New findings from the Denisova 3 genome at high coverage

Science today has released the new paper on the Denisova high-coverage genome by Mattias Meyer and colleagues from Svante Pääbo’s group: A high-coverage genome from an ancient Denisovan individual. There is a lot of material in the supplements of the new paper, and it will take some time to work through implications.

The basics are quite simple: The paper confirms the initial interpretation of the genome by David Reich and colleagues in most respects. The mixture with a whole-genome sample from Papua New Guinea is estimated at 6% Denisovan ancestry. Confirming the findings of a 2011 paper by Reich and colleagues, the new analysis finds no significant evidence of Denisovan ancestry in a mainland south Chinese (Han Dai) individual, and can exclude it down to a very small fraction:

However, in contrast to a recent study proposing more allele sharing between Denisova and populations from southern China, such as the Dai, than with populations from northern China, such as the Han (17), we find less Denisovan allele sharing with the Dai than with the Han (although non-significantly so, Z = 0.9) (Fig. 4B) (table S25). Further analysis shows that if Denisovans contributed any DNA to the Dai, it represents less than 0.1% of their genomes today (table S26).

That is a mystery to be explained. How did Asians end up lacking any evidence of Denisovan ancestry, when the peoples of Sahul (Australia and New Guinea) have six percent? It’s nutty! The early modern humans who were the ancestors of present Sahulian peoples surely came from Asia, and they surely mixed with Denisovans there somewhere, right? But today there’s no sign that present Asian peoples descended from those early Asian peoples.

We must, I think, conclude that there was at least one, and possibly several episodes of massive population movement across South and Southeast Asia.

I have recently completed a review of the analogous problem for Neandertals in Europe – late and early Neandertals themselves appear to have been a dynamic population. I’m now working on a review of the situation in Southeast Asia. We may fundamentally have to look at the archaeological record in a new, and much more dynamic, way than has been the case.

Neandertal gene flow

To me at the moment, this is the most interesting paragraph of the new paper:

Interestingly, we find that Denisovans share more alleles with the three populations from eastern Asia and South America (Dai, Han, and Karitiana) than with the two European populations (French and Sardinian) (Z = 5.3). However, this does not appear to be due to Denisovan gene flow into the ancestors of present-day Asians, since the excess archaic material is more closely related to Neandertals than to Denisovans (table S27). We estimate that the proportion of Neandertal ancestry in Europe is 24% lower than in eastern Asia and South America (95% C.I. 1236%). One possible explanation is that there were at least two independent Neandertal gene flow events into modern humans (18). An alternative explanation is a single Neandertal gene flow event followed by dilution of the Neandertal proportion in the ancestors of Europeans due to later migration out of Africa. However, this would require about 24% of the present-day European gene pool to be derived from African migrations subsequent to the Neandertal admixture.

My initial reaction to this difference is that it reflects the sharing of Neandertal genes in Africa. Meyer and colleagues filtered out alleles found in Africa, as a way of decreasing the effect of incomplete lineage sorting compared to introgression in their comparison. But if Africans have some gene flow from Neandertals, eliminating alleles found in Africans will create a bias in the comparison. If (as we think) some African populations have Neandertal gene flow, that probably came from West Asia or southern Europe. So as long as the present European and Asian (and Native American) samples have undergone a history of genetic drift, or if (as mentioned in the quote) they mixed with slightly different Neandertal populations, this bias will tend to make Asians look more Neandertal and Europeans less so.

Anyway, this demands further investigation. The Denisova genome makes a more compelling outgroup for these kinds of comparisons, because it is much closer to us than chimpanzees are. But it isn’t really an outgroup because it shares alleles by descent with Neandertals. So it takes some clever genetics to compare the distributions of derived alleles in these genomes in terms of introgression versus incomplete lineage sorting.

Denisovan demography

It has become possible to make some good estimates of demographic history using only a single diploid genome, using a technique developed by Li and Durbin (2011). Meyer and colleagues applied this technique to the Denisova genome, finding that its genetic history contrasts with that of living human populations:

To estimate how Denisovan and modern human population sizes have changed over time we applied a Markovian coalescent model (22) to all genomes analyzed. This shows that present-day human genomes share similar population size changes, in particular a more than two-fold increase in size before 125,000250,000 years ago (depending on the mutation rates assumed (23), Fig. 5B). Denisovans, in contrast, show a drastic decline in size at the time when the modern human population began to expand.

There is not yet enough data from Neandertal genomes to apply the same method, but to the extent that we understand their diversity, they show a similar picture. These archaic humans in Eurasia had much, much smaller effective population sizes than the ancient population of Africa. That’s not surprising, given what we understand about ancient hunter-gatherer population dynamics.

What may be a bit more surprising is the geography. We know that Neandertals of Europe and Central Asia lived in an environment that was relatively marginal for their technology and subsistence pattern. The Denisovan population could well have lived in parts of South or Southeast Asia – subtropical and tropical areas comparable to Africa in their ecological diversity and resource richness.

