Complementary methods to identify environment-responsive QTLs
Different approaches have been proposed in the literature to dissect GxE
into its genetic components (Malosetti et al., 2013; El-Soda et al.,
2014). We used a mixed linear model with a random genetic effect
accounting for the correlation structure of the MAGIC-MET design to
identify the QEI. Extending the use of mixed linear models to MAGIC
populations in the framework of MET analysis has been very rarely
applied in crops. To our knowledge, only Verbyla et al., (2014) applied
such approach in wheat and identified QEI for flowering time. Our model
was adequate to account for the complex mating design of the MAGIC
population by using the haplotype probabilities. Indeed, it allows
estimating the QTL effect for each parental allelic class and for each
environment at every SNP marker. Overall, 28 QEI were detected showing
significant marker x environment interaction for ten traits.
Methods using plasticity as a trait per se are also attractive to
identify environmentally sensitive QTLs. This strategy was applied in
maize, sunflower, barley and soybean to detect the loci governing GxE
(Lacaze et al., 2009; Gage et al., 2017; Kusmec et al., 2017; Mangin et
al., 2017; Xavier et al., 2018). With different plasticity parameters,
we identified a total of 63 plasticity QTLs and only 24% were also
identified with the QEI models. Thus, both methods, using plasticity or
mixed linear models, are complementary approaches to study the genetic
component of GxE.