MBE Advance Access originally published online on December 18, 2007
Molecular Biology and Evolution 2008 25(3):517-525; doi:10.1093/molbev/msm279
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Research Articles |
Contrasting Signatures of Population Growth for Mitochondrial DNA and Y Chromosomes among Human Populations in Africa
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* Department of Anthropology, University of Arizona
Arizona Research Labs Division of Biotechnology, University of Arizona
Department of Biology, Williams College
Department of Ecology and Evolutionary Biology, University of Arizona
|| Department of Animal and Human Biology, University La Sapienza, Rome, Italy
¶ Instituto Italiano di Antropologia, Rome, Italy
# Human Genomic Diversity and Disease Research Unit, National Health Laboratory Service and University of the Witwatersrand, Johannesburg, South Africa
** Department of Anthropology, University of Michigan
*** Unitat de Biologia Evolutiva, Facultat de Ciències de la Salut i de la Vida, Universitat Pompeu Fabra, Barcelona, Spain
E-mail: mfh{at}u.arizona.edu.
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Abstract |
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A history of Pleistocene population expansion has been inferred from the frequency spectrum of polymorphism in the mitochondrial DNA (mtDNA) of many human populations. Similar patterns are not typically observed for autosomal and X-linked loci. One explanation for this discrepancy is a recent population bottleneck, with different rates of recovery for haploid and autosomal loci as a result of their different effective population sizes. This hypothesis predicts that mitochondrial and Y chromosomal DNA will show a similar skew in the frequency spectrum in populations that have experienced a recent increase in effective population size. We test this hypothesis by resequencing 6.6 kb of noncoding Y chromosomal DNA and 780 basepairs of the mtDNA cytochrome c oxidase subunit III (COIII) gene in 172 males from 5 African populations. Four tests of population expansion are employed for each locus in each population: Fu's Fs statistic, the R2 statistic, coalescent simulations, and the mismatch distribution. Consistent with previous results, patterns of mtDNA polymorphism better fit a model of constant population size for food-gathering populations and a model of population expansion for food-producing populations. In contrast, none of the tests reveal evidence of Y chromosome growth for either food-gatherers or food-producers. The distinct mtDNA and Y chromosome polymorphism patterns most likely reflect sex-biased demographic processes in the recent history of African populations. We hypothesize that males experienced smaller effective population sizes and/or lower rates of migration during the Bantu expansion, which occurred over the last 5,000 years.
Key Words: population growth • Homo sapiens • sub-Saharan Africa • mtDNA • NRY • hunter-gatherer
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Introduction |
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TOPAbstractIntroductionMaterials and MethodsResultsDiscussionConclusionsSupplementary MaterialAcknowledgementsReferences |
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Several studies in the early 1990s concluded that the excess of low-frequency polymorphisms in human (mitochondrial DNA) mtDNA over the "expected" distribution under the standard neutral model was the signal of rapid population expansion (Di Rienzo and Wilson 1991
; Slatkin and Hudson 1991; Lundstrom et al. 1992; Aris-Brosou and Excoffier 1996). Many populations also exhibit unimodal peaks in the distribution of the number of pairwise differences (mismatch distributions) in mtDNA sequences, suggesting that populations expanded in size beginning 
30–130 kya (Di Rienzo and Wilson 1991; Rogers and Harpending 1992; Sherry et al. 1994; Rogers and Jorde 1995). This pattern of rapid expansion from small ancestral effective population size (Ne) has not been generally observed for autosomal and X chromosomal DNA sequencing data sets (Hey 1997; Harpending and Rogers 2000; Wall and Przeworski 2000; Excoffier 2002). X-linked and autosomal sequences typically show no excess of low-frequency polymorphisms, instead there is a tendency for non-African populations to have positive Tajima's D values and African populations to have only slightly negative values (Przeworski et al. 2000; Garrigan and Hammer 2006). There is disagreement on the causes of the discrepancy between mtDNA and nuclear data sets: some investigators favor models involving differential natural selection (Hey 1997; Harpending and Rogers 2000; Excoffier 2002), whereas others favor a demographic model featuring a recent bottleneck (Fay and Wu 1999; Hammer et al. 2004). For example, Fay and Wu (1999) pointed out that for a period of time following a population bottleneck, there is expected to be an excess of low-frequency polymorphisms in mtDNA and an excess of intermediate frequency autosomal polymorphisms simply because of the different effective population sizes of these loci.
