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 F ST) 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.
Mendelian randomization, which is instrumental variable analysis using genetic variants as instruments, is an increasingly popular method of making causal inferences from observ...
Abstract We formalize the use of allele frequency and geographic information for the construction of gene trees at the intraspecific level and extend the concept of evolutionary...
DNA sequences extracted from ancient remains are increasingly used to generate large population data sets, often spanning tens of thousands of years of population history. Bayes...
Patterns of restriction site variation within mitochondrial DNA (mtDNA) of 270 individuals were used to examine the current structure of savanna elephant populations and to infe...
We introduce the Bayesian skyline plot, a new method for estimating past population dynamics through time from a sample of molecular sequences without dependence on a prespecifi...
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