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- [Narrator] We are the paradoxical ape.
Bipedal, naked, large-brained,
long the master of fire, tools, and language.
But still trying to understand ourselves.
Aware that death is inevitable,
yet, filled with optimism.
We grow up slowly.
We hand down knowledge.
We empathize and deceive.
We shape the future from our shared understanding
of the past.
Charta brings together experts from diverse disciplines
to exchange insights on who we are and how we got here.
An exploration made possible
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(cheerful music)
- Or I can tell you about using ancient DNA
to try and understand evolution
and natural selection actually among humans today.
So, we've heard a lot about using ancient DNA
to understand archaic humans,
understand the relationship
between different archaic populations,
and the interactions between archaic
and modern human populations.
But, most of the work in ancient DNA
is actually focused on more recent populations,
particularly populations' movements
in the last 10 to 15,000 years.
And that's partly because of sample availability
and preservation of DNA,
but it's partly because that's a time period
when there are a large number of population movements,
if you will, migrations,
and I'm just showing you here
some of the recent population movements
that have been investigated in depth
using ancient DNA.
Now, what I'm interested in is trying to understand
not just how and when these population movements happened,
but as people moved into new environments,
how they adapted, and in particular,
how they adapted genetically to different environments.
Now, you see, there are a lot of arrows here in Europe
and there's been a lot of focus on Europe,
and I'm going to focus largely on Europe
and western Eurasia.
That's not because it's necessarily
the most important place,
or the most interesting place in the world,
but it's because it's really the only place,
as of today, that we have the large samples
that we need to study the kind of questions
we're going to look at.
So, what does the genetic structure
of Europe look like today?
Well, this is a rather famous figure
that shows you what genetic variation looks like in Europe.
So, here in the larger plot
is a principal component analysis of genetic data.
You can think of this as a genetic map.
People are close together on the map
if they are more genetically similar,
and further away if they're genetically more distant.
Now, each individual here is colored
according to their country of origin,
and if you look at the inset map,
you'll see the geographic structure,
and basically, the point here is if you look
at the genetic map and turn your head
and squint a little bit,
it looks like the geographic map.
So, the genetic structure of Europe
recapitulates the geographic structure.
So, by looking at genetic data,
we can learn about geographic structure.
And so, if I gave you an individual's genome,
you would be able to tell with some accuracy
which part of Europe they were from
or at least which part of Europe
their recent ancestors were from.
So, this is what ancestry testing companies do
in order to tell you what your ancestry is.
Now, the idea is that by looking at this kind of genetic map
we can learn not only about geographic structure
but also about historical structure.
And this is an idea that really goes back
to the work of Cavalli sports or in the 60s
and perhaps even earlier than that.
But there's a problem with trying to learn about
historical demography from this kind of data.
And that's that present day data
really gives us a very small window into the past.
So if I show you the same kind of plot,
including ancient Europeans,
you get a very different picture.
So now present day Europeans in this plot are in gray
and ancient Europeans dating back 45,000 years.
So this is essentially the entire time that
anatomically modern humans have been in Europe.
And the first thing you notice are that
the ancient Europeans are well outside the range of
present day genetic variation.
And it's not until the bronze age and even really
the late bronze age that you see ancient Europeans
who look genetically similar to present day people.
So you can immediately see it's going to be very difficult
to make inference about these past populations
if we're just trying to sort of look through the tiny window
that we get from present day data.
Now, the other thing we get from ancient DNA
that's very powerful is a very clear sense of time.
So here I'm showing you a time series of ancient Europeans
over the last 15,000 years.
So X axis is time.
Each column is an ancient individual
and they're colored according to how much ancestry they have
from each of three inferred source populations.
And you can immediately see just visually
looking at this plot, the genetic impact of events like
the introduction of agriculture and this transition
from this red hunter gatherer ancestry
to ancestry that's associated with early farming populations
in blue.
You can see those two groups continue to mix
over the next few thousand years.
And then around 4,500 years ago
we see the appearance of this green ancestry component,
which is associated with population movements
from the Eurasian steppe.
So in some sense, this is why people think of ancient DNA
as cheating because you can see these population movements
directly without having to infer them using
complicated statistical methods from present day data.
Now what I'm showing you here is a genome wide average.
So this is looking at if every average the ancestor
of each individual over over their whole genome,
we see these kind of systematic changes.
And what we want to know is, are there parts of the genome
that don't follow this pattern?
Because if there are parts of the genome which either
change more rapidly or in some cases less rapidly,
those are the parts of the genome which we think
are likely to have been targeted by natural selection.
