Population structure and eigenanalysis.
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.
Patterson
Nick
N
Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America.
Price
Alkes L
AL
Reich
David
D
D003198:Computer Simulation / Q000706:statistics & numerical data; D005819:Genetic Markers; D014644:Genetic Variation; D005826:Genetics, Medical / Q000379:methods* / Q000706:statistics & numerical data; D005828:Genetics, Population / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D025341:Principal Component Analysis / Q000379:methods*
D005819:Genetic Markers