Cecilia Bruno

Genetic variability in Chilean pepino (Solanum muricatum Aiton) fruit

Solanum muricatum Aiton is an herbaceous perennial fruit species native to the Andean region of Colombia, Ecuador, and Peru. In Chile, it was probably introduced in pre-Columbian times as a domesticated species and is presently grown in the coastal areas of the north-central regions of Coquimbo and Valparaiso. The species has been bred, but little information is available on its genetic variability in Chile. To characterize the genetic variability in this species, fruits were collected from 14 different ecotypes and seeds were sown to generate approximately 60 segregants from each accession. Segregants were planted at two different locations to characterize their fruits and fruiting habits. Fruit weight ranged from 30 to 485 g, while length was 3.5 to 16.7 cm, equatorial diameter 3.4 to 9.5 cm, pulp firmness 1.7 to 10 N, and soluble solids content 6.3 to 13.5° Brix. Fruit shape ranged from flat to oblong. When analyzing the estimated variance components with a mixed linear model, most of the variability between different ecotypes was in fruit shape, length, and weight, which resulted in a genetic contribution of 34.6%, 29.3%, and 18.1% of the total variability of these traits, respectively. Genetic variability was also found for pulp firmness and soluble solids content. Therefore, enough variability is available in seed-propagated pepinos from Chilean ecotypes to allow genetic improvement of these fruit quality traits. There was also variability from genotype x environment interactions; therefore, selections must be performed for specific environments or stable selections must be found.

A Cluster-Based Technique to Group Random Effects from Empirical BLUPs: An Application to Genotype Selection

Abstract Prediction of random effects arises in many applications, including plant breeding. Plant breeders strive to select genotypes according to predictions of unobserved genetic merits derived fromphenotypic data. There are several approaches to predict random effects, but a popular one is to use empirical BLUPs (EBLUP) of genetic effects to rank genotypes. The problem is how best to use EBLUP values to select a set of truly superior genotypes with high probability. In this paper, we propose an algorithm based on hierarchical cluster analysis to group genotypes according to their EBLUPs. Clustering algorithms produce no analytical expression to solve the problem of detecting the number of underlying groups. Via simulation, we obtain decision curves to group genotypes from an EBLUP-based dendrogram. The new algorithm has good operating characteristics and can be used to select genotypes with high genetic merit.