Random forest in plant genetics and breeding: an application in tomato as a model crop

Abstract

Random Forest approaches have been used in phenotyping at both morphological and metabolic levels and in genomics studies, but direct applications in practical situations of plant genetics and breeding are scarce. Random Forest was compared with Discriminant Analysis for its ability in classifying tomato individuals belonging to different breeding populations, exclusively based on phenotypic fruit quality traits. In order to take into account different steps in breeding programs, two populations were assayed. One was composed by a set of RILs derived from an interspecific tomato cross, and the other was composed by two of these RILs and the corresponding F1, F2 and backcross generations. Being tomato an autogamous species, the first population was considered a final step in breeding programs because promising genotypes are being evaluated for putative commercial release as new cultivars. Meanwhile, the second one, in which new variation is being generated, was considered as an initial step. Both Random Forest and Discriminant Analysis were able to classify populations with the aim of evaluating general variability and identifying the traits that most contribute to this variability. However, overall errors in classification were lower for Random Forest. When comparing the adequacy of classification between populations, errors of both statistical analyses were greater in the second population than in the first one, though Random Forest was more precise than Discriminant Analysis even in this initial step of plant breeding programs. Random Forest allowed breeders to get a reliable classification of tomato individuals belonging to different breeding populations.