Session 8: Human Evolution

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.

00:00:07.20 So in the second part of this lecture series,
00:00:10.13 I'm going to be discussing
00:00:12.03 African population history
00:00:14.02 based on patterns of genetic diversity.
00:00:18.23 So why do I think it's important
00:00:20.08 that we study African genetic variation?
00:00:22.28 Well, for one,
00:00:24.17 if we want to learn more about modern human origins,
00:00:26.23 we need to be looking in Africa,
00:00:28.15 which is the site of modern human speciation.
00:00:32.05 Secondly, if we want to learn more about African-American ancestry,
00:00:36.14 this will be an important region to study.
00:00:40.21 Third is that Africa is a region
00:00:42.13 with a very high level of infectious disease,
00:00:44.27 with HIV, malaria, and TB being three of the biggest killers,
00:00:49.21 but there's also an increasing level of
00:00:51.23 non-communicable diseases like diabetes, for example,
00:00:55.08 and cardiovascular disease.
00:00:57.11 And African populations have been greatly underrepresented
00:01:00.22 in the biomedical research,
00:01:03.00 and so we really need to give more focus
00:01:05.10 to these populations so that we can come up with better diagnostics
00:01:08.27 and better treatments for these diseases.
00:01:13.11 And lastly, we know that people differ in regards to drug response,
00:01:17.10 and this is likely due to variation at drug metabolizing genes,
00:01:21.02 but again, we currently know very little
00:01:23.03 about the extent of variation among Africans at these loci.
00:01:30.17 So first I have to give you a little bit of information
00:01:32.22 about African population history.
00:01:35.06 There are over 2,000 ethnic groups in Africa
00:01:37.26 speaking distinct languages,
00:01:40.12 and these languages have been classified
00:01:42.15 into four different language families.
00:01:45.17 So in blue are languages
00:01:48.15 classified as Afro-Asiatic.
00:01:50.27 They're found predominantly in the north and northeast of Africa,
00:01:55.23 and these would include, for example,
00:01:57.13 Semitic languages which are also spoken in the Middle East,
00:02:01.02 and they would also include Cushitic languages
00:02:04.14 spoken in northeast Africa.
00:02:07.02 And then in red we have populations
00:02:10.14 that are speaking Nilo-Saharan languages,
00:02:13.10 these tend to be pastoralist groups,
00:02:15.20 like the Maasai for example, who live in Kenya and Tanzania.
00:02:19.07 And these populations are mainly found
00:02:21.12 in central and eastern Africa
00:02:24.28 although there are a few groups who have migrated
00:02:27.14 to the west of Africa.
00:02:30.00 The most broad-spread language family
00:02:34.27 consists of the Niger-Kordofanian languages,
00:02:37.20 shown in yellow or orange here.
00:02:40.21 And the most common subfamily
00:02:43.18 is the family of Bantu languages.
00:02:47.06 Now, those are thought to have originated in Cameroon or Nigeria
00:02:51.02 around 5,000 years ago,
00:02:53.18 together with the development of iron tool technology,
00:02:56.26 which led to much better methods for practicing agriculture.
00:03:02.27 And so these populations
00:03:04.25 had a technological advantage in a sense,
00:03:07.20 and they were able to rapidly
00:03:09.29 expand across Africa into east Africa
00:03:13.12 and then south Africa,
00:03:15.14 or from west Africa
00:03:20.18 along the western coast into southern Africa.
00:03:24.14 The fourth language family, shown in green here,
00:03:28.05 is classified as Khoisan,
00:03:30.17 and it consists of languages that have click consonants.
00:03:34.03 So these are found predominantly
00:03:36.20 amongst the San hunter-gatherers in southern Africa,
00:03:40.24 and also amongst two groups called the Hadza and the Sandawe,
00:03:45.08 who live in Tanzania.
00:03:47.18 Now, despite the importance of studying Africa,
00:03:50.23 there have been relatively few genomics studies in that region,
00:03:54.05 and there's a number of reasons for that,
00:03:56.04 and one of which is just the challenges of
00:03:58.16 doing research in areas that sometimes
00:04:00.22 have little infrastructure.
00:04:02.25 And so I wanted to show you some examples of
00:04:05.21 the field work that we've done over the past 12 years.
00:04:08.25 We've mainly been studying
00:04:10.18 minority populations in Africa
00:04:12.13 who practice indigenous lifestyles,
00:04:14.22 and they live in very remote areas,
00:04:16.14 so we have to, for example, have a 4-wheel drive vehicle,
00:04:21.03 and this work has been done no only by myself,
00:04:23.21 but by my students and postdocs
00:04:25.27 and African collaborators over many years.
00:04:30.22 So here's an example, I like this,
00:04:32.13 it shows my postdocs Alessia Ranciaro and Simon Thompson,
00:04:37.06 and they were doing an expedition in Ethiopia in 2010.
00:04:41.04 We basically have to bring all of our lab equipment with us,
00:04:44.28 and I like this because it shows both the outside perspective of the car,
00:04:48.04 and also the inside perspective.
00:04:51.25 These are some of the other challenges that they faced.
00:04:54.20 They were there during the wet season,
00:04:56.03 making it extremely challenging to travel.
00:05:02.01 In each of these regions,
00:05:03.19 we typically start by doing what you could think of as
00:05:06.05 "Town Hall meetings", in which we explain the research
00:05:09.04 to the community,
00:05:11.01 and we explain both the risks and the benefits,
00:05:12.23 and make sure that they understand
00:05:14.11 why we're doing this research,
00:05:16.00 and how it might benefit or not benefit the community.
00:05:19.00 Ultimately though,
00:05:20.25 we have to obtain individual informed consent
00:05:23.12 to do this research.
00:05:27.09 We also measured a number of phenotypes,
00:05:29.11 like height and weight.
00:05:32.12 More recently, we've been looking at more detailed
00:05:34.29 anthropometric cardiovascular and metabolic traits.
00:05:41.13 From each of these samples,
00:05:42.24 we typically obtain blood intravenously,
00:05:45.29 and we've started to also obtain RNA.
00:05:50.07 But one of the challenges is processing these samples
00:05:53.12 in regions where there's no electricity.
00:05:55.25 So here's an example where we set up the so-called
00:05:58.19 "Bush Lab": we had to set up our centrifuge
00:06:00.28 and hook it up to the car battery.
00:06:05.02 But in other areas, we can find a local clinic,
00:06:07.08 they'll often have a generator,
00:06:09.06 and so then we're able to hook up a larger centrifuge.
00:06:12.06 One of the ways in which we obtain DNA...
00:06:15.20 and the DNA, I should note,
00:06:17.08 is only present in the white cells of blood,
00:06:19.24 so the first thing we're gonna do is we're gonna
00:06:21.13 break open all the red cells.
00:06:23.27 And we do that by adding a solution
00:06:27.00 that's going to cause them to burst open.
00:06:29.25 Then we're going to spin down the samples in this centrifuge,
00:06:34.04 and we have to repeat this several times,
00:06:36.09 and we're gonna end up with these little pellets at the bottom
00:06:39.01 of the white cells, and that's where we're gonna find the DNA.
00:06:45.10 Here are some other challenges of processing in the field.
00:06:48.13 After we've isolated the DNA,
00:06:50.03 we add another buffer, which is going to
00:06:53.01 preserve the samples at room temperature,
00:06:55.21 but here's a case where Simon Thompson
00:06:57.15 actually had to bring a generator with him
00:06:59.26 and set up the entire lab in the bush
00:07:02.18 when he was studying the Hadza hunter-gatherers of Tanzania.
00:07:08.15 Another very important thing
00:07:11.07 is to increase training and capacity building in Africa
00:07:15.13 so that they can do this research themselves,
00:07:17.21 and that's something that I've spent a lot of time doing,
00:07:20.09 and I think is very important.
00:07:23.23 Also equally important is actually
00:07:25.02 returning results to participants,
00:07:28.01 and it's really surprising how little this is done,
00:07:31.07 but I can assure you that people
00:07:32.26 really appreciate it when we return the results,
00:07:36.06 and I think it's also an ethical obligation
00:07:38.20 so that they can benefit from what we learn from these studies.
00:07:44.13 So I want to start by talking about some of the phenotypic variation
00:07:47.10 that we see in Africa.
00:07:49.03 This is an example of skin melanin levels,
00:07:52.07 or skin pigmentation.
00:07:54.09 So, the higher the value here,
00:07:56.15 the darker the skin color.
00:07:59.01 And I wanna just make the point that
00:08:00.16 we see a lot of variation in skin pigmentation levels
00:08:04.27 across diverse Africans.
00:08:07.18 And one of the things that we're interested in looking at is
00:08:10.10 correlations with vitamin D for example,
00:08:12.10 because we know that vitamin D is produced by UV light,
00:08:16.21 and that people with darker skin
00:08:18.12 may produce less vitamin D, for example.
