A method for identifying environmental stimuli and genes responsible for genotype-by-environment interactions from a large-scale multi-environment data set

It has not been fully understood in real fields what environment stimuli
cause the genotype-by-environment (G × E) interactions, when they
occur, and what genes react to them. Large-scale multi-environment data
sets are attractive data sources for these purposes because they
potentially experienced various environmental conditions. In this study,
we developed a data-driven approach termed Environmental Covariate Search
Affecting Genetic Correlations (ECGC) to identify environmental stimuli
and genes responsible for the G × E interactions from large-scale
multi-environment data sets. ECGC was applied to a soybean (Glycine max)
data set that consisted of 25,158 records collected at 52 environments.
ECGC illustrated what meteorological factors shaped the G × E
interactions in six traits including yield, flowering time, and protein
content and when they were involved. For example, it illustrated the
relevance of precipitation around sowing dates and hours of sunshine just
before maturity to the interactions observed for yield. Moreover,
genome-wide association mapping on the sensitivities to the identified
stimuli discovered candidate and known genes responsible for the G
× E interactions. Our results demonstrate the capability of
data-driven approaches to bring novel insights on the G × E
interactions observed in fields. This dataset provides the data used in
this study and supplementary tables cited in the manuscript.