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.

中文翻译：

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番茄基因组预测在高温下的良好性能和涉及耐热反应的基因座的鉴定

许多研究表明，番茄最适生长温度阈值高出几度会导致产量严重下降。因此，开发在高温条件下获得更高产量的番茄品种的创新策略是基础遗传研究和育种活动的主要目标。在本文中，对 F4 分离群体在热应激条件下的数量和质量性状进行表型评估。此外，已采用测序基因分型 (GBS) 方法来建立产量和可溶性固体含量 (SCC) 的基因组选择 (GS) 模型。测试了几个参数，包括训练种群大小、组成和标记质量，以预测热应激条件下的基因型表现。两个分析性状的预测准确度良好（0. 获得了产率 729 和 SCC 0.715)。预测的模型提高了下一个育种周期中选择的遗传增益，表明 GS 方法是加速番茄耐热性育种的一种有前途的策略。最后，将位于基因体区域的 SNP 注释与 QTL 分析相结合，可以识别出推定参与高温反应的五个候选者，并建立基于校准的 SNP 标记组的 GS 模型。