00:08:21.02 And vitamin D can have important health implications,
00:08:23.15 so this is relevant to know.
00:08:25.29 It's also an interesting trait to look at how people
00:08:28.09 have adapted to different environments.
00:08:31.21 Here are the results of a principal component analysis
00:08:34.17 for a number of cardiovascular traits,
00:08:37.11 and these are different populations.
00:08:40.27 If the populations cluster close to each other,
00:08:43.19 it means that they're very similar for these traits,
00:08:45.28 and we've color-coded them based on shared language and ethnicity.
00:08:50.26 And what's interesting is that they tend to cluster
00:08:53.04 based on language and culture.
00:08:55.12 So here are the Nilo-Saharan speakers,
00:08:57.06 here are the Afro-Asiatic speakers,
00:08:59.18 and in yellow here are the Niger-Kordofanian speakers,
00:09:04.08 but we see two exceptions.
00:09:06.13 These are two groups that live on the coast of Kenya,
00:09:09.00 in geographic proximity to the Bantu-speaking groups,
00:09:13.10 suggesting that not only are genetic factors important,
00:09:16.02 but environment factors are probably quite important as well.
00:09:22.18 And here we can see tremendous variation
00:09:25.16 for height, weight, and BMI in Africa.
00:09:29.00 And again, we're seeing that
00:09:31.07 populations tend to cluster based on shared ethnicity,
00:09:35.02 and at the extremes
00:09:36.23 we have the very short statured pygmies from central Africa,
00:09:40.13 and then we have the very tall and thin individuals
00:09:43.27 from Kenya and other places... and the Sudan.
00:09:49.02 And so, as we'll talk about in the last section of my lecture series,
00:09:52.18 this may be due to adaptation to different environments.
00:09:58.17 So now I want to tell you about the patterns of
00:10:00.21 genetic variation and genetic structure in Africa,
00:10:04.19 and this is based on a paper that we published several years ago,
00:10:08.14 in which we looked at genome-wide variable markers,
00:10:13.21 and these were genotyped in over 2,500 Africans
00:10:17.06 from 121 ethnic groups
00:10:19.04 that are shown by these dots here.
00:10:21.12 But note that even though this was
00:10:23.06 more than had ever been done before,
00:10:25.16 it still represents just a fraction of the
00:10:27.16 2,000 ethnic groups in Africa,
00:10:30.06 so we're still missing a lot of the variation.
00:10:33.06 We then looked at 98 African-Americans
00:10:36.20 from 4 regions in the US
00:10:38.22 and a comparative dataset of about 1,500 non-Africans.
00:10:44.13 So let me first tell you about the levels of genetic variation that we saw,
00:10:48.05 and that's indicated by the height of this bar.
00:10:51.03 And I've color-coded this by geographic region,
00:10:53.20 so shown in orange are people from Africa,
00:10:57.01 and as nearly every study has shown,
00:10:59.11 Africans have the highest level of genetic variation.
00:11:02.27 And then we see decreasing variation
00:11:05.00 as we move west to east
00:11:07.00 across Eurasia into
00:11:09.06 East Asia, Oceania, and the Americas.
00:11:13.10 So the patterns of genetic diversity that we're seeing
00:11:16.10 simply reflect our evolutionary and demographic history.
00:11:20.10 We see the highest levels of diversity in Africa,
00:11:22.20 which is the site of origin of modern humans,
00:11:25.11 and then when small groups of people
00:11:27.23 migrated out of Africa within the past 50,000-100,000 years,
00:11:32.03 there was a population bottleneck,
00:11:34.06 and so we see a decrease in genetic diversity.
00:11:37.26 And as humans migrated west to east across Eurasia
00:11:41.07 and into the Americas
00:11:43.00 and into Oceania and so on,
00:11:45.01 there were a series of founding events and again,
00:11:47.16 a concomitant decrease in genetic diversity.
00:11:51.21 So this is a phylogenetic tree
00:11:54.01 constructed based on pair-wise genetic distances
00:11:56.14 between populations.
00:11:58.07 You can't see any details on this tree,
00:12:00.13 I just want to point out some overall trends.
00:12:02.27 And I've color-coded these such that
00:12:05.24 the populations shown in black,
00:12:08.21 the black branches, are non-Africans,
00:12:12.11 and then the Africans are shown here.
00:12:14.23 So the first thing that you can see from this tree
00:12:16.27 is that non-Africans are distinguished from Africans,
00:12:20.19 and that the non-African populations
00:12:22.19 are clustering by major geographic region.
00:12:25.25 So we have people from India, central Asia, Europe,
00:12:29.07 Middle East, east Asia, and the Americas,
00:12:34.01 and then Oceania.
00:12:36.10 And even within Africa,
00:12:38.13 populations are clustering by major geographic region,
00:12:41.22 so here are populations from the north of Africa,
00:12:44.02 from eastern Africa,
00:12:45.15 from west-central Africa,
00:12:47.17 and then from southern Africa,
00:12:49.10 with one exception:
00:12:51.25 down here, at the root of this tree,
00:12:54.08 we see the San hunter-gatherers from southern Africa,
00:12:58.22 but clustering near the San are the pygmies,
00:13:01.17 who today live in central Africa.
00:13:04.08 And that's really intriguing and maybe telling us something
00:13:06.16 about the history of these populations,
00:13:08.24 and I'll discuss that more in a moment.
00:13:13.14 Now, we can also compare genetic distances,
00:13:17.02 which are shown on the y-axis,
00:13:19.10 to geographic distances between pairs of populations,
00:13:22.20 shown on the x-axis.
00:13:24.28 And we see a significant positive correlation,
00:13:28.19 but we can also see a lot of scatter here.
00:13:32.01 And what that means is that there are some populations
00:13:34.26 that are geographically very close,
00:13:38.17 but they're genetically very different,
00:13:41.17 and those probably represent recent migration events
00:13:44.24 of genetically differentiated populations.
00:13:47.19 And then on the other end of the spectrum,
00:13:50.02 we have some populations that are genetically very similar to each other,
00:13:53.27 but geographically very far apart.
00:13:56.21 And those may reflect, for example,
00:13:58.24 the Bantu people, who migrated from western Africa
00:14:02.03 to eastern and southern Africa,
00:14:03.18 so they're gone quite a long geographic distance,
00:14:07.03 but genetically they're still very similar to each other.
00:14:11.07 So now I want to move away from looking at populations
00:14:13.28 and I want to talk about looking at variation amongst individuals.
00:14:18.20 And the first thing I want to show you is
00:14:20.28 a principal component analysis based on individual genotypes.
00:14:25.07 And so each of these circles
00:14:28.10 actually represents a person,
00:14:30.16 and if they cluster together
00:14:32.21 it means that they're genetically similar to each other.
00:14:35.22 So, as shown here, the first principle component
00:14:38.15 accounts for as much of the variability in the data as possible,
00:14:42.08 and each succeeding component
00:14:44.08 accounts for as much of the remaining variability as possible.
00:14:47.28 So the first principal component
00:14:50.09 essentially is differentiating
00:14:52.24 the African groups
00:14:55.04 from the non-African groups.
00:14:57.14 The second principal component
00:14:59.23 is differentiating the Native Americas,
00:15:03.05 Eastern Asians,
00:15:04.20 and Oceanin populations
00:15:06.12 from the rest of the world.
00:15:07.26 And the third principal component
00:15:09.27 is distinguishing the Hadza hunter-gatherers from Tanzania
00:15:13.11 from the rest of the world.
00:15:15.12 This next result is based on a probabilistic analysis
00:15:20.24 that simultaneously infers ancestral population clusters,
00:15:26.11 which are represented by the different colors shown here,
00:15:29.27 and then we have...
00:15:31.28 this is actually composed of a series of lines,
00:15:34.21 and each line represents an individual.
00:15:37.18 And an individual can have mixed ancestry
00:15:42.04 from different ancestral population clusters.
00:15:45.18 So what we tend to see outside of Africa,
00:15:48.03 which is shown along the bottom here,
00:15:50.10 is that individuals are clustering
00:15:52.05 by major geographic region.
00:15:54.08 So, in blue we have individuals
00:15:56.26 who self-identify as European or Middle Eastern,
00:16:00.27 and then here we have individuals from southern India,
00:16:04.25 here we have individuals from Pakistan,
00:16:08.09 central Asia,
00:16:09.27 east Asia,
00:16:11.03 Oceania,
00:16:12.29 and the Americas.
00:16:14.27 But what I want you to note is all the colors
00:16:17.27 that we see here in Africa.
00:16:20.22 That's representing the very large amount of genetic diversity,
00:16:24.15 not only within,
00:16:26.11 but among African populations,
00:16:28.15 compared to the whole rest of the globe.
00:16:31.20 I'll just point out a couple of trends.
00:16:35.10 In orange colors are populations from central and west Africa
00:16:38.27 who speak Niger-Kordofanian and Bantu languages.
