A maximum-likelihood method for demographic inference is applied to data sets consisting of the frequency spectrum of unlinked single-nucleotide polymorphisms (SNPs). We use simulation analyses to explore the effect of sample size and number of polymorphic sites on both the power to reject the null hypothesis of constant population size and the properties of two-and three-dimensional maximumlikelihood estimators (MLEs). Large amounts of data are required to produce accurate demographic inferences, particularly for scenarios of recent growth. Properties of the MLEs are highly dependent upon the demographic scenario, as estimates improve with a more ancient time of growth onset and smaller degree of growth. Severe episodes of growth lead to an upward bias in the estimates of the current population size, and that bias increases with the magnitude of growth. One data set of African origin supports a model of mild, ancient growth, and another is compatible with both constant population size and a variety of growth scenarios, rejecting greater than fivefold growth beginning Ͼ36,000 years ago. Analysis of a data set of European origin indicates a bottlenecked population history, with an 85% population reduction occurring 000,03ف years ago.
P ATTERNS of genetic variation in contemporaryto be statistically independent of each other and the data are completely characterized by the number of populations can be used to make inferences about past population size changes. Ideally, likelihood methpolymorphic sites and the frequency spectrum. That is, we can represent the full data by m ϭ (m 0 , m 1 , m 2 , m nϪ1 ), ods using the full data would be applied to make such inferences. For the case of DNA sequence polymorphism where m 0 is the number of sites monomorphic in the sample, and, for i Ͼ 0, m i is the number of polymorphic and where no recombination occurs between the varisites in which the derived allele is present i times in the able sites, methods are available for carrying out such sample of n chromosomes. We assume all polymorphic inferences (