Under a simple bottleneck model with an equal breeding sex ratio, both the nonrecombining portion of the Y chromosome (NRY) and mtDNA are expected to experience a similar reduction in Ne. As the population recovers from this bottleneck, the 2 haploid loci should respond similarly with respect to changes in the frequency distribution of polymorphisms over time (Fay and Wu 1999). To date, there are no published studies designed specifically to test the bottleneck hypothesis. Although some studies of single nucleotide polymorphisms (SNPs) and short tandem repeats (STRs) on the NRY have supported models of demographic expansion (Pritchard et al. 1999; Thomson et al. 2000), the grid-sampling schemes employed have reduced power to distinguish between the effects of population expansion and population structure (Ptak and Przeworski 2002). For instance, Hammer et al. (2003) showed that sampling a few individuals from many global populations may lead to an upward bias in the number of singleton variants. They concluded that it is necessary to thoroughly sample within demes to obtain a robust estimate of the frequency spectrum. Moreover, inferences of population growth based on Y-STR data are difficult to interpret as a result of uncertainty associated with microsatellite mutation models (Pritchard et al. 1999; Wall and Przeworski 2000).
In this study, we use a population-based sampling strategy and DNA resequencing data and compare patterns of DNA polymorphism in the mtDNA and on the NRY in the same sample of 172 males from 5 sub-Saharan African populations. We chose to sequence approximately 780 basepairs (bp) of the mtDNA cytochrome c oxidase subunit III (COIII) gene and 6.6 kb of noncoding Y chromosomal DNA encompassing 13 Alu elements because the former has lower levels of homoplasy than the mtDNA control region and the latter has a 3-fold higher SNP density than other noncoding regions on the NRY (Wilder et al. 2004). The extent of mutation rate heterogeneity in a given region is a concern as this phenomenon may confound the signal of population growth (Aris-Brosou and Excoffier 1996). Previous DNA sequence surveys of the mtDNA control region indicated dramatically different signals of population expansion among African populations: Many food-producing populations exhibited unimodal mismatch distributions consistent with past demographic expansions, whereas most food-gatherers showed ragged distributions. This was interpreted to be the consequence of a recent contraction in effective population size as Bantu-speaking farmers infringed upon hunter-gatherer territory (Excoffier and Schneider 1999). Our samples comprise both food-producing (the Dogon, Dinka, and a group of Southeast [SE] Bantu speakers) and food-gathering groups (the Khoisan and the Bakola). We ask whether mtDNA and Y chromosomal genealogies reflect similar population histories: that is, do the food-producing populations better fit a model of population growth and food-gathering populations better fit a model of constant size for both haploid loci.
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Materials and Methods |
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Populations and Loci Surveyed
We surveyed DNA sequence variation in 5 sub-Saharan African populations (fig. 1) including the Dogon of central Mali (n = 40–49), the Dinka of southern Sudan (n = 23), the Bakola of southern Cameroon (n = 24–25), the Khoisan from Namibia (n = 25), and a group of Southeast Bantu (SE Bantu) speakers from southern Africa (n = 46–50) (table 1). The SE Bantu samples comprise a collection of Zulu, Ndebele, Khosa, Sotho, Swazi, Tswana, Pedi, and Tsonga (Lane et al. 2002
). Additionally, we analyzed orthologous DNA sequences from one common chimpanzee (Pan troglodytes) to determine ancestral states at each site. The Dinka DNA samples were obtained with written informed consent, and the protocols were approved by the Human Subjects Committee at the University of Arizona. The SE Bantu and Khoisan samples were obtained with either verbal or written consent with approval from the Committee for Research on Human Subjects, University of the Witwatersrand (protocol number M980553). The Dogon samples were collected with verbal consent with approval from the University of Michigan Health Sciences Institutional Review Board. The Bakola samples were collected by Gabriella Spedini and Giovanni Destro-Bisol with verbal informed consent and approval from the University of Rome "La Sapienza."