00:16:43.09 In purple are populations
00:16:45.17 that speak Afro-Asiatic languages
00:16:47.21 and originated from northern or northeast Africa.
00:16:51.26 In red are populations that speak Nilo-Saharan languages
00:16:55.23 and they most likely originated from southern Sudan.
00:17:01.11 We have populations that are speaking Chadic languages,
00:17:05.16 a group called the Fulani who are nomadic pastoralists.
00:17:08.28 Most of the north Africans
00:17:10.27 have a lot of European or Middle Eastern admixture.
00:17:14.23 And then we have the hunter-gatherer groups,
00:17:16.15 like the Hadza,
00:17:18.09 the Sandawe,
00:17:19.23 pygmies from central Africa,
00:17:21.22 and the San hunter-gatherers from southern Africa.
00:17:26.08 Now, we repeated this analysis within Africa,
00:17:30.01 and again we inferred 14 ancestral population clusters,
00:17:34.15 but for ease of viewing I'm just going to pool individuals together
00:17:37.20 and show them as pie charts.
00:17:39.25 Now, first I'm showing you the 3 populations
00:17:42.13 that had been studied as part of the
00:17:44.07 HapMap and Thousand Genomes Initiative.
00:17:47.06 These are NIH-funded programs
00:17:50.22 to characterize genetic variation
00:17:52.28 across ethnically diverse human populations
00:17:56.02 and making that data publically available
00:17:58.02 so that it could be used by a wide range of
00:18:00.16 biomedical research scientists.
00:18:04.00 Now, what I want to point out is that
00:18:05.15 when we look at the rest of Africa,
00:18:08.15 we see quite a bit more variation.
00:18:11.27 And so, for example, populations in east Africa
00:18:15.08 look distinct from populations in western Africa,
00:18:19.25 northern,
00:18:21.04 and southern Africa.
00:18:23.00 It's also interesting
00:18:24.16 because we can see remnants of historic migration events.
00:18:27.11 So for example, I mentioned to you the Bantu migration.
00:18:30.18 The people who speak Niger-Kordofanian or Bantu languages
00:18:33.23 are represented by shades of orange,
00:18:35.26 and you can actually see that they appear
00:18:38.13 to have originated, as I said,
00:18:40.11 in Cameroon/Nigeria region,
00:18:43.03 and then they migrated
00:18:45.10 across Africa into eastern Africa,
00:18:48.03 where they admixed with the indigenous populations there,
00:18:51.19 and they also migrated into southern Africa,
00:18:54.05 where the admixed with the populations there.
00:18:57.05 We can also see remnants of migration of individuals
00:19:01.08 from northeast Africa who speak Afro-Asiatic languages
00:19:04.28 into Kenya and Tanzania.
00:19:07.22 We see migration of people who speak Nilo-Saharan languages,
00:19:11.20 originating from southern Sudan.
00:19:13.11 There was one group that went west,
00:19:16.07 and we think that some of these people who speak Chadic languages,
00:19:20.19 which are actually classified as Afro-Asiatic,
00:19:22.25 genetically they look very similar to the Nilo-Saharans.
00:19:26.02 So in fact there may have been a language substitution
00:19:28.15 at some point in the past.
00:19:30.23 And then we have migration of the Nilo-Saharan pastoralists
00:19:34.08 into Kenya and into Tanzania.
00:19:38.23 We can also see that some of the hunter-gatherer groups are very distinct.
00:19:42.27 Here are the Hadza hunter-gatherers, who speak with a click in Tanzania.
00:19:47.08 Here are the Sandawe, who speak with a click, also in Tanzania,
00:19:50.04 but their languages are very divergent from each other.
00:19:53.14 Here are the San hunter-gatherers shown in light green,
00:19:56.05 also speaking with a click, but again,
00:19:58.06 their languages are very differentiated
00:20:00.06 from the other two populations who speak with clicks in Tanzania.
00:20:05.08 And then we have the pygmy populations from central Africa.
00:20:10.13 Interestingly, the pygmy population called Mbuti,
00:20:14.09 who lives the furthest to the east,
00:20:16.26 appears to possible share some common ancestry with the San.
00:20:22.01 And in fact several pieces of data that we've studied
00:20:26.15 suggest that there could have been a
00:20:28.16 proto Khoesan-Pygmy hunter-gatherer population in Africa
00:20:32.16 that probably existed greater than 50,000 years ago,
00:20:36.01 and then underwent population divergence and differentiation
00:20:40.06 and then migration within the past 50,000 years,
00:20:43.22 but there's still a lot of work to be done
00:20:45.14 to try to differentiate this population history.
00:20:48.23 So next I wanna talk about what we found
00:20:51.05 in terms of African American ancestry.
00:20:53.22 We looked at African Americans
00:20:55.21 originating from four regions in the US:
00:20:58.15 Chicago, Pittsburgh, Baltimore, and North Carolina.
00:21:02.05 Now, not surprisingly, you can see that the majority of ancestry
00:21:06.17 is this western Niger-Kordofanian ancestry,
00:21:10.05 shown in orange.
00:21:12.08 The other major component of their ancestry,
00:21:14.21 which is summarized here, is European ancestry,
00:21:17.19 which ranges from about 0% to greater than 50%.
00:21:22.18 And then we see small amounts of ancestry from other populations,
00:21:25.22 including some other African populations
00:21:29.18 who speak Chadic languages, for example,
00:21:33.09 from western Africa.
00:21:34.27 We see a small amount of ancestry from east Africa,
00:21:37.25 and also very small amounts of
00:21:40.07 east Asian and Native American ancestry,
00:21:43.02 at least in these particular populations.
00:21:45.23 If you look at populations from other regions,
00:21:48.17 you may see more ancestry from those regions.
00:21:54.02 And again, this is reflecting the history of the transatlantic slave trade,
00:22:00.15 originating from west Africa,
00:22:03.10 and actually a very large source of the slave trade
00:22:05.29 was from Angola,
00:22:07.24 and we currently know very little about genetic variation in that region.
00:22:11.12 And that's going to be important to know
00:22:13.28 for some studies in which knowing variation
00:22:17.29 in African ancestral populations will be important
00:22:20.15 for identifying disease risk alleles
00:22:23.28 in African American or Afro-Caribbean populations.
00:22:28.28 I want to tell you about another study that I did with collaborators,
00:22:32.18 in which we looked at
00:22:35.22 over 250,000 single nucleotide polymorphisms, or SNPs.
00:22:41.11 These are just regions of the genome
00:22:43.18 that differ at a single nucleotide,
00:22:46.24 and we looked at them predominantly
00:22:49.12 in western populations along the coast of Africa,
00:22:54.05 and one group from southern Africa.
00:22:57.10 And when we do this principal component analysis,
00:22:59.22 one of the interesting results
00:23:02.07 is that the distribution really reflects the geography of these populations,
00:23:08.02 and that's not a huge surprise.
00:23:09.29 It means that people who live near each other
00:23:12.06 tend to mate with each other,
00:23:13.28 and people who live further apart are not intermixing as often,
00:23:17.27 and so they tend to be more genetically differentiated.
00:23:23.23 We then did a principal component analysis
00:23:26.25 including the African American individuals,
00:23:30.08 shown here in sort of fuchsia color,
00:23:33.29 and shown in red are Europeans,
00:23:37.03 and then here we have the different west African populations.
00:23:42.01 And we could actually determine
00:23:44.00 the amount of European or African ancestry in any individual
00:23:49.06 -- African American individual --
00:23:51.19 by looking at their position along principal component 1.
00:23:56.05 So for example, this individual here,
00:23:58.24 this African American individual,
00:24:00.29 appears to have more European ancestry,
00:24:03.17 whereas this African American individual
00:24:06.04 seems to have more west African ancestry.
00:24:11.08 And then, using an approach that was developed by Carlos Bustamante's lab,
00:24:16.15 it was possible to actually scan along chromosomes,
00:24:19.16 so here we're showing
00:24:22.22 the different chromosomes starting at 22, 21, 20,
00:24:25.25 and so on down to chromosome 1.
00:24:28.12 And as you scan along the chromosome,
00:24:30.04 at any particular region,
00:24:32.11 you can infer if somebody has African ancestry,
00:24:36.25 which is shown in blue,
00:24:39.12 European ancestry, which is shown in red,
00:24:43.15 or mixed ancestry, which is shown in green.
00:24:47.27 And what we see is that most African Americans
00:24:50.23 have a mixture of ancestry.
00:24:53.01 So they tend to have a lot of,
00:24:54.16 not surprisingly, African ancestry shown in blue.
00:24:58.03 There are regions of mixed ancestry shown in green,
00:25:01.13 but also note that there are some regions of the genome
00:25:04.20 which are only of European ancestry,
00:25:07.26 and this differs quite a bit amongst different individuals.
00:25:10.11 Here's an example of someone who appears
00:25:12.13 to have undergone very recent admixture;
00:25:16.08 they have a lot of African ancestry.
