Genetic variability in tomato response to environmental
variation
Genotype x environment interaction is a long-standing challenge for
breeders and the predicted climate change has encouraged plant
geneticists to devote more attention into understanding its genetic
basis. Tomato is a widely cultivated crop adapted to a variety of
environmental conditions (Rothan et al. 2019). However, important
incidences of abiotic stress in the final productivity, fruit quality
and reproductive performance have been noticed (Albert et al. 2016;
Estañ et al. 2009; Mitchell et al. 1991; Xu et al. 2017). We quantified
the level of GxE and the subjacent phenotypic plasticity in a
multi-environment and multi-stress trial – involving induced
water-deficit, salinity and heat stresses – in a highly recombinant
tomato population. An important genetic variability was observed for the
plasticity traits related to yield, fruit quality, plant growth and
phenology (Supplemental Figure 6). This highlights the interest of the
MAGIC population as a valuable resource for tomato breeding in dynamic
changing environments. Tomato wild species have been also characterized
as an important reservoir for abiotic stress tolerance genes (Foolad,
2007). However, their effective use in breeding programs could be
difficult due to undesirable linkage drag, notably for fruit quality.
Unlikely, the MAGIC population characterized here is an intra-specific
population with high diversity regarding fruit quality components, which
provides a great advantage as a breeding resource compared to wild
populations.
Several statistical models are available to explore, describe and
predict GxE in plants (Yan et al., 2007; Malosetti et al., 2013).
Factorial regression model is among the most attractive as it allows to
describe the observed GxE regarding relevant environmental information.
We used the factorial regression model with different environmental
covariates that are readily accessible from year to year, which allowed
us to predict a variable proportion of the observed GxE (Supplemental
Figure 4). Besides, each MAGIC line was characterized for its
sensitivity to the growing climatic conditions opening avenues to
effectively select the most interesting genotypes for further evaluation
in breeding programs targeting stressful environments.
Interestingly we found significant correlation between the genotypic
sensitivities to the different environmental covariates and slopes from
the Finlay-Wilkinson regression model (Supplemental Figure 10). This
emphasizes the adequacy of the selected environmental covariates to
explain differences observed in the average performance of the genotypes
across environments. Conversely, slope and VAR showed less significant
correlations, although they were both correlated to mean phenotypes in
the same direction – except for SSC (Figure 2). This may be induced by
distinct genetic regulation of these two plasticity parameters which
reflect different types of agronomic stability (Lin et al. 1986).
Indeed, we identified 7 and 14 plasticity QTLs that were specific to VAR
and slope, respectively (Supplemental Table 4). The correlation pattern
of the different plasticity parameters evokes a complex regulation of
plasticity which besides is seemingly trait specific.
Significant correlation at phenotypic level might result from the action
of pleiotropic genes. The Figure 2 displays the correlations between
genotypic means and plasticity which were significant for almost every
trait at variable degree. These correlations were reflected at the
genetic level by 22 QTLs overlapping between genotypic mean and
plasticity parameters, representing about 21% of all identified QTLs.
However, a high proportion of the QTLs were specific either to genotypic
means or plasticity parameters (Supplemental Figure 11), hence
suggesting the action of both common and distinct genetic loci in the
control of mean phenotype and plasticity variation in tomato.