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In order to assess the relative influence of each individual year and its phenotypic contribution to the average (over the years) of cracking tolerance, two-factorial and three-factorial models were estimated for each trait and genotypic group (Fercer, Garnet, and RILs) separately (Table 8 ). Random regression residual (RR) options were obtained by the mean of using the year-specific parameters as fixed (i.e., the probability of each year for cracking tolerance was constant) or random (i.e., the probability of cracking tolerance was independent of the year). In the three-factorial model, the year effects were considered as random for both populations. The two-factorial RR (when RILs were considered) was always much better than the three-factorial RR (data not shown). Similar results were observed for the analysis of mean cracking incidence in the three RIL groups.
Tolerance Data 2009.2 crack
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In this study, the most likely location for a QTL for cracking tolerance on LG2 could be pinpointed on the region containing the major QTL QFGW5.25, for FW. 42 The highly significant SSR marker EV24032 (e-values from 1.0E-98 to 2.6E-05) was tightly linked to this QTL, by a recombination of 0.27. The physical position of the *e-value* of EV24032 on chromosome 2 was computed at 64,546,592-64,871,587bp, as shown in Fig. 2, well within the confidence interval for the FW QTL QFGW5.25.
Because this project focused on the adoption of new technologies, including the use of a new procedure to collect data, the sampling design was also different from those generally adopted by previous projects in sweet cherry, such as the North American cherry breeding program ( http://www.sweetcherrygenetics.info/ ). Therefore, a time-consuming and difficult procedure was needed to sample and classify cherries into three different regions. This procedure could therefore be considered an important source of variability, which was minimized in this study by the use of the kernel classification approach, a data-mining tool derived from the artificial neural network classifier and implemented in the MIX-7 software suite for Windows (MIX-7, Wintel, Inc., USA). Among the results, the approach of the kernel classification method provided a relatively unbiased association of FS cracking with all agronomical traits, with the advantage of allowing for the direct interpretation of the cracking ranking across all the cultivars. The kernel classification tool also allowed the adoption of a robust classifier, through a hyper-parameters optimization of the algorithm using the single-linkage clustering algorithm. Indeed, within the classifier, the top-ranked number of classes could be obtained by considering the number of pairs of cherries for which the FS cracking value 1 was the highest. This means that the FS cracking value was directly associated to a specific region of the cultivar and the latter was therefore directly associated with a specific agronomic trait. However, the highest value of FS cracking did not always correspond to the highest value of one of the agronomic traits in question. This is likely because the regions where the FS cracking is induced are related to all the other agronomic traits, but with a different intensity and impact depending on the environmental conditions, as seen in the two different years 35, 47 . In this study, this method allowed the identification of specific QTLs for FS cracking on LG2, on LG4, and on the whole genome, with the advantage of providing a precise localization of the significant QTLs in the genetic map, and an easy identification of the related regions in the cultivar.