Session 3: Evidence of Evolution

00:00:07.22 My name is Neil Shubin, I'm from the University of Chicago.
00:00:10.01 And I'm going to talk today about finding Tiktaalik.
00:00:12.06 My life's quest began in graduate school, in the second year
00:00:16.26 when I saw this exact slide. This is the slide my professor
00:00:19.29 showed back in 1986, and it showed what we knew at the time as
00:00:25.17 the fish to tetrapod transition, which is the transition from life in water
00:00:29.19 to life on land, as seen by these cartoons of these fossil creatures.
00:00:33.12 The one on top is a fish from 390 million years ago, the one on the bottom
00:00:38.18 is a tetrapod from about 365 million years ago. I remember as a student
00:00:43.10 seeing this diagram for the first time and thinking, "Holy cow!
00:00:46.20 What a first class scientific problem!" How did fish evolve to walk on land? It
00:00:52.00 seemed so utterly impossible go to from the thing on top to the thing
00:00:54.28 on the bottom. And that's what became my quest. So like any
00:00:58.00 good paleontologist, I applied the paleontological toolkit to try to find
00:01:01.09 an intermediate, say some sort of creature between these two.
00:01:04.14 And so what do we do when we try to find a new place to discover fossils?
00:01:08.18 What we do is we look for places in the world that have three things.
00:01:11.27 The first is rocks of the right age, you need rocks of the right age
00:01:16.08 to answer whatever question interests you. So in this case, clearly
00:01:19.19 we want something between 390 and 365 million years old.
00:01:24.05 That's of a time period known as the Late Devonian. The other thing
00:01:28.08 that was really important, of course, is to have rocks of the right type.
00:01:31.00 Not every kind of rock holds fossils. Some are super heated, some are
00:01:35.06 highly pressurized. So we needed a particular kind of rock, sedimentary
00:01:39.15 rocks, and not only that, sedimentary rocks in the right environment
00:01:43.20 to hold the kind of intermediates we're looking for. So what's the
00:01:47.14 other variable? Rocks of the right age. Rocks of the right type.
00:01:50.10 Well, it does me no good if my wonderful rocks of the right age
00:01:53.27 and the right type are buried five miles underground, right? These things
00:01:57.08 have to be exposed at the surface. So what we do is we look for places
00:02:00.28 in the world where we have naked bedrock, okay? So that's it. If you
00:02:04.10 ever want to run your own expedition someday, look for places in the world
00:02:07.18 that have rocks of the right age, rocks of the right type, and rocks that
00:02:10.09 are exposed at the surface. There's another criterion, and in this case
00:02:14.06 was accessibility. And this is where I began my first scientific expeditions.
00:02:19.06 These were accessible, because I had just begun my first academic
00:02:24.12 job at the University of Pennsylvania, here at the southeastern corner
00:02:28.00 of the state of Pennsylvania. And I dug out a geological map of
00:02:31.15 the state. And I'm showing you here what is sort of relevant for our
00:02:35.21 question. If you look at this map of Pennsylvania, what you see is
00:02:39.00 everything in purple are Devonian age rocks. So I have lots of
00:02:43.22 rocks of the right age throughout the state of Pennsylvania. But what about
00:02:47.02 that second variable, rocks of the right type? Well if you want to
00:02:50.29 think about what Pennsylvania looked like in the Devonian, get Pittsburg,
00:02:54.29 Harrisburg, Philadelphia, out of your brain. And think amazon delta.
00:03:00.14 This is a cartoon of what Pennsylvania looked like 365 million years ago.
00:03:04.24 You had highlands to the east, an inland sea to the west, and a series
00:03:09.12 of rivers draining from east to west. Now if you're a paleontologist
00:03:14.01 wanting to find fossils at the cusp of the transition from life in water
00:03:18.01 to life on land, this is perfect. Because if you have the right exposures,
00:03:22.19 you can sample ancient seaways, ancient rivers and streams, even ancient
00:03:27.15 upland deposits. Now the problem is Pennsylvania doesn't have a whole lot
00:03:31.21 of exposures. So that third variable is very tough. So my initial
00:03:36.06 expeditions, if you will, became following the Pennsylvania Department of
00:03:40.24 Transportation around as they build new roads. And if we were really lucky,
00:03:45.02 what they would do is they would be digging a new road in a place of Devonian
00:03:48.23 rock. And that's what you see here. This is a set of rocks about an hour north
00:03:53.02 of State College Pennsylvania. And this expanse extends about half a mile
00:03:57.21 long. It's a big red hill of rock -- it's called Red Hill, no surprise.
00:04:01.12 But what you see in these layers is the strata, the ancient layers
00:04:05.21 of ancient streams. And this is really perfect. Almost as soon as we
00:04:10.02 hit this site in the early '90s, we started to find fossils. The first thing
00:04:15.07 we started to find were teeth the size of railroad spikes. So big monsters, almost the size of
00:04:19.15 your thumb. Then we started to find the jaws of these creatures. Here's the
00:04:22.27 front end of one of these jaws. These jaws would be the length of your arm.
00:04:25.27 Alright, so they're really big. These big animals are probably about
00:04:29.13 16 feet long. Then we started to find all kinds of other fish, like
00:04:33.01 this little mashed thing here. This is actually a mashed fossil
00:04:36.25 of a lobed fin fish, which is related to lungfish today. So that's a head
00:04:41.14 and a body. But then my colleague, Ted Daeschler, who's been my partner
00:04:45.07 in all these expeditions for the last number of decades. Ted found
00:04:49.11 early tetrapods. One day I had left, one week I had left to do
00:04:53.28 expeditions elsewhere, and he had just discovered a series of
00:04:56.04 bones. Arm bones, leg bones, from creatures that represented
00:04:59.16 early limbed animals. And working with the National Geographic Society,
00:05:03.18 Ted and I were able to reconstruct what this ancient road in Pennsylvania
00:05:07.16 looked like 365 million years ago. I mean we had some of the first
00:05:12.10 forests, you can see this, and ancient streams, see that in the middle
00:05:16.04 here is that big monster fish, about 16 foot long fish. And then you see
00:05:20.28 a series of other kinds of fish, in particular, you see the little creatures
00:05:23.21 with limbs crawling around both on land and in the water.
00:05:27.16 Now this was great, but Ted and I realized we had a problem.
00:05:31.23 Why? We're in rocks about 365 million years old and we're already finding
00:05:38.00 pretty advanced limbed creatures. Remember what motivated this
00:05:41.02 whole quest was this whole slide I saw in graduate school.
00:05:44.22 We're not finding intermediates, we're already finding pretty advanced
00:05:47.20 limbed animals, I want something in the middle here. Well, to give you
00:05:50.18 a sense of what we're after, let's just go through some of the
00:05:52.26 traits that we look for. When you look at the creature on top, it has a
00:05:56.06 conical head with eyes on either side. The creature on the bottom
00:06:00.01 has a flat head with eyes on top. So really one of the kind of features
00:06:03.28 that changes in this transition is the shape of the head.
00:06:08.08 The other thing that changes is if you look at the creature on top,
00:06:10.27 the fish on top, you'll see the head is connected to the shoulder girdle by a series
00:06:14.21 of bones. It doesn't have a neck. Fish don't have necks where the head
00:06:18.20 can bend independently of the body. Early limbed animals, early
00:06:22.23 tetrapods, have a neck where the head can swivel independently
00:06:26.16 and it can look around as its legs are on the ground. Finally, another big feature
00:06:31.23 which obviously gives it its name, tetrapods, four-legged animal,
00:06:35.02 is fish have fins and the earliest tetrapods have limbs with fingers
00:06:39.03 and toes and wrists and ankles. We're finding a lot of fingers, toes, and wrists,
00:06:43.08 and ankles, right? We're not finding any intermediate. What Ted and I
00:06:46.14 are looking for is a flat headed fish with say fins or something in that
00:06:50.18 intermediate zone. To get that, what we had to do was move back in time,
00:06:55.02 back in time about 15 million years using analyses that other colleagues had done.
00:07:00.06 So Ted and I pulled out the paleontological rule book and began looking for
00:07:04.21 places that have rocks that are the right age, rocks that are the right type,
00:07:07.11 and rocks that are exposed. Wed had an idea to work in Brazil.
00:07:11.16 We had an idea to work in western North America. Then everything
00:07:15.28 changed one day in my office, back in the late '90s, when we had a
00:07:20.28 geological debate. And to settle the debate, I pulled out a college
00:07:24.15 geology textbook, this one right here. It's now in its eleventh edition.
00:07:28.19 So that gives you a sense of how old I am. But anyway, so we
00:07:31.01 finished the debate and I was paging through the book, and we ran
00:07:35.08 into this figure. This is the figure that changed the traction of research
00:07:39.25 for me, about 15 years ago. What it is is what we look for in a nutshell
00:07:45.01 as paleontologist. You'll see the caption, it says upper Devonian sedimentary
00:07:49.11 facies. Rocks more or less of the right age and the right type.
00:07:52.27 But here you see a map of North America, and superimposed on that
00:07:56.18 map is an interpretation of the geology of the Devonian age
00:08:00.27 rocks. Now you'll notice in the western part of North America,
00:08:03.20 there are Devonian rocks mapped as ancient oceans, but then the authors
00:08:08.05 identify three different areas, shown in red here, that had rocks
00:08:12.03 that had formed in ancient rivers and streams. Ancient rivers and stream
00:08:15.06 environments, delta systems. The first one, you've seen before.
00:08:20.01 That's the one I just showed you from Pennsylvania, that's the ongoing Catskill
00:08:23.08 project that we're working on along the roadsides of Pennsylvania.
00:08:26.29 There's another one on top here in East Greenland, that was well studied.
00:08:31.01 That was discovered in the '20s by Danes and Swedes. And
00:08:34.29 had produced a number of early tetrapods or limbed animals, in fact
00:08:38.03 the cartoon I've been showing you is the cartoon derived from
00:08:40.29 one of the fossils from this area. You see where I'm going?
00:08:43.25 Extending 1500 kilometers east to west across the Canadian arctic,
00:08:48.04 was mapped Devonian aged rock, completely unexplored. Not only that,
00:08:53.05 that rock was perfect. It was mapped as being from ancient rivers
00:08:56.03 and streams, ancient delta systems. So Ted and I were really excited,
00:08:59.13 and just a little anecdote here is, we were so excited. It's all happening
00:09:02.18 one morning, so we ran to a Chinese food restaurant across the street
00:09:05.23 and had a fortune cookie. And that fortune cookie said, "Soon you'll
00:09:09.24 be at the top of the world." So yes, here we go! We had a fortune cookie,
00:09:13.02 off we go! So anyway, this is a colorized version of one of the scientific papers
00:09:17.15 and it shows the stratigraphic section right here, and as well as the
00:09:21.07 map. And you can see, super-imposed over the map of the Canadian
00:09:25.15 arctic is a huge area, shown in red here, of Devonian age rock
00:09:30.18 So you can see it's all over. This is 1500 kilometers of section.
00:09:35.23 So now we had a new problem, instead of driving in our station
00:09:39.23 wagons to central Pennsylvania, we're now working several hundred
00:09:42.28 miles from the North Pole, where the arrow is in this slide.
00:09:46.02 To give you a sense, the nearest town is about 300 miles away.
00:09:50.06 I'm going to show you a picture of that town, with about 170 permanent residents.
00:09:54.07 This is a picture of that town in spring, Beresford, Canada. This is a big city!
00:09:58.22 And so this is resupply. Anyway, as you can imagine, much of our
00:10:02.16 supply is based on planes and helicopters. And so we're highly
00:10:06.20 dependent on aircraft. Now the way this affects science, is that the supply chain
00:10:12.23 is very long. And fossils are rocks, they're very heavy. So that means we
00:10:16.29 can't take home everything we find. So much of what we do in the field
00:10:20.26 is trying to determine whether we have really good material that is
00:10:23.20 worth spending the money to freight it home. So we started in 1998,
00:10:30.27 we had that fortune cookie in 1998. Our first expedition was in 1999.
00:10:35.02 It took us about a year to get the permits, the money, all that good stuff.
00:10:37.22 And we started with great hope in the western part of the Canadian arctic.
00:10:41.06 And you can see what that area looks like. It looks kind of flat, doesn't it?
