efficient clustering of populations using a minimal snp panel
The recent explosion in available SNP data requires exploration of satisfactory methods for efficiently clustering a set of individuals into their respective populations. As a practical matter, we describe a modified euclidean distance-based approach for successfully clustering 525 HapMap individuals into their 4 original populations; European, African, Japanese and Chinese. Our approach relies on the computation of the F st estimator using 4 distinct methods, and shows that that the k-means clustering of the 10 highest F st scoring SNPs is sufficient for producing an error-free description of the underlying population structure. A generalization of our approach represents a more "faithful" way of selecting the SNPs having the highest discriminating power and thus generating the most accurate population specific assignments of individuals, using the smallest possible SNP panel.
snp population clustering population stratification