So their evolution has been driven by selection
rather than in this case drift and admixture.
So if we do that, we get a map like this.
So what I'm showing you here is a scan for
selection across the genome using ancient DNA.
So on the X axis is chromosomal position.
Each point is a particular genetic marker.
And the Y axis is showing you the evidence that
that marker is not following this genome-wide
average pattern that I showed on the previous slide.
So this is called a Manhattan plot.
And when you see a skyscraper with a lot of variants
lined up, that means that that part of the genome
has a large number of markers
which are not behaving neutrally
and are likely to have been targeted by selection.
Now often when you do this kind of scan,
all of these hits, all of these signals are in
obscure regions of the genome and you can't figure out
what they do and it's very hard to interpret.
But fortunately when we did this analysis,
it turned out that almost all of these lay in genes
that were well annotated and which we can make
a pretty good guess of the function.
So that gives us some confidence that these are
real signals of selection.
So I'm showing you here the gene names.
If we ask, what do these genes do?
It turns out that they fall into three main categories.
So there are genes associated with diets,
genes associated with pigmentation,
particularly skin pigmentation and genes associated with
the immune system.
So this kind of makes sense.
This is the time which agriculture is introduced.
People's diets changed dramatically
that people are moving from lower latitudes into
Northern Europe.
We think a selection for lighter skin pigmentation
is related to vitamin D availability.
And of course people are moving into new environments
but also into denser populations and
living in close proximity to domesticated animals,
we expect them to experience new pathogen challenges.
And so see selection on the immune system.
So again, this set of genes is in some sense
what we expect and gives us some confidence that we are
finding real signals.
Now as I mentioned, one of the things we can do with
ancient DNA that's very powerful is to get a
very clear sense of the timing of selection
on some of these.
So if we look at the lactase persistence locus,
which is the strongest signal of selection
in the entire human genome, we can see exactly
when it starts to become common.
So many of you are familiar with this.
Most adults in the world today do not have the ability
to digest lactose as adults.
There are a number of mutations
in different parts of the world which allow this ability
to persist into adulthood.
And what I'm showing you here is the allele frequency
of the mutation, which today is relatively common in Europe.
In different parts of Europe.
And what you can see is that 5,000 years ago,
no one or almost no one in Europe had this mutation,
so no one had lactase persistence.
And that's interesting because
that's many thousands of years after the domestication of
cattle, goats and actually after archeological evidence of
dairying and use of milk products.
You see that in Britain and Ireland and in central Europe,
also in Scandinavia, not shown here,
the mutation starts to become in three to 4,000 years ago,
increases rapidly in frequency before actually leveling off
maybe around a thousand years ago.
On the other hand, in other parts of West Eurasia
where this mutation is common in Iberia and
in the Indus Valley, it's not selected until much later,
perhaps one to 2000 years ago.
It increases rapidly in frequency
and doesn't apparently seem to have leveled off.
This data stops maybe a few hundred years ago.
Now the idea is by looking at these kind of
very detailed temporal data and correlating with
archeological and cultural data,
we can start to try and understand why
this particular mutation would be so strongly selected.
As I said, it's the strongest signal of natural selection
in the entire human genome.
We still don't really understand the exact mechanism
of selection pressure.
Other interesting loci are also related to diet,
so one of my favorite genes is FADS.
There are a number of genes that the FADS locus associated
with fatty acid metabolism and we think that
the derived allele here is an adaptation to a diet
rich in plant fats as opposed to animal fats.
And you can see here that a Mesolithic hunter gatherers
do not carry this allele.
It appears in Europe and with early farmers
and then it's rapidly selected actually in the bronze age,
we think likely related to the advent of
intensive cereal agriculture.
One of the interesting things about this allele
is that today in medical genetic studies,
this is one of the strongest associations in the genome
with LDL cholesterol and triglycerides.
So this is an example where our evolutionary history,
in fact, our recent evolutionary history
has a big effect on our health today.
Now, some of the things that are actually very interesting
are not the things that are under selection,
but the things that are not under selection.
One example of that is salivary amylase.
So the salivary amylase gene
allows you to start breaking down starch in saliva.
It seems like the sort of thing that would be
an adaptation to an agricultural diet
based around starchy products.
Today, most humans have many copies of this gene.
In the top row here you can see the distribution of copies
in present day Europeans, you see most people have
on the order of six to 12 copies.