00:10:45.15 And you can see our camp just below me here. This is Devonian rock.
00:10:49.02 So we'd wake up in the morning and essentially walk all over these
00:10:51.25 Devonian rocks, looking for the bones that are weathering on the surface.
00:10:56.02 We realized we had a big problem, this is the wrong part of the Arctic.
00:10:58.28 We were in all these rocks, which were marine rocks formed in ancient
00:11:02.22 oceans. It was pretty clear based on other people's discoveries
00:11:06.12 that we needed to hit rocks in rivers and streams. So what that meant
00:11:11.07 is using the geological map, this is a version of what I showed you
00:11:14.29 before for Pennsylvania, it applied also to the arctic. We were in the
00:11:18.24 ancient seaways, shown in blue. We had to move upstream. To do
00:11:22.12 that geologically, it meant moving east. So we retooled, the next year we went back
00:11:27.23 and we moved east, here to Southern Ellesmere Island.
00:11:30.25 And as soon as we did that, you can see these beautiful landscapes
00:11:33.29 here, of mountains and areas. And these are all red beds that contain
00:11:38.09 Devonian rocks formed in ancient rivers and streams.
00:11:41.15 Took another few years, we eventually honed in on an individual
00:11:45.17 valley that had good fossil preservation. And this is that valley here.
00:11:49.04 What you see, in front of us, is a big hole in the middle of this
00:11:52.21 slide, that shows a layer that was preserving hundreds of fossil
00:11:56.13 fish, preserved one on top of the other. So, the arctic's a big place. Fossils
00:12:01.16 -- you know they're not very big, so you're trying to find the little
00:12:03.19 tiny patches of rock that will have those fossils inside. And what we use
00:12:08.01 are the tools of geology and sedimentology. Well, we worked this layer,
00:12:12.02 and everything changed one day in 2004, when my colleague,
00:12:16.09 Steve Gates, he's shown in blue here to the left, removed a rock
00:12:19.18 from this layer. And he saw a little notch, a little v, you can see that v
00:12:24.01 in the middle of this slide. If you look very carefully you can see it.
00:12:27.11 He says, hey guys, what's this? We run over to it and we saw what
00:12:31.04 we spent multiple years looking for and several decades of a quest. What we had
00:12:35.09 looking at us here is the snout of a fish, and not just any fish, the snout
00:12:39.22 of a flat headed fish. Remember I told you the conical head and the flathead
00:12:43.05 was the big piece of this transition? What I had here, in the middle of this
00:12:46.15 slide, was a flat headed fish sticking right out at me. With any luck at all,
00:12:50.11 the rest of the fish would be preserved in the rock. So we removed
00:12:53.29 this thing and they come home at the base of the helicopter, so this is a
00:12:57.08 first year graduate student for scale. And it comes back several hundred
00:13:00.17 miles to a supply base. And as we remove this first specimen, we
00:13:07.01 found several more. So at the end of the 2004 season, we had multiple
00:13:10.08 specimens of this flat headed fish. We come back to the lab, we're really
00:13:15.27 excited. We come back to the lab and the preparers start removing
00:13:19.05 the rock, grain by grain, to expose the fossils within. This is what
00:13:23.25 Steve's specimen looked like several months after preparation.
00:13:27.14 You can see what's emerging is sort of a flat head with eyes on top.
00:13:30.15 You can see the tool that's used here to remove the specimen, after
00:13:34.29 several more months, what's exposed. Look here we sort of have a flatheaded
00:13:38.14 animal with eyes on top, even a shoulder that looks separated from
00:13:42.00 the rest of the head. Remember what began this quest? In graduate
00:13:45.23 school I saw this slide, fish on top, tetrapod on bottom.
00:13:48.25 We wanted a flat headed fish. After several decades, using
00:13:52.05 tools of evolutionary biology, using tools of centrography, using the tools
00:13:55.22 of geology, this is the flat headed fish. This is Steve's specimen
00:13:59.19 as removed from that hole. Flathead, eyes on top, has a neck,
00:14:03.14 you can see the head is separated from the shoulder, it has fins,
00:14:06.22 it has fin rays, and it has scales on its back. Like a fish, it has
00:14:10.21 scales and fins, like an early limbed animal, it has eyes on top,
00:14:15.18 it has a neck. And indeed, when we crack open the fin, what
00:14:18.06 do we find? Bones that correspond with the upper arm, forearm, and even parts
00:14:22.13 of a wrist. This is a real mix of characteristics. Found at the right
00:14:25.28 part of the fossil record. So this creature we call Tiktaalik, Tiktaalik
00:14:30.05 roseae. And you can see here, what's sort of salient about it. It has a
00:14:33.16 mix of tetrapod and fish characteristics, like a lobed fin fish it has fins,
00:14:37.26 scales, and primitive jaws. Like an early tetrapod, it has a neck, a wrist,
00:14:43.16 a flathead, and so forth. So here's a creature with lungs and gills, here's a
00:14:49.26 creature with limbs that have limb bones in them but also fin bones,
00:14:52.25 as well. Fined rays. It's really a mix between fish and tetrapod.
00:14:56.19 And when we put it in the phylogenetic analysis, the evolutionary
00:14:59.29 tree, you can see Tiktaalik drawn right in the middle here. It really
00:15:04.03 shows a fine sequence of the emergence of tetrapod characteristics
00:15:08.11 throughout the Devonian. So what's the take home message here?
00:15:12.15 The take home message here is that we can predict likely and unlikely
00:15:16.04 places to find fossils. We can, in doing that, learn about the great
00:15:20.27 transitions in the history of life. In particular, this one, the transition
00:15:23.28 from life in water to life on land. But the story doesn't stop there, it begins there.
00:15:29.10 Why? Because we can trace the bones in a creature like Tiktaalik.
00:15:33.09 LIke the arm bone, the upper arm bone, the humerus of Tiktaalik,
00:15:36.17 we can trace it all the way to other mammals and to people.
00:15:39.23 This story of the origin of the neck, the origin of a wrist, is not
00:15:44.27 just some esoteric event in the history of life. It's an event
00:15:48.19 embedded in our own bodies. The origin of the neck, the first time we see
00:15:52.09 in Tiktaalik and its evolutionary cousins, is something that is to become
00:15:55.09 our own neck. Likewise with the arm bones. So every time you
00:15:58.11 shake your head, every time you bend your wrists, you can say
00:16:01.07 "Thank you, Tiktaalik and its evolutionary cousins that lived in the
00:16:04.22 Devonian streams 375 million years ago."

00:00:07.10 Hi, I'm Sarah Tishkoff.
00:00:08.23 I'm a professor at the University of Pennsylvania
00:00:11.02 in the Departments of Biology and Genetics,
00:00:13.24 and today I'm gonna tell you about my research
00:00:15.18 on African integrative genomics,
00:00:17.29 and implications for human origins and disease.
00:00:21.17 So in Part 1, I'm gonna tell you a bit about
00:00:23.24 human evolutionary history,
00:00:25.24 and what the implications are of that
00:00:27.20 on the patterns of genomic variation
00:00:29.18 that we see in populations today.
00:00:34.05 So I want to start by talking about some of the
00:00:35.26 key challenges in human genomics research.
00:00:38.19 And the first one is to characterize
00:00:40.27 the immense array of genomic and phenotypic diversity
00:00:44.29 across ethnically diverse human populations.
00:00:48.14 Secondly, to understand what the evolutionary processes are
00:00:51.16 that are generating and maintaining that variation.
00:00:54.14 And third, to better understand how
00:00:56.04 gene-gene, gene-protein, and gene-environment interactions
00:00:58.28 contribute to phenotypic variability.
00:01:01.27 So first let's start with the evolutionary history
00:01:05.00 of the hominin lineage
00:01:06.26 that's leading to modern humans,
00:01:10.13 which begins around the time that we
00:01:12.03 diverged from our closest genetic relative
00:01:14.04 the Chimpanzee,
00:01:15.18 sometime between 5-7 million years ago.
00:01:18.14 So shown here are some of the fossils
00:01:20.07 from the different species
00:01:22.17 preceding anatomically modern humans.
00:01:25.16 In blue are shown fossils from the oldest lineages,
00:01:30.06 and in fact one of the oldest is Sahelanthropus,
00:01:34.10 which has been dated to at least 7 million years ago,
00:01:37.29 and there's some debate about whether it even
00:01:39.14 belongs on the hominid lineage
00:01:41.09 or if it actually preceded the Chimpanzee and human divergence.
00:01:45.26 After that, in green,
00:01:47.14 we see the Australopithecus genus.
00:01:50.14 In yellow, we see Paranthropus genus.
00:01:54.09 In orange, we have the genus Homo
00:01:56.24 and the species proceeding anatomically modern humans
00:02:01.13 is Homo erectus, dated to about 2 million years ago.
00:02:06.14 And then we have the origins of
00:02:08.15 Homo neanderthalensis
00:02:11.02 and of anatomically modern humans.
00:02:13.24 Neanderthals are thought to have originated
00:02:16.00 somewhere between 300,000-400,000 years ago,
00:02:19.12 and modern humans originated
00:02:20.27 approximately 200,000 years ago.
00:02:24.03 Here's one of the best examples
00:02:26.11 of Australopithecus afarensis.
00:02:29.07 This was a set of fossils that was
00:02:31.24 discovered in the 1970's by Johanson and Gray,
00:02:36.02 named Lucy,
00:02:38.00 and Lucy was about...
00:02:41.04 she lived about 3.2 million years ago.
00:02:43.29 She was very small, only about 3 feet tall,
00:02:46.13 she had a very small brain,
00:02:48.07 and she was bipedal.
00:02:49.27 And being bipedal, in fact,
00:02:51.07 is one of the characteristics of the hominin lineage.
00:02:57.12 And, interestingly,
00:02:59.17 there have been some fossilized footprints
00:03:01.21 identified in Tanzania,
00:03:03.24 and we can see from these that there
00:03:06.08 appears to have been a mother,
00:03:08.27 from the species Australopithecus afarensis,
00:03:12.08 and she was holding the hands of her child.
00:03:14.29 And they must have been walking
00:03:16.15 in ash from recent volcanic activity,
00:03:20.06 and then that ash hardened and preserved these footprints
00:03:23.06 so that we can see them today,
00:03:24.21 and we can clearly see that they were bipedal.
00:03:29.08 So the species preceding modern humans
00:03:31.28 is called Homo erectus.
00:03:33.24 Homo erectus evolved around 2 million years ago,
00:03:39.02 and then after the origin of Homo erectus in Africa,
00:03:42.24 Homo erectus spread across Eurasia
00:03:47.17 and, indeed, shown here are some of the
00:03:49.21 oldest fossils of Homo erectus,
00:03:52.18 dated to as early as 1.9 million years ago (MYA) in Indonesia.
00:04:00.15 And this species was very successful,
00:04:03.14 lasting to as recently as 25,000 years ago
00:04:06.17 in Southeast Asia.
00:04:09.08 A very interesting recent finding was
00:04:11.20 a set of fossils identified on the island of Flores,
00:04:14.26 which is within Indonesia,
00:04:17.25 and these fossils actually show some characteristics
00:04:21.22 that look very similar to Homo erectus,
00:04:24.19 and for that reason it was proposed that
00:04:27.09 this species may have directly evolved
00:04:30.23 from a Homo erectus ancestor
00:04:33.20 that arrived on that island
00:04:36.07 about 1 million years ago
00:04:37.28 and then evolved in isolation.
00:04:39.25 And two of the very unique features of this species
00:04:42.17 is that they were very short, so again,
00:04:46.01 about the same size as Lucy, around 3 feet tall,
00:04:50.15 and secondly, that they had tiny brains.
00:04:53.14 And there's been a lot of debate about
00:04:55.01 whether this is an adaptation or in fact a pathology,
00:04:58.09 and there's still a lot of research being done,
00:05:01.03 but what was clear is that there were multiple species
00:05:04.01 outside of Africa
00:05:05.29 within the past 2 million years.
00:05:08.20 So now let's move on to the origins of
00:05:10.15 Homo neanderthalensis and Homo sapiens.
00:05:13.12 There's some question about the species preceding
00:05:16.28 Neanderthal and Homo sapiens.
00:05:19.17 Some say that it was heidelbergensis,
00:05:22.04 but there's debate about that.
