An 8,400-year-old skeleton from the Qihe cave archaeological site in Fujian, China.Credit: Xiujie Wu/Institute of Vertebrate Paleontology and Paleoanthropology
A genômica antiga está começando a desvendar a história do leste da Ásia. Os primeiros estudos em larga escala de genomas humanos antigos da região sugerem que muitos de seus habitantes descendem de duas populações outrora distintas que começaram a se misturar após o desenvolvimento da agricultura, cerca de 10.000 anos atrás.
Os estudos também revelam conexões entre humanos antigos que se estendem do sul da China ao Pacífico Sul, e um elo entre habitantes costeiros que poderia oferecer pistas sobre como os humanos se estabeleceram no leste da Ásia.
Os resultados são de um estudo de 14 de maio na Science que analisou os genomas de duas dúzias de chineses antigos1 e uma pré-impressão de março que analisou quase 200 genomas antigos de todo o leste asiático2.
Os asiáticos orientais contemporâneos descendem, em geral, de humanos que deixaram a África entre 50.000 e 100.000 anos atrás.
Mas os pesquisadores sabem pouco sobre as antigas mudanças populacionais que moldaram os genomas dos 1,7 bilhão de habitantes atuais da região. Apenas um punhado de genomas humanos antigos do Leste da Ásia foi publicado, e não está claro como eventos importantes, como a disseminação da agricultura - que alterou drasticamente a composição genética dos eurasianos ocidentais - afetaram essa parte do mundo.
Uma equipe liderada por Qiaomei Fu, geneticista populacional do Instituto de Paleontologia e Paleoantropologia de Vertebrados em Pequim, analisou os genomas de 24 indivíduos que viviam no leste da Ásia, incluindo o que é hoje a China, entre 9.500 e 300 anos atrás. A maioria dos genomas veio de sítios arqueológicos na Bacia do Rio Amarelo, no nordeste da China, ou a mais de 1.000 quilômetros de distância, na província de Fujian, no sudeste da China.
North–South divide
Durante o período neolítico inicial, cerca de 10.000 a 6.000 anos atrás, as pessoas dessas duas regiões geográficas eram geneticamente distintas, concluiu a equipe de Fu. Mas, com o tempo, começaram a se misturar: os chineses contemporâneos traçam grande parte de seus ancestrais aos grupos do norte, mas também estão relacionados ao antigo povo Fujian em graus variados (os do sul da China tendem a ser os mais próximos). A equipe de Fu não sabe exatamente quando os dois grupos começaram a se cruzar, mas viu sinais de que a assinatura genética do norte havia começado a se espalhar para o sudeste da China na época do final do neolítico de 5.000 a 4.000 anos atrás.
This suggests that farming
in East Asia could have spread through mixing of farmers and
hunter-gatherers, says Ling Qin, an archaeologist at Peking University
in Beijing. That’s different from what ancient-genome studies have found
in western Eurasia, where farmers with Middle Eastern ancestry largely
replaced hunter-gatherers in Europe3.
Farmers
in the Yellow River Basin also moved west. A team led by David Reich, a
population geneticist at Harvard Medical School in Boston,
Massachusetts, who is a co-author of Fu’s study, analysed the genomes of
20 5,000-year-old individuals from this region and found connections
with contemporary Tibetans. Their results are part of a study of 191
ancient individuals from East Asia that was posted to the bioRxiv
preprint server on 25 March.
Distant relatives
The studies
also revealed some surprising long-distance connections. Neolithic
people who lived near China’s coast, whether in the northeast or
southeast, shared some ancestry with ancient individuals from coastal
sites in southeast Asia and Japan. “That means the entire coast of East
Asia is a really important place for people to migrate,” says Fu. Reich
and his team found a similar connection, which they say could be
evidence that modern humans first settled in East Asia along a coastal
route.
Ancestry from southeastern China stretched even farther
afield. Neolithic individuals from Fujian and islands in the Taiwan
Strait were closely related to ancient islanders from Vanuatu in remote
Oceania, Fu’s team found. Previous ancient-genome studies had recorded
the spread of this ancestry from East Asia to Oceania4, and Fu’s study suggests that this group originated in southern China.