00:25:19.27 Here's someone who has very recent European ancestry,
00:25:23.01 so we see a lot of regions of the genome
00:25:24.24 where they're of mixed ancestry.
00:25:27.20 Here's someone who has...
00:25:29.24 they self-identify as African American,
00:25:31.27 but they have almost no African ancestry,
00:25:34.24 so that goes to show you that there can be a lot of genetic variation
00:25:38.00 that may not always correlate with self-identified ethnicity.
00:25:42.29 The other important point here is that,
00:25:45.29 in the future,
00:25:48.25 the ideal that we have is to develop
00:25:51.02 more personalized medicine
00:25:53.27 that is tailored for the individual.
00:25:56.14 And here's someone that, for example,
00:25:58.16 if they went to the doctor and they self-identified
00:26:00.22 as African American,
00:26:02.20 the doctor might prescribe certain drugs that, say,
00:26:04.26 are more effective in African Americans.
00:26:07.11 But what if, at that particular position,
00:26:09.27 where they have only European ancestry,
00:26:12.12 what if there was a drug metabolizing enzyme gene
00:26:16.01 at that particular point,
00:26:18.27 and so that would be of pure European ancestry,
00:26:21.24 and so that might be important to know.
00:26:24.00 So this has important implications for
00:26:26.00 future design of future personalized medical approaches for treatment.
00:26:32.25 So in conclusion, people from different geographic regions
00:26:35.29 are genetically more similar to each other,
00:26:38.08 so for example, Asian individuals
00:26:40.15 will be more similar to other Asian individuals,
00:26:43.02 Europeans more similar to other Europeans.
00:26:46.02 But in Africa,
00:26:47.25 there has been more time to accumulate genetic variation,
00:26:50.29 they're had larger effective populations sizes
00:26:53.18 so they've maintained a lot of variation,
00:26:55.26 and they've live in diverse environments,
00:26:58.05 so they tend to be highly differentiated from each other,
00:27:01.05 although we also can see that
00:27:03.16 there's been a history of admixture throughout much of Africa.
00:27:07.29 So therefore, Africans have the highest level of genetic variation,
00:27:12.16 both within and between populations,
00:27:15.01 and we saw that African Americans
00:27:17.05 have ancestry from west Africa and Europe,
00:27:19.21 and that the ancestry varies along chromosomes,
00:27:22.03 which has important implications for personalized medicine.
00:27:26.18 And that concludes this portion of my lecture,
00:27:28.26 and for this section I'd like to acknowledge
00:27:30.23 the many individuals who contributed,
00:27:34.28 together with our funding organizations.

00:00:07.17 For the last part of my lecture series,
00:00:10.11 I wanna talk about examples of natural selections in humans,
00:00:14.29 and the two particular examples
00:00:17.01 that I'm going to be talking about
00:00:19.00 are the evolution or lactose tolerance in east Africa,
00:00:22.10 and of pygmy short stature.
00:00:25.04 So if we're going to be talking about natural selection,
00:00:27.11 we have to first of course
00:00:28.29 acknowledge Charles Darwin,
00:00:31.12 who came up with the theory of natural selection.
00:00:36.10 In fact, to quote from Darwin, he said,
00:00:39.13 "This preservation of favourable variations
00:00:42.03 and the rejection of injurious variations,
00:00:44.21 I call Natural Selection.
00:00:47.06 Variations neither useful nor injurious
00:00:49.27 would not be affected by natural selection,
00:00:52.18 and would be left a fluctuating element,
00:00:55.01 as perhaps we see in the species called polymorphic."
00:00:58.10 And that was from his classic book
00:01:00.06 On The Origin of Species,
00:01:01.24 published in 1859,
00:01:03.25 and you might recognize from our first lecture,
00:01:07.03 that this is really talking about genetic drift,
00:01:09.27 random fluctuations.
00:01:13.13 However, part of the evolutionary change that we see
00:01:18.12 is not just going to be due to random genetic drift,
00:01:21.03 it's also going to be due to natural selection.
00:01:24.18 And so, according to that theory,
00:01:27.10 natural variation exists and is heritable,
00:01:30.02 more organisms are born than can survive,
00:01:32.11 and therefore organisms best suited to the environment
00:01:35.07 survive more often,
00:01:36.25 and slight differences can accumulate in a species over time.
00:01:40.24 So this is the idea of gradual evolution of a species
00:01:43.27 by natural selection.
00:01:45.18 And this is Huxley,
00:01:47.03 who was also known as Darwin's bulldog
00:01:50.11 because he was the big proponent of his theory,
00:01:52.17 and he said,
00:01:54.05 "How extremely stupid not to have thought of that!"
00:01:57.12 So when Darwin first came up with his theory of natural selection,
00:02:01.08 there was really no concept of genetics
00:02:04.12 as we know it today.
00:02:06.02 In fact, it wasn't until the late 1800s
00:02:08.12 that Mendel proposed his theory of genetics.
00:02:13.02 So in the 1930s and 1940s
00:02:15.22 there was sort of a synthesis of natural selection
00:02:19.09 and genetics and mathematics,
00:02:22.16 population genetics,
00:02:23.25 and at that time it was proposed that genetic variation in populations
00:02:27.05 arises by chance through mutation and recombination,
00:02:31.15 that evolution consists primarily of changes in the
00:02:34.12 frequencies of alleles between one generation and another,
00:02:37.25 largely as a result of genetic drift,
00:02:40.24 gene flow,
00:02:42.05 and natural selection.
00:02:43.27 And that speciation occurs gradually when populations
00:02:46.00 are reproductively isolated, for example,
00:02:48.20 by geographic barriers.
00:02:52.14 And so if we look at this timeline,
00:02:54.12 starting with the Origin of Species,
00:02:56.21 and then Mendelian inheritance
00:02:59.04 is actually rediscovered in 1900,
00:03:01.19 it was first proposed in the late 1880s,
00:03:04.01 but very few people knew about it at that time.
00:03:06.27 And then in the early 1900s
00:03:09.09 we have the theoretical foundations of population genetics
00:03:12.21 and then, as I mentioned,
00:03:14.09 the modern synthesis in the 30s.
00:03:16.19 And then in the 70s we have Kimura's theory of neutral evolution,
00:03:21.19 which was proposing that most changes and speciation events
00:03:25.07 are simply due to random genetic drift
00:03:27.22 and to new mutation events.
00:03:29.20 And I think that today we would say
00:03:31.14 it's a combination of all of the above.
00:03:33.19 There's certainly a lot of genetic drift that occurs,
00:03:36.02 but we know that natural selection
00:03:37.29 is having a very important influence
00:03:40.26 on the variation that we see
00:03:43.05 in terms of phenotypic variation and even disease susceptibility.
00:03:48.04 So let's look what happens
00:03:49.08 when a neutral mutation occurs in a population,
00:03:52.07 as indicated by this individual in green.
00:03:55.25 Let's look what happens as we proceed forward in generations,
00:03:59.08 and you can see there's not too many changes
00:04:01.21 in allele frequency.
00:04:03.29 But what happens when we have a beneficial mutation,
00:04:09.05 which means that it increases the fitness of the individual,
00:04:12.20 meaning that they're more likely to produce children,
00:04:16.12 and their children are more likely to produce more children,
00:04:19.00 and so on and so forth.
00:04:21.15 And so we can see that each generation,
00:04:24.04 this beneficial mutation is going to spread,
00:04:27.22 until eventually it may be nearly fixed
00:04:32.06 in the population.
00:04:34.17 So I want to tell you about some of our studies
00:04:37.20 focused in African populations
00:04:39.14 in which we're trying to identify
00:04:41.02 genetic signatures of natural selection,
00:04:43.19 and regions of the genome that are targets of natural selection.
00:04:47.29 And this is important
00:04:49.22 because it's thought that mutations associated with diseases
00:04:52.16 in modern populations,
00:04:54.13 like hypertension
00:04:56.03 , diabetes,
00:04:57.08 obesity,
00:04:58.10 and asthma,
00:04:59.08 may have been selectively advantageous or adaptive
00:05:01.23 in past hunter-gatherer environments.
00:05:04.04 So if we can identify these regions
00:05:06.25 that are targets of selection, or actual variable sites
00:05:09.18 that are targets of selection,
00:05:11.16 those may be functionally important
00:05:13.14 and may give us a clue about disease risk.
00:05:16.11 So here I'm showing you a few of the populations
00:05:18.12 that we've studied in Africa,
00:05:20.23 and we have people who are living at very different climates,
00:05:23.15 high altitude, low altitude,
00:05:26.03 savannah, and tropical environments, for example.
00:05:30.10 We have people who have very different diets,
00:05:32.22 so agriculturalists,
00:05:34.08 hunter-gatherers,
00:05:35.23 or pastoralists.
00:05:37.14 And they have very different infectious disease exposures,
00:05:40.05 so they've likely undergone local adaptation
00:05:42.13 to different environments.
