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Genomic prediction of morphometric and colorimetric traits in Solanaceous fruits

  • Selection of high-performance lines with respect to traits of interest is a key step in plant breeding. Genomic prediction allows to determine the genomic estimated breeding values of unseen lines for trait of interest using genetic markers, e.g. single-nucleotide polymorphisms (SNPs), and machine learning approaches, which can therefore shorten breeding cycles, referring to genomic selection (GS). Here, we applied GS approaches in two populations of Solanaceous crops, i.e. tomato and pepper, to predict morphometric and colorimetric traits. The traits were measured by using scoring-based conventional descriptors (CDs) as well as by Tomato Analyzer (TA) tool using the longitudinally and latitudinally cut fruit images. The GS performance was assessed in cross-validations of classification-based and regression-based machine learning models for CD and TA traits, respectively. The results showed the usage of TA traits and tag SNPs provide a powerful combination to predict morphology and color-related traits of Solanaceous fruits. TheSelection of high-performance lines with respect to traits of interest is a key step in plant breeding. Genomic prediction allows to determine the genomic estimated breeding values of unseen lines for trait of interest using genetic markers, e.g. single-nucleotide polymorphisms (SNPs), and machine learning approaches, which can therefore shorten breeding cycles, referring to genomic selection (GS). Here, we applied GS approaches in two populations of Solanaceous crops, i.e. tomato and pepper, to predict morphometric and colorimetric traits. The traits were measured by using scoring-based conventional descriptors (CDs) as well as by Tomato Analyzer (TA) tool using the longitudinally and latitudinally cut fruit images. The GS performance was assessed in cross-validations of classification-based and regression-based machine learning models for CD and TA traits, respectively. The results showed the usage of TA traits and tag SNPs provide a powerful combination to predict morphology and color-related traits of Solanaceous fruits. The highest predictability of 0.89 was achieved for fruit width in pepper, with an average predictability of 0.69 over all traits. The multi-trait GS models are of slightly better predictability than single-trait models for some colorimetric traits in pepper. While model validation performs poorly on wild tomato accessions, the usage as many as one accession per wild species in the training set can increase the transferability of models to unseen populations for some traits (e.g. fruit shape for which predictability in unseen scenario increased from zero to 0.6). Overall, GS approaches can assist the selection of high-performance Solanaceous fruits in crop breeding.show moreshow less

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Author details:Hao TongORCiDGND, Amol N. NankarORCiD, Jintao Liu, Velichka TodorovaORCiD, Daniela GanevaORCiD, Stanislava GrozevaORCiD, Ivanka TringovskaORCiD, Gancho PasevORCiD, Vesela Radeva-IvanovaORCiD, Tsanko GechevORCiDGND, Dimitrina KostovaORCiD, Zoran NikoloskiORCiDGND
DOI:https://doi.org/10.1093/hr/uhac072
ISSN:2052-7276
Pubmed ID:https://pubmed.ncbi.nlm.nih.gov/35669711
Title of parent work (English):Horticulture research
Publisher:Oxford Univ. Press
Place of publishing:Cary
Publication type:Article
Language:English
Date of first publication:2022/03/23
Publication year:2022
Release date:2024/03/15
Volume:9
Article number:uhac072
Number of pages:11
Funding institution:European Union [739582, 664620]; European Regional Development Fund; through the Bulgarian "Science and Education for Smart Growth"; Operational Programme [BG05M2OP001-1.003-001-C01]
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Biochemie und Biologie
DDC classification:5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie
Peer review:Referiert
Publishing method:Open Access / Gold Open-Access
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License (German):License LogoCC-BY - Namensnennung 4.0 International
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