Sex-biased demography in humans: differences in global and regional scale analyses

Wilkins, J. F. and F. W. Marlowe (2006). “Sex-biased migration in humans: what should we expect from genetic data?” BioEssays 28(3): 290-300.

There has been a debate on whether asymmetrical demographic history between two sexes is a result of small male effective population size due to polygyny (e.g., herehere and here) or increased female gene flow (e.g., here).  Many analytical methods used to address these questions assume equilibrium (e.g. migration rates were constant and the direction of gene flow stayed the same), but in reality migration rates and direction of gene flow always changes and they argue that the model that accounts for the changes is necessary.  Since very recent demographic changes affect a regional level genetic variation, but not global level, sampling of populations analyzed affects the results and interpretation.

First, they (Marlowe) reviewed the anthropological literatures and suggest that female migration rate increased after introduction of agriculture.  Foragers, hunter-gatherers, tend to have bilateral kinship system and flexible post-marital residence pattern, so in forager societies, both males and females moved.  On the other hand, in pastoralist and agriculturalist societies, patrilocal post-marital residence pattern is more dominant, so females tend to move more than males.

Second, they (Wilkins) conducted a series of computer simulations to show that statistics like FST, therefore that estimation of male to female effective population size ratio based on FST, are influenced by timing of migration rate change and whether researchers use local and dense global sampling or geographically sparse sampling.  So, when regional population samples or dens global population samples are analyzed, genetic data should show how recent female migrations after the introduction of agriculture affected genetic variation.  When geographically sparse population samples are analyzed, on the other hand, genetic data should reflect demography of archaic foragers that existed before introduction of agriculture.

As the title of the article says, this is what we should expect based on the theory, and this should be tested using empirical data.

Do males have smaller effective population than females?

Wilder, J., Z. Mobasher, et al. (2004). “Genetic evidence for unequal effective population sizes of human females and males.” Molecular Biology and Evolution 21: 2047-2057.

Pilkington, M., J. Wilder, et al. (2007). “Contrasting signature of population growth for mitochondrial DNA and Y chromosomes among human populations in Africa.” Molecular Biology and Evolution 25(3): 517-525.

While recognizing importance of female gene flow (Cox et al., 2008), Hammer and his colleagues (e.g., here) have been major critiques of Seielstad et al. (1998) and others who have shown that increased female gene flow is more important factor causing sex-biased demographic pattern.  Instead, they have argued that reduced male effective population size due to polygyny and other factors is the more important factor than increased female gene flow.

The reasons why they think reduced male effective population size, not increased female gene flow, is the major cause of asymmetrical demography are 1) shorter time to the most recent common ancestor (TMRCA) for Y chromosome than mtDNA, 2) clear evidence of demographic expansion for mtDNA, but not for Y chromosome, and 3) not significant difference in the population structure and differentiation between mtDNA and Y chromosome data.

To demonstrate that, Hammer and colleagues published a series of articles based on their analyses of mtDNA cytochrome c oxidase 3 and Y chromosome sequences, instead of mtDNA hypervariable region sequence and Y chromosome SNPs or STR.  Comparing different genetic markers with different mutation is problematic, so they chose these markers that have similar mutation rates.

First, Wilder et al. (2004) demonstrated that the TMRCA for Y chromosome is much shorter than for mtDNA.  They used a coalescent based program, GENETREE, to estimate the TMRCA.  This program allow users to estimate the TMRCA accounting for population growth, so the users can compare the difference in the TMRCA between mtDNA and Y chromosome accounting for differences in genetic diversity and demographic history

Second, Pilkington et al. (2007) focused on African populations and used several different methods to examine if mtDNA and Y chromosome variation show evidence of population expansion.  mtDNA variation of agriculturalists clearly show evidence of population expansion, while Y chromosome variation does not.

While these publications clearly show differences in mtDNA and Y chromosome variation resulting from differences in male and female demographic history, they did not really consider the effects of gene flow on genetic variation in these publications.

A reassessment of genetic diversity in Icelanders: Strong evidence from multiple loci for relative homogeneity caused by genetic drift

Helgason, A., G. J. Nicholson, et al. (2003). “A reassessment of genetic diversity in Icelanders: Strong evidence from multiple loci for relative homogeneity caused by genetic drift.” Annals of Human Genetics 67: 281-297.

