QTL were localized using the R/qtl package (Broman et al. 2003) implemented in the statistical software R (R Core Team 2014) as described previously (Barba et al. 2014). Multipoint probabilities were calculated using calc.genoprob with step = 1 and default parameters. Initial QTL positions were determined with the scanone function using a normal model, Haley–Knott regression and default parameters. LOD significance scores were determined by permutation tests (1000). Initial QTL positions were used to define QTL with the makeqtl function; significance of model terms was tested with fitqtl command and positions were refined with refineqtl. The addqtl command was used to test if another QTL was needed. A 1.5 LOD supported interval was determined using the lodint function, and QTL effects were calculated as the difference in the mean phenotype value of individuals within each genotype class at the marker or pseudomarker (a position between markers) with the highest LOD score, using the effectplot function in R/qtl.