We might have imagined that the Denisovan population would be more diverse than Neandertals – that it might have been comparable in diversity to part of Africa, if not the entirety of Africa. The genome is inconsistent with that picture.

How can we explain the apparent contrast?

Maybe Denisovans didn’t live in South or Southeast Asia at all. If not, that demands that we explain how Australians got their genes.
Maybe the population was geographically extensive and diverse, but the genome from Denisova Cave doesn’t represent it well. If so, we might discover that Sahulians actually have even more ancestry from this group. Alternatively, we might find that the early history of the population was widely shared, but the recent history diverged between Siberian and other branches of the Denisovan-inhabited region.
Maybe African diversity emerged from a much more complex series of interactions than we now appreciate. The demographic model of Li and Durban doesn’t encompass admixture, just the probability of gene coalescence across time. We have recently begun to appreciate the reality of ancient African population structure. If those initial African populations were more divergent from each other than Neandertals and Denisovans, their later mixture would give rise to a picture of early population expansion, even if each of them had relatively low (Denisovan-like) diversity.

This picture is already complicated. It will get more so. We have a long way to go before the archaeology of MSA and Middle Paleolithic peoples will be reconciled with these genetic models.

The "modern human" catalog

I think it’s tremendously interesting that the authors have compiled a list of gene variants shared by living humans that are absent from this high-coverage archaic human genome. It’s a first step to identifying networks of genes that have been subject to recent evolutionary change in human ancestors.

That being said, the list of genes itself doesn’t lend itself to concrete conclusions:

One way to identify changes that may have functional consequences is to focus on sites that are highly conserved among primates and that have changed on the modern human lineage after separation from Denisovan ancestors. We note that among the 23 most conserved positions affected by amino acid changes (primate conservation score ? 0.95), eight affect genes that are associated with brain function or nervous system development (NOVA1, SLITRK1, KATNA1, LUZP1, ARHGAP32, ADSL, HTR2B, CBTNAP2). Four of these are involved in axonal and dendritic growth (SLITRK1, KATNA1) and synaptic transmission (ARHGAP32, HTR2B) and two have been implicated in autism (ADSL, CNTNAP2). CNTNAP2 is also associated with susceptibility to language disorders (27) and is particularly noteworthy as it is one of the few genes known to be regulated by FOXP2, a transcription factor involved in language and speech development as well as synaptic plasticity (28). It is thus tempting to speculate that crucial aspects of synaptic transmission may have changed in modern humans.

Interesting. I can imagine a Ph.D. dissertation looking into the function of each of those genes. It is surely true that in the last 300,000 years, human brains have been evolving. But why these genes as opposed to others? And how many regulatory changes (as opposed to amino acid changes) may have been further involved?

Maybe even more interesting: How many times will the human alleles be found in some other Denisovan (or Neandertal) genomes, and how often will the “archaic” allele be found in anyone living now?

A limited series of comparisons is too small to exclude that the range of variation will overlap, as fossil analysts have known for a long time. So we will need to work on extending our knowledge of the range of variation within living people, by increasing the sample of genomes representing populations around the world, particularly in Africa.

The technology

Of course, the most exciting thing about the new paper is the proof of concept for future high-coverage archaic genomes. The lab was able to generate the high-coverage sequence using its existing samples, by sequencing single-strand DNA instead of requiring double-strand DNA. This is a massive advantage when working with ancient DNA, because damage to the sequence often prevents double-stranded DNA from being amplified.

The paper makes explicit that the Denisova phalanx simply has better endogenous DNA preservation than any other specimen known. That being said, the new sequencing method has greatly increased the sequence yield from the sample:

We applied this method to aliquots of the two DNA extracts (as well as side fractions) that were previously generated from the 40 mg of bone that comprised the entire inner part of the phalanx (2, 8). Comparisons of these newly generated libraries to the two libraries generated in the previous study (2) show at least a 6-fold and 22-fold increase in the recovery of library molecules (8), which is particularly pronounced for longer molecules (fig. S4).

It would be too soon to say that a similar increase in yield will happen for other specimens, but obviously, this may bring higher coverage into reach for several specimens that are currently only sequenced at very low coverage, including the Vindija, Mezmaiskaya, and El Sidron Neandertals. We will have to wait and see how the new technique affects ancient DNA recovery going forward.

I keep telling people that I think it’s exciting that research into human evolution is now pushing technology forward. It has often been that paleoanthropology uses technological advances in other fields. But with ancient DNA, we really see an organic growth of technology along with research questions about our evolution. In our work on the ancient genomes, we’re making some progress pushing forward knowledge about human biology by understanding human evolution. Evolution really is the fundamental principle of biology, but using evolution to learn about biology sometimes requires traveling through time. Ancient DNA gives us a time machine bringing new insights into reach.