Genetic diversity among alfalfa genotypes (Medicago sativa L.) of non-dormant cultivars using SSR markers and agronomic traits

The aim of this study was to assess genetic diversity among 40 alfalfa (Medicago sativa L.) genotypes of different non-dormant (FD=8) cultivars. Biomass yield, regrowth speed and reaction to spring black stem, lepto leaf spot, and rust were evaluated. Analyses of variances were performed using a mixed model to examine the agronomic variation among individuals. A principal component analysis on standardized agronomic data was performed. Agronomic data were also used to calculate Gower's distance and UPGMA algorithm. For the molecular analysis, six SSR markers were evaluated and 84 alleles were identified. The genetic distance was estimated using standard Nei's distance. Average standard genetic diversity was 0.843, indicating a high degree of variability among genotypes. Finally, a generalized procrustes analysis was performed to calculate the correlation between molecular and agronomic distance, indicating a 65.4% of consensus. This value is likely related to the low number of individuals included in the study, which might have underestimated the real phenotypic variability among genotypes. Despite the low number of individuals and SSR markers analyzed, this study provides a baseline for future diversity studies to identify genetically distant alfalfa individuals or cultivars.

Making genetic biodiversity measurable : a review of statistical multivariate methods to study variability at gene level

Measures of agro-ecosystems genetic variability are essential to sustain scientific-based actions and policies tending to protect the ecosystem services they provide. To build the genetic variability datum it is necessary to deal with a large number and different types of variables. Molecular marker data is highly dimensional by nature, and frequently additional types of information are obtained, as morphological and physiological traits. This way, genetic variability studies are usually associated with the measurement of several traits on each entity. Multivariate methods are aimed at finding proximities between entities characterized by multiple traits by summarizing information in few synthetic variables. In this work we discuss and illustrate several multivariate methods used for different purposes to build the datum of genetic variability. We include methods applied in studies for exploring the spatial structure of genetic variability and the association of genetic data to other sources of information. Multivariate techniques allow the pursuit of the genetic variability datum, as a unifying notion that merges concepts of type, abundance and distribution of variability at gene level.