Advancing molecular breeding from marker-assisted selection to genomic prediction in tomato

• Ano de defesa: 0203
• Autor(a) principal: Dariva, Françoise Dalprá
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Universidade Federal de Viçosa; Fitotecnia
• Palavras-chave em Português: Tomate - Melhoramento genético; Mapeamento cromossômico; Marcadores genéticos; CIENCIAS AGRARIAS::AGRONOMIA::FITOTECNIA
• Link de acesso: https://locus.ufv.br/handle/123456789/32618; https://doi.org/10.47328/ufvbbt.2023.474

This dissertation demonstrates how quantitative trait loci (QTL) mapping, marker-assisted selection (MAS), and genomic selection (GS) can be used to facilitate breeding for important agronomic traits in tomatoes. The first objective was to investigate the effect of deficit irrigation on yield and fruit quality attributes of four tomato introgression lines (IL) and hopefully locate QTLs for improved performance. Our results revealed losses in yield and fruit weight, but gains in soluble solids content, fruit redness, fruit firmness, and lycopene content as plants were subjected to deficit irrigation. QTLs for improved performance were detected for yield on tomato chromosome (Chr) 3, and lycopene content on Chr 2, and 3 under optimum irrigation, and for fruit firmness on Chr 3, and lycopene content on Chr 2, 3, and 7 under deficit irrigation. The second objective was to study the genetics of fruit puffiness, a physiological disorder that affects fruit quality and factory yield of tomatoes, and provide a solution to plant breeders that face this problem in their breeding populations. An advanced recombinant inbred line (RIL) and three-derived processing tomato populations were used for mapping and validation purposes, respectively. A dominant QTL for increased fruit puffiness was mapped on Chr 1 explaining from 5 to 22.5% of the total phenotypic variation. Missing heritability issues suggest polygenic control of fruit puffiness in tomatoes. A GS model developed from the mapping set predicted fruit puffiness in the validation set with an accuracy of r = 0.52 (p = 2.36e -12 ). MAS using the markers solcap_snp_sl_20440 and solcap_snp_sl_18619 associated with the QTL on Chr 1 was as effective as GS. The third objective was to investigate genomic prediction accuracy of yield-related traits in tomato hybrids. First, we imputed a total of 22,681 tomato hybrids using SNP information for 303 tomato parents. Seven GS models using three-related populations and all their possible combinations were then developed. Fifty hybrids were actually created for further use in field validation trials. With correlation coefficients as high as 0.42 for yield and 0.58 for fruit weight, genomic prediction of tomato hybrids showed to be very accurate. Fruit weight predictions were better than yield predictions. Increase in training population size improved yield predictions ofhybrids. For fruit weight, better predictions were obtained from the model in which training lines were more genetically related to selected hybrids. Overall, these results suggest that GS may help breeders to choose which hybrids they should invest in. This dissertation provides useful information about QTL discovery and the role of MAS and GS tools in applied tomato breeding. Keywords: QTL mapping. Marker-assisted selection. Genomic selection. Molecular breeding. Processing tomato.