00:05:24.15 However, what is clear is that the Neanderthals species
00:05:28.10 arose somewhere within the past 300,000-400,000 years,
00:05:32.15 and Homo sapiens arose within the past 200,000 years.
00:05:38.04 And this is a fossil from Neanderthals,
00:05:40.29 we can see a few features such as
00:05:44.02 the double arched and very wide brow ridges,
00:05:47.08 a broad nose,
00:05:48.28 a very large brain size,
00:05:50.27 and a retromolar space,
00:05:52.21 and in fact these species were very robust.
00:05:55.16 The males would have been over 6 feet tall,
00:05:57.15 they had very big bones,
00:05:59.19 and they had rather big brains.
00:06:02.20 In fact, here are some reconstructions of Neanderthal.
00:06:06.28 We have the old reconstruction
00:06:09.03 and then the more recent one as well.
00:06:12.11 So, anatomically modern humans, Homo sapiens sapiens,
00:06:16.06 arose approximately 200,000 years ago.
00:06:19.02 In fact, here these red dots
00:06:21.09 are representing locations where fossils have been found
00:06:24.11 of anatomically modern humans,
00:06:26.27 and the oldest fossil is
00:06:28.22 dated to around 150,000-195,000 years ago,
00:06:32.19 in Southern Ethiopia.
00:06:36.23 We also see evidence of early modern human behavior
00:06:40.10 dated to 70,000 years ago,
00:06:42.11 or even as old as 120,000 years ago,
00:06:45.16 in caves in south Africa
00:06:47.13 and also some from east Africa as well.
00:06:51.05 So after modern humans arose in Africa within the past 200,000 years,
00:06:55.08 one or a few small groups of individuals
00:06:57.25 migrated across the rest of the globe
00:07:00.11 within the past 50,000-100,000 years.
00:07:03.23 Indeed, we think that Europeans...
00:07:07.15 there were no people in Europe, actually,
00:07:09.06 until about 40,000 years ago,
00:07:11.13 and then modern humans crossed the Bering Straits
00:07:14.15 and went into the Americas
00:07:16.28 within the past 30,000 years.
00:07:19.05 The earliest migration event was actually into Australo-Melanesia,
00:07:23.11 dated to about 40,000-60,000 years ago.
00:07:26.14 And then we have much more recent migration events,
00:07:29.03 such as into the Pacific Islands,
00:07:31.12 within the past few thousand years.
00:07:34.11 Now, interestingly,
00:07:36.16 when modern humans migrated out of Africa
00:07:39.08 within the past 50,000-100,000 years,
00:07:42.05 they would have run into Neanderthals,
00:07:44.10 in fact they overlapped in their distribution.
00:07:47.08 So shown here is the distribution of Neanderthals,
00:07:50.22 and the modern humans who lived at that time
00:07:52.25 were referred to as Cro-Magnon,
00:07:55.17 and in fact we did not see anatomically modern humans
00:07:59.09 in this region, in Europe, until about 40,000 years ago.
00:08:03.03 They would have been in the Middle East a little bit earlier,
00:08:05.23 but it appears they overlapped
00:08:08.18 for about at least 10,000 years with Neanderthals.
00:08:12.13 And as we'll discuss later,
00:08:13.27 there is some evidence that there could have been actual admixture
00:08:17.05 between Neanderthal and anatomically modern humans
00:08:20.18 during that time.
00:08:22.26 So now I want to discuss the evolutionary forces
00:08:25.27 that influence the patterns of genetic variation
00:08:28.08 that we see today.
00:08:30.04 And these include mutation,
00:08:32.14 genetic drift,
00:08:33.29 migration,
00:08:35.09 and natural selection.
00:08:37.16 So let's first introduce some terminology.
00:08:40.05 The gene pool refers to the set of all genomes
00:08:42.25 in a specified population,
00:08:44.10 and here we have an example from a population of warthogs.
00:08:47.22 So where we have at a single genetic locus
00:08:51.03 two alleles, big B or little b,
00:08:54.17 and here's an example of an individual
00:08:56.11 who is homozygous for the big B allele,
00:08:59.07 and an individual homozygous for the little b allele,
00:09:02.12 and here's an individual who is heterozygous
00:09:05.08 for big B and little b.
00:09:07.12 And together, the set of alleles in that population
00:09:10.19 represents the gene pool.
00:09:13.28 So when we are doing population genetics analyses,
00:09:16.25 we can't actually go out and look at every genotype
00:09:21.00 for every individual in the population,
00:09:23.14 that would be unfeasible.
00:09:25.13 So what we typically do is to
00:09:26.23 infer frequencies by estimating them
00:09:30.10 from a random sample.
00:09:32.25 So in population genetics
00:09:35.01 generation, each new individual
00:09:37.16 is viewed as drawing from a set of gametes
00:09:39.20 with alternative alleles,
00:09:41.08 so let's use an example here
00:09:43.01 in which we have a set of marbles in a bowl.
00:09:46.05 And initially, we have a distribution of
00:09:51.26 60 of the white marbles
00:09:54.13 relative to 40 of the green marbles,
00:09:56.27 and these, the white and the green,
00:09:58.08 are representing different alleles.
00:10:00.14 So let's say that we're gonna pick...
00:10:02.04 we're gonna reach into this bag
00:10:04.04 and we're gonna randomly draw out
00:10:06.09 another hundred of these marbles.
00:10:09.01 And now in the next generation
00:10:10.26 we have 80 of the white and we have 20 of the green.
00:10:15.02 We're gonna reach back in,
00:10:16.01 we're gonna grab another set of a hundred,
00:10:18.09 and now in the next generation
00:10:20.15 we have 100 of the white alleles and 0 of the green.
00:10:26.08 And this is a demonstration of
00:10:27.15 how we get changes in allele frequency over time.
00:10:31.25 Allele frequencies will also change over time
00:10:34.23 due to genetic drift,
00:10:36.21 which is defined as random fluctuations
00:10:39.01 of allele frequencies from generation to generation,
00:10:42.03 simply due to chance.
00:10:44.19 So as we see, sometimes things could happen,
00:10:47.16 like these bugs are getting squashed,
00:10:50.00 and that's gonna change, perhaps,
00:10:52.07 the allele frequency in the next generation.
00:10:55.19 Here's another example from some lady bugs,
00:10:58.23 and we can see that, perhaps,
00:11:01.03 in the next generation, just by chance,
00:11:03.10 we're gonna see more of these ladybugs
00:11:04.29 with the dark colors,
00:11:06.12 or we might see more that are with the medium colors and dots.
00:11:10.16 And the fact is that drift is just an inevitable fact of life.
00:11:16.15 I also want to define what we mean by neutral evolution.
00:11:20.08 So we define a selectively neutral allele
00:11:22.10 as one that does not affect reproductive fitness of individuals
00:11:25.20 who carry that allele,
00:11:27.20 so it's frequency in the population
00:11:29.25 changes by chance or genetic drift alone.
00:11:32.18 And here we have an example:
00:11:35.04 this is just a substitution
00:11:37.22 in the third position of the codon,
00:11:41.02 and when we have substitutions
00:11:44.09 of nucleotides in the third position,
00:11:46.20 very typically they result in a silent or synonymous change.
00:11:51.05 So here there's been a substitution,
00:11:53.00 but there's no change in the amino acid;
00:11:55.02 it remains as valine.
00:11:57.26 So the rate at which genetic drift occurs
00:12:00.01 is going to inversely proportional to the population size, N,
00:12:03.23 and it's going to be very fast in small populations.
00:12:06.27 And here's an example that we can look at
00:12:08.23 based on computer simulation.
00:12:11.20 So let's assume here that we're looking at a single locus
00:12:15.15 and it has two alleles
00:12:18.06 that are at 50% frequency each,
00:12:21.25 as we can see here.
00:12:23.22 We have a sample size of 25,
00:12:27.06 and we're going to do the simulation
00:12:29.03 over 80 generations.
00:12:31.14 Now, each of these lines here
00:12:34.03 represents a different simulation,
00:12:36.27 and what we can see is that
00:12:38.23 over time alleles are either going to
00:12:44.02 be lost from the population
00:12:46.08 or they're going to reach fixation,
00:12:48.17 which means that they go to 100% frequency.
00:12:52.10 And the rate at which this occurs
00:12:54.00 is going to depend on the sample size.
00:12:56.09 So in a small sample it's gonna be very rapid,
00:12:59.19 but in this example where we have a larger sample, now N=300,
00:13:03.26 you can see that it just takes more time.
00:13:05.23 There's not as much genetic drift occurring.
00:13:08.19 Now, the end result is gonna be the same,
00:13:10.15 it just takes more time.
00:13:14.09 The change in allele frequency also is going to depend
00:13:17.27 on the initial allele frequencies.
00:13:19.20 So in this particular case,
00:13:21.05 we've now changed the starting frequency:
00:13:23.20 it's not 50%, it's now 10%.
00:13:27.06 And you can see that there's much more
00:13:29.28 probability of loss of the allele in this case,
00:13:34.11 and here we have just one of the alleles reaching fixation.
00:13:42.08 So again, in this particular case,
00:13:44.05 about 1 out of 10 will eventually become fixed,
00:13:47.14 or reach 100% frequency.
00:13:51.09 Now here's an example from a large population.
00:13:54.01 It'll take longer for this to occur,
00:13:56.02 but the proportion of alleles are gonna be
00:13:58.12 roughly the same,
00:13:59.29 so again roughly 1 out of 10 will go to fixation,
00:14:03.06 it's just gonna take longer.
00:14:05.16 Other important terms in population genetics
00:14:07.26 are bottleneck and founder effects,
00:14:10.08 and this is because genetic drift
00:14:11.23 has a large effect on allele frequencies
00:14:14.10 when a population originates
00:14:16.05 via a small number of people from a larger population.
00:14:19.16 So here we have an example of a bottleneck,
00:14:22.10 and what a bottleneck means is that
00:14:24.01 there's been a decrease in population size
00:14:26.21 at some time in the past.
00:14:28.14 So you can think of it as a population crash.
00:14:31.10 And what happens when the population is very small,
00:14:34.28 you're going to have a higher rate of genetic drift,
00:14:37.12 and we can see here that these alleles,
00:14:39.20 which are represented by the different colors,
00:14:42.00 have shifted from what we're seeing
00:14:44.18 back at this earlier time.
00:14:46.25 Now we go through the bottleneck,
00:14:48.19 and now we're seeing predominantly
00:14:50.07 these white and black alleles.
00:14:53.09 Another example we can look at is a founder event,
00:14:57.20 which is sort of a special case of a bottleneck event.
00:15:00.11 And in this case it's where a population, a small population,
00:15:05.03 breaks off from the larger population,
00:15:07.25 and again there's going to be increased genetic drift
00:15:10.26 in this initially small population
00:15:13.12 and here, by chance,
00:15:15.05 we just happened to see more of these dark blue
00:15:18.12 and light blue alleles.
00:15:21.09 The pattern of variation that we see
00:15:22.23 in the human genome
00:15:24.09 is also dependent on the effective population size,
00:15:27.17 which we distinguish as capital N sub e.
00:15:32.10 And the definition of the effective population size
00:15:35.10 is the number of breeding individuals in a population.
00:15:38.19 So estimates of Ne
00:15:40.17 are most strongly influenced by population sizes
00:15:43.07 when they're at their smallest,
00:15:45.10 and it could take many generations
00:15:47.02 to recover from a bottleneck event.
00:15:49.11 So estimates of Ne in modern populations
00:15:51.21 reflect the size of the population
00:15:53.20 prior to population expansion.
00:15:56.22 Pretty consistently, studies of nuclear sequence diversity in humans
00:16:00.24 have estimated an effective population size
00:16:03.15 of about 10,000.
00:16:05.19 Now, by contrast, if we look at Chimpanzees,
00:16:08.29 the estimate is closer to 35,000.
00:16:12.14 And so what that means is that
00:16:14.01 humans have undergone a bottleneck
00:16:16.18 sometime during their evolutionary history.
00:16:19.22 So the pattern of genomic variation
00:16:21.25 that we see in modern populations today
00:16:24.00 is a reflection of our evolutionary and demographic history.
00:16:27.14 So how much do we differ?
