Description of the data
The data described here were produced from the ANR projects ADAPTOM (ANR-13-ADAP-0013) and TomEpiSet (ANR-16-CE20-0014). An 8-way tomato MAGIC population was phenotyped over 12 environments including three geographical location (France, Israel and Morocco) and four conditions (control, and water-deficit, high-temperature and salinity stress). A set of 397 MAGIC lines were genotyped for 1345 markers, used together with the phenotypic traits for linkage mapping analysis. Genotype-by-environment interaction (GxE) was evaluated and phenotypic plasticity computed through different statistical models. Each file in the dataset has its own description below.
• Phenotype files
The Phenotypes files contain the 10 phenotypic traits that were evaluated. Phenotypic data averaged per genotype and environment are in the file “Phenotype_per_Environment”. The input phenotypes for the linkage mapping analysis are in the file “Pheno_Input_QTL_detection”. They represent for each trait the estimated average performance, slope and variance from the Finlay & Wilkinson regression model and sensitivity to environmental covariates from the factorial regression model, respectively.
• MAGIC Genotyping information
This file presents the genetic map with 1345 SNP markers used in linkage mapping analyses. The genotypic information of the eight founders and 397 MAGIC lines are also presented
• Daily recorded climactic parameters
This file presents the daily climatic parameters recorded within the greenhouses. The different parameters were computed over 24 hours.
• Custom R script for the two-stage analysis of GxE
The file “Two-stage-analysis_magicMET.txt” contains the custom R script used for analysis of factorial regression and Finlay-Wilkinson regression models. Average performance and plasticity parameters were derived from these analyses. Example have been given for fruit weight phenotype averaged per genotype and environment. The input file “Var_environment_P2P3” presents the average climatic parameters used particularly for the factorial regression model.
• Custom R script for QEI modelling
The files “QEI_Glbal_marker_effect_model5.txt” and “QEI_main_plus_interactive_effect_model6.txt” describe the custom R script used for the detection of interactive QTLs (QEI). Example of fruit weight phenotype have been developed. The input files for the script are “FW_pheno_GxE.csv”, the average phenotypic data per genotype and environment for fruit weight example and the parental haplotype probabilities “Proba_parents.txt” that were computed from R/qtl2 package with the function calc_genoprob. The “Geno_ID.csv” file gives the correspondence between genotype name and ID.