00:05:45.25 And I'm going to, as I mentioned,
00:05:47.16 tell you about two examples today.
00:05:49.15 The first one is the evolution of lactose tolerance
00:05:51.29 in east African pastoralist populations.
00:05:57.07 So, the ability to digest the sugar lactose,
00:06:00.21 which is quite common in milk,
00:06:03.07 is due to an enzyme called lactase-phlorizine hydrolase,
00:06:07.15 or known as lactase for short.
00:06:09.28 And lactase is expressed specifically
00:06:13.01 in the brush border cells of the small intestine,
00:06:16.25 and in individuals who maintain high levels of this enzyme
00:06:20.29 as adults,
00:06:22.23 they're able to break down the complex sugar lactose
00:06:26.16 into glucose and galactose,
00:06:29.14 which is rapidly taken up into the bloodstream.
00:06:32.23 However,
00:06:35.19 most mammals, and most humans,
00:06:38.19 shut down lactase activity
00:06:40.23 shortly after weaning.
00:06:42.28 So, as adults, they do not have an active form of this enzyme.
00:06:46.24 And what's going to happen is
00:06:48.19 they're not going to be able to break down that complex sugar.
00:06:51.26 It's going to go down into the lower gut,
00:06:54.13 it's going to be attacked by bacteria,
00:06:56.25 and you're going to have severe intestinal distress.
00:07:01.00 Now, it has been noted for many years by anthropologists
00:07:04.27 that there is a very strong correlation
00:07:06.27 between the lactose tolerance trait,
00:07:09.19 or you could think of it also as the lactase persistence trait,
00:07:13.26 because there's persistence of the enzyme activity as adults.
00:07:18.15 And they've seen a strong correlation
00:07:20.20 between the prevalence of that trait
00:07:23.14 with populations who traditionally practice cattle domestication
00:07:28.04 and dairying.
00:07:30.05 So for example, this trait is most common in northern Europe,
00:07:33.19 it decreases in frequency as one moves
00:07:36.23 into southern Europe
00:07:38.29 and into the Middle East.
00:07:40.24 It's very uncommon in eastern Asia
00:07:43.26 and in the Americas,
00:07:46.13 and it's uncommon in western Africa,
00:07:48.26 which is one of the reasons that we see high levels
00:07:51.17 of lactose intolerance in African Americans, for example.
00:07:55.24 But in regions of Africa where there's a high prevalence
00:07:59.04 of cattle domestication, pastoralism, and dairying,
00:08:03.14 we see a high prevalence of this trait.
00:08:07.15 So, in 2002,
00:08:10.18 there was an elegant study done
00:08:12.20 by Leena Peltonen's group in Finland,
00:08:14.28 in which they identified a genetic mutation
00:08:17.08 that regulates lactose tolerance in Europeans.
00:08:20.20 And it was located near the...
00:08:23.25 upstream of the lactase gene.
00:08:26.01 When we sequenced that region in east African pastoralists,
00:08:29.04 they didn't have it,
00:08:31.10 so we knew they must have something else.
00:08:33.13 So in order to identify those mutations,
00:08:35.21 we did something that's called a lactose tolerance test.
00:08:38.29 So, basically what we do is
00:08:42.11 we give people the sugar lactose in a powdered form,
00:08:46.20 we add water, and it basically tastes like orange Kool-Aid,
00:08:51.09 and then we have to line people up
00:08:54.17 and have them drink the lactose at the same time.
00:08:57.27 This is a group of Maasai women from Tanzania.
00:09:03.28 This is a group of pastoralists from southern Ethiopia.
00:09:11.23 And then we can use a standard diabetes monitoring kit,
00:09:16.03 and what we can do is to measure the blood glucose,
00:09:19.29 starting at baseline before they drink the lactose,
00:09:23.29 and then every 20 minutes we're gonna measure this,
00:09:27.06 over a period of about an hour.
00:09:30.02 And then we're gonna look at the maximum rise
00:09:32.25 in blood glucose.
00:09:35.15 If individuals have a rise
00:09:37.09 that is greater than 1.7 millimolar (mM)
00:09:39.20 we consider them to be lactose tolerant,
00:09:42.20 or to have the lactase persistent trait,
00:09:45.03 shown in light blue.
00:09:47.07 And if they have a rise that is less than 1.1 mM,
00:09:51.12 they're considered to be intolerant,
00:09:53.25 shown in dark blue.
00:09:55.18 So, we measured this trait
00:09:57.12 in nearly 500 individuals
00:09:59.17 from Tanzania, Kenya, and the Sudan,
00:10:02.00 and then we looked for association
00:10:04.10 with genetic variation that we identified
00:10:06.21 by resequencing the region
00:10:08.28 where the European variant had been identified.
00:10:13.13 And in doing so we identified
00:10:15.12 three novel genetic polymorphisms
00:10:18.21 that are associated with the lactose tolerance trait in east Africa,
00:10:22.17 and those are shown here by the boxes.
00:10:26.29 The most common was this one at position 14010,
00:10:31.06 but we also saw those others
00:10:32.24 at positions 13915 and 13907,
00:10:36.03 located roughly 14,000 basepairs
00:10:38.25 upstream of the lactase gene
00:10:41.18 which is located on chromosome 2.
00:10:44.07 Now, one of the really interesting things about this is that,
00:10:48.11 one, these regulatory mutations were pretty far away,
00:10:51.28 about 14,000 basepairs from the gene,
00:10:54.26 and they were located in an intron
00:10:57.25 in a non-coding region of a neighboring gene called MCM6.
00:11:03.00 So this is demonstrating that
00:11:04.25 functionally important variation
00:11:07.13 can actually be located in non-coding regions,
00:11:10.21 and we were able to show,
00:11:13.13 using in vitro cell line studies,
00:11:16.20 that these variants that are derived,
00:11:20.19 shown in the different colors here,
00:11:23.22 that they regulate expression
00:11:26.15 of the lactase gene using the lactase promoter.
00:11:31.10 Now, they're located very close to the mutation
00:11:35.01 associated with lactose tolerance in Europeans,
00:11:38.20 located at position 13910,
00:11:41.14 but they arose independently
00:11:43.17 due to a process called convergent evolution,
00:11:46.13 and probably due to a very strong
00:11:48.27 selective force to be able to drink milk that contains lactose,
00:11:56.15 in these different regions of the world.
00:12:00.27 What's also interesting
00:12:02.19 is that the variants that we identified
00:12:04.16 have a very distinct geographic distribution.
00:12:07.09 So the one that we found that was most common in our study
00:12:10.06 was at position 14010,
00:12:12.08 and we can see that it is pretty localized
00:12:15.01 to east Africa, to Tanzania and Kenya,
00:12:17.26 and that's the most likely site of origin of that mutation.
00:12:21.11 Interestingly, we also see it a bit in south Africa,
00:12:26.01 probably reflecting migration of pastoralists
00:12:28.29 from east Africa into that region.
00:12:32.16 The variant position at 13915
00:12:35.08 appears to have originated in the Middle East,
00:12:37.21 and we could see that it was introduced into northeast Africa,
00:12:40.17 probably by migration.
00:12:43.10 And then the variant at position 13907
00:12:46.26 likely arose in northeast Africa.
00:12:49.21 But again, one of the important take-home points is that
00:12:53.02 we have a functionally important variant
00:12:55.07 that's occurring at high frequency, sometimes as high as 40%,
00:12:59.12 and it's very geographically restricted,
00:13:02.22 and there are likely to be other mutations like that,
00:13:05.06 some of which may have implications for disease susceptibility,
00:13:09.11 again emphasizing the importance
00:13:11.23 to look amongst ethnically diverse Africans.
00:13:16.29 So the next thing we wanted to do
00:13:19.21 was to look for a signature of positive selection,
00:13:23.17 and this is the method in which we can do that.
00:13:27.21 So imagine, here in red,
00:13:30.25 imagine that this is a new mutation that has occurred, say,
00:13:34.15 one of the mutations associated with lactose tolerance.
00:13:38.00 And it's adaptive,
00:13:39.10 meaning that it increases the fitness of individuals who have it,
00:13:43.25 meaning that they're more likely to have children,
00:13:45.27 and their children are more likely to have children,
00:13:47.22 and so on.
00:13:49.28 And so it's going to increase in frequency
00:13:52.24 in the population,
00:13:54.29 and it's going to drag with it
00:13:57.15 the neighboring variants nearby.
00:14:00.03 So, you can see that when it originated, it had...
00:14:02.29 it was on a chromosome with a green variant
00:14:05.18 and a black variant.
00:14:08.03 And now these got dragged along to high frequency,
00:14:11.04 through a process known as hitchhiking.
00:14:14.07 Now, if this had gone to fixation,
00:14:17.12 meaning that everybody has it,
00:14:19.00 we would have called it a full selective sweep.
00:14:21.20 In this case, it hasn't quite reached a full selective sweep,
00:14:26.15 so we call it a partial sweep.