00:16:29.17 Well, identical twins
00:16:31.27 have no differences at the nucleotide level.
00:16:35.06 If we compare unrelated humans,
00:16:36.29 we differ at about 1 out of 1,000 nucleotide sites.
00:16:41.12 And if we compare humans to our closest genetic relative, the Chimpanzee,
00:16:45.02 we differ at about 1 out of 100 sites.
00:16:47.29 So, as a whole, our species is very similar,
00:16:50.27 and that simply reflects our recent common ancestry
00:16:54.05 from Africa within the past 100,000 years.
00:16:57.06 But when you consider that there are
00:16:58.27 over 3 billion DNA bases in the genome,
00:17:02.02 that results in 3 million differences
00:17:04.16 between each pair of genomes,
00:17:06.05 more than enough to generate the diversity
00:17:08.29 that will make each of us unique.
00:17:12.02 Now I want to introduce a statistic
00:17:14.13 that we typically use to look at how much variation
00:17:17.06 there is among populations,
00:17:20.01 and this is referred to as an Fst statistic.
00:17:24.00 And it's simply looking at the proportion of genetic variation
00:17:27.03 that is within populations,
00:17:29.06 relative to that which is between populations.
00:17:32.18 Fst can be measured based upon heterozygosity,
00:17:37.20 and heterozygosity is simply a measure of genetic variation,
00:17:41.26 which is very simply calculated as
00:17:44.15 1 minus the sum of the allele frequencies squared.
00:17:49.09 And so once we calculate
00:17:51.26 the heterozygosity for each locus,
00:17:53.29 we can look at the average,
00:17:55.23 and we can look at the average within a subpopulation,
00:17:58.03 or in the total combined population.
00:18:00.29 Now, just as an example,
00:18:03.15 if we were to see here that
00:18:06.22 in the case of Fst = 1,
00:18:09.12 it means that there is no overlap at all in the allele frequencies.
00:18:13.15 So we can see that in population 1 they have all A's,
00:18:16.13 and in population 2 they have all B's.
00:18:19.15 And in the case of Fst = 0,
00:18:22.18 there is complete similarity,
00:18:26.08 so here we see exactly the same number
00:18:28.13 of A alleles and exactly the same number of B alleles.
00:18:32.01 And then here's an intermediate case
00:18:33.29 where we have about 0.11, 11%,
00:18:39.07 showing that there's just a small amount of differentiation
00:18:43.04 between these two populations.
00:18:46.09 So what do we see in humans?
00:18:47.29 Well, the average Fst between human populations
00:18:51.04 is about 15%,
00:18:53.15 and what that means is that the majority of genetic variation
00:18:56.04 is found within a population,
00:18:59.07 and only about 15% of the genetic diversity
00:19:02.08 differs between populations.
00:19:04.23 Again, this is reflecting our recent common ancestry in Africa,
00:19:09.00 within the past 50,000-100,000 years.
00:19:14.13 Now, interestingly,
00:19:16.09 if we were to do this calculation from Chimpanzee populations,
00:19:19.08 we see that the value is around 32%,
00:19:22.15 so there's actually a lot more differentiation
00:19:25.04 among Chimpanzee populations
00:19:27.07 than among human populations,
00:19:29.18 again reflecting our overall close genetic similarity to each other.
00:19:36.19 So I now want to talk about the
00:19:38.04 different sources of DNA that we use
00:19:40.04 to reconstruct human evolutionary history.
00:19:43.01 One source of DNA is
00:19:45.29 that which is present in the nuclear genome
00:19:48.06 that's located in the nucleus of the cell.
00:19:51.03 And there's another type of genome
00:19:53.20 which is present in the mitochondria of the cell,
00:19:56.15 and the mitochondria is the energy-producing organelle.
00:20:02.13 So what is the difference between these different genomes?
00:20:06.03 Well, the nuclear genome
00:20:08.09 consists of 22 autosomal pairs of chromosomes
00:20:12.26 and then the sex chromosomes,
00:20:14.15 XX for females and XY for males.
00:20:17.27 The nuclear genome is about 3.4 billion bases in size,
00:20:22.02 and it consists of about 20,000 coding genes.
00:20:25.10 It's inherited from both parents,
00:20:27.21 but it also undergoes extensive recombination each generation.
00:20:32.07 But, one of the reasons it's useful is that there's
00:20:34.18 so many different locations where we can study variation,
00:20:38.08 given that there are 3 billion nucleotides,
00:20:41.02 it's just a little bit more difficult to trace them back
00:20:43.29 to a single common ancestor.
00:20:46.20 By contrast, the mitochondria DNA genome
00:20:50.21 is very small, it's only about 16,000 nucleotides in size,
00:20:55.14 and it's circular,
00:20:57.17 and it's passed on only through the maternal lineage.
00:21:00.19 There's also no recombination
00:21:02.17 and it has a very high mutation rate.
00:21:05.00 All of these features make it very useful
00:21:07.01 for tracing evolutionary history.
00:21:09.27 So let me give you another example of what I'm referring to.
00:21:13.12 The mitochondrial DNA is inherited through the maternal lineage,
00:21:17.05 whereas the nuclear DNA is inherited from both parents.
00:21:22.08 So if we were to trace back from a present day individual,
00:21:25.26 they will have inherited their nuclear genome
00:21:28.20 from their parents,
00:21:30.17 their parents would have inherited from their set of parents,
00:21:33.28 and then their set of parents, and so on.
00:21:36.15 So we can trace it back to a large number of ancestors.
00:21:39.16 But by contrast, if we're tracing back mitochondrial DNA lineages,
00:21:44.00 we can see that they're only passed on
00:21:46.25 through the maternal lineage,
00:21:49.10 so they're essentially inherited from a single lineage.
00:21:52.03 We can trace them back to a single common female ancestor,
00:21:56.01 and that's why they're been very useful
00:21:57.29 for human evolutionary genetics studies.
00:22:00.21 So for example, if we were to consider
00:22:02.26 these dots to be mitochondrial DNA lineages,
00:22:06.20 and let's start at generation 11 at the bottom,
00:22:10.12 shown by the red dots,
00:22:12.06 and imagine those are different mitochondrial DNA sequences
00:22:15.00 from different individuals.
00:22:17.10 At some time in the past, these two individuals, for example,
00:22:22.06 coalesced back to a common ancestor,
00:22:24.26 and then this group coalesces back to a common ancestor here,
00:22:29.29 and ultimately they all coalesce back
00:22:32.20 to a single common ancestor.
00:22:35.03 Now, in the popular literature,
00:22:36.22 the single common ancestor for mitochondrial DNA
00:22:39.04 is often referred to as "mitochondrial Eve",
00:22:42.21 but one thing to remember is that
00:22:45.17 Eve was not alone, she lived within a population,
00:22:49.06 as we can see here by the other colors.
00:22:51.22 But those lineages just never made it
00:22:54.22 down to the present day.
00:22:57.25 So this is a phylogenetic tree
00:23:00.11 constructed by sequencing mitochondrial DNA
00:23:03.10 whole genome lineages
00:23:05.02 from ethnically diverse individuals.
00:23:07.19 So each individual actually represents
00:23:10.29 a branch on this tree,
00:23:13.02 and if two individuals are very closely related to each other,
00:23:16.05 they'll be very close to each other
00:23:19.01 in the tree.
00:23:21.03 So one of the first things you can see
00:23:22.19 using Chimpanzee as an outgroup
00:23:25.01 is that all modern human lineages
00:23:27.25 coalesce at about 170,000 years ago,
00:23:31.12 and so that corresponds very well with the
00:23:33.05 time of origin of anatomically modern humans.
00:23:36.23 So another thing that we can see is that
00:23:39.25 all of the oldest genetic lineages
00:23:42.26 are from African individuals.
00:23:45.22 We can also see that
00:23:48.12 the very oldest lineages
00:23:50.15 are from the San and the Mbuti pygmy hunter-gatherers,
00:23:54.28 and then the more recent lineages
00:23:57.13 are from non-African populations.
00:24:00.01 And that is a pattern that's very consistent
00:24:02.17 with the model of a recent African origin
00:24:05.12 of modern humans.
00:24:07.23 Now, another way that we can compare mitochondrial DNA sequences
00:24:11.21 is to simply count up the number of sites
00:24:14.04 at which they differ when we compare any pair of sequences.
00:24:17.23 And when we do this,
00:24:19.09 we observe that
00:24:22.11 any two African lineages will differ from each other
00:24:25.03 at many more sites than any two non-African lineages.
00:24:29.06 And again, that means that there has been more time
00:24:32.02 for variation to accumulate in Africa,
00:24:34.16 and is consistent with an African origin
00:24:37.08 of modern humans.
00:24:39.20 When we sequence the mitochondrial DNA lineages,
00:24:42.21 we can classify them as haplotypes,
00:24:45.10 and those haplotypes belong to
00:24:47.16 larger subsets of haplogroups.
00:24:50.01 You can think of a haplotype as simply
00:24:52.14 the arrangement of genetic variants along a chromosome,
00:24:55.19 or in the case of the mitochondrial DNA
00:24:57.22 there's just a single genome,
00:24:59.14 so it's really just the different nucleotide differences
00:25:02.27 amongst different mitochondrial DNA lineages.
00:25:06.24 And one of the first things that you can note is that
00:25:09.26 there are different haplogroups
00:25:11.29 in different regions of the world.
00:25:13.19 So here are some that seem to be pretty specific to Africa,
00:25:16.20 but are also present in some regions
00:25:18.20 where there may have been some gene flow
00:25:20.20 from Africa.
00:25:22.21 Then we have others that may be more common in Europe,
00:25:25.12 or in east Asia,
00:25:28.18 or in the Americas.
00:25:30.19 And for that reason,
00:25:32.11 mitochondrial DNA can be very useful for
00:25:34.11 tracing recent human migration events.
00:25:38.13 Now, by contrast,
00:25:40.02 the Y chromosome is also inherited with no recombination,
00:25:45.14 and so it can also be very useful for tracing back
00:25:48.01 through the male lineages.
00:25:50.16 And here is a phylogeny constructed from Y chromosome variation,
00:25:55.07 and as with the mitochondrial DNA,
00:25:58.08 what we see is that the oldest lineages
00:26:01.19 are specific to Africans,
00:26:04.02 and the more recent lineages
00:26:06.05 are found predominantly in Non-Africans,
00:26:08.13 although we do see some in Africans as well.
00:26:11.25 Again, this is consistent with the recent African origin of modern humans.
00:26:18.14 We can also look at Y chromosome haplogroups,
00:26:22.09 and one of the things that's a little bit different
00:26:24.04 is you can see that they're a bit more differentiated
00:26:26.16 between geographic regions.
00:26:29.03 So for example,
00:26:30.24 here we just see haplogroups that are in blue,
00:26:34.04 and we see very distinct haplogroups
00:26:36.20 in the Americas, shown in purple.
00:26:39.26 And one of the reasons for that may have to do with
00:26:43.08 sex-biased migration,
00:26:46.01 that you may have, for example,
00:26:47.16 one male traveling long distances.
00:26:50.06 And it may also have to do with patterns of mating structure.
00:26:54.20 So for example, in some populations or ethnic groups,
00:26:57.23 you may have one male who has many different wives,
00:27:01.05 and because of that the effective population size of the Y chromosome
00:27:07.01 is actually smaller than the mitochondrial DNA,
00:27:09.28 and we tend to get more genetic differentiation
00:27:12.27 around the world.
00:27:15.07 So now I want to talk about analyses of ancient DNA,
00:27:18.27 for example, in this case from Neanderthal,
00:27:22.12 and these are some images of scientists
00:27:25.20 working on a Neanderthal fossil.
00:27:29.10 And this type of analysis is very challenging
00:27:32.01 for a number of reasons.
00:27:33.25 One is that DNA which is that old,
00:27:38.04 on the order of say 30,000 years old
00:27:40.10 to even 100,000 years old,
00:27:42.06 is going to be highly degraded,
00:27:44.24 and if there's any contamination
00:27:46.25 with modern human DNA,
00:27:49.02 that is much more likely to amplify
00:27:51.19 than the old degraded DNA
00:27:54.01 from the archaic species,
00:27:56.21 so one has to be extremely careful when analyzing this DNA.
