Comparison of multivariate methods for the analysis of genetic resources and adaptation in Phytolacca dodecandra using RAPD

Abstract 

The extent of genetic differentiation among 17 Ethiopian populations (249 individuals) of Phytolacca dodecandra (Endod) sampled along altitudinal gradients that varied from 1600 to 3000 m was investigated using random amplified polymorphic DNA (RAPD). The populations were classified into three altitude groups: lowland (1600–2100 m), central-highland (2101–2500 m) and highland (2500–3000 m). Seventy polymorphic loci scored from 12 RAPD primers, singly or in combination with ecogeographical variables (altitude, longitude, latitude, temperature and rainfall), were used for principal component, discriminant, correlation, and stepwise multiple regression analyses. Principal component analysis (PCA) clearly differentiated lowland and the central-highland populations from those of the highlands independent of their geographical regions. Canonical discriminant analysis separated the lowland plants from those of the highlands with the central-highland plants being intermediate. Classificatory discriminant analysis corrected classification of 92.8% of the 249 plants into their respective three altitude groups. Multiple regression analysis identified a strong association between some RAPDs and altitude, temperature and rainfall, while the variation in most RAPDs was explained by combinations of the different ecogeographical variables. It is hypothesised that the different altitude groups may be (1) chemical and/or physiological ecotypes produced as a result of complex interactions of altitude with climatic and/or edaphic factors, or (2) different in ploidy levels. The significant correlations obtained between population means from some RAPDs and altitude and temperature as well as the strong association of some RAPDs with the ecogeographical variables in the multiple regression analysis suggest that part of the RAPD polymorphism could be adaptive, and responsive to environmental selection.