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Data on six protein polymorphisms (19 alleles) from the human population of Tenerife are presented and discussed along with other classical markers in relation to the origin of the Canarians. Genetic influences from three population groups were considered: the Iberians, and the Berbers and non-Berbers (Arabs) from north Africa. The systems examined show the Tenerife population lies within the limits of variation described for various Iberian groups, with a slight tendency towards the characteristics of north African populations. When blood groups, red cell enzymes and serum protein data were considered, the similarity of the Canary population to Iberians seems strengthened (70% estimated contribution of Iberian peninsula genes to the present-day Canarian pool), while some relation with north African groups is shown. Genetic distances between Canarians and Arabs and Canarians and Berbers are lower than those between the two north African groups, indicating a relative and comparable contribution of each to the present-day gene pool of the Canarian population. The Arab contribution could be attributable to the slaves who were introduced to these islands after the conquest in the 15th century, while the Berber contribution could be the remnants of the extinct aboriginal peoples of the islands (Guanches) or a more recent immigration due to slavery. Genetic data do not allow us to distinguish between these two possibilities.

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Cluster analysis has proven to be a useful tool for investigating the association structure among genes in a microarray data set. There is a rich literature on cluster analysis and various techniques have been developed. Such analyses heavily depend on an appropriate (dis)similarity measure. In this paper, we introduce a general clustering approach based on the confidence interval inferential methodology, which is applied to gene expression data of microarray experiments. Emphasis is placed on data with low replication (three or five replicates). The proposed method makes more efficient use of the measured data and avoids the subjective choice of a dissimilarity measure. This new methodology, when applied to real data, provides an easy-to-use bioinformatics solution for the cluster analysis of microarray experiments with replicates (see the Appendix). Even though the method is presented under the framework of microarray experiments, it is a general algorithm that can be used to identify clusters in any situation. The method's performance is evaluated using simulated and publicly available data set. Our results also clearly show that our method is not an extension of the conventional clustering method based on correlation or euclidean distance.

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