00:28:01.03 Now, more recently,
00:28:02.24 there was a pinky finger bone
00:28:05.07 identified in a cave in Siberia
00:28:07.22 from a region called Denisova,
00:28:10.11 so it's referred to as the Denisova
00:28:13.21 or Denisovan genome.
00:28:16.11 Here I'm presenting a phylogenetic tree
00:28:18.29 based on mitochondrial DNA variation
00:28:21.24 comparing modern humans, shown in blue here,
00:28:26.09 to Neanderthals shown in red,
00:28:29.01 and the Denisova individual shown in yellow.
00:28:32.23 And what we can see is that the
00:28:34.17 time to most recent common ancestry in humans,
00:28:37.08 as we've already discussed,
00:28:39.00 is about 200,000 years ago.
00:28:41.13 The time to most recent common ancestry
00:28:43.14 between humans and Neanderthals
00:28:46.01 is about 500,000 years ago,
00:28:48.13 for the mitochondrial DNA lineages.
00:28:51.03 And the time to most recent common ancestry
00:28:53.20 with the Denisova mitochondrial lineages
00:28:57.08 is about 1 million years ago.
00:29:00.05 So this is demonstrating a couple of things.
00:29:02.20 From the mitochondrial DNA perspective,
00:29:05.07 there's no evidence of any admixture
00:29:07.13 with anatomically modern humans.
00:29:10.02 The Neanderthal sequences are clearly
00:29:12.18 very distinct from modern humans.
00:29:14.28 It's also showing you that there was another species, Denisova,
00:29:18.15 that appears to be distinct from the Neanderthals,
00:29:21.07 and they diverge even earlier than Neanderthals
00:29:24.09 from modern humans.
00:29:26.21 So if we were to compare pairwise nucleotide diversity,
00:29:31.01 for example,
00:29:33.02 among anatomically modern humans shown in blue,
00:29:35.24 you can see that there's not a lot of diversity,
00:29:38.15 as expected considering that
00:29:40.13 we all have a very recent common ancestry.
00:29:43.04 If you compare the modern human mitochondrial genomes to Neanderthal,
00:29:48.03 you can see that they're more divergent,
00:29:50.07 as expected, given that the mitochondrial DNA lineage
00:29:54.04 diverged about 500,000 years ago.
00:29:57.02 If we compare to the
00:29:59.03 Denisovan mitochondrial DNA lineage,
00:30:01.10 they're even more divergent.
00:30:04.04 And then if we compare to Chimpanzee,
00:30:06.14 of course as expected,
00:30:08.11 given that they diverged at least 5 million years ago,
00:30:11.14 they are the most different in terms of sequence variation.
00:30:15.13 Now, several years ago
00:30:18.13 there was a draft sequence produced of
00:30:21.20 the Neanderthal genome using next-generation sequencing technology.
00:30:25.25 And this was an absolutely amazing feat,
00:30:28.17 but at the time they had very low coverage,
00:30:31.07 meaning that any particular region of the genome
00:30:33.19 was sequenced only about once or twice.
00:30:36.20 Now, more recently,
00:30:38.07 as the technology has improved,
00:30:40.05 they've gotten much better coverage of the Neanderthal sequence,
00:30:43.04 and quite recently they now have a 30-fold coverage,
00:30:46.22 meaning that on average most sites
00:30:49.03 will have sequenced 30 times.
00:30:51.22 And so you'll have a much better accuracy
00:30:54.23 when determining nucleotide variation.
00:31:01.07 So, when the Neanderthal genome
00:31:03.25 was compared to the human genome,
00:31:06.11 what you can do is first
00:31:08.10 look at how much divergence has occurred
00:31:11.02 since modern humans differentiated from Chimpanzees
00:31:15.10 within the past 6.5 million years.
00:31:18.12 And you can look at the divergence
00:31:20.24 that has occurred specifically in the human lineage
00:31:24.06 since they diverged from Neanderthal,
00:31:26.21 and they've only accumulated
00:31:29.07 about 8% of this total divergence.
00:31:34.08 And so the estimate of the time of population divergence
00:31:38.06 between humans and Neanderthals
00:31:40.15 is about 400,000 years ago.
00:31:43.09 Furthermore, it has been estimated that
00:31:45.24 there may have been a small amount of admixture
00:31:48.16 between Neanderthals and anatomically modern humans,
00:31:52.01 as shown by this red arrow here.
00:31:54.18 So the estimated amount of admixture is about 1-2%,
00:32:00.15 of the modern human genome,
00:32:02.17 may be of Neanderthal ancestry.
00:32:05.03 But what is of interest is to note that
00:32:07.24 this is only present in Non-Africans.
00:32:10.13 It is not present in African genomes.
00:32:13.05 And so what we can infer from that is
00:32:15.16 that this admixture event probably occurred
00:32:18.25 before modern humans spread across the globe.
00:32:22.01 It may have occurred, for example, in the Middle East,
00:32:24.28 and that's why we're seeing it present in all Non-Africans,
00:32:29.18 and we don't see it at all in Africans.
00:32:32.15 Now, more recently, there has been
00:32:34.22 whole genome sequencing of the Denisovan individual,
00:32:39.20 and what that has shown is that
00:32:42.09 the Denisovan species, or this individual,
00:32:45.15 appears to have diverged from modern day humans
00:32:48.13 around 800,000 years ago,
00:32:51.09 consistent with what we saw from the mitochondrial DNA.
00:32:55.21 They also observed low levels of heterozygosity in Denisova,
00:32:59.21 suggesting that they may have had
00:33:01.19 a small population size.
00:33:04.06 Additionally, when a phylogenetic tree
00:33:07.24 was constructed from the nuclear DNA variation,
00:33:11.13 they could see that the modern humans
00:33:15.11 tend to cluster together,
00:33:17.09 and as we expect they're divergent
00:33:19.01 from the Denisova and the Neanderthals.
00:33:21.29 The Neanderthals tend to cluster together,
00:33:24.06 so they're clearly divergent from Denisova.
00:33:27.03 But what's interesting is if you look at how much
00:33:31.01 variation there is amongst the modern humans,
00:33:34.11 as indicated by the length of these lineages,
00:33:38.06 and then you compare that to Neanderthals,
00:33:40.14 which have very short branches.
00:33:43.06 What that suggests is
00:33:44.28 that there was not a lot of genetic variation
00:33:47.09 amongst the Neanderthals,
00:33:49.23 and therefore they may have undergone a bottleneck,
00:33:52.11 so they might have undergone a population crash
00:33:54.20 at some point in the past.
00:33:57.07 So in summary,
00:33:59.04 what we can see is that
00:34:01.23 Homo erectus left Africa
00:34:04.05 within the past 2 million years,
00:34:06.28 and spread throughout Eurasia,
00:34:09.09 giving rise, possibly,
00:34:11.09 to species like Homo floresiensis,
00:34:14.17 and surviving until quite recently,
00:34:17.12 as recently as around 25,000 years ago.
00:34:20.28 Then we have other species like Neanderthal and Denisovans,
00:34:27.02 who may have originated from a different species,
00:34:30.07 such as heidelbergensis,
00:34:33.10 and they differentiated sometime
00:34:36.12 around 600,000 or 700,000 years ago in the case of Denisova,
00:34:39.29 or in Neanderthals around 400,000 years ago.
00:34:43.05 And then we have the modern human lineage,
00:34:46.11 Homo sapiens,
00:34:49.00 which arose around 200,000 years ago
00:34:51.07 and spread out of Africa.
00:34:53.21 And when they did so,
00:34:55.02 they would have encountered these other species,
00:34:57.09 and there may have then been low levels of gene flow.
00:35:01.20 And in fact for the case of the Denisovan genome,
00:35:03.23 it appears that the gene flow
00:35:05.26 was predominantly with populations from Oceania,
00:35:10.01 implying that this admixture
00:35:12.17 may have occurred in a different location and a different time.
00:35:16.00 Now, we still don't know exactly
00:35:18.05 how much admixture there may have been
00:35:20.12 between archaic species
00:35:22.23 and modern humans in Africa,
00:35:25.01 but there's some preliminary data suggesting that
00:35:27.10 this has occurred there as well.
00:35:29.14 The problem is that the fossils don't preserve as well in Africa,
00:35:32.19 so we don't have any DNA sequences
00:35:34.26 from archaic lineages in Africa at this point.
00:35:40.01 So in conclusion,
00:35:41.18 Africa has the most genetic diversity in the world.
00:35:44.15 Human dispersions out of Africa
00:35:46.11 populated the entire world,
00:35:48.15 and we are the last of a series of hominin dispersal events
00:35:51.14 out of Africa.

00:00:00.04 Hi, my name is Hopi Hoekstra and I'm a professor at Harvard
00:00:03.10 University. And in this second segment, what I would like to do is
00:00:06.15 tell you a story about the genetic basis of evolutionary
00:00:09.22 change. And in particular, we're going to focus on the story
00:00:12.06 of a morphological trait. I'm going to tell you this story in the context
00:00:17.15 of making links between environment, phenotype, and
00:00:20.08 genotype. In particular, I'm going to tell you today a three-part story. The first
00:00:24.18 part of the story, I'm going to tell you about a role for natural
00:00:28.09 selection in driving differences and traits in a natural population.
00:00:31.14 And then I'm going to take you from the field into the lab,
00:00:34.11 where we've been doing work to understand the genetic basis
00:00:37.06 or the genes that control this phenotypic difference. And it's
00:00:40.12 not just that we want to know the genes, but I'll tell you
00:00:42.07 about how changes in these genes through development
00:00:44.25 actually produce variations in the phenotype. And then once we
00:00:48.02 made all the links, then we'll have a more complete understanding
00:00:50.14 of the process of adaptation. And here's where I think things can get
00:00:53.24 really fun. Because we can go back out into the wild
00:00:56.22 and ask how these traits evolved in natural populations. So let's
00:01:00.10 start today at the level of phenotype. So the phenotype I'm going to
00:01:03.23 tell you about is one that we've been studying in my group for almost
00:01:06.03 a decade. And that is color variation. So why do we study color?
00:01:10.25 Well, color is one of the primary ways in which organisms
00:01:14.11 interact with their environment. And it varies tremendously
00:01:18.04 between species, and it can also vary between species. And in
00:01:22.04 particular, we know at least in some cases, even small changes
00:01:25.03 in color difference can have a huge impact on fitness.
00:01:27.22 The ability of organisms to reproduce and survive in the wild.
00:01:31.03 So here I've just given you a number of examples of how
00:01:34.21 color is used. It can be used, for example, in terms of reproduction
00:01:38.21 in that it can be used for mate choice. So we have the canonical example of
00:01:41.29 the peacock's tail. But flowers also use color to attract pollinators
00:01:46.26 which is the way that they reproduce. Color can also be
00:01:49.20 used not to attract other species or other individuals,
00:01:52.18 but it can be a way to warn them of let's say a distasteful
00:01:56.29 poison. As is the case in these poison frogs.
00:02:00.29 Now, no good biological story comes without having
00:02:04.16 cheaters. So we also have mimics, those species that
00:02:08.14 are themselves not toxic, but mimic those that are toxic.
00:02:13.09 And thereby are also avoided by predators. But by
00:02:17.00 far, color is used most commonly in terms of camouflage or
00:02:21.23 in terms of crypsis. So that's what I'm going to focus on today.
00:02:25.00 In addition to being ecologically relevant, the other reason we
00:02:29.23 study color is because we can rely on nearly a decade's worth of work
00:02:34.22 by geneticists who have been tracking down genes involved
00:02:37.16 in spontaneous mutations that occur in laboratory populations
00:02:40.28 of mice. So here what I've done is I've made a list, which again
00:02:44.04 I don't expect you to read, but just to appreciate that we know a lot
00:02:47.25 of genes. And if you mutate that gene, it's going to have an effect on
00:02:51.08 color. And here are some of the mutant color differences
00:02:55.00 that have spontaneously arose in laboratory colonies of
00:02:57.26 mice. So in some sense, we have a little bit of a headstart because
00:03:00.26 we know a number of what I'll refer to as candidate genes,
00:03:03.29 genes that contribute to color differences in mammals, but may
00:03:08.05 also contribute to natural variation in color. Now unfortunately,
00:03:12.06 laboratory mice or their wild equivalents, while very common
00:03:15.14 in nature, for example, in our houses, vary a lot in laboratory
00:03:19.23 populations. They don't actually vary that much in the wild.