In the article published in 2000, Helgason and his colleagues compared mtDNA variation of the Icelaners to that of other European populations and argued that Icelanders experienced bottleneck recently.  They observed large θπ but small θk and θS among Icelanders and argue that genetically heterogeneous Icelanders reduced their genetic diversity after the historic population declines.  Arnason (2003) responds to Helgason and his colleagues and says that mtDNA variation of Icelanders is product of admixture during the founding or history of Iceland. 

In this 2003 article, Helgason and colleagues respond back to Arnason, and they show that admixture and genetic drift affect mtDNA sequence diversity differently.  If a new population was founded by two genetically distinct populations, the new population is expected be genetically more diverse than source populations.  However, they show that when a population was founded by two genetically similar populations (for example two European populations), the new population has Tajima’s D and cumulative frequency of polymorphic haplotypes which are intermediate between two source populations or are very similar to those of source populations.

On the other hand, the populations that experienced reduction in effective population size, so genetic drift, have reduced number of rare haplotypes and higher number of haplotypes in intermediate frequencies.  In the end, they concluded that number of distinct haplotypes (k), number of polymorphic sites (S), θk, θS, and Tajima’s D are sensitive to recent demographic events, so genetic drift reduce number of distinct haplotypes (k), number of polymorphic sites (S), θk, and θS.  The haplotype diversity (heterozygosity) and mean pairwise nucleotide differences are less sensitive to recent demographic events and are less likely to be affected by genetic drift.

I believe this is a very interesting case study showing how a historically known event may have affected mtDNA variation of modern Icelanders, and the results of this study can be applied to other genetic studies investigating mtDNA variation of anthropologically interesting populations that may have experienced recent bottleneck.  Reconstruction of past demographic events among these populations tends to be challenging, because they lack written documents.  Unfortunately, probably because their focus is Icelanders, they did not address how you can tell the genetic signature of admixture from demographic expansion.


Árnason, E. (2003). “Genetic heterogeneity of Icelanders.” Annals of Human Genetics 67: 5-16.

Helgason, A., S. Siguroardottir, et al. (2000). “mtDNA and the origin of the Icelanders: Deciphering signals of recent population history.” American Journal of Human Genetics 66: 999-1016.

mtDNA variation in South America and evolutionary history of native South Americans

In Central Andes, exploitation of marine resources and introduction of intensive agriculture caused population size to increase early in its prehistory.  Also, since its prehistory, there were constant movements of people in the Andes due to vertical use of the ecosystem, series of state expansions, forced migration by Inca, and migration to larger cities after European contact.  These events probably had great effects on the genetic variation, but what extent these event influenced genetic variation is still uncertain.  In this post, I reviewed recent mtDNA data from Central Andes to address these questions.

Following Tarazona-Santos et al. (2001), Fuselli et al. (2003) argues that mtDNA data supports Y chromosome data and Western part of South America, mainly Andes, and Eastern part of South America, mainly Amazon, had separate evolutionary histories.  The Andean populations have high within-population genetic diversity and they are genetically similar to each other.   They argue that both large effective population size and gene flow contributed for large within-population genetic diversity.  The Amazonian populations, on the other hand, have low within-population genetic diversity and they are genetically differentiated, because without gene flow, genetic drift had a great effect on geographically isolated small populations.

Lewis et al. (2005, 2007) analyzed mtDNA sequence variation of five highland Peruvian populations and compared to two of three highland Peruvian populations that Fuselli et al. (2003) analyzed and lowland populations.  Supporting the argument that Fusellli et al. put forth, Lewis et al. found high within-population genetic diversity among highland Peruvian populations and Andeans populations were genetically homogeneous compared to Amazonian populations.  However, their AMOVA results suggest that there is no significant genetic difference between Andean group and Amazonian group.  Lewis and Long (2008) found more mtDNA variation in Eastern South American and less genetic variation in the Andean region than previously reported, when regional variation was accounted for. 

Analyses of mtDNA variation among the populations that occupy in the transitional zone between Andes and Amazon provide more complicated perspectives on the South American evolutionary history.  Bert et al. (2004) and Corella et al. (2007) analyzed mtDNA variation of lowland Bolivians from the Department of Beni and Cabana et al. (2006) analyzed that of Gran Chaco.  In general, these populations have genetic diversity values intermediate of Andean and Amazonian populations and they are genetically differentiated from each other.  These patterns are expected from smaller relatively isolated populations. 