This
conclusion makes sense, says Matthew Spriggs, an archaeologist at
Australian National University in Canberra. Archaeological and genetic
evidence has linked the South Pacific migration to ancient humans in
Taiwan, whose Neolithic inhabitants probably came from southern parts of
the mainland. “I am very pleased to see this paper,” he says of Fu’s
study.
Qin says that the ancient southern Chinese people in Fu’s
study lived in an isolated pocket that might not be representative of
the wider area. A priority should be sequencing DNA from early farmers
from the Yangtze River Basin in southern China, a centre for rice
domestication and a potential source of other migrations, she adds.
The
studies give researchers hope that ancient genomics will help them
delve even deeper into East Asia’s early history. Pontus Skoglund, a
population geneticist at the Francis Crick Institute in London, would
love to know whether the first Homo sapiens to settle in the
region interbred with Denisovans, an extinct group of hominins. “I think
one of the most exciting open big questions of the region relates to
the pre-Neolithic settlement — who were the earliest modern humans in
the region?” adds Martin Sikora, a geneticist at the University of
Copenhagen.
doi: 10.1038/d41586-020-01456-9
References
1.
M. A. Yang et al.Science https://doi.org/10.1126/science.aba0909 (2020).
sexta-feira, 28 de setembro de 2018
Inferring Human Demographic Histories of Non-African Populations from Patterns of Allele Sharing
Estudos recentes de genética humana chegaram a conclusões diferentes sobre como e quando os humanos modernos se espalharam da África para o resto do mundo. Eu apresento aqui uma análise simples baseada em parcimônia que sugere que os asiáticos e melanésios orientais são grupos irmãos, e discuto quais implicações isso tem para alegações recentes feitas sobre as histórias demográficas de populações não africanas.
Introduction
Anatomically modern humans are thought to have evolved in sub-Saharan Africa 150,000–200,000 years ago
(150–200 Kya) and from there to have eventually colonized the rest of
the world. It is unclear, though, how and when modern humans first left
Africa and whether there was one major migration out of Africa or more
than one. One theory, the “coastal migration model” (CMM), posits that
the first modern humans to leave Africa departed from the Northeast
through the Arabian peninsula, then along the coasts of India and
Southeast Asia until they reached Australia roughly 50 Kya.
Descendants of this first wave of migration are hypothesized to include
aboriginal Australians, Melanesians, and possibly so-called Negrito
groups in South and Southeast Asia. Then, a second dispersal out of
Africa led to the colonization of the rest of the world, including
mainland Europe, Southeast Asia, and the Americas. The other major
hypothesis posits that there was a single major migration out of Africa
and that all extant non-African populations are descended from these
first migrants.
From
a genetic perspective, it is not always clear what predictions
different models make. Researchers have focused instead on questions
that are more easily answerable. Figure 1
shows two possible branching orders for West Africans, Europeans, East
Asians, and Melanesians, as well as interpretations in terms of waves of
migration out of Africa (OOA) and into Asia (I2A). With the recent
proliferation of whole-genome sequence datasets from diverse
populations, it should be easier to distinguish between these competing
demographic models. Rasmussen and colleagues
published the genome of an aboriginal Australian individual and
observed that this individual’s genome shows a greater divergence from
African genomes than do genomes from mainland Eurasia. They interpreted
this observation as evidence for the CMM and suggested that the greater
divergence was due to an older separation time. However, this
observation has an alternative explanation in light of the fact that
aboriginal Australians (but not mainland Eurasians) have experienced a
substantial amount of admixture from Denisovans.
posited that Melanesians contain some ancestry from an older
out-of-Africa migration (which we call “ghost” admixture) and implied
(though did not state explicitly) that Figure 1A is the correct branching order (see, e.g., their Extended Data Figure 4A). In contrast Figures 1 and 3 in Mallick et al.
suggested that Europeans are an outgroup in comparison to East Asians and Melanesians (i.e., Figure 1B).
