DOI: 10.5061/dryad.rr4xgxd6r
title: A method for identifying environmental stimuli and genes responsible for genotype-by-environment interactions from a large-scale multi-environment data set

description: 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.
description: <strong>Supplementary Tables</strong> (Tables S1-S21)
 are provided in a single excel file (SupplementaryTables.xlsx).
 <strong>Supplementary Data</strong> (Supplementary Data 1-4)
 are provided as independent four csv files
 (SupplementaryData1-4.csv).<strong> An example R script to execute
 ECGC</strong> is also provided. This script uses Table S5 and Table
 S11. For Supplementary Tables, captions are included in the file. Captions
 for the Supplementary Data are provided below. <b>Supplementary Data
 1</b> IDs of records, phenotypic values,
 and meteorological measurements are included. The meteorological
 measurements include from the sowing dates (day 0) to
 245<sup>th</sup> day after sowing. The column names of
 meteorological measurements are composed of abbreviations of
 meteorological factors and days after sowing. The abbreviations
 are: T, mean temperature Tmax, maximum
 temperature Tmin, minimum temperature
 Pr,
 precipitation e, vapour pressure VPD, vapour
 pressure deficit RH, relative humidity
 RHmin,
 minimum relative humidity u, wind speed u10max,
 maximum wind speed N, hours of sunshine Sd, solar
 radiation EP, potential
 evapotranspiration Ph, photoperiod. <b>Supplementary Data
 2</b> Genomic relationship matrix used for
 mixed models is included. The column and row names are the variety
 IDs. <b>Supplementary Data
 3</b> Adjusted phenotypic values used for
 estimating genetic correlation are included. Phenotypic values were
 adjusted for fixed effects at each environment. Then adjusted phenotypic
 values at each environment were matched to those of each of the other
 environments. The column names are composed of environment IDs and
 traits. <b>Supplementary Data
 4</b> Record IDs of Supplementary Data 3
 are included. Record IDs indicate the positions in Supplementary Data
 1. <strong>Trait
 abbreviations</strong> In these files, traits are often
 referred as abbreviations: DTF, days to flowering; DTM, days to
 maturity; SL, stem length (cm); PR, protein content of seeds (%); YI,
 yield (kg/a); SW, seed weight (g/100 seeds).