However, they found evidence suggesting that these populations were not reproductively isolated and there were gene flows among these populations as well as between Andeans and populations in the transitional zone responding to state expansion from Andean highland or reorganization of indigenous societies during the colonial era.  First, some of these small populations from lowland Bolivia have unexpectedly high within-population genetic diversity.  Second, when the Ayoreo is excluded from analyses, Gran Chaco populations were very homogeneous.  Finally, some these populations are genetically very similar to Andeans.

The population size in Central Andes may have been large enough for them to be genetically more diverse than other populations in South America, but the constant movements, or interactions, of people made populations in Central Andes genetically homogeneous and potentially genetically more diverse.  This interaction sphere may have extended into the transitional zones making populations from the transitional zone genetically diverse and similar to Central Andeans.  My review of articles on Central Andeans mtDNA variation, however, shows that no one has examined whether female effective population size or gene flow contribute more on mtDNA variation of these populations.

Bert, F., A. Corella, et al. (2004). “Mitochondrial DNA diversity in the Llanos de Moxos: Moxo, Movima and Yuracare Amerindian populations from Bolivia lowland.” Ann Hum Biol 31: 9-28.

Cabana, G. S., D. A. Merriwether, et al. (2006). “Is the genetic structure of Gran Chaco populations unique? Interregional perspectives on native South American mitochondrial DNA variation.” American Journal of Physical Anthropology 131(1): 108-119.

Corella, A., F. Bert, et al. (2007). “Mitochondrial DNA diversity of the Amerindian populations living in the Andean Piedmont of Bolivia: Chimane, Moseten, Aymara and Quechua.” Annals of Human Biology 34(1): 34-55.

Fuselli, S., E. Tarazona-Santos, et al. (2003). “Mitochondrial DNA diversity in South America and the genetic history of Andean highlanders.” Molecular Biology and Evolution 20(10): 1682-1691.

Lewis, C. M. J., B. Lizárraga, et al. (2007). “Mitochondrial DNA and the peopling of South America.” Human Biology 79: 159-178.

Lewis, C. M., Jr. and J. C. Long (2008). “Native South American genetic structure and prehistory inferred from hierarchical modeling of mtDNA.” Molecular Biology and Evolution 25(3): 478-486.

Lewis, C. M. J., R. Y. Tito, et al. (2005). “Land, language, and loci: mtDNA in Native Americans and the genetic history of Peru.” American Journal of Physical Anthropology 127: 351-360.

Tarazona-Santos, E., D. R. Carvalho-Silva, et al. (2001). “Genetic Differentiation in south amerindians is related to environmental and cultural diversity: Evidence from the Y chromosome.” American Journal of Human Genetics 68(6): 1485-1496.

Intra-deme molecular diversity in spatially expanding populations

Ray, N., M. Currat, et al. (2003). “Intra-deme molecular diversity in spatially expanding populations.” Molecular Biology and Evolution 20: 76-86

Using computer simulation, Ray and his colleagues analyzed effects of spatial, or range, expansion and gene flow on within-population genetic diversity and demonstrated that when migration rate (Nm) is large, demes have genetic signature of expansion similar to demographic expansion (N is deme size or effective population size of deme and m is rate of out-migrating individuals in a population that are replaced by incoming immigrants in each generation). 

In the spatial expansion model, the center of the expansion sends migrants to previously unoccupied areas and as deme size increases, migrants are sent to new areas.  When the migrants are sent to the previously occupied demes, gene flow takes place.  They show that spatially expanding populations with large Nm have large genetic diversity and large negative values of two neutrality tests.  They argues that spatial expansion model explains observed genetic pattern better than pure demographic model which assumes that populations are not subdivided. 

Up until 1990s, many population genetics methods to reconstruct demographic history used an unrealistic model.  The model assumes that populations are unsubdivided and people in the populations are randomly mating.  In reality, human populations are subdivided in a very complex way and there are many cultural factors that regulate mating patterns. 

More recently, population geneticists and anthropological geneticists try to understand how migration/gene flow between demes in subdivided populations affects population subdivision and demographic history.  Today, we start understanding that migration/gene flow between different ethnolinguistic and geographic groups was common and gene flow can affect genetic variation of populations in various ways.

Based on Ray and his colleagues’ finding and new perspective on gene flow and genetic variation, we need to reevaluate the model of human colonization and expansion, such as Neolithic expansions among European farmers, Bantus, and others.  One extreme version of the Neolithic expansion model suggests that the Neolithic farmers spread after pure demographic expansion at the core area without contribution of preexisting foragers or without much of gene flow after expansion.  The results of their simulation suggest that populations that experienced Neolithic expansion have genetic evidence of expansion either due to demographic expansion or increased gene flow.

