Genetic Diversity Analysis of Onion (Allium cepa L.) from the Arid Region of Tunisia Using Phenotypic Traits and SSR Markers

The present study was carried out to evaluate and characterize, for the first time, the genetic potential of a collection of onion (Allium cepa L.) local accessions from the arid region of southern Tunisia. The genetic diversity among 135 onion individuals, belonging to 23 accessions, was evaluated using microsatellite (SSR) markers and phenotypic information. A total of 35 alleles were generated with 11 SSRs. The polymorphic information content (PIC) value ranged from 0.08 to 0.95, with an average PIC of 0.43. The expected heterozygosity averaged 0.37, and the observed heterozygosity averaged 0.42. The analysis of molecular variance (AMOVA) revealed that 79% of genetic variation existed within individuals. Structure and cluster analysis grouped the accessions into two major clusters: landraces and pre-breeding lines. For the phenotypic traits evaluated, field trials were conducted in two different environments. Significant differences among accessions were shown, and for most traits, there was also a significant environmental effect and a significant interaction between environment and accession. Great variability was found for all the traits that could be exploited to create new varieties of onion adapted to local conditions by selecting appropriate parents in hybridization breeding.Esta investigación fue financiada en parte por el laboratorio de investigación LR21AGR03-Producción y Protección para una Horticultura Sostenible, financiado por el Ministerio de Educación Superior e Investigación Científica de Túnez y parcialmente financiado por el proyecto A11-20R financiado por el Gobierno de Aragón y por el programa AGROALNEXT apoyado por MCIN con financiación de la Unión Europea NextGenerationEU (PRTR-C17.I1).diversitylandracesmicrosatellitesbreedingphenotypicG × E interactionPublishe

A new feature selection approach based on ensemble methods in semi-supervised classification

International audienc

Semi-supervised superpixel classification for medical images segmentation: application to detection of glaucoma disease

International audienc

An analysis of ambulatory blood pressure monitoring using multi-label classification

International audienc