INTRODUCTION
Plants are sessile organisms which have to cope with environmental
fluctuations to ensure species reproduction for persistence in nature.
For a given genotype, the expression of different phenotypes according
to the growing environment is commonly called phenotypic plasticity (PP)
(Bradshaw, 1965). It offers the possibility to plants to adapt to new
environments, notably new locations, changes in climatic conditions or
seasonal variations. In agriculture, the range of environmental
variation for crop cultivation may also include different cultural
practices or growing conditions, leading to the expression of PP on
agronomic traits and unstable performance. When different
genotypes/accessions are examined for PP within a species,
inter-individual variations in their responses usually lead to the
common phenomenon of genotype-environment (GxE) interaction (El-Soda et
al., 2014). Understanding the genetic mechanisms driving PP and GxE in
plants is a crucial step for being able to predict yield performance of
crop cultivars and to adapt breeding strategies according to the
targeted environments.
In plants, the genetic basis of PP has been investigated to assess
whether PP has its own genetic regulation and thus could be directly
selected. Three main genetic models, widely known as the over-dominance,
allelic-sensitivity and gene-regulatory models were proposed in the
literature as underlying plant PP (Scheiner, 1993; Via et al., 1995).
The over-dominance model suggests that PP is negatively correlated to
the number of heterozygous loci (Gillespie and Turelli, 1989). The
heterozygous status is favored by allele’s complementarity in this case.
Allelic-sensitivity and gene-regulatory models are assumed to arise from
the differential expression of an allele according to the environment
and epistatic interactions between structural and regulatory alleles,
respectively. The latter assumes an independent genetic control of mean
phenotype and plasticity of a trait. Using a wide range of environmental
conditions, the prevalence of the allelic-sensitivity or gene-regulatory
model in explaining the genetic architecture of PP was explored in
different crop species including barley (Lacaze et al. 2009), maize
(Gage et al., 2017; Kusmec et al., 2017), soybean (Xavier et al., 2018)
and sunflower (Mangin et al., 2017).
Quantification of PP is however a common question when analyzing the
genetic architecture of plasticity since different parameters for PP
estimation are available as reviewed by Valladares et al. (2006). At a
population level, when multiple genotypes are screened in different
environments, different approaches can be used to assess plasticity
(Laitinen and Nikoloski, 2019). The most common of these approaches is
the joint regression model (Finlay and Wilkinson, 1963) that uses the
average performance of the set of tested genotypes in each environment
as an index on which the individual phenotypes are regressed. This
model, commonly known as the Finlay-Wilkinson regression model, allows
to estimate linear (slopes) and non-linear plasticity parameters (from
the residual errors) that presumably have different genetic basis
(Kusmec et al., 2017). If the detailed description of the environments
is available, the environmental index used in the Finlay-Wilkinson
regression model can be replaced by environmental covariates such as
stress indexes through factorial regression models (Malosetti et al.
2013). Thus plasticity could be estimated as the degree of sensitivity
to a given stress continuum (Mangin et al., 2017).
Climate change is predicted to increase the frequency and intensity of
abiotic stresses with a high and negative impact on crop yield (Zhao et
al., 2017). Plants respond to abiotic stresses by altering their
morphology and physiology, reallocating the energy for growth to defense
against stress (Munns and Gilliham, 2015). Consequences on agronomic
performances are apparent and detrimental to productivity. The most
common abiotic stresses studied across species are water deficit (WD),
salinity stress (SS) and high temperature stress (HT). The negative
impact of these stresses on yield have been underlined for major
cultivated crops; however, positive effects of WD and SS on fruit
quality have been observed in fruit trees and some vegetables notably in
tomato (Costa et al. 2007; Mitchell et al. 1991; Ripoll et al. 2014).
Tomato is an economically important crop and a plant model species which
led to numerous studies that contributed much in understanding the
genetic architecture of the crop and its response to environmental
variation. However, most of the studies that addressed the genetic
architecture of tomato response to environment were conducted on
experimental populations exposed to two conditions (i.e. controlvs stress). Albert et al. (2018) for example identified different
WD-response quantitative trait loci (QTL) in a bi-parental population
derived from a cross of large and cherry tomato accessions. Tomato
heat-response QTLs were also identified in different experimental
populations including interspecific and intraspecific populations
(Grilli et al., 2007; Xu et al., 2017a; Driedonks et al., 2018). These
studies investigated heat-response QTLs using mostly reproductive traits
screened under heat stress condition. Villalta et al. (2007) and Diouf
et al. (2018) investigated the genetic architecture of tomato response
to SS and identified different QTLs for physiological and agronomic
traits, involved in salinity tolerance. However, no QTL study has yet
been conducted on tomato plasticity assessed under a multiple stress
design, although the coincidence of different stresses is a more
realistic scenario in crop cultivation, especially with the climate
change.
Tomato benefits of a large panel of genetic resources that have been
used in multiple genetic mapping analyses (Grandillo et al. 2013).
Bi-parental populations were first used in QTL mapping and permitted the
characterization of plenty of QTLs related to yield, disease resistance
and fruit quality. In the genomic era, new experimental populations were
developed offering higher power and advantages for QTL detection. These
include mutant collections, BIL-populations and multi-parent advanced
generation intercross (MAGIC) as described in Rothan et al. (2019). The
first tomato MAGIC population was developed at INRA-Avignon in France
and is composed of about 400 lines derived from an 8-way cross (Pascual
et al. 2015). This population showed a wide intra-specific genetic
variation under control and stress environments and is highly suitable
for mapping QTLs (Diouf et al., 2018).
In the present study, we used the 8-way tomato MAGIC population
described above and evaluated its response in a multi-environment trial
(MET) design. The population was grown in 12 environments including
control and several stress conditions (WD, SS and HT), and agronomic
traits related to yield, fruit quality, plant growth and phenology were
measured. Different plasticity parameters were computed and used
together with mean phenotypes to decipher the genetic control of
response to environmental variation. Multi-environment QTL analysis was
performed in addition to detection of interactive QTLs (QEI) along with
QTL mapping for plasticity traits.