Session 8: Human Evolution

Transcript of Part 2: African Genomics: African Population History

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.