Revista de la Facultad de Ciencias Agrarias (Jul 2021)

Methodological proposal for the characterization of accessions in Germplasm Banks using Generalized Procrustes Analysis applied to incomplete but connected trials

  • Andrea Lina Lavalle,
  • Raquel Defacio,
  • Mariano De Leo,
  • Sergio Jorge Bramardi

DOI
https://doi.org/10.48162/rev.39.004
Journal volume & issue
Vol. 53, no. 1

Abstract

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Characterization of plant material conserved in germplasm banks allows the study and analysis of the genetic variability within a collection. When germplasm banks have a large number of accessions, field evaluation should be performed using assays with manageable accession subsets. Common checks connecting the different assays are required to compare these accession subsets. In this study, the Generalized Procrustes Analysis was proposed as a basis for obtaining a factorial plane where all individuals are projected. This technique is applied to genotypes common to all assays, iteratively generating scale factors and rotation matrices. Accessions only belonging to a given assay are considered supplementary elements. This proposal was illustrated using datasets of 54 maize accessions from the Pergamino Active Germplasm Bank of the Experimental Station at the Instituto Nacional de Tecnología Agropecuaria (INTA) in Argentina. The proposal achieved highly satisfactory results. Highlights: In field evaluation of large germplasm collections, the material must be divided into manageable experimental trials, in which different accession subsets are evaluated in different environments. A new algorithm based on Generalized Procrustes Analysis (GPA) allowed to find the consensus of several configurations of individuals connected by common checks. The characterization data analysis strategy was illustrated using a set of accessions from the Argentine Maize Germplasm Bank. The new proposal stands as a useful tool for evaluate germplasm collections, providing good results with easy implementation and considering the multivariate structure of the data set.

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