Now, looking at the ancient populations going down the list,
you see that actually most of the ancient populations
we look at have copy numbers, which are not
significantly different from present day Europeans.
In particular, Mesolithic hunter gatherers do not have
less copy number than present day Europeans.
So it seems like in this case,
what seems like it should be an adaptation to agriculture
actually predates the introduction of agriculture per se.
Finally, all of the variants I've talked about so far
have been relatively recent mutations,
but actually some of these mutations
turn out to be very old.
And in particular some of the mutations associated with
the immune system turn out to have introgressed
from archaic humans.
So here I'm showing you two figures from two papers
which demonstrate that the selected alleles of
two of these immune loci are actually clustering with
the Neanderthal loci and in fact appear to be introgressed.
What's interesting here is that
we're talking about selection in the past 10,000 years,
Neanderthal introgression happened 50,000 years ago.
So these are not variants that were immediately adaptive.
They're variants that have contributed to immune diversity
and become selected much later.
So ancient DNA allows us to identify
individual variants under very strong selection.
And one of the interesting things we discover is that
actually genetic adaptation to agriculture
doesn't seem to coincide very closely
with the introduction of agriculture itself.
Now, all the things I've talked about so far
have been relatively simple traits.
If you have the lactase persistence mutation,
you have lactase persistence.
If not, you don't.
But actually most human traits,
certainly most morphological traits and most disease traits
are not like that.
So the canonical example here is height.
So I'm showing you here another Manhattan plot.
This time I'm not looking for evidence of selection,
I'm looking for evidence of association with height.
And it turns out that there are many thousands,
in fact probably tens of thousands of genetic variants
across the genome that are all significantly associated
with heights.
They all contribute to variation in height.
Almost every complex trait that's been studied in any detail
has this kind of architecture.
Of course, the effect of each of these variants are tiny,
so the average effect size is on the order of
plus or minus one millimeter.
But if we sum up the effect of all of these variants,
we can actually start to build relatively accurate
predictions of height.
For example, we can explain on the order of
20% of the phenotypic variance.
Now, the other reason we like studying height
is because it's one of the few phenotypes we can actually
measure in skeletons, okay?
So if we do that,
we see very interesting changes throughout Europe.
So this is data collected by our collaborator, Chris Ruff.
So the first thing you notice is that
actually in the early upper paleolithic,
these early humans in Europe are relatively tall.
Actually almost within the range of present day variation.
There's a dramatic decrease in height over the LGM,
which lasts really until the neolithic,
before an increase again going into the bronze age.
These data, and about a thousand years ago, of course,
in the 20th century, there's a dramatic increase in height.
But we think that's largely not genetic in origin.
So if we take our ancient samples
and what we know about the genetic basis of height today,
and we try and predict, make genetic predictions of
height of these ancient samples, it turns out we can
largely recover this pattern.
So what I'm showing here is genetic predictions of heights
based on something like 1200 ancient Europeans.
And you can see that we do predict that
early upper paleolithic populations are taller.
There's a decrease continuous into the neolithic
and then an increase going into the bronze age.
If I show you the stature data on the same model,
you can see at least, they're qualitatively consistent.
Technical reasons, we don't necessarily expect them to be
exactly the same.
Another interesting observation is that
if we do this analysis for standing heights,
sorry for sitting heights,
it turns out we see very little change.
Okay, so sitting height in the skeletons doesn't change
to genetic prediction, we predict a significant change,
but it's actually very, very small in magnitude.
So what this is telling us is that the known observation
that this change in height is driven by
change in limb length actually has a genetic basis.
We think this is likely an adaptation to climate
following Allen's rule, reduced limb length
reduces heat loss in colder environments.
So ancient DNA allows us to track not only the evolution of
simple traits, but actually of
polygenic complex traits as well.
And potentially we can use this ability to actually predict
the evolution of traits,
which we can't measure directly from skeletons.
Now, in some sense, we can use this information to tell
when phenotypes are changing because of evolution
rather than plastic developmental responses.
And to separate out those two effects.
I'll end with a caveat though, which is that
it feels like many of these changes should be adaptive,
but actually it turns out to be very difficult to provide
genetic evidence for that. Okay?
And I think particularly in, even in the case of heights,
which is probably the best studied example,
we don't have solid evidence
whether this is actually driven by natural selection
or whether this is genetic drift.
And I think that's one of the big questions
that we're trying to challenge over the next few years.
So I'd like to thank my lab at Penn,
collaborators elsewhere, our funding sources,
and I'd like to thank you for listening.
Thank you.
(applause)
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