00:14:29.24 Now, that could just mean that
00:14:31.17 there hasn't been time for it to go to a full sweep,
00:14:33.06 or it could be that for some reason
00:14:35.17 there may be some negative aspects of having it,
00:14:38.02 and there's a reason that both variants are maintained in the population.
00:14:43.17 Now, after the sweep occurs,
00:14:45.19 you're going to have new mutation events
00:14:47.26 and new recombination events
00:14:49.21 shuffling up the variants
00:14:52.04 that are linked to the mutation that's adaptive.
00:14:56.12 And so that will decrease the association
00:15:01.00 observed between the mutation and the flanking variation.
00:15:05.00 And in fact,
00:15:06.15 if we have an estimate of the recombination rate,
00:15:08.27 we can use computational methods
00:15:10.24 to estimate how old this mutation is.
00:15:14.18 And that's exactly what we did here.
00:15:17.12 So shown on top
00:15:19.28 is an example from the most common mutation
00:15:22.27 that we found associated with lactose tolerance,
00:15:25.03 at position 14010.
00:15:27.18 Individuals who have the C variant
00:15:29.26 are able to digest milk,
00:15:31.22 and individuals who are homozygous are shown as red.
00:15:35.28 And what we did is we genotyped markers
00:15:38.28 going a distance of about 3 million nucleotides,
00:15:42.26 and what we would do is that if someone is homozygous,
00:15:46.20 starting at the lactose tolerance mutation,
00:15:49.02 and then we go to the next mutation.
00:15:51.02 If they're homozygous,
00:15:53.00 then we continue going.
00:15:55.13 If they underwent a recombination,
00:15:57.05 we stop the line.
00:15:59.13 And what we can basically see is that homozygosity
00:16:02.28 extends about 2 million basepairs
00:16:06.01 on chromosomes that have the lactose tolerance mutation.
00:16:09.15 But if we look at chromosomes that have the ancestral mutation,
00:16:14.00 they have almost no extended haplotype homozygosity.
00:16:19.07 And so this is a classic signature of a selective sweep.
00:16:22.25 It means that this variant
00:16:24.16 was under very strong positive selection
00:16:28.14 and it rapidly increased in frequency in the population,
00:16:32.04 dragging with it the neighboring variation.
00:16:38.16 Now, here I'm showing the European variant,
00:16:41.17 in this case the T variant
00:16:43.20 is associated with lactose tolerance,
00:16:45.27 and it shows a very similar pattern.
00:16:50.22 So using computational approaches,
00:16:52.27 we were able to estimate the age of the African mutation
00:16:57.22 to be somewhere between about 3,000-7,000 years of age.
00:17:01.28 These are the populations
00:17:03.25 that had the oldest age estimates,
00:17:06.08 and they include individuals
00:17:08.07 who speak Cushitic languages.
00:17:10.04 They came from Ethiopia,
00:17:12.10 and they practiced agro-pastoralism.
00:17:15.01 They came into Kenya and Tanzania
00:17:17.16 within the past 5,000 years.
00:17:20.23 And then we saw it at very high prevalence
00:17:23.26 and an old age estimate in Nilo-Saharan-speaking groups,
00:17:27.11 and these would include, for example, the Maasai.
00:17:30.09 Now, they came into the region more recently,
00:17:32.17 from southern Sudan,
00:17:34.08 within the past 3,000 years, so if I were to guess,
00:17:37.05 I would think perhaps this mutation
00:17:39.04 arose in the Cushitic speaking populations.
00:17:42.03 But irregardless, it quickly, rapidly spread
00:17:45.07 to all of the populations in the area
00:17:47.21 because it was so selectively advantageous
00:17:51.22 and adaptive to have this mutation.
00:17:55.03 Now, because we see the correlation
00:17:59.18 between the practice of cattle domestication and pastoralism
00:18:04.11 and the rise in this mutations,
00:18:06.16 this is a really excellent example
00:18:08.22 of gene-culture co-evolution.
00:18:12.03 And in fact, what's really interesting is
00:18:15.01 that the date estimates that we came up with correlate really well
00:18:18.25 with the archaeological data,
00:18:20.17 which shows that cattle domestication
00:18:22.14 arose in the Middle East or north Africa
00:18:27.04 somewhere between 8,000-10,000 years ago,
00:18:29.26 and that corresponds with the age estimate for the European mutation,
00:18:33.18 which we inferred to be about 9,000 years old.
00:18:37.25 But cattle domestication was not introduced
00:18:40.25 south of the Saharan desert
00:18:44.20 until roughly 5,000 or 5,500 years ago,
00:18:48.21 correlating very well with the age estimate
00:18:52.03 for the mutation we found in eastern Africa.
00:18:54.24 And then it was introduced
00:18:56.13 much more recently into southern Africa.
00:19:00.12 But one could argue that perhaps Mendelian traits like lactose tolerance,
00:19:05.04 which are regulated by a single locus or gene of major effect,
00:19:10.23 are in a sense the low hanging fruit;
00:19:12.20 they're the easiest to identify.
00:19:15.04 So one thing that my lab is interesting in doing
00:19:17.10 is looking at more complex traits,
00:19:19.23 and perhaps one of the most classic complex traits is height.
00:19:23.28 So, height is highly heritable,
00:19:26.19 genome wide association studies in tens of thousands of Europeans
00:19:30.12 have identified hundreds of loci,
00:19:33.06 each of very small effect,
00:19:35.06 and explaining only a very small proportion of the variation in height.
00:19:39.20 Now, interestingly, most of these are not part of
00:19:42.22 the growth hormone/IGF1 pathway,
00:19:45.07 which we know plays a very important role in idiopathic short stature,
00:19:49.16 for example.
00:19:53.05 Now, in Africa, we see some of the broadest distributions,
00:19:56.21 or ranges in height,
00:19:59.02 ranging from the very short statured Pygmies in central Africa,
00:20:03.28 and then we see some of the tallest individuals
00:20:07.13 in the Sudan and in eastern Africa.
00:20:10.23 And it's thought that these differences
00:20:12.14 may be partly due to adaptation
00:20:14.29 to different environments.
00:20:16.27 So what I want to tell you today is about
00:20:18.19 our genetic studies of short stature
00:20:22.01 in Pygmy populations from central Africa.
00:20:25.14 And, for you to fully understand and appreciate the work we've done,
00:20:29.17 I think I should first tell you a little bit about
00:20:32.07 how we went about collecting these samples
00:20:34.07 and how challenging it could be.
00:20:35.25 So, this is...
00:20:37.18 to get to one of the groups that we studied in Cameroon,
00:20:39.27 you have to cross this river,
00:20:41.28 and you have a person who has a ferry,
00:20:44.05 he's actually using a hand crank here
00:20:47.13 to get us across.
00:20:50.12 And I guess I'm very fortunate
00:20:52.19 because as a woman, I was able to get shade,
00:20:54.15 but not everybody was that lucky.
00:20:56.28 And here are some other hazards that we run into,
00:20:59.15 but I'm smiling because the head is cut off of this snake.
00:21:03.01 But I actually have to give credit to Dr. Alain Froment,
00:21:06.24 who has been studying the Pygmy populations in Cameroon
00:21:09.16 for greater than 30 years,
00:21:11.20 and he did the majority of the sample collection
00:21:14.01 in this case.
00:21:16.21 So, the genetic basis of short stature in Pygmies
00:21:19.26 is a question that's been of tremendous interest
00:21:22.08 to endocrinologists and human geneticists alike
00:21:25.07 for most than 50 years.
00:21:27.08 The particular populations that we studied
00:21:29.25 are located in Cameroon, three different groups from Cameroon,
00:21:34.27 who mean male height is 152 cm.
00:21:40.11 And they live in very close connection and interaction
00:21:44.29 with neighboring populations who speak Bantu languages
00:21:47.29 and practice agriculture,
00:21:50.06 and their mean male height is 170 cm,
00:21:54.04 so that's quite a difference between the two.
00:21:58.16 So, the Pygmy short statured phenotype in humans
00:22:01.26 has arisen independently in different global populations.
00:22:05.12 Typically, these are populations
00:22:07.03 that live in tropical environments,
00:22:09.14 so there have been a number of hypotheses
00:22:11.11 about why this trait might be adaptive.
00:22:14.28 And these include thermoregulation,
00:22:19.04 limited food resources,
00:22:21.19 locomotion - that it may be easier to move
00:22:23.28 in a dense tropical environment if you're short,
00:22:26.18 and more recently there's a theory
00:22:30.10 that this is due to a life-history tradeoff,
00:22:32.10 and I'm going to focus on that theory.
00:22:35.08 And that has to do with the fact that
00:22:37.14 Pygmies have a remarkably short lifespan.
00:22:40.11 Their chance of living to age 15
00:22:42.06 is only about 40%,
00:22:44.18 and if they make it to age 15,
00:22:46.23 the expected lifespan is only around 25 years of age.