Female-to-male breeding ratio in modern humans – an analyziz based on historical recombination

Labuda, D., J.-F. Lefebvre, et al. (2010). “Female-to-male breeding ratio in modern humans – an analyziz based on historical recombination.” American Journal of Human Genetics 86: 1-11.

Following Keinan et al., Labuda et al. also challenge Hammer’s argument and argue that polygyny was not a major factor influencing asymmetrical demographic pattern between males and females.  Hammer et al. and Keinan et al. obtained X chromosome to autosome variation or effective population size ratios from mutational diversity, but Labuda et al. based their analysis on population recombination rate (ρ) to estimate female-to-male breeding ratio (β).

If males have large reproductive variance, so if some males are reproductively more successful and have more children than others through polygyny or other polygynous sexual practices, male effective population size should be smaller than female effective population size, so the female-to-male breeding ration is larger than 1.  If polygyny was not major factor, on the other hand, male effective population size should be similar to female effective population size, so the female-to-male breeding ratio is expected to be close to 1.

They obtained the breeding ratio of 1.4 among the Yoruba from West Africa, 1.3 among the Europeans, and 1.1 among the East Asians.  They believe that there is slight excess of breeding females per male (10-40%) in the samples analyzed and they concluded that polygyny was not a major factor.

Our estimates of the breeding ratio are close to but greater than 1, suggesting some polygyny in the history of human populations…Excessive manifestation of polygyny are documented in the recent history of Asian populations, but this may be the exception rather than the rule.  Human beings are usually characterized as monogamous with polygamous tendencies.

Instead of polygyny, they suggest that serial monogamy and longer generation time of males can also increase the breeding ratio.

I agree with Labuda et al.  I doubt polygyny had that big of impacts on Europeans and Asian (Chinese and Japanese) genetic variation, but polygyny is more common in Africa and recent shift from flexible to more male-centric social structure may have had some impacts on the Yoruban genetic variation.

So, why Hammer’s argument is challenged by Keinan et al. and Labuda et al.?  Both Keinan et al. and Labuda et al. used HapMap data and findings from mutational diversity based analysis conducted by Keinan et al. were somewhat supported by recombination based analysis conducted by Labuda et al.  It might be safe to say that polygyny was not major factor affecting the genetic variation of the European and Asian populations analyzed by these two groups of re searchers.  I believe that Hammer’s data does not agree with these findings, not only because Hammer and his colleagues have smaller genome coverage, but also because they used different sample populations.  Probably, the same level of analyses that Keinan et al. and Labuda et al. conducted using more population samples is necessary to address how polygyny played a role affecting genetic variation.

Accelerated genetic drift on chromosome X during the human dispersal out of Africa

Keinan, A., J. C. Mullikin, et al. (2009). “Accelerated genetic drift on chromosome X during the human dispersal out of Africa.” Nat Genet 41(1): 66-70.

Keinan and his colleagues provide data that challenges Hammer’s argument.  While Hammer and his colleagues have argued that female effective population size is larger than male effective population size largely due to polygynous practices, comparing X chromosome variation to autosomal variation, Keinan and his colleagues show that female effective population size was reduced outside of Africa (note that females carry two X chromosomes and males carry one, so X chromosome variation reflect female demographic history more than male).   

Compared to Hammer et al., Keinan et al. used bigger genome data.  They analyzed 130,000 SNPs using subset of the HapMap data, 1,087 additional SNPs that they discovered in two West African copies of X chromosomes, and sequence data consist of over a billion base pairs of DNA from five North Europeans, four East Asians, and five Africans. 

First, using SNP data, they obtained the ratio of X chromosome and autosomes allele frequency differentiation between two populations (FST) to estimate the amount of genetic drift.  The ratios obtained between North European and East Asian were not significantly different from expected ratio (3/4 = 0.75), but the ratios between African and non-African were reduced.

Second, they compared the X chromosome and autosomes SNP allele frequency distribution within each population.  The shape of allele frequency distribution for X chromosome and autosomes was significantly different for non-Africans.  Non-Africans have more high-frequency derived allele on X chromosome than expected and the X chromosome allele frequency distribution of non-Africans does not fit the expected distribution.

Third, they obtained the X-to-autosome sequence divergence ratios for each population.  West African has ratio close to expected, but non-Africans have significantly smaller ratio than expected (0.635 for North European and 0.690 for East Asian).

