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Article|01 Oct 2021|OPEN
Tomato genomic prediction for good performance under high-temperature and identification of loci involved in thermotolerance response
Elisa Cappetta1,2, Giuseppe Andolfo1, Anna Guadagno1, Antonio Di Matteo1, Amalia Barone1, Luigi Frusciante1 & Maria Raffaella Ercolano1,
1Department of Agricultural Sciences, University of Naples Federico II, Via Università 100, 80055 Portici, Naples, Italy
2Present address: Institute of Bioscience and BioResources, National Research Council, Via Università 100, 80055 Portici, Italy

Horticulture Research 8,
Article number: 212 (2021)
doi: 10.1038/hortres.2021.212
Views: 51

Received: 27 Feb 2021
Revised: 05 Jul 2021
Accepted: 14 Jul 2021
Published online: 01 Oct 2021

Abstract

Many studies showed that few degrees above tomato optimum growth temperature threshold can lead to serious loss in production. Therefore, the development of innovative strategies to obtain tomato cultivars with improved yield under high temperature conditions is a main goal both for basic genetic studies and breeding activities. In this paper, a F4 segregating population was phenotypically evaluated for quantitative and qualitative traits under heat stress conditions. Moreover, a genotyping by sequencing (GBS) approach has been employed for building up genomic selection (GS) models both for yield and soluble solid content (SCC). Several parameters, including training population size, composition and marker quality were tested to predict genotype performance under heat stress conditions. A good prediction accuracy for the two analyzed traits (0.729 for yield production and 0.715 for SCC) was obtained. The predicted models improved the genetic gain of selection in the next breeding cycles, suggesting that GS approach is a promising strategy to accelerate breeding for heat tolerance in tomato. Finally, the annotation of SNPs located in gene body regions combined with QTL analysis allowed the identification of five candidates putatively involved in high temperatures response, and the building up of a GS model based on calibrated panel of SNP markers.