One difficulty in assessing the merits of these competing claims is
that they are based on the output from complicated inference tools such
as Fastsimcoal,
Given the intricate nature of these methods, it is difficult to assess
how sensitive they are to model assumptions and sampling biases or to
determine whether they have been implemented correctly. In addition,
both MSMC and fineSTRUCTURE require (haplotype) phased data, making them
less reliable for analyses involving human populations not included in
the HapMap
In
this study, I present a straightforward, easy-to-understand analysis of
patterns of human genetic variation. Based on the phylogenetic concept
of parsimony, this analysis can be thought of as an estimate of the
average branch lengths, across the whole genome, of internal branches in
the (unobserved) genealogies of sampled individuals, and it is similar
to previously proposed approaches, such as D-statistics
for inferring branching orders and detecting admixture. The results
reported here suggest that the true branching order of the major
non-African populations is easy to recover, even with relatively
unsophisticated analytical methods.
Material and Methods
Human Sequence Data
I started with the variant calls used in Pagani et al.
(downloaded from the Estonian Biocenter website) and filtered them to
consider only biallelic autosomal SNPs. I further considered only those
variants where the reference allele was the ancestral allele and where
the genotype was homozygous reference in 21 West and Central African
(nine Yoruba, four Luhya, and eight Pygmy) genomes and in Neanderthal
and Denisovan genomes. For each such variant, I then tabulated the
frequency of the alternative allele across two test populations from
Europe, East Asia, and Melanesia (i.e., Tuscan, Croat, Han, Japanese,
Kosipe and Koinanbe).
Allele-Sharing Statistics
Suppose we have samples from a European, an East Asian, and a Melanesian population with sample sizes nE, nA, and nM, respectively. Then, for a set of S SNPs, we use ei, ai and mi to denote the number of copies of the derived allele at the i-th SNP in the European, East Asian, and Melanesian samples (0 ≤ ei ≤ 2nE, etc.). We then define KEA as follows:
The
term in the summation is the probability that randomly chosen single
alleles from each of the three non-African populations will yield a
derived allele shared between the European and East Asian samples and an
ancestral allele in the Melanesian sample. KEM and KAM
are defined analogously. Note that it is straightforward to modify the
denominator to handle missing data in any of the samples. Finally, we
define PEA, PEM, and PAM as KEA/(KEA+KEM+KAM), KEM/(KEA+KEM+KAM), and KAM/(KEA+KEM+KAM),
respectively. Using a parsimony assumption, these three values reflect
the proportions of phylogenetically informative SNPs that support each
of the three possible tree topologies (Figure 2,
assuming the West and Central African samples described above are an
outgroup). Although the true topologies are expected to vary across the
genome due to incomplete lineage sorting and additional demographic
factors not considered here (e.g., migration), the relative number of
variants supporting each topology is informative about the average
genealogical history of the samples and thus the true branching order of
the populations. This same approach was used in some of the work that
showed that chimpanzees (and bonobos) are our closest living relatives.
All of these are counting statistics that condition on the presence or
absence of derived alleles across individuals. For example, although
enhanced D-statistics count sites that are homozygous ancestral in
sub-Saharan Africans but derived in the Denisovan genome, the method
introduced here counts variants that are homozygous ancestral in
sub-Saharan Africans, Neanderthals, and Denisovans.
Simulations Using the Malaspinas et al. Model
I obtained the exact simulation parameters used for generating data under the model shown in Malaspinas et al.’s Figure S07.3
to simulate sequence data. I assumed (diploid) sample sizes of 21, 3,
3, and 3 for the West African, European, East Asian, and Australian
populations. ms0ancient2 is a simple modification of Hudson’s ms;
it changes branch lengths to allow for the past sampling of two archaic
genomes. To sample non-African sequences roughly 42 Kya, I shifted the
scaled times of events by 0.032324 (I used the same time scaling as in
Malaspinas et al.
).
Both sets of simulations included a total of 2.05 Gb of simulated
sequence to roughly approximate the total length of the genome that was
available after filtering.
Simulations Modeled after Pagani et al.
