There were some questions about the Indian ancestry of the Thai. The dataset released by the Reich lab has some Thai. I pulled that data, and some other Southeast Asian groups, and Tamils and Tajiks. The merging only left 62,000 SNPs, but that’s probably enough to answer this question. The PCA above shows the West Eurasian shift of some groups. The Thai definitely seem pulled to the Tamils, and are similar to the Cambodians, but with a bit more Indian ancestry and less “southern” Southeast Asian.
Below the fold are admixture and TreeMix plots. Basically you see what I’m talking about but in more detail. The Indian-like ancestry in the Luzon samples is really Spanish. The Ami and Atyal are Taiwanese aborigines. You see that they have the least West Eurasian ancestry. Even southern mainland Chinese seem to have some of that, indicating long-distance gene flow. But groups like Miao, Vietnamese/Kinh, and Dusun (Austronesians from Borneo) don’t the Indian ancestry that Thai/Lao/Cambodians/Malay have.
One of the somewhat surprising things we have learned over the last decade is that massive admixture and homogenization has occurred between distinct human lineages over the last 10,000 years. By this, I mean that we’re not talking simply about continuous gene-flow between neighboring populations, but massive expansions of small groups and assimilation of very different groups from the expanding groups. As a stylized fact, it looks like “Early European Farmers” we as distinct from Mesolithic hunter-gatherers as modern Northern Europeans are from Han Chinese (pairwise Fst ~0.10). The fusion of these two groups later merged in much of Europe with migrants from the east, the western edge of the forest-steppe.
The empirical pattern seems to be that cultural innovations (e.g., agriculture) trigger demographic revolutions, which homogenize and admix vast regions. This is a story of demographic history. Phylogeography.
But there is another aspect, natural selection. Humans are not exempt from this. Selection operates upon genetic variation, which is preexistent (“standing variation”), or, comes from new mutations (de novo).
It seems plausible that cultural innovation has resulted in a great deal of selection over the last 10,000 years. So where did the raw material come from? One argument that has been playing out is between those who argue that it’s from variation within human populations that is ancestral and shared, and new variation. This is where admixture comes into play.
A new preprint on bioRxiv uses the 1000 Genomes data in the New World to suggest that admixture resulted in the introduction of a lot of adaptive alleles into populations of mostly European and Native background from African ancestry. Basically, it seems likely that the American tropics were colonized by African tropical diseases, which entailed adaptations which were already existent within African populations. Admixture-enabled selection for rapid adaptive evolution in the Americas:
Background: Admixture occurs when previously isolated populations come together and exchange genetic material. We hypothesized that admixture can enable rapid adaptive evolution in human populations by introducing novel genetic variants (haplotypes) at intermediate frequencies, and we tested this hypothesis via the analysis of whole genome sequences sampled from admixed Latin American populations in Colombia, Mexico, Peru, and Puerto Rico. Results: Our screen for admixture-enabled selection relies on the identification of loci that contain more or less ancestry from a given source population than would be expected given the genome-wide ancestry frequencies. We employed a combined evidence approach to evaluate levels of ancestry enrichment at (1) single loci across multiple populations and (2) multiple loci that function together to encode polygenic traits. We found cross-population signals of African ancestry enrichment at the major histocompatibility locus on chromosome 6, consistent with admixture-enabled selection for enhanced adaptive immune response. Several of the human leukocyte antigen genes at this locus (HLA-A, HLA-DRB51 and HLA-DRB5) showed independent evidence of positive selection prior to admixture, based on extended haplotype homozygosity in African populations. A number of traits related to inflammation, blood metabolites, and both the innate and adaptive immune system showed evidence of admixture-enabled polygenic selection in Latin American populations. Conclusions: The results reported here, considered together with the ubiquity of admixture in human evolution, suggest that admixture serves as a fundamental mechanism that drives rapid adaptive evolution in human populations.