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We generated DNA sequence data for 780 bp of the mitochondrial cytochrome c oxidase subunit III (COIII) (supplementary material 1, Supplementary Material online) and for 6.6 kb of noncoding NRY encompassing 13 Alu elements in the Y
5 subfamily (see Wilder et al. 2004; primers and protocols available upon request, supplementary material 2 [Supplementary Material online]). mtDNA and NRY resequence data were collected from the same individuals; however, sample numbers varied slightly between loci. Nucleotide sequences were obtained using an ABI Prism 3730 automated sequencer and aligned using Sequencher version 4.1 (Genecodes Corporation, Ann Arbor, MI). Insertion/deletion polymorphisms were excluded from all analyses.
Population Genetic Analyses and Tests of Population Growth
Population parameters such as nucleotide diversity (
and 
) and Tajima's D were calculated using DnaSP version 4.0 (Rozas et al. 2003). Inferences of population expansion were made using 4 different methods. First, we calculated Fs statistic of Fu (1997), which is based on the probability of having a number of haplotypes greater or equal to the observed number of samples drawn from a constant-sized population. To complement this method, we calculated the R2 statistic (Ramos-Onsins and Rozas 2002), which is based on the difference between the number of singleton mutations and the average number of nucleotide differences. Ramos-Onsins and Rozas (2002) demonstrated that these statistics have the greatest power to detect population expansion for nonrecombining regions of the genome under a variety of different circumstances, especially when population sample sizes are large (50, Fu's Fs) or when sample sizes are small (10, R2). They also found that the power of the R2 statistic is relatively high when the number of segregating sites is low (e.g., <20). The significance of Fu's Fs and R2 was obtained by examining the null distribution of 5,000 coalescent simulations of these statistics using DnaSP. Significantly large negative Fu's Fs values and significantly positive R2 values were taken as evidence of a population expansion.
To further test for evidence of population expansion, we used coalescent simulations to generate maximum likelihood estimates of population parameters for mtDNA and the NRY under the infinite sites model using the program GENETREE version 9.0 (R. C. Griffiths, http://www.stats.ox.ac.uk/griff/ software.html). GENETREE uses a coalescent Markov chain Monte Carlo approach to search the state space of DNA sequence evolution (Bahlo and Griffiths 2000). Unique gene trees describing both mtDNA and NRY mutational histories for each of the 5 African populations were constructed using Seq2tr (Wilder et al. 2004). For each population, we generated maximum likelihood estimates of the population mutation rate (ml) under a model of constant population size and a model of exponential population growth, where we specified the range of growth parameters (β). The maximum likelihood estimate of ml was then used to calculate Ne (or No) under each of the demographic models. Likelihood ratio tests of the log likelihood of the ml produced under models of constant population size versus population growth were performed. The significance of ml was evaluated using a simple chi-square test with one degree of freedom. To calculate effective population size, we used previously published mutation rate estimates (1.58 x 10–8 mutations per site per year for the mtDNA COIII region and 4.19 x 10–9 mutations per site per year for the NRY) with a generation time of 25 years (Wilder et al. 2004; Wilder and Hammer 2007).
The mismatch distributions were also examined (Rogers and Harpending 1992) using the program, ARLEQUIN version 2.000 (Excoffier et al. 2005). The number of observed differences between pairs of mtDNA or NRY haplotypes was compared with the expected distribution of differences under a specified demographic model (i.e., constant population size or population growth). The mismatch distribution method uses estimated parameters of the expansion (
, 0, and 1) to perform coalescent simulations of stepwise expansions and create new estimates of the same parameters (*, 0*, and 1*). The estimated demographic model is tested by obtaining the sum of the squared differences (SSDs) between the observed and the estimated mismatch distributions (Schneider and Excoffier 1999). A significant SSD P value is interpreted here as departure from the estimated demographic model, which is population expansion when > o and 1 > 0 and population stationarity when 
= 0 or 
1 = 0. Population stationarity was also inferred if the 95% confidence intervals for 1 and 0 overlapped, even if the P value of the SSD was not significant (Excoffier and Schneider 1999). To take into account known deviation from the infinite sites model for the mtDNA, the Kimura 2-parameter model was used (Kimura 1980) and mutation rates followed a gamma distribution with a shape parameter = 0.22 (Schneider and Excoffier 1999).