I constructed five simple branching models (without migration) based on the split times described in Pagani et al.
are actual population split times. Models 1–3 are simple branching models (Figure 3A)
with a split time between Europeans and East Asians of 30 Kya, a split
time between Eurasians and Papuans of 40 Kya, and a split time between
Eurasians and West Africans of 75 Kya. Papuans also derived 0%–5% of
their ancestry from an unsampled “ghost” population that was completely
isolated from 38–120 Kya. Models 1, 2, and 3 have 0%, 2%, and 5% “ghost”
admixture, respectively. These models also assumed 4% admixture from
Denisovans into Papuans 35 Kya, 2% Neanderthal admixture into
non-Africans 65 Kya, an Altai Neanderthal sampling time of 130 Kya, a
Denisovan sampling time of 100 Kya, an intra-Neanderthal split time of
150 Kya, an intra-Denisovan split time of 350 Kya, a
Neanderthal-Denisovan split time of 425 Kya and an archaic-modern split
time of 650 Kya. These parameter values, while somewhat arbitrary, were
taken from the literature
I also tried to construct a model that followed a similar branching order as in Extended Data Figure from Pagani et al.16
(but without the West European hunter-gatherer population).
Specifically, I assumed that the ancestral European population split
10 Kya into two groups (i.e., ANE and Basal European). One of these
(ANE) merged with the ancestral East Asian population 30 Kya, whereas
the other (Basal European) merged with the ancestral
Melanesian–East-Asian population 45 Kya (Figure 3B).
All other model parameters were assumed to be the same as above. Models
4 and 5 assumed a 20%–80% or an 80%–20% split of European ancestry into
the ANE and Basal European groups, respectively.
Additional Simulations
I tested additional demographic models to better understand how specific parameter values affect the relative values of PEA, PEM, and PAM.
These models were similar to the Pagani models 1–3 described above. One
set of simulations took Pagani model 2 and added a single pulse of
migration from the East Asian population into the Melanesian population
10–20 Kya (Figure 3C).
This pulse accounted for 20%–90% of the subsequent Melanesian gene
pool. The other set of simulations used the same population branching
order as in Figure 1B,
but with all other model parameters (e.g., Neanderthal and Denisovan
admixture, “ghost” admixture, and African–non-African divergence time)
being the same as in Pagani models 1–3. These simulations had
East-Asian–Melanesian split times from 40–45 Kya and European–East-Asian
split times of 50–60 Kya. For each parameter combination, we simulated
2.05 Gb of sequence and estimated PEM, PEA, and PAM.
We also calculated these parameters on sites that conditioned on the
homozygous reference allele in only the 21 sub-Saharan Africans and the
Neanderthal, to see what effect the Denisovan conditioning had on the
proportions.
Results
I
focused on variants that were not present in sub-Saharan Africans or
archaic humans and that are thus likely to have arisen after the
dispersal of modern humans out of Africa. I also assumed that West
Africans are an outgroup with respect to all non-Africans (although this
assumption can be relaxed). Given a single representative sequence from
each of three non-African groups, there are three possible tree
topologies, each containing a single internal branch and a unique
phylogenetically informative variant (PIV) pattern (Figure 2).
Specifically, if exactly two out of three of the sequences share a
derived allele, then the genealogy at this site has (under the parsimony
assumption) the two populations with a derived allele as sister groups.
In analyzing the whole-genome sequence data described in Pagani et al.,
I considered a panel of 21 West and Central African samples (nine
Yoruba, four Luhya, and eight pygmies), two representative European
populations (Tuscans and Croats), two representative East Asian
populations (Han and Japanese) and two representative Melanesian groups
(Kosipe and Koinanbe). I tabulated the proportion of PIVs supporting
each topology (PEA, PEM, and PAM, cf. Figure 2)
across each of the eight combinations of one European, one East Asian,
and one Melanesian population; these proportions were averaged over all
possible choices of a single haploid sequence from each population. The
results were highly consistent across the different population
combinations; PAM ranged from 0.418–0.430, and PEA and PEM were always <0 .3="" a="" class="scroll-into-link" href="https://www.cell.com/ajhg/fulltext/S0002-9297(17)30146-5#tbl1" id="back-tbl1">Table 1
).