00:03:22.15 So, instead of studying these mice, we've decided to do
00:03:25.17 a study on a closely related group of mice, mice in the genus
00:03:29.20 peromyscus called deer mice, shown here in their slightly
00:03:32.21 flattened form. So this is a common way that mice are kept
00:03:36.25 in museum collections. And it nicely illustrates the variation in
00:03:40.24 color that I'm going to talk about today. So in this first image,
00:03:43.28 you can see that these deer mice vary tremendously in their
00:03:47.00 dorsal coat. They vary from almost completely white to
00:03:50.23 almost completely black. But they also vary not just in the
00:03:54.28 pigments and individual hairs on their dorsal coat, but they
00:03:57.19 also differ in the distribution of pigments across the body.
00:04:00.10 Or variation in color pattern, as shown here. So these candidate
00:04:05.04 genes that I showed you in the previous slide, many of those
00:04:07.23 are implicated in variation in dorsal color. But in fact,
00:04:11.04 we know very little about how patterns are made. So by studying
00:04:14.10 these mice, not only can we take advantage of this vast
00:04:17.00 range of variation in the color, but we also may learn something
00:04:20.02 new about color patterning. So you may have already noticed
00:04:24.09 that these slides were both taken by someone called Sumner back
00:04:28.07 in the 1920's. Well, this is Francis Sumner, shown here.
00:04:32.09 In his field regalia. This is actually the outfit that he would wear
00:04:36.16 when he would go out into the wild and trap mice. He's a
00:04:39.10 classic natural historian, he was associated with initially the
00:04:42.13 University of Michigan natural history museum. And he spent
00:04:46.29 a bulk of his career trying to answer the question of why
00:04:50.10 populations vary so much in nature. And to do this,
00:04:53.25 he would drive around the U.S. and catch mice, and document
00:04:57.20 variation in a number of traits, including color variation.
00:05:02.17 So Francis Sumner's favorite species of deer mouse, and
00:05:05.28 mine as well, is a particular species call peromyscus polionotus.
00:05:09.17 Peromyscus polionotus is often referred to as the old field
00:05:13.19 mouse. And that's because much of its range in the southeastern
00:05:16.27 U.S., as seen here, so Alabama, Georgia, South Carolina,
00:05:20.08 and Northern Florida. It occurs in these old fields, which are
00:05:24.13 really overgrown agricultural fields. Now throughout
00:05:28.13 most of its range, it may look to you like a typical mouse.
00:05:31.13 It's got a dark brown coat, a gray scruffy belly, and a striped
00:05:35.01 tail. But what's particularly interesting to us about these mice
00:05:38.19 is that they've recently invaded these sandbar islands and
00:05:44.15 sandy dune habitats on the Gulf coast of Florida, as well as
00:05:48.03 the Atlantic coast of Florida. So each one of these
00:05:51.16 numbers on this map refers to a different subspecies
00:05:54.13 of what I'll refer to as "beach mice," because the mice actually live
00:05:57.20 on the beach. Now, the first part of the story, I'm going to
00:06:00.29 focus on one of these subspecies. Here number 3.
00:06:04.04 And that is the Santa Rosa Island beach mouse. So let me show you
00:06:07.06 a picture of their habitat. So unlike their mainland counterparts
00:06:12.02 that live in dark loamy soils, these mice live on these beautiful
00:06:15.14 sandy islands off the Gulf coast of Florida. And here's a picture
00:06:20.03 of one of our field sites. So you'll notice that there are two
00:06:24.05 dramatic differences in habitat that these mice occupy.
00:06:26.25 So first, you can tell that the soil, in this case, this granulated
00:06:33.02 sand that's almost like walking on hills of granulated sugar.
00:06:36.07 It's much lighter in color than the dark loamy soils of the mainland
00:06:40.00 subspecies. But in addition, these beach habitats also have
00:06:45.17 much less vegetative cover, so these mice are exposed to
00:06:48.11 really high levels of predation. And I'll tell you more about
00:06:51.05 their predators in a minute. So it may not be surprising then,
00:06:55.01 when we go out and catch mice in these beautiful beaches,
00:06:58.08 the mice look different. So here's a picture of one of those beach
00:07:01.29 mice. And I should mention that this is not to scale.
00:07:04.02 So both of these mice are about the same size and they're about
00:07:06.23 the size of a ping pong ball. But what this slide does serve to illustrate
00:07:10.17 is the dramatic differences in pigmentation. So this particular
00:07:13.25 mouse, you can see is lacking pigment on its nose,on its sides, and if you could see the tail, it's also missing that
00:07:19.17 strong tail stripe. The other thing I want to mention to you
00:07:22.23 about this system is that we know something about the geological
00:07:25.14 age of these islands. They're about 6000 years old, which suggests
00:07:30.12 that the difference in coat color that you see here may have
00:07:33.17 evolved in just a few thousand years. So you may all be
00:07:37.24 thinking that this makes perfect sense, right? That these mice are
00:07:41.00 running around in these beautiful white sand beaches and having
00:07:43.04 a light color coat would make them more camouflaged.
00:07:46.15 And that's a great idea, but we wanted to actually prove that.
00:07:49.23 So as scientists, what we wanted to do is empirically
00:07:53.00 demonstrate that color matters for survival. So actually
00:07:56.07 do an experiment. We wanted to know how much it matters,
00:07:59.03 in other words, we want to estimate the strength of selection.
00:08:01.27 How favorable is it to actually match the background?
00:08:05.17 And then finally, we wanted to know the agent of selection.
00:08:07.25 Who's doing the selection? And in this case, who are the
00:08:10.17 predators? So first I want to tell you about the experiments we did
00:08:15.10 to try to make this link between color variation and the differences
00:08:18.25 in environment that these mice live in. And in particular, implicate a role
00:08:22.09 for natural selection. Now if I could do any experiment
00:08:25.13 in the world, here's what I'd love to do. I'd love to catch
00:08:28.25 let's say 100 light mice and 100 dark mice, maybe give them all
00:08:32.16 a tag. And let's say release half of them, equal numbers of
00:08:36.11 light and dark, in dark habitat. The other half, equal numbers
00:08:40.06 of light and dark, in light habitat. And then come back, let's say a few
00:08:43.21 months later, and see who survived. And I'd have the expectation
00:08:46.22 that mice that are lighter would survive better in light habitat,
00:08:49.11 and those that are darker in dark habitat. Now for a number of reasons,
00:08:53.03 that experiment is quite hard to do. So instead, what we
00:08:56.16 did is what I'd argue is the next best experiment. And in some ways,
00:08:59.17 it may be even better. So here's the experiment that we actually
00:09:02.22 did. Instead of using live mice, we made mice. So here's
00:09:07.05 a picture of my postdoc, Sacha Vignieri, who along with
00:09:10.05 an undergraduate from Harvard, Joanna Larson, made
00:09:13.09 hundreds of plasticine mice. Half of them were painted
00:09:17.13 dark to mimic the mainland mice, and half of them were painted light
00:09:20.18 to mimic the beach mice. Now this experiment in some
00:09:24.28 ways, as I mentioned may even be better than using live
00:09:27.20 mice, because here the only difference in these mice is
00:09:31.04 their color. So they're made from the same mold, they all look the same,
00:09:34.09 they all smell the same. Whereas with live mice, the beach mice
00:09:37.09 and mainland mice may differ in let's say smell, in escape
00:09:39.28 behavior, in activity patterns. So here, the experiment completely
00:09:44.08 focuses on the difference in color and not correlated traits.
00:09:47.05 But the downside of this experiment is would it actually
00:09:50.18 work? Could we actually fool predators into attacking these
00:09:54.00 plasticine models of mice and not live mice?
00:09:56.29 Well, I wouldn't be telling you about this experiment if it didn't actually
00:09:59.26 work. So what Sacha did was she released equal numbers
00:10:03.22 of these light and dark mice in both light and dark habitats, where
00:10:06.20 live mice of the species actually occurred. And then counted
00:10:10.29 predation events. So here's a predation event. So what you're
00:10:13.29 looking at here is a dark mouse that was put out on light
00:10:16.16 soil. And you may notice that it's missing part of its
00:10:20.03 left ear, and it's got a big chunk taken out of its back.
00:10:23.05 And this is a classic predation event, and in fact, not only
00:10:26.26 can we tell it's been attacked, but we can actually say
00:10:30.08 something about who's doing the attacking. Because these marks
00:10:33.02 are consistent with an avian predator. So here's the results of
00:10:38.10 the larger experiment that Sacha did, where she was counting
00:10:41.14 the number of predation events in different habitat types.
00:10:44.23 So, what you're looking at here are the results of this experiment.
00:10:50.08 So let's first focus on this far panel at the light habitat.
00:10:54.17 What you can see is that there's cryptic and what we'll refer
00:10:57.11 to as non-cryptic mice. And these bars indicate the relative level of
00:11:03.01 predation in both of these two types of mice in this particular habitat.
00:11:06.28 And what you can immediately see is the level of predation here in
00:11:10.19 these cryptic mice is much lower, in fact, it's about half
00:11:14.00 of non-cryptic mice. So what this means is that both mice
00:11:17.22 were attacked by predators, but the mismatched mice were attacked
00:11:21.15 about twice as often. Now when we look at the dark habitat
00:11:24.28 we see a very similar pattern, but in reverse. Here we can
00:11:28.17 see the dark mice survived much better, and while still attacked,
00:11:33.22 they were attacked about half as often as the mismatched mice.
00:11:36.24 So what this first thing tells us is that color seems to matter.
00:11:40.10 And in fact it matters a lot. We can take these numbers and sort
00:11:44.16 of translate that into a selection intensity. I'm not going to go
00:11:48.12 into details about this, but let me just say that color matters
00:11:51.17 a great deal for these mice. And in fact, mice that match their
00:11:55.21 habitat have about a 50% increased chance of survival
00:11:59.21 compared to those that are mismatched. And the final thing
00:12:02.27 I want to say, as I mentioned, we can tell in some cases
00:12:05.08 who's doing the predating. And about half the attacks
00:12:09.16 were due to avian predators, and about half of the attacks
00:12:12.14 were due to mammalian predators like coyotes and foxes.
00:12:16.03 So together, what this experiment is telling us is that
00:12:20.06 the differences between color variation are tightly linked
00:12:22.17 to the environment and that it's natural selection that
00:12:26.01 is playing a role in driving these color differences. So now that
00:12:30.18 we've implicated a role for natural selection, the next thing I
00:12:34.00 want to do is to take you from the field into the lab,
00:12:36.15 where I'll tell you about how we're going about identifying
00:12:39.15 the genes that are responsible for these differences in adaptive color
00:12:42.12 variation. So the first thing I want to do is give you a general sense
00:12:47.23 or an overview of the approach that we're taking. And I don't
00:12:50.22 want you to get caught up in the details, but more appreciate
00:12:53.01 this general approach. So what we're able to do is
00:12:57.05 take these mice from the field and bring them into the lab, so
00:13:00.13 we have both dark mice and light mice. And they have
00:13:03.27 differences in their genomes, in their chromosomes, so
00:13:06.24 I'm going to illustrate this by the dark mice having dark chromosomes
00:13:09.27 and the light mouse having light chromosomes. And what we can
00:13:12.29 do is take a dark mouse and a light mouse, one male and one
00:13:15.05 female. Put them in a cage together and they'll actually
00:13:17.11 reproduce. And they'll produce what we refer to as hybrids.
00:13:21.04 Then we take those hybrid individuals, and we breed them
00:13:24.20 together. And then what happens is in this next generation
00:13:27.16 is their genomes get shuffled. So some individuals are going to have
00:13:31.16 different parts of their chromosomes that come from the light parent,
00:13:34.08 and some from the dark parent. So we're effectively shuffling
00:13:38.06 up their genomes. Now in this population, this second generation of hybrids,
00:13:42.13 what we do in all those individuals is we measure their coat
00:13:45.16 color pattern, and then using genetics or molecular biology,
00:13:49.16 we're able to sort of characterize their chromosomes and determine what regions
00:13:53.08 come from each of the parents. And then what we do is
00:13:56.16 -- I'll just simplify and say we do a nice statistical analysis
00:13:59.20 and ask, are there regions of the genome that seem to be
00:14:03.05 correlated with different aspects of different color variation?