They think that X chromosome experienced accelerated genetic drift and sex-biased demographic processes rather than natural selection is likely explanation.  However, the data do not support that polygyny is one of the process, because polygyny increases the ratio, but they observed decreased ratios.  Alternatively, they suggest that non-Africans received long-range male migration from Africa or females have longer generation time than males.  Also, some females were reproductively more successful than the others during out-of-Africa dispersal.

Two different conclusions were obtained from different groups of researchers, maybe because of several factors.  First, the samples used by two groups were different.  Hammer et al. have more sample populations that Keinan et al did not use.   Second, although Hammer et al have sequence data from more individuals than Keinan et al., they used much smaller genomic data.  Third, two groups used very different analytical methods.

Sex-Biased Evolutionary Forces Shape Genomic Patterns of Human Diversity

Hammer, M. F., F. L. Mendez, et al. (2008). “Sex-Biased Evolutionary Forces Shape Genomic Patterns of Human Diversity.” PLoS Genet 4(9): e1000202.

Hammer and his colleagues analyzed X chromosome sequence variation and compared to autosomal sequence variation to understand the male-female asymmetrical demographic patterns observed.  They believe that the large variance in male reproductive success due to wide spread practice of polygyny is the major contributing factor for sex-biased demographic pattern.

Hammer et al. used the same 90 individuals from six populations (Biaka, San, Mandenka, Han Chinese, Oceanians, and Basque) that Cox et al. analyzed, and they analyzed about 210 kb sequence from 40 independently evolving non-gene coding regions on autosomes and X chromosome. 

Then, they examined the ratio of effective population size between X chromosome and autosomes (NX/NA).  Note that females carry two X chromosome and males carry one X chromosome, but both sexes carry a pair of autosomal chromosome.  So, the expected ratio (NX/NA) is 0.75, if male and female effective population sizes are equal.  The observed ratios, on the other hand, ranges from 0.85 to 1.08 indicating that female effective population size is larger than male effective population size.

They explored the possible explanations (sequencing error, background selection, changes in population size, sex-biased migration, and high variance in male reproductive success) for the observed pattern.  They believe that sequencing error is minor and background selection, changes in population size, and sex-biased migration do not alter the ratio significantly. 

They hypothesize that high variance in male reproductive success is the major factor affecting the ratio, though all other factors may have contributed.  They explain:

The human mating system is considered to be moderately polygynous, based on both surveys of world populations and on characteristics of human reproductive physiology.  The practice of polygyny, in both the traditional sense and via ‘effective polygyny’ (whereby males tend to father children with more females than females do with males-a common practice in many contemporary western culture), would tend to increase the variance in reproductive success among males.

It is important to note that Hammer and colleagues have argued that wide spread practice of polygyny causes sex-biased demographic history, but in this article they are saying polygyny is not only cultural practice that causes some males to be reproductively more successful than the others, but they extend into other polygynous reproductive behaviors (not specific about what these cultural practices are) or physiological processes (e.g., sperm competition).  In addition, age structure, lower male survival rate to the adulthood or higher mortality rate among young male, and delayed maturity of males also influence the ratio.

Another important thing to note is that Hammer and his colleagues in Cox et al. (2008) argued that female gene flow was important factor understanding human demographic history.  Yet, they did not ask how female gene flow, sex-biased migration might have affected the ratio in detail, and they conducted computer simulations, but I think the two-deme island model they used is not realistic.

Intergenic DNA sequences from the human X chromosome reveal high rates of global gene flow

Cox, M., A. Woerner, et al. (2008). “Intergenic DNA sequences from the human X chromosome reveal high rates of global gene flow.” BMC Genetics 9(1): 76.

Cox and his colleagues analyzed X chromosome sequence variation and using isolation-with-migration (IM) model, they tried to understand how gene flow affected global genetic pattern.  They found larger migration rates than they expected considering the geographic distance between each population analyzed and concluded that gene flow is an important factor for understanding human demographic history.

Cox et al. analyzed 98kb sequence from 20 independently evolving non-gene coding regions of 90 individuals from six populations (Biaka, San, Mandenka, Han Chinese, Oceanians, and Basque).  They used IM, the Markov chain Mont Carlo Bayesian method to estimate effective population size of two demes and their ancestral population, asymmetrical migration rate between demes, and time of divergence since two demes sprit off.  Traditional method, Fst, can be used to examine how two populations are genetically similar, but it does not differentiate effects of divergence time and gene flow.  IM, on the other hand, estimates migration rates accounting for other factors, such as divergence time.  Using this method, they estimated migration rates (Nm) ranging from 0.2 to 5.0 and global average of 2.4.

