Tuesday, May 02, 2006

Clusters, clines, and geographic dispersion

This paper appeared in PLoS last December. It uses the CEPH panel of 1050 or so individuals from around the world. It looks at about 1000 markers (mostly microsatellites, and some I/D), and uses STRUCTURE to look at the effect of number of loci, sample size, number of clusters and geographic dispersion of the sample on clustering of individuals. Surprisingly, geographic dispersion does not have an effect on clustering.

Clines, Clusters, and the Effect of Study Design on the Inference of Human Population Structure
Noah Rosenberg, Saurabh Mahajan, Sohini Ramachandran, Chengfeng Zhao, Jonathan K. Pritchard, Marcus W. Feldman
PLoS Genetics December 2005; 1: 660-671

Abstract: Previously, we observed that without using prior information about individual sampling locations, a clustering algorithm applied to multilocus genotypes from worldwide human populations produced genetic clusters largely coincident with major geographic regions. It has been argued, however, that the degree of clustering is diminished by use of samples with greater uniformity in geographic distribution, and that the clusters we identified were a consequence of uneven sampling along genetic clines. Expanding our earlier dataset from 377 to 993 markers, we systematically examine the influence of several study design variables—sample size, number of loci, number of clusters, assumptions about correlations in allele frequencies across populations, and the geographic dispersion of the sample—on the “clusteredness” of individuals. With all other variables held constant, geographic dispersion is seen to have comparatively little effect on the degree of clustering. Examination of the relationship between genetic and geographic distance supports a view in which the clusters arise not as an artifact of the sampling scheme, but from small discontinuous jumps in genetic distance for most population pairs on opposite sides of geographic barriers, in comparison with genetic distance for pairs on the same side. Thus, analysis of the 993-locus dataset corroborates our earlier results: if enough markers are used with a sufficiently large worldwide sample, individuals can be partitioned into genetic clusters that match major geographic subdivisions of the globe, with some individuals from intermediate geographic locations having mixed membership in the clusters that correspond to neighboring regions.

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