"We can only uncover structure in the samples being analyzed. As pointed out in [39], the sampling strategy can affect the apparent structure. Rosenberg et al. [29] give a detailed discussion of the issue, and of the question of whether clines or clusters are a better description of human genetic variation. However, our “axes of variation” are likely to be relatively robust to this cline/cluster controversy. If there is a genetic cline running across a continent, and we sample two populations at the extremes, then it will appear to the analyst that the two populations form two discrete clusters. However, if the sampling strategy had been more geographically uniform, the cline would be apparent. Nevertheless, the eigenvector reflecting the cline could be expected to be very similar in both cases."
Population Structure and Eigenanalysis
Nick Patterson, Alkes L. Price, David Reich
PLoS Genetics December 2006, 2(12)
Abstract: Current methods for inferring population structure from genetic data do not provide formal significance tests for population differentiation. We discuss an approach to studying population structure (principal components analysis) that was first applied to genetic data by Cavalli-Sforza and colleagues. We place the method on a solid statistical footing, using results from modern statistics to develop formal significance tests. We also uncover a general “phase change” phenomenon about the ability to detect structure in genetic data, which emerges from the statistical theory we use, and has an important implication for the ability to discover structure in genetic data: for a fixed but large dataset size, divergence between two populations (as measured, for example, by a statistic like FST) below a threshold is essentially undetectable, but a little above threshold, detection will be easy. This means that we can predict the dataset size needed to detect structure.
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