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Results |
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TOPAbstractIntroductionMaterials and MethodsResultsDiscussionConclusionsSupplementary MaterialAcknowledgementsReferences |
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Patterns of Nucleotide Diversity
Table 1 summarizes nucleotide diversity in the sub-Saharan African populations studied here. The number of segregating sites (s) identified at the 2 loci is similar: for mtDNA COIII s ranges from 6 in the Bakola to 16 in the SE Bantu samples, whereas s ranges from 8 in the Dinka to 14 in the Bakola samples for the NRY. The average value of
per locus is 1.6 for mtDNA and 2.7 for the NRY, whereas the average value of per locus is similar for both loci (2.6 and 2.5, respectively). In contrast, the average number of singleton substitutions is almost twice as high for mtDNA (5.0) compared with the NRY (2.6), although this difference is not statistically significant. The number of mtDNA singleton mutations ranges from 2 in the food-gatherer populations to 12 in the Dinka (mean of 7 in the food-producers), whereas the number of NRY singletons only ranges from 1 to 4 (mean of 3 in the food-producers). For mtDNA, Fu's Fs values for all populations are negative, with the 3 food-producing populations (SE Bantu and Dinka) having statistically significant negative Fs values (P < 0.001). For the NRY, 2 populations have positive Fs values (not statistically significant) and the others have values close to zero. Only a single population had a statistically significant R2 value (the Dinka for mtDNA; P < 0.001). To test for the effect of differing sample sizes on Fu's Fs statistic, we randomly subsampled 20 individuals from each population 100 times for both mtDNA and the NRY (table 1). The overall trend observed from reducing the sample sizes to 20 was for Fu's Fs to become less negative, except for situations where sample size was already small. For mtDNA, the only noticeable effect of subsampling was for the Dogon, which no longer had a significant Fu's Fs value. For the NRY, subsampling did not change any of the inferences (i.e., no population has a statistically significant Fu's Fs value).
To test whether the mtDNA and NRY Fu's Fs values were significantly different from each other, we conducted coalescent simulations to create a sampling distribution of the difference between the mtDNA and NRY Fu's Fs. We simulated neutral genealogies, conditioned on the values estimated from GENETREE under the null model of constant population size and using the sample size of each population (which ranged from 23 to 50). We calculated Fu's Fs for each of 10,000 simulated data sets after assuring that the average number of segregating sites generated was comparable with those observed (table 2). We arbitrarily subtracted the NRY Fu's Fs values from the mtDNA Fu's Fs values and asked whether the observed mtDNA–NRY Fu's Fs was significantly small under the distribution ( = 0.05). We find that the mtDNA and NRY Fu's Fs values are significantly different for the SE Bantu and the Dinka (both food-producers), whereas they were not significantly different for the Dogon (a food-producer), Khoisan, and Bakola (both hunter-gatherers).
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GENETREE Simulations
Table 3 lists the maximum likelihood estimates of the population mutation rate, growth rates, effective population sizes, and likelihood ratio tests for each population for each locus. For mtDNA, 3 food-producing populations better fit a model of exponential growth (likelihood ratio score [LRS] = 6.494, 6.720, 4.783 for the SE Bantu, Dinka, and Dogon, respectively), whereas the 2 hunter-gatherer populations better fit a model of constant size (Khoisan and Bakola). In contrast, all NRY populations better fit a model of constant population size.
