A comparative analysis of genomic and phenomic predictions of growth-related traits in 3-way coffee hybrids.

Genomic prediction has revolutionized crop breeding despite remaining issues of transferability of models to unseen environmental conditions and environments. Usage of endophenotypes rather than genomic markers leads to the possibility of building phenomic prediction models that can account, in part, for this challenge. Here, we compare and contrast genomic prediction and phenomic prediction models for 3 growth-related traits, namely, leaf count, tree height, and trunk diameter, from 2 coffee 3-way hybrid populations exposed to a series of treatment-inducing environmental conditions. The models are based on 7 different statistical methods built with genomic markers and ChlF data used as predictors. This comparative analysis demonstrates that the best-performing phenomic prediction models show higher predictability than the best genomic prediction models for the considered traits and environments in the vast majority of comparisons within 3-way hybrid populations. In addition, we show that phenomic prediction models are transferrable between conditions but to a lower extent between populations and we conclude that chlorophyll a fluorescence data can serve as alternative predictors in statistical models of coffee hybrid performance. Future directions will explore their combination with other endophenotypes to further improve the prediction of growth-related traits for crops.

A Solanum lycopersicoides reference genome facilitates insights into tomato specialized metabolism and immunity.

Wild relatives of tomato are a valuable source of natural variation in tomato breeding, as many can be hybridized to the cultivated species (Solanum lycopersicum). Several, including Solanum lycopersicoides, have been crossed to S. lycopersicum for the development of ordered introgression lines (ILs), facilitating breeding for desirable traits. Despite the utility of these wild relatives and their associated ILs, few finished genome sequences have been produced to aid genetic and genomic studies. Here we report a chromosome-scale genome assembly for S. lycopersicoides LA2951, which contains 37 938 predicted protein-coding genes. With the aid of this genome assembly, we have precisely delimited the boundaries of the S. lycopersicoides introgressions in a set of S. lycopersicum cv. VF36 × LA2951 ILs. We demonstrate the usefulness of the LA2951 genome by identifying several quantitative trait loci for phenolics and carotenoids, including underlying candidate genes, and by investigating the genome organization and immunity-associated function of the clustered Pto gene family. In addition, syntenic analysis of R2R3MYB genes sheds light on the identity of the Aubergine locus underlying anthocyanin production. The genome sequence and IL map provide valuable resources for studying fruit nutrient/quality traits, pathogen resistance, and environmental stress tolerance. We present a new genome resource for the wild species S. lycopersicoides, which we use to shed light on the Aubergine locus responsible for anthocyanin production. We also provide IL boundary mappings, which facilitated identifying novel carotenoid quantitative trait loci of which one was likely driven by an uncharacterized lycopene ß-cyclase whose function we demonstrate.