There are two interesting points to note.  First, IM has been used for studies of human evolution, but the older version that Cox et al. used is not suitable for this.  The older version of IM assumes that only two populations are exchanging genes after they were separated, but many human populations are interacting with multiple populations at the same time.  The new version is released in 2010 and it allows to analyze 2-10 demes.  Second, Hammer and his colleagues have argued that polygyny rather than female gene flow is the main cause for sex-biased demographic history.  Since a female carries two X chromosome, X chromosome variation is largely determined by females.  In this article, they have shown that female gene flow was common in our evolutionary history.

DNA genome of an unknown hominin from southern Siberia

Krause, J., Q. Fu, et al. (2010). “The complete mitochondrial DNA genome of an unknown hominin from southern Siberia.” Nature advance online publication.

This article is very interesting and also covered by and Prancing Papio.  I believe the research findings presented in this article provide an interesting perspective on the human evolution and genetic diversity existed in the past.

The complete mitochondrial DNA genome of the fossil remain from Denisova Cave in the Altai region of Russia dated to 48 to 30 kyr ago was analyzed.  Their results of analyses show that the Denisova individual was genetically very different from Neanderthals or modern humans.  An average of nucleotide position differences was 385 between the Denisova individual and modern human, which is about twice as many difference between Neanderthals and modern human (202 positions) (Figure 2).

The phylogenetic treesof complete mtDNA show that the ancestors of the Denisova individual sprit from the ancestors of Neanderthals and modern human, before archaic human lineages began diverge (Figure 3).  TMRCA of all three lineages is about one million years ago (mean=1,04,900 with 95% C.I. ranging 779,300-1,313,500).

So, who is this Denisova individual?  Home erectus left Africa and around 1.9 myr ago and was in Asia by 1.7 myr ago, so the Denisova individual was probably not H. erectus (TMRC of three lineages is about one myr ago.  That is after H. erectus spread into East Asia).  If the Denisova is H. erectus much older TMRCA is expected (> 1.9 myr?).  Homo heidelbergensis, probable ancestors of Neantherthals, emerged after divergence of three lineages.  However, since the 95% C.I. of TMRCA slightly overlaps with the time that H. heidelbergensis existed, so we cannot reject the hypothesis of the Denisova individual = a descendant of H. heidelbergenesis, but if H. heidelbergenesis were ancestors of Neanderthals, the ancestors of Neanderthals and the Denisova individual were genetically quit different.

The findings from this project generally support Huff et al. (2010) and these two projects have shown that great genetic diversity existed in the past (> 30,000 years ago).  It is very interesting that there were many species or subspecies of Home may have co-existed in some parts of the world.  Around time the Denisova individual lived, there is also possible existence of Neanderthal and anatomically modern human in the area (Don’t forget H. erectus existed in East Asia about same time).  However, only anatomically modern human survived and others disappeared without leaving clear genetic evidence of ancient admixture.

Update (April 1, 2010)

I forgot about H. ergaster and that is another possibility in addition to H. heidelbergensis.  If we believe that Asian H. erectus was a different species from African H. ergaster who were direct ancestors of H. heidelbergenesis, H. neanderthalensis, and H. sapiens, the Denisova individual could be a descendant of H. ergaster who took very different evolutionary path from Neanderthals and Anatomically modern human.  The 95% C.I. of TMRCA (1.-0.7) also slightly overlap with the time H. ergaster existed in East Africa (1.8-1.3 mya).  If we believe this scenario, first there was an out of Africa event of H. erectus into Asia and then another out of Africa event of H. ergaster into Western Eurasia.  However, TMRCA is too young for Asian H. erectus and all others to share the common ancestor that recent, so mtDNA of the Denisova individual is not that of H. erectus.  Of course, we are talking about only maternal side of evolutionary history.

Updata (April 3, 2010)

I considered the possibility of an unsampled Neanderthal, but I thought that the TMRCA is too old, considering that Neanderthals analyzed so far is genetically not diverse and effects of drift affecting mtDNA is strong because of small effective population size of mtDNA.  If, in fact, the Denisova individual was a Neanderthal, Neanderthal was genetically much more diverse than many genetic researchers thought and phylogenetic tree suggests that Neanderthals were ancestors of modern human.   Judging from the genetic evidence we have, this is unlikely scenario.  Of course, we should not conclude that the Denisova individual was not Neanderthals, because we do not know enough about this individual or human evolution.