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Mismatch Distributions
Figure 2 shows mismatch distributions for mtDNA COIII and the NRY. For mtDNA, the SE Bantu and the Dinka fit a model of population growth. The Khoisan mtDNA mismatch distribution also appears to fit a model of growth; however, the
0 and 1 95% confidence intervals are very close to overlapping (0 = 0.000–1.416 and 1 = 2.269–602.722) and in fact do overlap for their 99% confidence intervals (0 = 0.000–2.455 and 1 = 0.913–6818.972). Thus, similar to Excoffier and Schneider (1999), the Khoisan mtDNA mismatch distribution is interpreted as being consistent with population stationarity. SSD was significant for the Dogon and the Bakola mtDNA mismatch distributions, indicating a poor fit to the stepwise growth model. In contrast to mtDNA, all populations reject a model of population expansion for the NRY (fig. 2).
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Although there is a great deal of consistency among the methods used to infer changes in population size, some differences are noteworthy. For example, the SE Bantu mtDNA Fu's Fs, GENETREE, and mismatch distributions are consistent in supporting a model of growth, whereas the R2 statistic is not statistically significant (i.e., does not reject a model of constant population size). This apparent contradiction may be explained by the finding the R2 statistic has less power than Fu's Fs to detect expansion when sample sizes are large, as is the case here (n = 50). The mismatch distribution test provided the least consistent results as judged from the results of the other tests.
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Discussion |
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TOPAbstractIntroductionMaterials and MethodsResultsDiscussionConclusionsSupplementary MaterialAcknowledgementsReferences |
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Here we present the first comparative analysis of DNA resequence data from both the NRY and mtDNA in the same set of sub-Saharan African population samples and ask whether the genealogical histories of these 2 loci are concordant in their signals of population expansion. Similar to previous observations, we find that the mtDNA results indicate that, in general, the food-producers better fit a model of population expansion, whereas the hunter-gatherers better fit a model of population stationarity. In contrast, the NRY resequence data indicate that all 5 populations better fit a model of constant population size. In the following sections, we explore 3 possible explanations for why different patterns of population growth were inferred from these mtDNA and NRY resequencing data sets: 1) variation in mutation rate and mode, 2) natural selection, and 3) sex-specific demographic processes.
Variation in Mutation Rate and Mode
Two concerns with comparing patterns of mtDNA and NRY sequence variation are that mtDNA has much higher mutation and homoplasy rates than nuclear DNA (Horai et al. 1995; Takahata and Satta 1997). As pointed out by Fay and Wu (1999), after passing through a stepwise bottleneck, the more rapid and severe fluctuations in the frequency spectrum of mitochondrial versus autosomal DNA are due only to the mitochondrial genome's smaller population size and have nothing to do with its higher mutation rate. However, the higher mutation rate for mtDNA compared with the NRY may contribute to a difference in power to detect population growth. For this reason, we sequenced much longer segments of the NRY to ensure that a comparable number of segregating sites were recovered from the 2 haploid systems. Indeed, and (calculated per sequence) are actually consistently higher for the NRY than for mtDNA (table 1). As such, it does not appear that mutation rate differences between the NRY and mtDNA are contributing to the differing patterns of growth observed here. This is also illustrated by the fact that the Dogon mtDNA shows a signal of growth, whereas the NRY does not, despite a greater number of segregating sites on the NRY. We incorporated a shape parameter () for the gamma distribution of the mutation rate. This minimizes the influence of mutation rate heterogeneity, which can lead to erroneous inference of population growth. (Slatkin and Hudson 1991; Rogers and Jorde 1995; Aris-Brosou and Excoffier 1996). Importantly, different patterns of population growth for food-producers and food-gatherers were initially inferred from D-loop sequences, a region of the mitochondrial genome suffering the highest levels of rate heterogeneity (Meyer et al. 1999; Ingman et al. 2000). Therefore, we do not believe that either mutation rate or rate heterogeneity are important factors contributing to the observed discrepancy between our mtDNA and NRY results.
