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Distance from Africa, not climate, explains within-population phenotypic diversity
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[QUOTE]Originally posted by MindoverMatter718: [QB] [b]Support from the relationship of genetic and geographic distance in human populations for a serial founder effect originating in Africa [/b] [URL=http://www.pnas.org/content/102/44/15942.full.pdf+html?sid=183403b8-e6b9-4c82-b5b1-38d11aea6cd5]Link[/URL] Sohini Ramachandran*†, Omkar Deshpande‡, Charles C. Roseman§, Noah A. Rosenberg¶, Marcus W. Feldman*, and L. Luca Cavalli-Sforza† [i]Abstract Equilibrium models of isolation by distance predict an increase in genetic differentiation with geographic distance. Here we find a linear relationship between genetic and geographic distance in a worldwide sample of human populations, with major deviations from the fitted line explicable by admixture or extreme isolation. A close relationship is shown to exist between the correlation of geographic distance and genetic differentiation (as measured by FST) and the geographic pattern of heterozygosity across populations. Considering a worldwide set of geographic locations as possible sources of the human expansion, we find that heterozygosities in the globally distributed populations of the data set are best explained by an expansion originating in Africa and that no geographic origin outside of Africa accounts as well for the observed patterns of genetic diversity. Although the relationship between FST and geographic distance has been interpreted in the past as the result of an equilibrium model of drift and dispersal, simulation shows that the geographic pattern of heterozygosities in this data set is consistent with a model of a serial founder effect starting at a single origin. Given this serial-founder scenario, the relationship between genetic and geographic distance allows us to derive bounds for the effects of drift and natural selection on human genetic variation.[/i] [i]Results Testing whether a serial founder effect could give rise to the decay of expected heterozygosity with distance observed in Fig. 4A requires appropriate demographic models for calculating the effect of drift. We performed simulations of evolutionary processes to assess whether we could recover a similar pattern to what was computed from the data as shown in Fig. 4A (37). Assume for simplicity that we begin with a parental population, and there are n serial bottleneck episodes starting at the origin (the location of the parental population). In each bottleneck, a sample of individuals of size Nb founds the next colony, which is established at some distance from the previous colony and which remains isolated from all other colonies. This subsampling generates a succession of colonies in time, each of which grows to a large size K before generating the next colony in the chain. Each bottleneck episode decreases expected heterozygosity in the new colony by a factor of 1 1(2Nb) (39). To be precise, this computation includes the drift effect only of the first generation after the bottleneck. Based on this simple model of n bottlenecks with Nb founders at each bottleneck, an approximation for the total loss of expected heterozygosity from the beginning to the end of the expansion from the parental population due to the sequence of bottlenecks alone will be Regressing heterozygosity on distance from the parental colony, we can estimate Hby calculating the difference between the intercept of the regression line and the fitted value for the last population in the expansion (the furthest population from the origin). In Fig. 4A, the observed H is 0.12. Because n and Nb are unknown, Eq. 8 only allows the estimation of their ratio. Moreover, this simple model assumes no intermigration among colonies after their founding; it only accounts for genetic drift that occurs as a result of the bottlenecks in the serial founder effect, ignoring genetic drift (i) during the growth period where the founding population increases in size to carrying capacity and (ii) while the population stays at carrying capacity as the subsequent colonies are formed. These components will increase the amount of drift experienced by populations over that which would ensue from a population of constant size K. Simulation enables the evaluation of these components of the evolutionary process by using estimable quantities, such as the mutation rate of microsatellites and the sizes of populations (see Supporting Text, which is published as supporting information on the PNAS web site, for more discussion). Fig. 4B shows that simulation can produce heterozygosity values similar to those observed in the data set, giving a simulated value for H of 0.12, very close to the observed value. Hsim will differ from H˜ in Eq. 8 (see Supporting Text). The main assumption in the simulation (Fig. 4B) is that Nb, the number of founders at each bottleneck, is of the order of a hunter–gatherer tribe (35, 36). Discussion Geographic distance is a good predictor of genetic distance on a global scale (Fig. 1). The pattern’s robustness is indicated by our ability to reasonably explain anomalies (Fig. 2) based on what is generally believed to have occurred during the past 100,000 years of modern human history (29). We also find a close relationship between the correlation of FST and geographic distance (Fig. 1) and the geographic pattern of heterozygosity across populations (Fig. 4A). An increase in genetic distance with geographic distance has been observed in the past and has been attributed to equilibrium models of isolation by distance, but simulation results show that the geographic pattern of heterozygosities in the HGDP-CEPH populations is consistent with a serial founder effect starting at a single origin. Further, the observed pattern of within-population diversity is best explained by an origin in Africa (Fig. 5). By studying the relationship between genetic and geographic distance, we can assess the relative importance of genetic drift and natural selection in determining the genetic variation observed among human populations. The average contribution of drift generated by the serial founder effect might be estimated from the properties of the regression in Figs. 1B and 4A. Because our regressions explain 76–78% of the observed genetic variation, this quantity is therefore an estimate of the minimum influence that drift, due to the serial founder effect, has on the total variation observed. In other words, the fraction of the variation in heterozygosity across human populations that is explained by drift is at least 76–78%. If stabilizing selection has been a major force in human evolution, then the decrease of average heterozygosity would be reduced, and the slope in Fig. 4A would be less negative (by an unknown amount). The residual 22–24% of genetic variation not explained by the regression is generated by population-specific selection, drift, and mutational histories. The deviation from the regression of each individual population (Fig. 4A) or of each population pair (Fig. 2) is a consequence of each population’s particular demographic history (40). But it is clear that part of these deviations also may be due to different selective conditions met by these populations in the different environments to which they have been exposed. Therefore, we estimate that 76–78% can be considered a lower bound on the effect of drift, and 22–24% an upper bound on the effect of selection, in the genetic differentiation of human populations.[/i] [/QB][/QUOTE]
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