The period after 1492 is easy for us to think about. But what ancient DNA has shown us is that it’s not as uncommon a phase as we might have thought.
Adult height was one of the earliest putative examples of polygenic adaptation in human. By constructing polygenic height scores using effect sizes and frequencies from hundreds of genomic loci robustly associated with height, it was reported that Northern Europeans were genetically taller than Southern Europeans beyond neutral expectation. However, this inference was recently challenged. Sohail et al. and Berg et al. showed that the polygenic signature disappeared if summary statistics from UK Biobank (UKB) were used in the analysis, suggesting that residual uncorrected stratification from large-scale consortium studies was responsible for the previously noted genetic difference. It thus remains an open question whether height loci exhibit signals of polygenic adaptation in any human population. In the present study, we re-examined this question, focusing on one of the shortest European populations, the Sardinians, as well as on the mainland European populations in general. We found that summary statistics from UKB significantly correlate with population structure in Europe. To further alleviate concerns of biased ascertainment of GWAS loci, we examined height-associated loci from the Biobank of Japan (BBJ). Applying frequency-based inference over these height-associated loci, we showed that the Sardinians remain significantly shorter than expected (~ 0.35 standard deviation shorter than CEU based on polygenic height scores, P = 1.95e-6). We also found the trajectory of polygenic height scores decreased over at least the last 10,000 years when compared to the British population (P = 0.0123), consistent with a signature of polygenic adaptation at height-associated loci. Although the same approach showed a much subtler signature in mainland European populations, we found a clear and robust adaptive signature in UK population using a haplotype-based statistic, tSDS, driven by the height-increasing alleles (P = 4.8e-4). In summary, by examining frequencies at height loci ascertained in a distant East Asian population, we further supported the evidence of polygenic adaptation at height-associated loci among the Sardinians. In mainland Europeans, we also found an adaptive signature, although becoming more pronounced only in haplotype-based analysis.
The whole literature on selection and height is confused. This is definitely an unformed and new area of exploration, so I wouldn’t put my money on any particular result. But, it is important to note I think that the association of particular genetic variants with differences in height is stronger than the signature of selection on those variants. Second, the preprint is hard to follow because there are all sorts of factors like ascertainment in the huge datasets necessary to do analysis on polygenic traits that date from the way the data were generated in the late 2000s (as well as new datasets coming online).
I think looking at variants in East Asians, and how they impact Europeans, is pretty neat. Obviously, some of the variants that impact polygenic traits are going to be rare, and so not shared between populations, but a lot of it is probably “standing variation” that dates back to before the Out of Africa event. In other words, the key thing is to look at differences in frequencies of alleles which are present in most populations, not different alleles which are not present in all populations.
One element that jumps out at me is the trajectory of selection, and how much is due to events that date deep into the past, to such an extent that it might not make sense to talk about populations as we understand them today. So, for example, they talk about selection events going back to beyond 10,000 years…but all the populations that we survey today did not really exist that deeply in time. This doesn’t mean that selection didn’t happen. “Populations” is a human construct, alleles are alleles. They may have been subject to selection in a variety of populations which admixed themselves out of existence in turn (there was selection for larger brains on and off for millions of years up until about ~200,000 years ago in various hominin populations).
The strongest selection result in this preprint seems to be that something is going on with Sardinians, the most direct descendants of Neolithic farmers. As noted on Twitter I think this has more to do with the nature of calorie restriction, or lack thereof, than selection on height per se. A lot more has to be done on understanding how the “secondary products revolution” (going from simple cereal farming to agro-pastoralism) impacted on human nutrition to understand selection on height, which does seem to be a reoccurring signal across human groups.
More than twenty years ago L. L. Cavalli-Sforza published The History and Geography of Human Genes. Based on decades of analysis of ‘classical’ markers, this work lays out results of statistical genetic analyses based on a few hundred genes, as well as displaying Cavalli-Sforza’s encyclopedic ethnographic knowledge. A close look at this book will yield some familiar population groups to readers of this weblog. The reason for this is simple: the cell lines continued onward to contribute to the HGDP data set.
