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BAG. Journal of basic and applied genetics

On-line version ISSN 1852-6233

Abstract

CABODEVILA, V.G; PICARDI, L.A  and  PRATTA, G.R. A multivariate approach to explore the genetic variability in the F2 segregating population of a tomato second cycle hybrid. BAG, J. basic appl. genet. [online]. 2017, vol.28, n.1, pp.7-17. ISSN 1852-6233.

Segregating progeny from the tomato Second Cycle Hybrids (SCH) that were obtained from crossing RIL (Recombinant Inbred Lines) allows the detection of new genetic combinations that could increase genetic variability in F2 populations. The objectives of the present study were to evaluate eleven tomato quality traits in a segregating F2 population obtained from a SCH and, then, to characterize the molecular diversity by six AFLP (Amplified Fragment Length Polymorphism) primer combinations. Different multivariate analyses were used to assess the degree of concordance among these two approaches to detect genetic variability. Sixty-nine F2 plants were obtained by selfing the SCH (ToUNR18xToUNR1). The parental RIL were derived from an interspecific cross between S. lycopersicum cv. Caimanta and the accession LA722 from S. pimpinellifolium after five cycles of antagonist and divergent selection for fruit weight and fruit shelf life. Principal Components Analysis (PCA) was applied to these data and we found that the first two components explained 77 % of variability. The molecular characterization showed 62 % of polymorphic bands. The Principal Coordinate Analysis (PCoA) showed that the first ten coordinates explained 75 % of variability. The Generalized Procrustes Analysis (GPA) showed a consensus between morphological and molecular data of 65 %. High values of broad sense heritability (H2) were found for all traits together with a high level of molecular polymorphism. The morphological and molecular data showed a high consensus proportion suggesting that it could be possible to detect QTL for these fruit traits exploring this new population.

Keywords : Plant breeding; Plant genetic resources; Fruit quality; AFLP; Multivariate analysis.

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