Population structure and eigenanalysis.
Bottom Line:
Current methods for inferring population structure from genetic data do not provide formal significance tests for population differentiation.We place the method on a solid statistical footing, using results from modern statistics to develop formal significance tests.This means that we can predict the dataset size needed to detect structure.
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PubMed Central - PubMed
Affiliation: Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America.
ABSTRACT
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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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Mentions: We first made a series of simulations in the absence of population structure. (Some additional details are in the Methods section.) Our first set of runs had 100 individuals and 5,000 unlinked SNPs, and the second 200 individuals and 50,000 unlinked SNPs. In each case we ran 1,000 simulations and show in Figure 2A and 2B probability–probability (P–P) plots of the empirical and TW tail areas. The results seem entirely satisfactory, especially for low p-values in the top right of Figures 2A and 2B. For assessment of statistical significance, it is the low p-value range that is relevant. The simulations show more generally that the TW theory is relevant in a genetic context, that the normalizations of Equations 5–7 are appropriate, and that the calculation of the effective marker size has not seriously distorted the TW statistic. |
View Article: PubMed Central - PubMed
Affiliation: Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America.