analysis of large scale data taken from the world groundnut (Arachis hypogaea L.) germplasm collection. I. Two-way quantitative data

• Published in: Euphytica 1997, Vol.95 (1), p.27-38
• Main Authors: Harch, B.D, Basford, K.E, DeLacy, I.H, Lawrence, P.K
• Format: Article
• Language: English
• Subjects: agronomic traits; Agronomy. Soil science and plant productions; Arachis hypogaea; Biological and medical sciences; descriptors; emergence; flowering date; Fundamental and applied biological sciences. Psychology; Generalities. Genetics. Plant material; Genetic resources, diversity; genetic variation; Genetics; Genetics and breeding of economic plants; Germplasm; Growing season; leaves; length; Mathematical models; Ordination; Peanuts; phenetics; phenotype; plant genetic resources; Plant material; pods; post rainy season; principal component analysis; Principal components analysis; quantitative traits; seasonal variation; seed weight; seeds; statistical analysis; Studies; taxonomy; Tropical environments; wet season; width
• ISSN: 0014-2336; 1573-5060
• DOI: 10.1023/A:1002971207770

Data associated with germplasm collections are typically large and multivariate with a considerable number of descriptors measured on each of many accessions. Pattern analysis methods of clustering and ordination have been identified as techniques for statistically evaluating the available diversity in germplasm data. While used in many studies, the approaches have not dealt explicitly with the computational consequences of large data sets (i.e. greater than 5000 accessions). To consider the application of these techniques to germplasm evaluation data, 11328 accessions of groundnut (Arachis hypogaea L) from the International Research Institute for the Semi-Arid Tropics, Andhra Pradesh, India were examined. Data for nine quantitative descriptors measured in the rainy and post-rainy growing seasons were used. The ordination technique of principal component analysis was used to reduce the dimensionality of the germplasm data. The identification of phenotypically similar groups of accessions within large scale data via the computationally intensive hierarchical clustering techniques was not feasible and non-hierarchical techniques had to be used. Finite mixture models that maximise the likelihood of an accession belonging to a cluster were used to cluster the accessions in this collection. The patterns of response for the different growing seasons were found to be highly correlated. However, in relating the results to passport and other characterisation and evaluation descriptors, the observed patterns did not appear to be related to taxonomy or any other well known characteristics of groundnut.[PUBLICATION ABSTRACT]