00:22:50.01 Now, that is due largely to very high infectious disease burden
00:22:54.05 and a very challenging life in dense tropical forests.
00:22:59.24 Now, what the study showed is that
00:23:02.18 Pygmies appear to be reaching reproduction...
00:23:05.25 they appear to be reproducing and reaching puberty
00:23:08.09 at a significantly earlier age
00:23:11.01 than other Africans.
00:23:13.13 And the growth trajectory in Pygmies
00:23:14.28 appears to be similar to other populations until the point of puberty,
00:23:19.21 and then they lack the adolescent growth spurt.
00:23:22.15 So this may be some sort of a tradeoff:
00:23:24.18 there's selection to reproduce earlier
00:23:26.22 because they're dying very young,
00:23:28.22 but that may be a tradeoff,
00:23:30.24 in that they're not undergoing the adolescent growth spurt.
00:23:35.20 Now, there have been only a handful
00:23:37.28 of physiologic and metabolic studies in Pygmies,
00:23:42.00 but nearly all of these are pointing towards
00:23:44.18 disruptions of the growth hormone/IGF1 pathway,
00:23:47.19 so this is in contrast to what we're seeing in European populations.
00:23:52.05 However, there's been quite a bit of dispute of
00:23:54.28 where along this pathway these disruptions are occurring.
00:24:00.04 So, in order to try to address these questions,
00:24:03.06 we genotyped one million single nucleotide polymorphisms
00:24:08.06 in 67 pygmy individuals
00:24:10.23 and 58 of the neighboring Bantu individuals.
00:24:14.14 And here we can see a plot,
00:24:17.10 similar to what I've shown you before,
00:24:19.09 based on structure analysis.
00:24:21.09 And to remind you,
00:24:23.02 this is composed of a series of lines,
00:24:24.23 and each line represents a person,
00:24:26.16 and they can have ancestry
00:24:28.08 from different ancestral populations,
00:24:31.01 represented by the different colors.
00:24:33.04 So here in orange
00:24:34.29 are individuals who speak the Bantu language
00:24:38.00 and practice agriculture,
00:24:40.08 and in dark green are individuals who self-identify as Pygmies.
00:24:44.19 And what you can see is that there's been
00:24:46.22 a lot of admixture between the Pygmies
00:24:49.29 and the neighboring Bantu people.
00:24:52.03 Now, interestingly, this tends to be unidirectional,
00:24:54.28 and it tends to be gene flow between males
00:24:57.27 from the Bantu population
00:25:00.03 with females of the Pygmy population.
00:25:02.22 This is largely due to socioeconomic factors.
00:25:06.23 Now, when we look at a correlation
00:25:08.26 between ancestry and height,
00:25:11.03 we observed a very strong and significant positive correlation.
00:25:15.04 So, we can see that Pygmies who have more of the Bantu ancestry
00:25:19.23 tend to be taller.
00:25:21.19 And, so this is showing
00:25:22.25 that there's a strong genetic component to this trait.
00:25:26.17 We've also worked with collaborators
00:25:28.11 to develop methods
00:25:30.19 to infer tracts of Pygmy and Bantu ancestry
00:25:35.11 across the chromosome.
00:25:36.29 So here, these are the different chromosomes,
00:25:38.18 starting with chromosome 1
00:25:40.05 and going up to chromosome 22,
00:25:42.25 and here I'm showing you an example from chromosome 3.
00:25:46.04 And in blue is showing tracts of the genome
00:25:49.03 that are Pygmy ancestry,
00:25:50.24 and in red are tracts of the genome that are Bantu ancestry,
00:25:54.23 and what we tend to see are very, very short tracts of Bantu ancestry.
00:25:58.28 And that's reflected in the fact that admixture
00:26:01.08 has been occurring over thousands of years.
00:26:06.11 Now, the next question that we wanted to address
00:26:08.17 is how do the genomes of the Pygmy hunter-gatherers
00:26:12.04 differ from the genomes of the Bantu agriculturalists
00:26:17.00 and from other groups, such as the Maasai pastoralists
00:26:20.28 from east Africa.
00:26:22.28 And to do that,
00:26:25.03 we use a number of scans of natural selection
00:26:27.28 across the genome.
00:26:29.29 Without getting into detail about the methods,
00:26:32.26 I'll just point out that you can see by the different colors here
00:26:37.00 across the different chromosomes,
00:26:39.00 here's chromosome 22 and going down to chromosome 1,
00:26:42.04 that we found a number of regions of the genome
00:26:44.22 that are targets of selection.
00:26:47.05 But there was one region in particular,
00:26:49.26 on chromosome 3,
00:26:52.04 where we saw a cluster of targets of natural selection.
00:26:57.01 And this was over about a 15 million basepair region.
00:27:01.14 Now, given our small sample size,
00:27:03.20 we have very little power
00:27:05.15 to detect a genome-wide association.
00:27:09.04 And so what we did is,
00:27:10.26 under the hypothesis that this is an adaptive trait,
00:27:13.17 we just focused on the regions of the genome
00:27:16.07 that are targets of selection, shown here,
00:27:19.11 and then we looked for an association with height.
00:27:22.10 And one of the strongest, most significant associations
00:27:25.09 was exactly in that same 15 million basepair region
00:27:29.19 of chromosome 3.
00:27:31.23 And indeed, it encompassed several genes,
00:27:34.15 one of which is DOCK3,
00:27:36.18 which has been shown to be associated with height
00:27:39.09 in non-African populations,
00:27:41.08 so we replicated that finding.
00:27:43.20 But nearby was another gene called CISH,
00:27:47.09 which is a member of the cytokine signaling family,
00:27:50.10 plays a very important role in regulating
00:27:52.28 IL-2 cytokine signaling pathway,
00:27:56.18 and studies have shown that it's associated
00:27:58.26 with resistance to a number of infectious diseases
00:28:01.18 in Africa.
00:28:04.01 Now, interestingly,
00:28:05.29 CISH also directly inhibits
00:28:07.14 human growth hormone receptor action
00:28:10.06 by blocking the STAT5 phosphorylation pathway.
00:28:13.15 And so we know that studies in mice
00:28:15.17 show that when this gene is overexpressed,
00:28:18.06 the mice are short statured.
00:28:20.23 Now, this led me to the hypothesis that,
00:28:24.14 could it be that there could actually be selection
00:28:26.19 for immune function
00:28:28.11 that is indirectly resulting
00:28:30.05 in short stature in Pygmies,
00:28:32.05 because that gene plays an important role in both.
00:28:35.29 And we need to do further functional studies,
00:28:38.20 and look at differences in gene expression
00:28:40.13 to test this hypothesis.
00:28:44.04 The last study I wanna tell you about is a study
00:28:46.20 in which we sequenced the entire genomes,
00:28:49.15 at high coverage,
00:28:51.07 of 15 African hunter-gatherers,
00:28:53.22 including 5 Pygmies,
00:28:55.28 5 Hadza,
00:28:57.10 and 5 Sandawe.
00:28:59.26 We identified over 13 million variants,
00:29:02.29 3 million of which are completely novel;
00:29:05.29 they have never previously been identified.
00:29:08.13 And that's just from 15 individuals,
00:29:10.14 so you can imagine how much variation is out there.
00:29:13.16 Many of these are novel variants...
00:29:15.27 many of these novel variants are in known regulatory sites.
00:29:21.04 So now, combining the two studies,
00:29:24.08 we wanted to ask the question,
00:29:26.03 which pathways are enriched for genes near targets of selection?
00:29:29.16 And these enriched pathways
00:29:31.25 include genes involved in neuro-endocrine signaling,
00:29:35.01 reproduction,
00:29:36.06 metabolism,
00:29:37.11 and immune function,
00:29:38.22 and interestingly, based on the whole genome sequencing study,
00:29:42.08 we saw an enrichment for genes
00:29:44.06 that play a role in pituitary function in Pygmies,
00:29:47.13 including follicle-stimulating hormone receptor,
00:29:50.13 growth hormone receptor,
00:29:52.11 HESX1, which I'll tell you more about in a moment,
00:29:55.11 and thyrotropin-releasing hormone receptor.
00:29:58.15 In fact, TRHR was one of the biggest hits
00:30:02.13 that we saw in terms of these studies of selection.
00:30:05.17 And what's interesting is that this gene
00:30:08.22 plays an important role in the hypothalamic-pituitary-thyroid axis,
00:30:12.28 influencing a number of traits that could potentially
00:30:15.14 be of adaptive significance in Pygmies.
00:30:18.26 And also of interest was that anthropologists
00:30:21.18 have noted that there is a significant difference
00:30:24.23 in the prevalence of Goiter
00:30:27.00 among Pygmies and neighboring Bantu groups.
00:30:29.24 So the Pygmies have a much lower frequency of Goiter
00:30:33.16 compared to the neighboring Bantu populations,
00:30:36.16 and this could reflect a biological adaptation in Pygmies
00:30:41.20 to a low iodine environment.
