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. 2005 Jun;3(6):e193.
doi: 10.1371/journal.pbio.0030193. Epub 2005 May 24.

On the number of New World founders: a population genetic portrait of the peopling of the Americas

Jody Hey  1
Affiliations

On the number of New World founders: a population genetic portrait of the peopling of the Americas

Jody Hey. PLoS Biol. 2005 Jun.

Abstract

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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Figures

Figure 1
Figure 1. Isolation with Migration Models
(A) The basic IM model. The demographic terms are effective population sizes (N1, N2, and NA), gene flow rates (m1 and m2), and population splitting time (t). Also shown are parameters scaled by the neutral mutation rate (u), as they are actually used in the model fitting. Terms for basic demographic parameters, including N, m, t, and u, are not italicized. Note that the migration parameters are identified by the source of migrants as time goes backward in the coalescent. In other words, the migration rate from population 1 to population 2 (i.e., m1) actually corresponds to the movement of genes from population 2 to population 1 as time moves forward. (B) The IM model with changing population size. An additional parameter, s, is the fraction of NA that forms N1 (i.e., the fraction 1 − s gives rise to N2)
Figure 2
Figure 2. Approximate Geographic Locations, and Sample Sizes per location, for Each Locus Listed in Table 1
In some cases locations are based on actual geographic locations, in other cases the locations are the approximate center of the geographic region occupied by ethnic groups identified in the original references (Table 1).
Figure 3
Figure 3. Marginal Posterior Probability Densities
Probability densities for each of the parameters described in Figure 1 are shown, as follows: (A) θ1; (B) θ2; (C) θA; (D) t (i.e., t/u); (E) t shown on a scale of years over the range corresponding to a maximum t value of 0.2; (F) s; (G) m 1; and (H) m 2. The analysis in which a high upper limit on the prior distribution for t was used is identified as “high tupper,” while those analyses with a smaller upper limit on the prior distribution of t are identified as “low tupper.” Each curve is based upon the results of multiple simulations over millions of Markov chain updates (see Materials and Methods), and is plotted over the specified prior range of that parameter.
Figure 4
Figure 4. The Marginal Densities Obtained by Fitting the Model with Population Size Change to Simulated Data
The input parameters for the simulations were as follows: (A) θ1 = 10; (B) θ2 = 10; (C) θA = 10; (D) t =2.5, (E) s = 0.2, (F) m 1= 0.04; (G) m 2= 0.2 ; and t = 5 (t/2NA = 0.5). For each simulated dataset, coalescent simulations were done for each of 20 loci with identical mutation rates under an infinite sites mutation model, each with sample sizes of 10 for each of the two populations. Each simulated dataset was analyzed using wide uniform prior distributions for each parameter. Each analysis began with a burn-in period of 300,000 steps followed by a primary chain of 3 million to 10 million steps. The curves for parameters θ1 through m2 are shown in (A) through (G), respectively. For each figure, the true parameter value used in the simulations is shown as a black vertical bar, and the mean of the estimates for the 20 simulations (based on peak locations) is shown as a gray vertical bar.
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