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Article|23 Mar 2022|OPEN
Genomic prediction of morphometric and colorimetric traits in Solanaceous fruits
Hao Tong1,2,3 , Amol N. Nankar1 , Jintao Liu2 , Velichka Todorova4 , Daniela Ganeva4 , Stanislava Grozeva4 , Ivanka Tringovska4 , Gancho Pasev4 , Vesela Radeva-Ivanova4 and Tsanko Gechev1 , Dimitrina Kostova1,4 , Zoran Nikoloski,1,2,3 ,
1Center of Plant Systems Biology and Biotechnology, Plovdiv, 4000, Bulgaria
2Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, Potsdam, 14476, Germany
3Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, 14476, Germany
4Maritsa Vegetable Crops Research Institute, Plovdiv, 4003, Bulgaria.
*Corresponding author. E-mail: nikoloski@mpimp-golm.mpg.de

Horticulture Research 9,
Article number: uhac072 (2022)
doi: https://doi.org/10.1093/hr/uhac072
Views: 20

Received: 31 Oct 2021
Accepted: 16 Mar 2022
Published online: 23 Mar 2022

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.