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  Genomic prediction of morphometric and colorimetric traits in Solanaceous fruits

Tong, H., Nankar, A., Liu, J., Todorova, V., Ganeva, D., Grozeva, S., et al. (2022). Genomic prediction of morphometric and colorimetric traits in Solanaceous fruits. Horticulture Research, 9: uhac072. doi:10.1093/hr/uhac072.

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 Creators:
Tong, H.1, Author              
Nankar, A.N.1, Author              
Liu, Jintao2, Author
Todorova, Velichka2, Author
Ganeva, Daniela2, Author
Grozeva, Stanislava2, Author
Tringovska, Ivanka2, Author
Pasev, Gancho2, Author
Radeva-Ivanova, Vesela2, Author
Gechev, Tsanko2, Author
Kostova, Dimitrina2, Author
Nikoloski, Z.1, Author              
Affiliations:
1Mathematical Modelling and Systems Biology - Nikoloski, Cooperative Research Groups, Max Planck Institute of Molecular Plant Physiology, Max Planck Society, ou_1753310              
2external, ou_persistent22              

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 Abstract: 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. 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.

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Language(s): eng - English
 Dates: 2022-03-23
 Publication Status: Published in print
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 Identifiers: DOI: 10.1093/hr/uhac072
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Title: Horticulture Research
  Alternative Title : Hortic Res
Source Genre: Journal
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Pages: - Volume / Issue: 9 Sequence Number: uhac072 Start / End Page: - Identifier: ISBN: 2052-7276