00:14:06.20 So for example, in these chromosomes here, do all these mice
00:14:10.08 that have the light allele from this parent in this region
00:14:15.04 of the chromosome, if all those mice have, let's say, light
00:14:18.13 tails. That suggests that a gene controlling tail color
00:14:21.20 may reside in this part of the chromosome. And this is what we
00:14:25.11 refer to as a QTL analysis, or quantitative trait locus analysis.
00:14:29.03 But it's really just this simple statistical association.
00:14:32.10 And then what we do with this statistical association is that
00:14:36.06 we take that region of the chromosome, and we lookfor its homologous region, the same region in the mouse
00:14:43.01 genome, which we have the complete genome sequence.
00:14:45.12 We know where all the genes are. And we look for
00:14:48.02 candidate genes, remember that list of candidate genes
00:14:51.07 that I showed you earlier in the talk, and ask do any of those
00:14:54.04 candidate genes fall within this particular chromosome region.
00:14:57.16 With the ultimate goal of finding a mutation in that gene
00:15:01.04 whether it's in the protein structure of that gene or
00:15:04.10 maybe a mutation that controls the regulation of this gene,
00:15:07.21 that we can then link to the color differences between the parents.
00:15:10.24 So now that I've given you this overview, next what I want to do is walk
00:15:14.02 you through each of the pieces. So as I mentioned the first
00:15:17.02 thing we do is this cross. So we brought mice in from the wild,
00:15:21.15 we brought in a mainland species and one of these beach
00:15:24.09 mouse subspecies. And here they are, shown again in their
00:15:26.28 flattened form. You can see a dark parent, and over here
00:15:29.13 the light parent. And here is that F1 hybrid shown here. And you can
00:15:34.16 already see this F1 hybrid has traits from both of the parents.
00:15:38.26 So for example, it doesn't have a tail stripe like the light parent
00:15:41.29 but it has a fully pigmented face like the dark parent.
00:15:45.01 That suggests both dominant and recessive alleles
00:15:49.01 contribute to this light color adaptive beach mouse
00:15:52.23 phenotype. Then as I mentioned, we take these F1 hybrids and we breed
00:15:56.06 them together, and that's when we get these F2s.
00:15:58.23 And this is where we've now with recombination shuffled
00:16:02.00 up their genomes. So, for example, presumably this mouse
00:16:05.22 has more pigment alleles from the light parent, and the mouse over here
00:16:10.08 has more pigment alleles from the dark parent. But what you can
00:16:13.05 see is that there's a continuum of variation. And what this
00:16:16.11 immediately tells us that this color difference we observe
00:16:19.11 between beach mice and mainland mice is not controlled by
00:16:22.06 a single gene, but in fact is controlled by a handful of genes.
00:16:26.10 And the reason I say a handful and not hundreds is because
00:16:29.04 you can see in this population we get mice that look like
00:16:31.15 the dark parent, and we get mice that look like the light parent.
00:16:34.16 And this suggests there are not hundreds of genes, because it would
00:16:37.26 be a very small chance that we would get all the light
00:16:40.11 -- all hundred of those light alleles in one individual.
00:16:43.06 But instead, probably just a handful. And from this variation,
00:16:46.27 we estimate there are about 3-5 genes. But I'll tell you more about
00:16:50.05 those genes in just a minute. So as I mentioned, we then take
00:16:53.19 the color variation in these mice and we measure them in
00:16:57.01 all these individuals. And then we also genotype them to
00:17:00.08 figure out what regions of the genome come from the light and dark parent.
00:17:03.06 We did this using molecular techniques. And here's the results
00:17:08.10 of that. So what this is is each one of those lines shown
00:17:11.29 here represents a chromosome. And each one of these markers is
00:17:15.14 a difference between the light and the dark parent.
00:17:17.12 So in each one of these markers we can tell whether a particular
00:17:20.08 individual has that region of the genome comes from the dark
00:17:23.05 parent or the light parent. Then what we do is the statistical
00:17:27.05 analysis. And what we found was, there are three regions
00:17:30.22 of the genome, which I've highlighted here, that seem to
00:17:33.27 control color. That is there's genes in these regions of
00:17:36.15 the genome that control different aspects of color patterning.
00:17:39.25 And lucky for us, in each one of these regions, there's one
00:17:44.17 of these candidate genes that I mentioned earlier.
00:17:47.00 Oh, I should also mention that the differences in the size
00:17:50.12 of the arrows reflects the amount of variation that a particular
00:17:53.14 locus explains. So the Agouti locus way over here, explains
00:17:58.12 a larger proportion of variation compared to Corin, which explains
00:18:02.05 the smallest amount. So what this is telling us is that there's
00:18:06.06 3 regions of the genome, and each one of those contains
00:18:08.29 what I'll refer to as a candidate gene. So next what I'd like
00:18:12.13 to do is just tell you about what of these genes. And then I'll
00:18:15.22 summarize and tell you about all three of them at the end.
00:18:17.28 So the gene I'm going to focus on is this gene up here,
00:18:21.07 Mc1r, or the melanocortin 1 receptor. And one of the
00:18:24.26 reasons we focused on this gene is because we actually
00:18:27.13 know a lot about the structure and the function of this gene.
00:18:30.03 So, the melanocortin receptor is a classic g-protein coupled
00:18:35.24 receptor. That is, it's found in the membrane, and it's got
00:18:39.18 extracellular and intracellular regions. Each one of these
00:18:43.22 little circles represents a different amino acid, and what
00:18:46.06 I've done is I've color coded those amino acids that we know
00:18:49.23 when you change that amino acid, it has an effect on
00:18:52.29 color, and on particular species. And those that make
00:18:56.20 -- when you make that change, an individual darker, it's shown
00:18:59.24 in black and those lighter is shown in gray. So you can see
00:19:03.12 summarized over a number of different studies, that
00:19:07.13 there are multiple mutations in this receptor that can affect
00:19:11.06 color. And it can either make an individual lighter or darker.
00:19:14.20 So the first thing to note is that there are many different
00:19:17.17 mutations. The second thing to note is that they're found throughout
00:19:20.28 the receptor. And the third thing to note is that even a single
00:19:23.27 amino acid mutation can have an effect on color. So one change
00:19:27.26 can have a big effect on phenotype. Now, the first thing we did is
00:19:32.29 we sequenced this gene in both the mainland and the beach
00:19:37.24 form, and asked are there mutational differences between
00:19:40.15 the two? And in fact, we found one and it's highlighted
00:19:44.03 here in red. And the mutation is a single nucleotide change that
00:19:48.03 caused the change in amino acid, whereas the mainland species had
00:19:51.20 an arginine at position 65, beach mice had a cysteine.
00:19:56.02 And this is a charged changing mutation, so it actually
00:19:58.21 changed the charge of that amino acid so that it's more likely
00:20:01.06 to have an effect on the structure and function of melanocortin
00:20:04.14 1 receptor. Now the unfortunate thing, though, was that it didn't
00:20:09.03 overlap with any of the other mutations that had been previously
00:20:11.07 characterized in other species. But that's okay because we can do an experiment
00:20:14.18 to test whether this mutational change had an effect on the way
00:20:18.03 that this receptor functions. So first thing I want to do is tell you a little bit
00:20:21.25 about what this receptor does. And then I'll tell you about our
00:20:24.25 experiment. So what I'm showing you here is a melanocyte.
00:20:28.19 Now a melanocyte is a pigmentation producing cell.
00:20:31.07 And in mammals, we produce two types of pigment,
00:20:34.03 a dark brown to black eumelanin, and a yellow to blonde
00:20:37.19 pheomelanin. So you can look at your own hair and
00:20:39.12 determine whether you have eumelanin or you have pheomelanin.
00:20:42.25 Now, this melanocyte has the ability to produce both types
00:20:47.21 of pigments. But which pigment it produces at any one time
00:20:50.14 is largely controlled by the melanocortin-1 receptor.
00:20:54.05 Which essentially acts like a switch. When it's turned on,
00:20:57.11 that is when let's say alpha-MSH, which activates Mc1r,
00:21:02.03 is around, Mc1r turns on and it signals by increasing intracellular
00:21:06.11 cyclic AMP levels, and you get the production of dark pigment.
00:21:10.00 By contrast, when Mc1r is turned off, then you get the production
00:21:15.15 of less cyclic AMP intracellularly, and the result is light
00:21:19.21 pigment. So Mc1r is very much a switch that determines which pigment is
00:21:24.27 produced. Now this leads to a nice prediction. So in beach mice we have
00:21:29.03 a mutation in Mc1r that we think leads to light coloration.
00:21:32.14 So our prediction is this, is that mutation of arginine to cysteine
00:21:36.18 change at position 65 will reduce receptor activity, which will result
00:21:41.13 in lighter pigmentation. So we can test this doing an experiment.
00:21:45.02 And so what we did is we took both the light allele and the dark
00:21:49.03 allele, that remember, differs by one nucleotide change,
00:21:51.26 we cloned it into an expression vector, we put it into
00:21:55.03 cells. And then we added alpha-MSH in increasing amounts.
00:22:01.14 To activate Mc1r, and then as a proxy for activity, we measured
00:22:05.13 cyclic AMP. So here are the results of this experiment.
00:22:09.17 So here what we did is you can see we added increasing
00:22:14.16 amounts of alpha-MSH, which turned on Mc1r, and you can
00:22:18.21 see that its signaling higher and higher cyclic AMP
00:22:21.22 until it sort of plateaus out. And this is a normal sigmoidal response curve
00:22:26.09 that we see in the mainland mouse. Now when we did the same
00:22:30.02 experiment with the beach mouse allele that differs by again
00:22:32.22 that one nucleotide change, what you can see is there's a dramatically different
00:22:36.14 pattern of activity. And that is no matter how much alpha-MSH
00:22:40.28 we added, there's still a relatively low level of receptor activity.
00:22:44.21 So what this suggests is that one nucleotide change changes the
00:22:48.26 function of the receptor, and it's in the direction we expect.
00:22:53.04 That it has lower activity, which is consistent with producing
00:22:55.21 lighter pigmentation. So at this point, it's worthwhile stepping
00:22:59.07 back and sort of thinking about what I've just showed you. So here I've
00:23:02.22 showed you a single nucleotide change affects the activity of the receptor,
00:23:07.04 we know this receptor affects color variation, and we know
00:23:11.08 that color variation affects survival in the wild. So what we
00:23:14.13 have done here is made a link between a single nucleotide
00:23:17.14 change and fitness or survival in the wild. But I don't want
00:23:22.24 to leave you with the impression that this is the whole story, because
00:23:26.28 as I mentioned, color difference is controlled not by a single
00:23:29.03 gene, but by multiple genes. And so the two other genes,
00:23:33.23 if you are paying attention you might have noticed, actually
00:23:37.02 interact with Mc1r. So Agouti, for example, represses Mc1r
00:23:42.02 activity. And what we see in beach mice, is that higher
00:23:45.26 levels of Agouti expression are associated with lighter pigmentation.
00:23:49.07 And so the allele in beach mice has increased expression
00:23:52.13 of Agouti, which leads to lower activity of Mc1r and light pigment.
00:23:57.02 Corin is the third gene, and while this gene is just newly
00:24:02.04 discovered in the last few years. We don't know its exact function
00:24:05.14 but we know it interacts with Agouti, and again, increased
00:24:09.05 expression of Corin is associated with this light pigmentation
00:24:11.26 in beach mice. So these genes are interacting together
00:24:15.11 to produce light pigmentation. So what we've done in this second part
00:24:20.28 is to tell you about the genes that affect coloration and a little
00:24:25.08 bit about how changes in those genes actually cause
00:24:28.09 these differences in color patterning. And now that we have a much
00:24:32.20 more complete picture of adaptation, here's where I think things can get
00:24:36.08 really fun. So I'm going to take you now back from the lab into the
00:24:39.16 wild. And we're going to talk about how these traits may have
00:24:42.05 evolved in natural populations of beach mice. So to do this, this
00:24:46.14 involves us actually going back to the field. So here's a picture
00:24:49.15 of me and my postdoc, Vera Domingues, when we're out
00:24:53.04 on the Atlantic coast catching mice. Here's a mouse
00:24:57.07 currently being weighed. So we hang it by its tail on a little
00:25:00.09 pesola. We take measurements including their weight,
00:25:03.15 the size of their ears and feet, et cetera. And what Vera's doing here
00:25:07.20 is measuring their coat color. And what's nice about this
00:25:11.23 work in the wild is we take these measurements, we measure their
00:25:14.21 coat color, we also give them a little ear tag, and we take a little
00:25:18.05 snippet of DNA from their tail, and we release them back
00:25:21.19 in the wild. So here we have a DNA sample from each of
00:25:24.12 these mice and we have a record of their coloration.
