Linkage map construction and QTL analysis

Polymorphic markers genotyped in both populations were incorporated into previous marker datasets [16, 23] to develop new linkage maps. Segregating data were analyzed for goodness of fit to the expected 1:1 ratio using the chi-square test and the JoinMap version 4.0 software was used for linkage map construction [58, 59]. Markers were grouped using the maximum likelihood option at a minimum LOD score of 4.0 and maximum recombination fraction of 0.25. Recombination fractions were converted to centimorgans (cM) using the mapping function of [58, 59].

QTL analysis was conducted using the MAPQTL 5 software [60]. Considering previous evaluation performed in both faba bean populations, QTLs for disease resistance were identified using interval mapping (IM). An initial set of cofactors was selected from the interval mapping results, and a backward elimination procedure was used to select significant markers. Only markers significant at P = 0.01 were used as cofactors in the multiple QTL analysis (MQM) [61]. The threshold for the detection of a QTL was determined using 1000 permutations [62] and a significance (P-value) level of 0.05. The presence of a QTL was accepted based on its P-value and co-location with QTLs for different resistance traits. An estimation of the additive effect and the total variance explained (R2) by the QTL at the position with the highest LOD score was given by MapQTL 5.0. Uncertainty of the QTL position was indicated by a 1-LOD and 2-LOD support intervals [63–65]. MapChart software [65] was used to produce the QTLs figures for ascochyta and broomrape resistance.