Natural Selection
Positive directional selection is another process that may mimic the effects of population growth. Both haploid regions are particularly prone to the effects of periodic selective sweeps (genetic hitchhiking; Maynard Smith and Haigh 1974), which are expected to lead to transient phases when there is an excess of rare variants over neutral expectations (Kaplan et al. 1989; Braverman et al. 1995). Although positive selection is thought to have reduced variation on the NRY (Malaspina et al. 1990; Dorit et al.1995; Jaruzelska et al. 1999; Pritchard et al. 1999) and to have influenced mtDNA variation in some geographic regions (Mishmar et al. 2003; Ruiz-Pesini et al. 2004), there is still no strong evidence that differential selection underlies contrasting patterns of mtDNA and NRY variation in human populations (Wilder et al. 2004). None of the 5 populations examined here has a significant excess of rare NRY variants; and although it remains possible that directional selection is the underlying cause of an excess of rare mtDNA variants over neutral expectations, this explanation requires a selective agent that affects food-producers but not food-gatherers.
Another form of selection that could mimic the signal of population growth is weak purifying selection, which has been implicated as the factor underlying an observed excess of low-frequency nonsynonymous polymorphism in mtDNA (Nachman et al. 1994; Nachman 1998; Ballard and Dean 2001). However, the excess of rare mtDNA polymorphism in some African populations is not caused solely by replacement polymorphisms: When the mtDNA data are reanalyzed using only third position sites, results for Fu's Fs and the R2 statistics are similar to results of analyses including all sites (data not shown). Interestingly, the mtDNA data for the food-producers do exhibit an excess number of polymorphic nonsynonymous sites, whereas those of the hunter-gatherers do not (McDonald-Kreitman test Fisher's exact test P value = 0.0001 and 0.070, respectively). This suggests weaker purifying selection on the food-producer versus the food-gatherer populations, which may in turn suggest larger effective population sizes for the food-gatherers in the past.
Sex-Specific Demographic Processes
In this section, we turn to demographic explanations for the observed discrepancy between mtDNA and NRY results. The different mtDNA patterns for food-producers and food-gatherers were initially explained by a model of Pleistocene demographic expansions followed by more recent population crashes for marginalized hunter-gatherers, effectively erasing any signature of population growth (Excoffier and Schneider 1999). Later work by Ray et al. (2003) examined the effects of a spatial expansion in a subdivided population. Their computer simulations demonstrated that a population with low levels of gene flow among neighboring demes (Nem) during a spatial expansion usually do not exhibit the signature of expansion, whereas demes experiencing high Nem values tend to show statistically significant negative values of Fu's Fs statistic (Ray et al. 2003). When range expansions are simulated with small deme sizes followed by recent demographic growth (i.e., deme sizes and levels of gene flow with surrounding demes increase well after the spatial expansion), the outcome is similar to simulations of spatial expansions with demes of constant size that always exchanged large numbers of individuals with their neighbors (Ray et al. 2003). In other words, spatial expansions followed by relatively recent population growth with differing rates of gene flow among neighboring demes (Nem) can also produce different signatures of population growth at the molecular level.
Drawing upon the simulation results of Ray et al. (2003), we suggest that a similar difference between effective population sizes and/or rates of gene flow (Nem) for males versus females may explain the discrepancy observed between our mtDNA and NRY results. For example, food-producing males may have experienced either a smaller effective population size (Ne) or a lower rate of migration (m) than females during a phase of expansion. What are the possible causes of lower Ne or m for males of food-producing populations? Two common cultural practices may lead to lower values of Nem for the NRY: polygyny and patrilocality. Polygyny, a marriage practice that allows males (but not females) more than one spouse, is widespread in many parts of the world, especially in Africa (Dorjahn 1959; Konotey-Ahulu 1980; Spencer 1980; Strassmann 2003). When males father children with more females than females do with males, the result is an increase in the variance in reproductive success among males, which lowers their Ne relative to females (Low 1988). Interestingly, food-producers are generally described as substantially more polygynous than hunter-gatherers (Cavalli-Sforza 1986; Biesele and Royal 1999). It is also well established that the widespread practice of patrilocality (defined as the tendency of females to migrate and males to remain in their natal groups) can result in lower rates of male migration (Murdock 1981). Although most agricultural societies are patrilocal (Murdock 1967), hunter-gatherer groups are typically referred to as bilocal, that is, as spending time living with both the father's and the mother's families (Marlowe 2004). The importance of polygyny and patrilocality in shaping patterns of maternally and paternally inherited variation has been suggested previously for African populations (Destro-Bisol, Donati, et al. 2004; Wood et al. 2005). Assuming that these processes evolved as populations shifted from foraging to food-producing lifestyles, they may have played an important role in shaping the distinctive patterns of mtDNA and NRY polymorphism observed here.