So, although the “true” demographic history of non-African populations
was certainly very complex, on average Melanesian and East Asian
sequences are more genetically similar (and share a more recent common
ancestor) than do European and East Asian or European and Melanesian
sequences, and Figure 1B
is likely to be the true population branching order. Note that the same
conclusion was reached in the neighbor-joining tree shown in Figure 1a of Mallick et al.
). My a priori expectation was that PEA would be slightly larger than PEM and PAM because their model, like Figure 1A,
has Melanesians as an outgroup with respect to Europeans and East
Asians. Surprisingly, I found instead that simulations under their model
produce large PAM values, as expected under the branching order shown in Figure 1B (with Europeans as an outgroup with respect to East Asians and Melanesians) and as observed in the actual data (Table 1).
A closer look at the parameters in their model (Tables S07.3 and S07.5)
explains this apparent discrepancy. The estimated split times for
Aboriginal Australians (58 Kya) and ancestral Eurasians (57 Kya) are
almost identical, and more importantly, these two ancestral populations
are connected by high scaled migration rates (4Nm > 10, cf. Table
S07.5 in Masaspinas et al.
)
after the split. One consequence of the high migration rates is that
the ancestral Australians do not start diverging genetically from
ancestral Europeans and ancestral East Asians until after the
European–East-Asian split 42 Kya (when the migration rates decrease). I
verified that this is the case by simulating sequences that were sampled
from each non-African population right after the European–East-Asian
split under the Malaspinas model. I found that the expected FST
B.
Furthermore, any model, including models with all possible branching
orders for non-African populations, that has the European, East Asian,
and Australian populations splitting roughly simultaneously 42 Kya (and
with the same migration rates since then) should have roughly the same
likelihood as the model presented in the Malaspinas paper.
I also tried to explore the claims made by Pagani and colleagues.
Although they do not propose a specific demographic model, they do
assume in the text that median genetic-split times estimated from MSMC
analyses can be thought of as genetic divergence times between
populations. I constructed five simple population models by using
MSMC-estimated split times and assuming that Papuans have 0%–5% of their
ancestry from an unsampled “ghost” population, which branched off from
other modern human groups 120 Kya (Figure 3). Three of these models used the topology shown in Figure 1A
because Pagani et al. estimated a Papuan-Eurasian MSMC split time of
∼40 Kya and a European–East-Asian MSMC split time of ∼30 Kya,
whereas two models (based on Pagani et al.’s extended Data Figure 10)
incorporate heterogeneity in the true population topology across the
genome (see Material and Methods for details).
As expected, simulations under these models produce larger PEA values and smaller PAM values than what was found in the actual data (Table 1).
These results are insensitive to assumed “ghost” admixture proportions
(i.e., there is little difference between the results of models 1–3) but
rather are a simple consequence of the models’ underlying population
topology.
To test whether other,
similar demographic models might produce results more in-line with
observations, I also ran simulations under a model with the topology
shown in Figure 1A, but with recent (one-way) migration from East Asians into Melanesians (Figure 3C), and under a model with the same topology as in Figure 1B. These simulations show that one-way migration increases the relative value of PAM (Table S1
in the Supplemental Data available with this article online).
Qualitatively, this is because those regions of the genome affected by
recent migration have a true topology with Europeans as an outgroup, and
the overall values of PEA, PEM, and PAM
reflect a weighted average of the different true genealogical trees
across the genome. However, the magnitude of this effect does not seem
to be enough to produce PAM values as large as those that are
observed, even if Melanesians derive 80%–90% of their genome from East
Asian migrants as recently as 10 Kya. However, such an extreme migration
model would be inconsistent with the roughly constant levels of
Denisovan ancestry observed across Melanesian and aboriginal Australian
populations.
