Spatial modeling increases accuracy of selection for Phytophthora infestans‐resistant tomato genotypes

Abstract

At initial breeding stages, using a replicated check design is a viable alternative to reduce experimental field area as well as financial and operational costs. In this situation, spatial modeling could act to increase prediction accuracy of plant genotypic values. The objectives of this study were to demonstrate how spatially adjusted models can be used to reduce experimental error and how to compare statistical models in order to identify the best model for accurate genotype selection. For this purpose, we assessed 200 F 3:4 tomato families for their resistance to Phytophthora infestans isolates. NC1CELBR, NC25P, and the cultivar Santa Clara were used as checks. Under field conditions, plants were inoculated with P. infestans isolates and scored according to their level of disease severity. Nine statistical models were adjusted to estimate family genotypic values. The selection of the fittest model was based on residual variance values, accuracy, Akaike and Bayesian information criteria, and the maximum likelihood ratio test. We observed spatial patterns within the experimental field area. Spatial modeling decreased error, which is indicated by the better experimental variation distribution. Residual variance decreased, while genotypic variance increased ∼10% when spatial analysis was used. Spatial analysis improved selection accuracy by 19% compared with the traditional analysis. Therefore, we recommend incorporating spatial modeling into data analysis in breeding trials for disease resistance because it can provide higher gains from selection compared with traditional modeling approaches, depending on the experimental condition.

Tomato families possessing resistance to late blight also display high-quality fruit

“…We assessed 81 F 3:5 tomato families (Solanum lycopersicum) obtained from successive self-pollination cycles of the cultivar Iron Lady (F 1 ), which was previously selected as late blight resistant by Copati et al (2021). This cultivar is known for carrying the late blight resistance genes Ph2 and Ph3 (Ozores-Hampton & Roberts, 2014).…”

“…A strategy to overcome this problem is to perform two separate trials: one to select disease-resistant genotypes and another where the fruit quality of these genotypes can be measured without the presence of the pathogen. In a previous work, Copati et al (2021) uncovered a set of genotypes possessing resistance to late blight. In the current work, the main goal was to select late blight-resistant tomato families displaying enhanced fruit quality using the FAI-BLUP index.…”

Spatio-temporal modeling of high-throughput multi-spectral aerial images improves agronomic trait genomic prediction in hybrid maize

“…plots that are close to each other are more interdependent than those farther away), but these models often make simplifying assumptions of a consistent rate of decay in interdependency across the entire field. Nonetheless, statistically modeling spatial effects using linear mixed models has improved experimental accuracy in plant breeding (Rodríguez-Álvarez et al 2018;Robbins, Backlund, and Schnelle 2012;Copati et al 2021;Bernardeli et al 2021;Smith, Cullis, and Thompson 2005).…”