Mixed data types and the use of pattern analysis on the Australian groundnut germplasm data

Harch, B.D., Basford, K.E., DeLacy, I.H., Lawrence, P.K., & Cruickshank, A. (1996) Mixed data types and the use of pattern analysis on the Australian groundnut germplasm data. Genetic Resources and Crop Evolution, 43(4), pp. 363-376.

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Data in germplasm collections contain a mixture of data types; binary, multistate and quantitative. Given the multivariate nature of these data, the pattern analysis methods of classification and ordination have been identified as suitable techniques for statistically evaluating the available diversity. The proximity (or resemblance) measure, which is in part the basis of the complementary nature of classification and ordination techniques, is often specific to particular data types. The use of a combined resemblance matrix has an advantage over data type specific proximity measures. This measure accommodates the different data types without manipulating them to be of a specific type. Descriptors are partitioned into their data types and an appropriate proximity measure is used on each. The separate proximity matrices, after range standardisation, are added as a weighted average and the combined resemblance matrix is then used for classification and ordination. Germplasm evaluation data for 831 accessions of groundnut (Arachis hypogaea L.) from the Australian Tropical Field Crops Genetic Resource Centre, Biloela, Queensland were examined. Data for four binary, five ordered multistate and seven quantitative descriptors have been documented. The interpretative value of different weightings - equal and unequal weighting of data types to obtain a combined resemblance matrix - was investigated by using principal co-ordinate analysis (ordination) and hierarchical cluster analysis. Equal weighting of data types was found to be more valuable for these data as the results provided a greater insight into the patterns of variability available in the Australian groundnut germplasm collection. The complementary nature of pattern analysis techniques enables plant breeders to identify relevant accessions in relation to the descriptors which distinguish amongst them. This additional information may provide plant breeders with a more defined entry point into the germplasm collection for identifying sources of variability for their plant improvement program, thus improving the utilisation of germplasm resources.

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ID Code: 72833
Item Type: Journal Article
Refereed: Yes
Additional Information: Cited By (since 1996):6
Export Date: 26 May 2014
Source: Scopus
Additional URLs:
Keywords: classification, genetic diversity, groundnut, mixed data types, ordination, Arachis hypogaea
DOI: 10.1007/BF00132957
ISSN: 0925-9864
Divisions: Current > QUT Faculties and Divisions > Science & Engineering Faculty
Deposited On: 16 Jun 2014 00:13
Last Modified: 16 Jun 2014 00:13

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