Abstract
Despite the availability of newer approaches,
traditional hierarchical clustering remains very popular in genetic diversity studies in plants. However, little is known about its suitability for molecular marker data. We studied
the performance of traditional hierarchical clustering techniques using real and simulated molecular marker data. Our study also compared the performance of traditional
hierarchical clustering with model-based clustering (STRUCTURE). We showed that the cophenetic correlation coefficient is directly related to subgroup differentiation
and can thus be used as an indicator of the presence of genetically distinct subgroups in germplasm collections. Whereas UPGMA performed well in preserving distances
between accessions, Ward excelled in recovering groups. Our results also showed a close similarity between clusters obtained by Ward and by STRUCTURE. Traditional cluster analysis can provide an easy and effective way of determining structure in germplasm collections using molecular marker data, and, the output can be used for sampling core collections or for association studies.
Original language | English |
---|---|
Pages (from-to) | 195-205 |
Journal | Theoretical and Applied Genetics |
Volume | 123 |
Issue number | 2 |
DOIs | |
Publication status | Published - 2011 |
Keywords
- multilocus genotype data
- genome-wide association
- forming core subsets
- population-structure
- data set
- linkage disequilibrium
- f-statistics
- number
- inference
- diversity