Linking genotype and phenotype is a fundamental challenge in biology. In this respect, machine learning is playing a pivotal role in systems biology. As a central phenotypic trait, fruit development and its relative growth rate (RGR) result from interactions between gene regulation, metabolism and environment. In the present study, we carried out a multispecies transcriptomic analysis of nine different fruits. To illustrate fruit transcriptomes, transcripts were first compared using multivariate methods, revealing main similar profiles. They were then used as variables to predict four growth traits, i.e. RGR, developmental progress, fruit weight and protein content, using generalised linear models (GLMs) to decipher the mechanisms involving gene expression in development. The predictions were very satisfactory despite disparities when the model did not include the entire panel of fruit species. Based on orthogroups derived from BLAST and annotated consensus sequences from gene ontology (GO) terminology, variables annotated for metabolic processes, especially those involving cell wall carbohydrates and proteins, were found to be the most effective in predicting growth. In addition, predictions were improved for RGR when introducing a seven-day lag between transcript contents and growth traits, suggesting the necessity of considering the proteins produced to enhance phenotypic trait predictions. These original results showed that growth traits can be predicted very well with GLMs based on orthogroups from multi-species transcriptomes.Read less <