinants of G ´ E for yield in
a phenologically uniform biparental population. Careful
model building surprisingly supports the hypothesis that
variation in thermal time and solar radiation across years
generates more G ´ E than soil water availability.
Taking it to the Next Level
Factors such as variable climate conditions, increasing
world population, improvements in diets, and a growing demand for alternative uses for agricultural products
are expected to increase the need for more efficient agricultural production. Meeting this challenge will largely
depend on our ability to generate and manage crop varieties with high productivity in the context of variable
environments. Current indicators suggest that traditional
breeding techniques are unlikely to meet this demand.
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crop science, vol. 56, september– october 2016
Plant genome research has generated copious amounts
of genomic data and tools. A bottleneck in phenotyping
subsists, however, for employing this data to improve agricultural crops. Plant productivity is a direct consequence
of how well adapted the genotype of an individual is to
the surrounding environment. Useful phenotypic characterization is therefore location-specific and represents the
integration of the entire lifecycle of the plant and the local
environmental conditions. To truly increase efficiency
in agricultural production, a key component will be to
predict accurately the performance of specific genotypes
across a wide range of variable environments.
An overwhelming number of published studies have
investigated the role of G ´ E in plant breeding. This
wealth of information has mostly focused on developing
tools to quantify its magnitude and utilize that information to identify strategies to minimize its potentially
negative effect in cultivar advancement and deployment to
appropriate environments. While this wealth of information has contributed to our ability to manage G ´ E in a
plant breeding context, these studies have also highlighted
the complexity of this phenomenon, as the opportunities
for genotypes and environments to interact throughout
the lifetime of a plant are effectively unlimited.
Yield is the single most important measure of plant
productivity. Its integration of all environmental conditions over the season, however, obscures the process by
which the plant achieved its end of the season state. Technological advances are expected to allow the deployment
of small and inexpensive sensors and robotic units for highthroughput field phenotyping. Plants are dynamic living
systems that change constantly. Our ability to measure the
effect of environmental influences affecting intermediate
developmental stages (maybe to the level of daily changes)
through the deployment of high-throughput phenotyping tools provide the opportunity to dissect G ´ E into
smaller time sensitive components and as such contribute
to our understanding of the main factors limiting end of
the season productivity.
The combination of rich genomic information and
detailed environmental assessments allows not only an
enhanced ability to quantify and mitigate the unrepeatable portion of G ´ E, but also to genetically characterize
it in economically important crops in the context of relevant production conditions. A deeper understanding of
the types of genetic architectures and mechanisms controlling G ´ E will provide additional opportunities to
engineer crop varieties with enhanced capacity to tolerate
and thrive in ever-changing sets of environments. Similarly, a deeper understanding of the specific environmental
components that generate crossover G ´ E interactions at
specific plant developmental stages is expected to enhance
our ability to determine the value of on-the-fly management decisions.
crop science, vol. 56, september– october 2016 Despite the intricate nature of G ´ E interactions,
breeding efforts have overcome, and at times exploited,
G ´ E: well-adapted superior varieties have been generated in virtually all species where selection has been
systematically applied and across a very wide range of
environmental conditions. The challenge for breeders in
the future is that, to improve phenotype prediction, they
will be required to deploy ever more sophisticated plant
growth and development monitoring tools. The diversity
of knowledge required to manage breeding programs is
expected to require large interdisciplinary teams that combine expertise in diverse areas such as genetics, genomics,
computation, engineering, environmental sciences,
physiology, and modeling. Groundbreaking advances are
taking place involving the incorporation of crop growth
modeling tools directly into prediction machinery. The
biological and genetic information gained through the
use of these technological advances will continue to be
directly fed into statistical models to further improve predictions of end-of-the-season phenotypes.
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