My
simulations with Europeans as an outgroup with respect to other
non-African groups showed several things. First, as expected, increasing
the difference between the European and East Asian and between the East
Asian and Melanesian divergence times increases the relative value of PAM because it increases the average internal branch length in Figure 1B. Second, there are parameter combinations that produce PEA, PEM, and PAM
values similar to those that are observed, but these require Europeans
to be a clear outgroup relative to East Asians and Melanesians (Table S1).
Third, both sets of simulations show that conditioning on the absence
of alleles in the Denisovan genome has almost no effect on PEA, PEM, and PAM.
Finally, the presence of low (e.g., ≤5%) levels of “ghost” admixture
has only a minor effect on allele-sharing statistics and is completely
unnecessary for explaining the observed values of PEA, PEM, and PAM.
Discussion
In
summary, we have shown that East Asian and Melanesian samples have an
excess of shared derived variants, reflecting their genetic similarity
to each other. (I reached the same qualitative conclusions with the
Malaspinas et al.
dataset, but the genomes are not fully publicly available.) These
results are incompatible with simple versions of the CMM because an
older divergence of Melanesian populations would lead to larger PEA
values. My focus on shared variants is crucial because it concentrates
on the internal branches present during the times when
non-African populations diverged from each other. If instead I had just
counted the number of derived variants that were present in non-Africans
and homozygous ancestral in 21 sub-Saharan Africans, the Neanderthal
genome, and the Denisovan genome, then Melanesians would have more of
these sites (an average of 62,546) compared with East Asians (an average
of 57,574) or Europeans (an average of 47,054). These “non-African
alleles” are less informative about the true population branching order
because they reflect a combination of other demographic factors, such as
ancient admixture between Melanesians and Denisovans, as well as more
recent admixture between Southern Europeans and sub-Saharan Africans.
Although I cannot formally rule out Pagani et al.’s claim of “ghost” admixture into the ancestors of Melanesians,
there are several factors that make this highly unlikely. My simulations suggest that the large observed PAM values are only possible with models having Europeans as an outgroup with respect to East Asians and Melanesians (Table 1 and Table S1). However, this branching order is inconsistent with the observed MSMC split times,
and these same split times were used prominently in Pagani et al.’s
justification for their claim of “ghost” admixture. Simulations suggest
that haplotype-based methods such as MSMC are extremely sensitive to
phasing errors,
and this problem is most likely exacerbated by the small number of
Melanesians included in the Pagani et al. study (just three Kosipe and
three Koinanbe).
Finally, the old Papuan–West-African split time estimated by Pagani and colleagues,
and used as motivation for their claim of “ghost” admixture, is
probably a simple consequence of Denisovan admixture into Melanesians.
It is important to emphasize that the large genetic distance between the
Altai Denisovan genome and the genomes of the Denisovans that interbred
with the Melanesian lineage
make it impossible to successfully “mask” out all Denisovan ancestry
tracts in contemporary human genomes. This is evident from the fact that
PEA > PEM even in simulations without “ghost”
admixture and from the small effect that masking putative Denisovan SNPs
has on PIV proportions (Table S1).
In addition, PEA and PEM correspond to groups 2 and 1, respectively, in the D4P test,
so Rasmussen et al.’s evidence for the CMM (i.e., that group 2 is larger than group 1, or equivalently that PEA > PEM)
can be directly ascribed to the effects of Denisovan admixture.
Furthermore, my analyses suggest that effective methods for
distinguishing between the models listed in Rasmussen et al.’s Figure 1A would include sites ancestral in Europeans but derived in East Asians and Melanesians or aboriginal Australians (i.e., PAM).
The analyses presented here have been primarily qualitative. Although PEA, PEM, and PAM
could in principle be used as summary statistics in a likelihood-based
parameter estimation framework, they are more useful as heuristic
descriptors of the data. As such, they can serve as a “sanity-check” on
the conclusions of more sophisticated (but also more opaque) analytical
tools. The challenge then remains for proponents of models featuring
multiple major waves of migration of modern humans out of Africa to
produce an explicit, testable model of human demography that at a
minimum can produce the same qualitative patterns of genetic variation
as those described above.
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