00:30:43.24 It's very deleterious to get Goiter
00:30:46.22 because it can also lead to a diseased called Cretinism,
00:30:49.27 which of course is going to be very deleterious.
00:30:52.18 So again, here's an example
00:30:54.10 where something like adaptation to diet
00:30:56.13 could indirectly influence growth
00:30:58.28 or other phenotypes in the Pygmy population.
00:31:04.01 The last thing we wanted to do
00:31:06.01 was to look for regions of the genome,
00:31:08.08 using the whole genome sequencing data,
00:31:10.13 that are specific to Pygmies,
00:31:12.20 and those are shown in green here.
00:31:16.02 Now, we identified 25 clusters in the genome,
00:31:19.23 and the largest cluster
00:31:22.27 was right in that same region of chromosome 3
00:31:25.14 that we had previously identified.
00:31:28.00 But we had missed it in the prior study,
00:31:30.11 and the reason why is because
00:31:32.17 it contains these Pygmy-specific variants,
00:31:35.08 that were not captured by the SNP array that we used,
00:31:39.17 and thus demonstrating the great importance
00:31:42.00 of doing resequencing for identifying novel
00:31:44.24 and potentially functionally important variation
00:31:47.15 in ethnically diverse populations.
00:31:50.28 Now, this cluster consisted of
00:31:55.10 44 SNPs in 100% association with each other
00:31:59.16 over 170,000 nucleotide,
00:32:03.06 shown here,
00:32:05.24 and it contained a very interesting candidate gene called HESX1.
00:32:10.10 HESX1 codes for a transcription factor
00:32:13.05 that plays a very important role
00:32:15.04 in regulating the development
00:32:17.15 at the anterior pituitary in the brain,
00:32:20.14 and that's the site of production of growth hormone,
00:32:22.23 as well as other reproductive hormones.
00:32:25.11 Now, interestingly,
00:32:27.06 we identified a non-synonymous,
00:32:29.28 so an amino acid change, basically,
00:32:33.23 in this gene
00:32:36.03 that had been previously associated
00:32:38.13 with idiopathic short stature in humans.
00:32:41.26 But it turns out that this varian
00:32:44.01 t is present at about a 20% frequency in other Africans.
00:32:47.12 So what we hypothesize is that
00:32:49.13 there's something about this region
00:32:51.22 that may be altering gene expression of HESX1
00:32:55.07 or other genes in that region.
00:32:58.01 Upstream, we found another cluster
00:33:01.18 near this gene POU1F1, also known at Pit-1 in mouse,
00:33:07.13 and again this codes for a transcription factor
00:33:09.18 that plays a critical role in regulating growth hormone expression.
00:33:14.23 So another excellent candidate gene.
00:33:17.28 Now, what is interesting is that
00:33:19.27 both of these clusters, or genes,
00:33:23.18 are amongst the most differentiated regions
00:33:26.27 of the Pygmy genomes,
00:33:28.27 compared to genomes from elsewhere in Africa.
00:33:31.29 So we then picked out some of the SNPs in these regions
00:33:37.13 and genotyped them in a larger set
00:33:39.19 of western and eastern Pygmies,
00:33:41.26 and we showed that they are statistically
00:33:44.02 associated with short stature in Pygmies.
00:33:47.29 So the next step is going to be
00:33:49.24 to try to make transgenic models
00:33:52.01 that express these variants using transgenic mouse models,
00:33:56.06 and see what the phenotype looks like.
00:34:00.19 So that leads us to a number of hypotheses.
00:34:03.19 One, is that alterations in the growth hormone/IGF1 pathway
00:34:07.15 play a role in the short stature trait in Pygmies.
00:34:13.01 Two, is that anterior pituitary hormones
00:34:15.10 may play a central role in the Pygmy phenotype,
00:34:18.09 influencing growth, reproduction,
00:34:20.15 metabolism, and immunity.
00:34:24.00 And thirdly, that short stature
00:34:26.16 could be a byproduct of selection
00:34:28.11 acting on pleiotropic loci.
00:34:31.04 So if we look here,
00:34:32.21 one of the candidate loci that we identified is HESX1.
00:34:36.13 That's going to influence expression and development
00:34:39.20 of the anterior pituitary,
00:34:42.02 site of production of growth hormone.
00:34:44.20 Growth hormone expression is also regulated
00:34:46.23 by this other gene we found, POU1F1.
00:34:50.04 And this CISH regulates growth hormone receptor.
00:34:54.17 Now, if we look at the downstream effects
00:34:56.24 of growth hormone,
00:34:59.07 growth hormone, when it binds to growth hormone receptor,
00:35:02.18 will trigger off expression of IGF1,
00:35:06.12 predominantly from the liver, but from other tissues as well.
00:35:10.06 IGF1 will have an effect on muscle growth
00:35:13.14 and also on bone growth and height,
00:35:16.02 but the other impact, or the other role of growth hormone
00:35:20.12 is that it also influences insulin metabolism,
00:35:24.06 it influences fat metabolism.
00:35:28.01 And then we know that infectious disease
00:35:30.01 alters immune response and cytokine levels,
00:35:33.08 and that these can influence gene expression from CISH,
00:35:36.11 or other genes that are in this pathway.
00:35:40.09 So, when we go back to Africa to study the Pygmies,
00:35:42.28 what we would ultimately like to do next
00:35:45.16 is to measure all of the phenotypes,
00:35:48.01 because if you want to understand something
00:35:50.04 like the evolution of short stature in Pygmies,
00:35:52.19 I think you can't just be looking at stature
00:35:55.09 because the growth hormone pathway
00:35:58.25 plays a role in all of these different traits,
00:36:01.01 so we need to be looking at this as an integrative picture.
00:36:06.01 And in fact, our approach in the future
00:36:08.26 is to use an integrative genomics approach
00:36:11.24 combining whole genome data,
00:36:14.15 data on protein variation from blood,
00:36:17.25 epigenetic variation,
00:36:19.21 which can be influenced by diet and environment,
00:36:22.12 gene expression,
00:36:24.10 we're starting to look at the microbiome,
00:36:27.16 which is the spectrum of bacteria in the gut,
00:36:32.05 because that can not only be influenced by diet,
00:36:35.16 it can also have an influence on the metabolome,
00:36:38.12 or the set of all the metabolites, for example,
00:36:40.27 in blood.
00:36:42.20 And we want to combine that information
00:36:44.25 together with information on diet
00:36:46.22 and other environmental factors,
00:36:48.29 to try to identify genetic and environmental factors
00:36:52.15 that play a role in short stature
00:36:55.05 and in other anthropometric,
00:36:56.25 cardiovascular,
00:36:58.01 and metabolic traits.
00:37:00.20 One of the other approaches we can take
00:37:02.20 to distinguish the role of genetics and environment is, for example,
00:37:06.00 to look at individuals of the same or similar ethnic background,
00:37:10.29 but living in an urban versus a rural environment.
00:37:16.21 We can also take a different...
00:37:18.14 the opposite approach.
00:37:20.00 We can look at individuals who have
00:37:22.06 very different genetic ancestries,
00:37:25.03 but live in similar environments.
00:37:27.13 So for example,
00:37:29.20 this is a girl who is from the Fulani population,
00:37:33.17 and here's a neighboring...
00:37:35.19 an individual from the Tupuri population.
00:37:38.26 So they are genetically very differentiated,
00:37:41.20 but live in a similar environment,
00:37:43.16 yet the Fulani seem to have some innate resistance
00:37:47.06 to malaria infection.
00:37:50.03 By contrast, in the San,
00:37:53.09 from southern Africa,
00:37:54.29 are very differentiated from the Bantu,
00:37:57.15 but the San seem to have an innate susceptibility
00:38:01.09 to TB infection.
00:38:03.20 So again, by contrasting populations with different ancestry,
00:38:07.26 and living in different environments,
00:38:09.11 we may identify clues about the genetic basis
00:38:12.10 of differences in phenotypic variation
00:38:14.26 and disease susceptibility.
00:38:17.23 So in conclusion,
00:38:20.20 Africans have the highest levels of genetic diversity
00:38:23.04 within and among populations.
00:38:26.28 The demographic history of Africans
00:38:29.00 and local adaptation to different environments
00:38:31.04 has resulted in population
00:38:33.01 or region specific genetic variation.
00:38:36.25 And we need to be including
00:38:38.21 ethnically diverse Africans in genomic studies
00:38:41.17 to better identify both unique rare, and common variants
00:38:45.28 which may be of functional importance,
00:38:47.28 including those that play a role in disease risk
00:38:50.13 in these populations.
00:38:52.14 And I will just end by thanking
00:38:54.04 the many individuals
00:38:55.25 who contributed to these studies,
00:38:57.29 and my funding agencies,
00:39:00.16 and particular thanks to the Africans
00:39:02.20 who have contributed to these studies.