Erratum: Microsatellite data show recent demographic expansions in sedentary but not in nomadic human populations in Africa and Eurasia

Aimé, Carla; Verdu, Paul; Ségurel, Laure; Martínez‐Cruz, Begoña; Hegay, Tatyana; Heyer, Évelyne; Austerlitz, Frédéric

doi:10.1038/ejhg.2014.276

Cited by 1 publication

(8 citation statements)

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“…In Aimé et al, 4,25 we suggested that these contrasted findings may result from the specificities of each type of genetic markers and indicate two successive expansion events in the studied populations. Indeed, as explained above, the higher mutation rate of microsatellites as compared to DNA sequences may increase their sensitivity to recent events.…”

Section: Introductionmentioning

confidence: 72%

“…Using Simcoal version 2.1.2, 26 we simulated here both DNA sequences (mitochondrial or autosomal) and microsatellite (autosomal or from the Y chromosome) data sets under several scenarios involving either one or two successive expansion events, starting at different points in time, consistent with either a Paleolithic or a Neolithic expansion. These data sets were similar to those used in Aimé et al 4,20,25 in terms of numbers of loci.…”

Section: Introductionmentioning

confidence: 81%

“…For Africa, several authors also inferred Palaeolithic expansion events, with onsets ranging from 80 000 to 25 000 YBP. [21][22][23][24] However, in a study on microsatellite data, 25 we did not find signals of Palaeolithic expansions, either in Eurasia or Africa: the observed signals of expansion events were indeed consistent with the Neolithic transition. Finally, in Aimé et al, 4 we also found signals of expansion events that were consistent with the Neolithic transition for Eurasia using Y-chromosome microsatellite data.…”

Section: Introductionmentioning

confidence: 87%

“…We then inferred the dates of expansion onsets (t) using the following formula: t = (1/g) × ln(N 1 /N 0 ), applied to each step of the MCMC algorithm. 25 …”

Section: Discussionmentioning

confidence: 99%

“…The simulated data were analysed using the parametric approach implemented in BEAST v1.8, following the same procedure as in Aimé et al 4,20,25 Four demographic models are implemented in BEAST: constant effective population size (N 0 ; constant model), population expansion with an increasing growth rate (g) (exponential model), population expansion with a decreasing growth rate (logistic model) and the expansion model, in which N 0 is the present day population size, N 1 the population size that the model asymptotes to going into the distant past and g the exponential growth rate that determines how fast the transition is from near the N 1 population size to N 0 population size. As in Aimé et al, 20 we selected the best-fitting demographic model by estimating marginal likelihoods using two methods: path sampling and stepping-stone sampling.…”

Section: Discussionmentioning

confidence: 99%

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Different kinds of genetic markers permit inference of Paleolithic and Neolithic expansions in humans

Aimé¹,

Austerlitz²

2016

Eur J Hum Genet

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Recent population genetic studies have provided valuable insights on the demographic history of our species. However, some issues such as the dating of the first demographic expansions in human populations remain puzzling. Indeed, although a few genetic studies argued that the first human expansions were concomitant with the Neolithic transition, many others found signals of expansion events starting during the Palaeolithic. Here we performed a simulation study to show that these contradictory findings may result from the differences in the genetic markers used, especially if two successive expansion events occurred. For a large majority of replicates for each scenario tested, microsatellite data allow only detecting the recent expansion event in that case, whereas sequence data allow only detecting the ancient expansion. Combined with previous real data analyses, our results bring support to the ideas that (i) a first human expansions started during the Palaeolithic period, (ii) a second expansion event occurred later, concomitantly with the Neolithic transition. INTRODUCTIONDemographic changes are known to leave footprints on allelic frequencies. Together with the increased availability of large genetic data sets, numerical methods stemming from Kingman's coalescent theory 1 allow inferring the past demography of populations from their present-day patterns of genetic diversity. 2,3 Several types of genetic markers can be used, each category of markers having its own specificities. For instance, as they are uni-parentally transmitted, mitochondrial sequences are informative only about maternal lineages, whereas the Y-chromosome provides information about paternal lineages. In Aimé et al., 4 it has been shown that these two types of markers sometimes provide different inferences on the demographic history of human populations, especially as females generally exhibit higher effective population sizes and higher migration rates than males. 5,6 Conversely, autosomal markers provide synthetic information about both maternal and paternal lineages. When these loci are carefully selected in order to avoid pairwise linkage disequilibria, they also offer the possibility to consider each of them as an independent replicate. Among these autosomal markers, sequences or microsatellites data also exhibit contrasted properties. In particular, microsatellites have higher mutation rates than sequences. 7,8 Moreover, microsatellites are subject to strong homoplasy, which can lead to false signals when using models where this phenomenon is not efficiently controlled for. One simulation-based study 9 showed that when microsatellites are included into an Approximate Bayesian Computation analysis, 10 they provide substantially better estimation for recent admixture events than sequence data. However, they did not investigate the question of inferring other kind of demographic events such as expansions. Moreover, an empirical study on a non-human species (the black sea porpoise) showed that integrating microsatellites in an ABC analy...

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Section: Introductionmentioning

confidence: 72%

Section: Introductionmentioning

confidence: 81%

Section: Introductionmentioning

confidence: 87%

“…We then inferred the dates of expansion onsets (t) using the following formula: t = (1/g) × ln(N 1 /N 0 ), applied to each step of the MCMC algorithm. 25 …”

Section: Discussionmentioning

confidence: 99%