00:25:26.24 So next what I'd like to do is tell you about what we've learned
00:25:30.02 about natural populations of these mice and how these color
00:25:33.15 differences may have evolved. So just by way of reminder,
00:25:36.29 so far what I've done is I've focused on only one of these
00:25:40.22 populations, that is population number 3, the Santa Rosa
00:25:42.29 Island beach mouse. But next what I'd like to do is tell you about
00:25:46.02 variation among these subspecies. So the five subspecies
00:25:49.26 on the Gulf coast and the three subspecies on the Atlantic coast.
00:25:53.13 So, when we go out and catch these mice and record their
00:25:58.12 color differences. We find some very striking patterns, which I'm going to
00:26:02.19 show you in the next slide. So here what I'm showing you
00:26:05.18 are cartoons that represent the different subspecies of
00:26:10.12 beach mice. So each one of these cartoons shows you the typical
00:26:14.09 color of a beach mouse from each of these populations, compared
00:26:17.07 here to a mainland mouse. So the first thing you may notice
00:26:21.19 is that all the beach mice are much lighter in color
00:26:26.00 compared to the mainland mouse. But that each of these
00:26:28.07 subspecies differs in their color pattern, and in fact,
00:26:31.23 they're so distinct that if you went for let's say spring break
00:26:35.06 down to the Gulf coast of Florida and you brought me back a
00:26:38.13 beach mouse, I would say with about 95% certainty
00:26:42.03 just by looking at the color of the beach mouse, I could tell you
00:26:44.16 what subspecies it is. But, instead if you went to Florida and you
00:26:49.09 didn't tell me if you went to the Atlantic coast or the Gulf
00:26:51.28 coast, and you just brought me back a beach mouse, I'd probably
00:26:55.08 have a 50/50 chance of knowing what subspecies it was
00:26:58.17 and that's because the mice on the Atlantic coast are very similar
00:27:02.09 to mice on the Gulf coast. So let me highlight that here.
00:27:05.26 So for example, these two subspecies, even though they're
00:27:08.29 separated by over 300 kilometers, are very similar
00:27:12.18 in their overall color pattern. And in fact, I can't tell them
00:27:16.10 apart. Likewise, these mice are very similar. And these mice
00:27:20.26 are very similar. So what this suggests is that on the Atlantic
00:27:26.02 coast and Gulf coast, the mice have convergent color patterns.
00:27:31.08 And so what we wanted to do first was ask the question,
00:27:34.16 did these mice evolve these similar color differences independently?
00:27:39.14 And if so, did they use the same genes? So the first
00:27:44.02 thing I want to show you is a tree, or a topology that shows
00:27:48.24 you the relationships among these different subspecies.
00:27:52.00 So this is a simplified version of a tree that we generated
00:27:54.28 using molecular data, but it highlights the relationships
00:27:58.12 among these subspecies within peromyscus polionotus.
00:28:02.07 And what you can see is the Gulf coast beach mice, shown here,
00:28:06.08 all five of those subspecies cluster together. They're very
00:28:09.09 closely related. But they're actually not that closely related
00:28:12.16 to Atlantic coast beach mice, shown here. In other words,
00:28:17.11 it looks like light coloration has evolved independently
00:28:20.02 on two coasts. So the Gulf coast beach mice probably
00:28:23.07 arose from a dark colored ancestor sometime in the past,
00:28:26.22 that was probably from the Panhandle of Florida. Whereas,
00:28:29.23 the Atlantic coast beach mice independently evolved light
00:28:32.20 coloration, probably from an ancestor in Central Florida
00:28:35.26 that was dark in color. Okay, but here's the cool part,
00:28:40.04 now that we now at least in one of these Gulf coast subspecies
00:28:43.07 that the melanocortin-1 receptor is involved, we can
00:28:45.22 ask in these independently evolved light colored beach mice
00:28:48.22 on the Atlantic coast, is that same gene and same mutation
00:28:51.15 involved? So to do this, we returned back to this melanocortin-1 gene.
00:28:56.21 And we sequenced the DNA in the mice that we collected
00:28:59.19 on the Atlantic coast and asked, do we find that same
00:29:02.24 arginine to cysteine change at position 65? So we simply
00:29:07.28 genotyped that one particular site and asked, is it present in the
00:29:12.00 Atlantic coast? Now, despite the fact that mice from the Gulf
00:29:15.09 coast and the Atlantic coast are so similar in coat color,
00:29:18.06 let me just say that we never found that cysteine change in any of the
00:29:22.09 mice from the Atlantic coast. So what that tells us is that
00:29:26.01 the same mutation isn't involved in that convergently evolved
00:29:29.20 light coloration on the Atlantic coast. So you may be thinking,
00:29:32.13 well, it's not the same mutation, but maybe it's a new mutation.
00:29:36.09 Remember there's lots of mutations in Mc1r that can cause
00:29:39.08 color differences, I told you this earlier. So maybe a new mutation
00:29:42.12 in Mc1r is causing light coloration on the Atlantic coast. So
00:29:45.23 it may not be the same mutation, but it could be the same gene.
00:29:48.18 So we went back to the Atlantic coast mice and sequenced
00:29:50.20 the entire melanocortin-1 receptor, and asked are there any
00:29:54.08 new mutations that are correlated with color. Well, in fact,
00:29:57.08 we found four new mutations in the melanocortin-1 receptor.
00:30:01.02 But none of them were perfectly correlated with color, and
00:30:04.11 when we did those pharmacological assays like the ones
00:30:07.04 I showed you earlier, none of them had an effect on the
00:30:11.17 activity of Mc1r. So what this tells us is that it's not just
00:30:16.12 the same mutation, it's also not the same gene that's
00:30:19.11 responsible for the convergent evolution of light coloration in the
00:30:23.10 Atlantic coast mice. So this is the case for melanocortin-1 receptor,
00:30:26.25 but we're still checking these populations for changes in Agouti and Corin.
00:30:31.27 So what I've done today is I've told you a story about
00:30:37.00 how identifying not only the ultimate causes of phenotypic
00:30:41.12 variation, but also the genetic causes can tell us something about how
00:30:44.13 traits evolved in the wild. And this story revolved around a single
00:30:47.24 species, in which different mutations, at least in regards to
00:30:52.04 the melanocortin-1 receptor, are involved in generating
00:30:55.04 similar coat color patterns on two coasts of Florida.
00:30:57.25 But I want to end by telling you a story about convergent
00:31:01.14 evolution. Because sometimes the same genes and same
00:31:04.13 mutations are involved in similar phenotypes in very different
00:31:08.01 organisms. So, if you think about mammals, think about what
00:31:13.02 is the most different mammal from a mouse. Often the answer
00:31:17.03 I get is an elephant, and it's close. I'm going to tell you a story about
00:31:21.11 mammoths. So, mammoths were the subject of intense
00:31:26.17 study, especially about five years ago when there was a big
00:31:30.06 interest in sequencing ancient DNA. And mammoths were
00:31:33.28 a great target because they occurred in Siberia, and
00:31:38.05 their DNA was essentially frozen in permafrost about 14,000
00:31:43.03 years ago. So this is almost like keeping DNA in a giant freezer,
00:31:46.15 which is the best conditions possible. So about five years ago,
00:31:50.07 the goal was to sequence an entire gene from an extinct
00:31:53.25 organism. Now today of course, we're sequencing entire
00:31:57.03 genomes of extinct organisms, but just five years ago, we
00:32:01.00 wanted to sequence an entire gene from the nuclear genome.
00:32:04.29 So this is what a group in Germany, in Leipzig, headed by
00:32:10.12 Michael Hofreiter did. And the gene they chose to study
00:32:14.00 was the melanocortin-1 receptor, because it's a very
00:32:17.13 simple gene. Remember I told you, we know a lot about its
00:32:20.27 structure function, it's only about 1,000 base pairs in length,
00:32:23.05 and there's no introns. So it's very simple and it's a great starting
00:32:26.05 point. So they sequenced the melanocortin-1 receptor,
00:32:29.10 in DNA extracted from mammoths. And here's what they found.
00:32:35.14 They found a mutation, and what mutation was it?
00:32:39.01 Well, it was an arginine to cysteine change at the exact
00:32:41.19 same position that we found in beach mice. Now of course
00:32:45.15 the DNA it was extracted from was bone, so they didn't know
00:32:48.18 the phenotype or the coat color of mammoths, but based on our
00:32:52.06 work in beach mice, what this suggests is that mammoths
00:32:55.01 like beach mice, may have been polymorphic in color. Now the
00:32:59.07 question of why they were polymorphic in color, we don't know the
00:33:01.26 answer to. This could be due to survival differences, other
00:33:06.25 suggested that it's due to sexual selection. So when this
00:33:10.07 work was published, the press line was that blonde mammoths have
00:33:14.15 more fun. But I'm not going to give you any explanation, I'll leave that
00:33:19.08 up to your imagination. So in this case, we have very
00:33:22.21 divergent organisms, mice and mammoths that use
00:33:26.03 not only the same gene, but the same mutation.
00:33:29.12 And in fact, as I alluded to earlier, melanocortin-1 receptor
00:33:33.06 is involved in a number of different color variants.
00:33:36.05 And a number of organisms that you may be familiar
00:33:38.24 with. So in this case, it was the same mutation, but we know
00:33:42.25 that other changes in the melanocortin receptor can also
00:33:46.21 cause differences in color through different mutations. So I just wanted to
00:33:51.02 give you some examples. So some work that I've been doing with a colleague
00:33:54.12 at UC Berkeley, Erica Rosenblum, shows that changes in the
00:33:57.26 melanocortin-1 receptor is responsible for the production of these
00:34:00.22 very adorable lizards that differ in color, when they occur
00:34:05.14 in White Sands, New Mexico. So you can guess which
00:34:09.02 lizard occurs on white sands and which one occurs off white sands.
00:34:13.06 Color differences are also involved in animals you may see
00:34:17.09 every day, including cows. Also, many of you may know
00:34:22.12 of or even have a labrador dog. Well, they come in
00:34:26.21 generally two colors, there's the black labs and blonde labs,
00:34:30.12 again this is caused by a change in the melanocortin-1 receptor. And much more recently, again work out of Germany,
00:34:36.23 this time Svante Paabo's group, has shown that color
00:34:41.06 changes in the melanocortin receptor, in addition to being
00:34:45.20 responsible for human color differences, may also be
00:34:48.29 responsible for color differences or hair differences in neanderthals.
00:34:53.03 So they showed that changes in the melanocortin-1 receptor
00:34:56.02 or variants in the melanocortin-1 receptor were found in
00:34:59.24 neanderthals, suggesting even back in those days,
00:35:05.03 there could have been redheads as well. So what I hope to have done
00:35:09.07 today is to suggest to you that by making the link between
00:35:13.23 environment, phenotype -- in this case a morphological trait,
00:35:17.22 and genotype, we're able to say something about
00:35:21.05 how organisms evolve in the wild. The work that I presented
00:35:25.12 today was done by a large number of people. These are some
00:35:30.22 of the people in my lab group that contributed to the work,
00:35:34.05 shown here looking their mousiest. And in particular,
00:35:38.00 the work I talked about today was done by folks like
00:35:41.21 Cynthia Steiner, Marie Manceau, Vera Domingues, Sacha
00:35:44.23 Vignieri, Holger Rompler, and Lynne Mullen, as well as
00:35:47.29 an undergraduate, Joanna Larson. And we were funded by
00:35:52.03 a number of sources shown here, as well. And with that,
00:35:55.10 thank you for your attention. I hope you enjoyed this segment,
00:35:58.03 and you'll stick around for segment three. Thank you very much.