Massive Phenotyping of Cranberry Fruits Reveals Novel QTLs for Quality Traits and Provides and Excellent Framework for Genome Prediction

Breeding progress to produce new cranberry cultivars (Vaccinium macrocarpon L.) has been limited because of reliance on mainly phenotypic selection, challenges due to perennial habit, little genetic knowledge of the crop, and lack of incentive due to small scale production around the world. Recently, most cranberry breeding efforts have focused on increasing yield and enhancing disease resistance, however, other traits, mainly those related with fruit quality, are important in the industry and merit further study. Therefore, we combined high-throughput phenotyping strategies and genome-wide quantitative trait loci (QTL) mapping for the genetic dissection of fruit quality trait variation in cranberry. Firstly, we developed a software package called GiNA for massive phenotyping of cranberry fruit traits based on digital imaging. Using GiNA, we massively phenotyped three biparental populations for fruit morphology and color-related traits. Then, we obtained multivariate genomic BLUPs (MGBLUPs) for all the traits and QTL mapping was performed, in which we found a large amount of co-located QTLs among the populations analyzed. In addition, we explored genomic selection as a means of increasing genetic gain in cranberry fruit quality by studying the predictive ability of MGBLUPs for all the fruit quality traits evaluated, finding high correlations for most of the cases. With this study, we provide a powerful phenotyping tool for cranberry fruits (and other horticultural crops) as well as identified QTLs for fruit quality traits which were confirmed to be present in multiple breeding populations.