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Conclusions |
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TOPAbstractIntroductionMaterials and MethodsResultsDiscussionConclusionsSupplementary MaterialAcknowledgementsReferences |
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This work constitutes the first direct comparison of mtDNA and NRY resequence data from the same African samples for the purpose of examining patterns of population expansion. The 4 tests that we employed (especially the Fu's Fs, the R2 statistic and coalescent simulations methods) were very consistent in revealing different patterns of population size change for the 2 haploid compartments of the genome. Although all tests may be sensitive to unknown structure within populations (Ptak and Przeworski 2002
; Hammer et al. 2003), we emphasize that the mismatch distribution test actually has the weakest power to detect growth (Ramos-Onsins and Rozas 2002). We find that mtDNA COIII data from African food-producers better fit a model of population growth, whereas those for food-gathering populations better fit a model of stationarity. In striking contrast, NRY data from both the food-producers and the food-gatherers sampled here better fit a model of constant population size. We hypothesize that these patterns are the result of differences in sex-specific demographic processes—in particular, asymmetrical migration and/or reduced male effective population sizes. These sex-biased demographic processes are expected to significantly alter the frequency spectrum of mtDNA and NRY polymorphisms during large spatial/demographic expansions (Ray et al. 2003). Such an expansion is known to have occurred recently as farmers speaking Niger-Congo Bantu languages expanded from a southern Cameroonian homeland over most of subequatorial Africa beginning 4,000 years ago (Holden 2002; Lane et al. 2002; Destro-Bisol, Coia, et al. 2004; Wood et al. 2005). It still remains to be resolved whether the Bantu expansions and the spread of farming in Africa, or changes in population size that took place much earlier in the Pleistocene (Excoffier and Schneider 1999; Harpending and Rogers 2000), are responsible for the differential patterns observed here. Along theses lines, further analysis of populations with known differences in marriage customs or migration patterns would be extremely valuable. For example, the finding of similar signals of strong growth for both mtDNA and the NRY in a nonpolygynous (or bilocal) food-producing population (i.e., thereby mitigating a reduction in male effective population size) would support the hypothesis that recent changes in cultural practices made an impact on patterns of genetic diversity (Wilkins and Marlowe 2006). The collection of additional genetic data sets that minimize ascertainment biases, along with the development of more realistic models of human demography that incorporate nonequilibrium processes, will aid future analyses of the effects of sex-biased processes on patterns of genetic variation. |
Supplementary Material |
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Supplementary materials 1 and 2 are available at Molecular Biology Evolution online (http://www.mbe.oxfordjournals.org/).
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Acknowledgements |
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TOPAbstractIntroductionMaterials and MethodsResultsDiscussionConclusionsSupplementary MaterialAcknowledgementsReferences |
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We would like to thank Amy Russell, Heather Norton, Stephen Zegura, and Michael Nachman for helpful discussions. Publication of this work was made possible by grant GM-53566 from the National Institute of General Medical Sciences (to M.F.H.). Its contents are solely the responsibility of the authors and do not necessarily reflect the official views of the National Institutes of Health. Funding was also provided by National Science Foundation (NSF) Doctoral Dissertation Improvement Grant (to M.M.P.), and an NSF Integrative Graduate Education and Research Tranineeships grant (to M.M.P.).
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Footnotes |
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Yoko Satta, Associate Editor
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