Principal components analysis was used to evaluate finger ridge-count variability as an indicator of genetic relationships between populations. The analysis was carried out on American White, American Black and African Black samples, each including both sexes. Each individual is represented as a vecotr of 20 counts, a radial and an ulnar count for each digit. No assumptions were made prior to analysis concerning the number of meaningful components, and all were examined sequentially. The first five eigenvectors extracted from the within-groups correlation matrix have loadings very similar to those previously described by Roberts and Coope ('75). However, it is the component scores derived from the sixth eigenvector which show the most marked variation, accounting for 45% or more of the D2 in all Black-White comparisons. A number of other components also show significant intergroup heterogeneity, but they often do not accord with what is known of the genetic relationships between the populations. Apparently a large amount of ridge-count variation is not genetically meaningful, at least as far as these populations are concerned.

Mean finger ridge-count data were obtained, primarily from literature sources, for 31 male and 24 female sub-Saharan African samples. The 10 finger ridge-counts and total ridge-count were used as independent variables in a multiple regression analysis, latitude and longitude serving in turn as the dependent variables. The results show that it is not the magnitude of the ridge-counts themselves that is important, but rather contrasts between groups of digits. The most important geographically patterned variation in ridge-counts consists of contrasts between digits 4 and 5 and digits 2 and 3. South and south-east African populations are characterized by low contrasts, west Africans by high contrasts and, south-west Africans are intermediate. The geographical patterning of the contrast agrees well with known patterns of gene flow into and within the continent as determined by serological genes. Principal components analysis was also carried out to determine whether within-group components corresponding to the geographically relevant between-group variation could be identified. The third, fourth and fifth components drew the same types of contrasts between the groups of digits identified in the multiple regression analysis, but they were relatively unimportant. The geographically important principal components would have been overlooked in a traditional multivariate analysis of finge ridge-counts, since the analysis would have been dominated by pattern size. We conclude that finger ridge-counts are potentially very useful in population studies, but account must be taken of their multicomponent nature.

This paper addresses the question of the extent to which finger ridge-count data are useful features with which to study population variation in Subsaharan Africa. Each subject was represented by a vector of 20 ridge-counts, a radial and an ulnar count for each digit. Such data were available from 11 African groups, nine of which were indigenous Africans, and two, the South African Colored and South African Indians, contained a portion of non-African ancestory. The ridge-counts were first transformed to principal component scores and these were subjected to multivariate analysis of variance and distance analysis to elucidate intergroup variation. The primary findings were that ridgecounts provide a good reflection of variation on at least two levels, that of African versus non-African, and variation among Africans. Also, the principal components that reveal variation at these two levels are very different. We conclude that ridge-counts can only be useful in population studies if full account is taken of their multicomponent nature.

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