There are 3 files associated with the QTL mapping #### 1. geno.csv This file contains the the genotype information for all 544 genotyped RILs. This includes the 404 lines in this study, as well as addiditonal lines included in a previously published study mapping flowering time in controlled growth chamber conditions that simulated temperature and pohotperiod at the Italian and Swedish sites (Dittmar et al. 2014. Molecular Ecology. 23:4291-4303). #### 2. pheno.csv This file contains the phenotype information for mean days to first flower for the 6 site*year combinations of this study, as well as mean days to first flowering from the two environments in Dittmar et al. 2104. F09; F10; and F11 in the column names signifies that this data is from the field in 2009/2010; 2010/2011; and 2011/2012 respectively. SW and IT in the column name signifies that the data is from the Swedish and Italian environments respectively. ED in the column name signifies that this data is from Dittmar et al. 2014. Each of these phenotypes is represented as untransformed data, as well as a column that has been quantile normalized (Prefix of "QN_" in the column name) using the following snippet of code: # Snippet of code to perform quantile normalization quantnorm<-function(x) { n=sum(!is.na(x),na.rm=T) x=rank(x)/(n+1) x=qnorm(x) x[is.infinite(x)]=NA x } #### 3. MapQTL_F09IT_DTFF This file contains an R script to do the qtl mapping. This is an example script for one site*year combination. For the stepwise procedure, the best model is first determined with quantile-normalized data. This model is then re-fit with the raw data to get effect size estimates on the raw scale. Note: This is an automated script and should be adapted to other analyses only after all quality control steps have been performed. All QTL identified from the stepwise procedure should be carefully validated. For more details on R/qtl, see: Broman KW, Sen ? (2009) A Guide to QTL Mapping with R/qtl Springer, New York. Note: DO NOT run this script on a desktop/laptop without making a few changes. This script was designed to run on a server/cluster node with 20 cores (64GB of RAM) to run 10K permutations in parallel in under 30 minutes Change n.perm=10000, n.cluster=20 to n.perm=1000, n.cluster=1, in order to run 1K permutations on a single core (4GB RAM). It may take about 2 hours depending on the system Make sure you have the qtl library installed Set the working directory to where the genfile and phefile are located Run the script in batch mode from the command line $ R CMD BATCH MapQTL_F09IT_DTFF.R