On the number of New World founders: a population genetic portrait of the peopling of the Americas.
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The model permits estimation of founding population sizes, changes in population size, time of population formation, and gene flow.The estimated effective size of the founding population for the New World is fewer than 80 individuals, approximately 1% of the effective size of the estimated ancestral Asian population.Analyses of Asian and New World data support a model of a recent founding of the New World by a population of quite small effective size.
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Affiliation: Department of Genetics, Rutgers, the State University of New Jersey, Piscataway, New Jersey, USA. hey@biology.rutgers.edu
ABSTRACT
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The founding of New World populations by Asian peoples is the focus of considerable archaeological and genetic research, and there persist important questions on when and how these events occurred. Genetic data offer great potential for the study of human population history, but there are significant challenges in discerning distinct demographic processes. A new method for the study of diverging populations was applied to questions on the founding and history of Amerind-speaking Native American populations. The model permits estimation of founding population sizes, changes in population size, time of population formation, and gene flow. Analyses of data from nine loci are consistent with the general portrait that has emerged from archaeological and other kinds of evidence. The estimated effective size of the founding population for the New World is fewer than 80 individuals, approximately 1% of the effective size of the estimated ancestral Asian population. By adding a splitting parameter to population divergence models it becomes possible to develop detailed portraits of human demographic history. Analyses of Asian and New World data support a model of a recent founding of the New World by a population of quite small effective size. |
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Mentions: The IM computer program [33] was modified to include the additional parameter. The program is available from http://lifesci.rutgers.edu/~heylab/HeylabSoftware.htm#IM. For the Markov chain simulation that is implemented by the program, it is difficult to assess how well the method works, because of the need to generate large numbers of simulated datasets and because of the long run times required [33]. To conduct testing, a program was written to generate simulated datasets under the models in Figure 1. Datasets were simulated in groups of 10 or 20, each having 10–20 loci, for a given set of parameter values, and for a range of parameter values. Figure 4 shows the marginal posterior densities estimated from each of 20 independent simulations for a case of modest population growth with the following parameter values. θ1 = 10; θ2, = 10; θA = 10; t = 2.5; s = 0.2; m1 = 0.04; and m2 = 0.1. For each parameter, the mean of the 20 estimates is shown, and in general these are fairly close to the true value, though there is considerable variance for the peak locations in individual runs. To test whether the locations of these distributions are consistent with the true values of the parameters (i.e., the values used in the simulations), probabilities were combined by treating each simulation as an independent test of the same hypothesis [60]. For each posterior density pi, i = 1,…,20, is the chance that a parameter value is more extreme (i.e., departs more from the mean of the distribution) than is the actual true value. That is, if x is the area of the curve to the left of the true value then pi = 2x if x < 0.5 and pi = 2(1 − x) if x > 0.5. If the pi's are uniformly distributed, then the quantity |
View Article: PubMed Central - PubMed
Affiliation: Department of Genetics, Rutgers, the State University of New Jersey, Piscataway, New Jersey, USA. hey@biology.rutgers.edu