Genetic architecture of fresh-market tomato yield

Genomic selection for crop improvement in fruits and vegetables: a systematic scoping review

To ensure the nutritional needs of an expanding global population, it is crucial to optimize the growing capabilities and breeding values of fruit and vegetable crops. While genomic selection, initially implemented in animal breeding, holds tremendous potential, its utilization in fruit and vegetable crops remains underexplored. In this systematic review, we reviewed 63 articles covering genomic selection and its applications across 25 different types of fruit and vegetable crops over the last decade. The traits examined were directly related to the edible parts of the crops and carried significant economic importance. Comparative analysis with WHO/FAO data identified potential economic drivers underlying the study focus of some crops and highlighted crops with potential for further genomic selection research and application. Factors affecting genomic selection accuracy in fruit and vegetable studies are discussed and suggestions made to assist in their implementation into plant breeding schemes. Genetic gain in fruits and vegetables can be improved by utilizing genomic selection to improve selection intensity, accuracy, and integration of genetic variation. However, the reduction of breeding cycle times may not be beneficial in crops with shorter life cycles such as leafy greens as compared to fruit trees. There is an urgent need to integrate genomic selection methods into ongoing breeding programs and assess the actual genomic estimated breeding values of progeny resulting from these breeding programs against the prediction models.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-024-01497-2.

Whole genome sequence data of 516 F2 plants of tomato (Solanum lycopersicum)

The large-fruited fresh-market tomato cultivated in the U.S. represents a unique fruit market class of contemporary (modern) tomatoes for direct consumption. The genomes of F2 plants from crosses between inbred contemporary U.S. large-fruited fresh-market tomatoes were sequenced. 516 F2 individual plants randomly selected from five different biparental segregating populations were used for DNA extraction. The polymerase chain reaction (PCR)-free, paired-end (2 × 150 bp) sequencing libraries (350 bp DNA fragment length) were prepared, and sequenced on average 5 Gb for each plant using the Illumina next-generation sequencing technologies [1,2]. Raw Illumina reads with adapter contamination and/or uncertain nucleotides constitute (Ns, >10 % of either read; Q-score 5 or lower, >50 % of either read) were removed. This data article will contribute to improving our knowledge of the genetic recombination and variation in tomato.

Genome sequence data of the contemporary fresh-market tomatoes

OBJECTIVE

The fresh-market tomato (Solanum lycopersicum) is bred for direct human consumption. It is selected for specific traits to meet market demands and production systems, and unique genetic variations underlying fresh-market tomato yields have been recently identified. However, DNA sequence variant-trait associations are not yet fully examined even for major traits. To provide a rich genome sequence resource for various genetics and breeding goals for fresh-market tomato traits, we report whole genome sequence data of a pool of contemporary U.S. fresh-market tomatoes.

DATA DESCRIPTION

Eighty-one tomatoes were nominated by academic tomato breeding programs in the U.S. Of the 81 tomatoes, 68 were contemporary fresh-market tomatoes, whereas the remaining 13 were relevant fresh-market tomato breeding and germplasm accessions. Whole genome sequencing (WGS) of the 81 tomatoes was conducted using the Illumina next-generation sequencing technology. The polymerase chain reaction (PCR)-free, paired-end sequencing libraries were sequenced on an average depth per sequenced base of 24 × for each tomato. This data note enhances visibility and potential for use of the more diverse, freely accessible whole genome sequence data of contemporary fresh-market tomatoes.

Using machine learning and partial dependence to evaluate robustness of best linear unbiased prediction (BLUP) for phenotypic values

Best linear unbiased prediction (BLUP) is widely used in plant research to address experimental variation. For phenotypic values, BLUP accuracy is largely dependent on properly controlled experimental repetition and how variable components are outlined in the model. Thus, determining BLUP robustness implies the need to evaluate contributions from each repetition. Here, we assessed the robustness of BLUP values for simulated or empirical phenotypic datasets, where the BLUP value and each experimental repetition served as dependent and independent (feature) variables, respectively. Our technique incorporated machine learning and partial dependence. First, we compared the feature importance estimated with the neural networks. Second, we compared estimated average marginal effects of individual repetitions, calculated with a partial dependence analysis. We showed that contributions of experimental repetitions are unequal in a phenotypic dataset, suggesting that the calculated BLUP value is likely to be influenced by some repetitions more than others (such as failing to detect simulated true positive associations). To resolve disproportionate sources, variable components in the BLUP model must be further outlined.