Evidence map›Paper›PMID 34593775›Full record

ArticleHorticulture research2021

Tomato genomic prediction for good performance under high-temperature and identification of loci involved in thermotolerance response.

Elisa Cappetta, Giuseppe Andolfo, Anna Guadagno, Antonio Di Matteo, Amalia Barone, Luigi Frusciante, Maria Raffaella Ercolano

Abstract read
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Article in Horticulture research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

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0cells of the map it votes in
20citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

20 citing papers in PubMed.

  1. Article
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  4. Assessment of genomic prediction capabilities of transcriptome data in a barley multi-parent RIL population.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025
    Article
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  7. Molecular breeding of tomato: Advances and challenges.Journal of integrative plant biology · 2025
    Review
  8. An advanced 10K SNP panel for genotyping tomato (Frontiers in plant science · 2025
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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Elisa CappettaDepartment of Agricultural Sciences, University of Naples Federico II, Via Università 100, 80055, Portici, Naples, Italy.
Giuseppe AndolfoDepartment of Agricultural Sciences, University of Naples Federico II, Via Università 100, 80055, Portici, Naples, Italy.
Anna GuadagnoDepartment of Agricultural Sciences, University of Naples Federico II, Via Università 100, 80055, Portici, Naples, Italy.
Antonio Di MatteoDepartment of Agricultural Sciences, University of Naples Federico II, Via Università 100, 80055, Portici, Naples, Italy.ORCID http://orcid.org/0000-0001-9081-5315
Amalia BaroneDepartment of Agricultural Sciences, University of Naples Federico II, Via Università 100, 80055, Portici, Naples, Italy.
Luigi FruscianteDepartment of Agricultural Sciences, University of Naples Federico II, Via Università 100, 80055, Portici, Naples, Italy.
Maria Raffaella ErcolanoDepartment of Agricultural Sciences, University of Naples Federico II, Via Università 100, 80055, Portici, Naples, Italy. ercolano@unina.it.ORCID http://orcid.org/0000-0002-4475-6112

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Identifiers

PMID34593775
PMCPMC8484564

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.