In 2002 Rosenberg et al. used these populations in Genetic Structure of Human Populations by looking at “377 autosomal microsatellite loci.” Microsatellites are highly variable genetic regions. They pack a lot of diversity per locus. With more input variation Rosenberg et al. advanced beyond Cavalli-Sforza’s earlier work (instead of pairwise comparisons between populations, one could infer individual relatedness as displayed in a bar plot).
But times change, and in 2008 the same data set was used in Worldwide human relationships inferred from genome-wide patterns of variation, which utilized a 650,000 marker SNP-array. Though Rosenberg et al.’s work advanced the ball considerably, the move to genome-wide analysis was even bigger. For many years this data set has been a widely used benchmark and reference (these markers and populations were part of the early basis of 23andMe’s analyses in terms of population genetic inference). As the 1000 Genomes Project moved us beyond the SNP-array period, looking the whole genome, as opposed to a specific set of SNPs, the HGDP populations were still an important complement.
The reason was simple: Cavalli-Sforza was an ethnographic genius in comparison to most geneticists and had selected very interesting and informative populations. In some ways, the original motivation given for selecting these groups, that they may have preserved phylogenetic patterns obscured in cosmopolitan populations, has only been partially justified.
Ancient DNA has shed light on the reality that almost all populations, indigenous and cosmopolitan, come out of periods of admixture between lineages which had heretofore been distinct and separate. But some of Cavalli-Sforza’s populations have been inordinately important in informing us about branches of the human family tree less well represented in the cosmopolitan samples accessible in the 1000 Genomes Project (or earlier, the HapMap). I’m thinking here of the Kalash (a relatively good proxy for “Ancestral North Indians”), Sardinians (the best representatives in the modern world of “Early European Farmers”), and African hunter-gatherers (who carry the deepest diverging lineage within the modern human clade).
With all that, finally, theHGDP whole genome preprint is out. Anders Bergstrom superstar!
Genome sequences from diverse human groups are needed to understand the structure of genetic variation in our species and the history of, and relationships between, different populations. We present 929 high-coverage genome sequences from 54 diverse human populations, 26 of which are physically phased using linked-read sequencing. Analyses of these genomes reveal an excess of previously undocumented private genetic variation in southern and central Africa and in Oceania and the Americas, but an absence of fixed, private variants between major geographical regions. We also find deep and gradual population separations within Africa, contrasting population size histories between hunter-gatherer and agriculturalist groups in the last 10,000 years, a potentially major population growth episode after the peopling of the Americas, and a contrast between single Neanderthal but multiple Denisovan source populations contributing to present-day human populations. We also demonstrate benefits to the study of population relationships of genome sequences over ascertained array genotypes. These genome sequences are freely available as a resource with no access or analysis restrictions.
The authors were able to make recourse to many more subtle analytic methods with their phasing, which seems to have been considerably superior to population phasing (the HGDP does have some closely related individuals due to endogamy, but no traditional trios). Because their population set included some undersampled groups with a lot of diversity (e.g., San Bushmen), they detected about as many variants with ~1,000 individuals at good coverage as the 1000 Genome Project with ~2,500 individuals at variable coverage.
And there are major lacunae within the 1000 Genome Project data set even after taking into account ethnographically and historically important groups such as the San Bushmen. There are no Middle Eastern populations in the 1000 Genome Project. The HGDP has the Druze, Bedouin, Palestinians, and Mozabites.
The preprint requires a lot of deep reading. There is much in there that one can mull over (frankly, I’m excited about the supplementary text, but that’s just me). One thing that came to mind is that ancient DNA and other more narrow studies laid the groundwork for the interpretations that naturally fall out of this extremely rich potential set of analyses. For example, by looking at shared variants across western and central Africa the authors confirm the likely result that there is a basal human population of some sort mixed into peoples of far western Africa. And, they also confirm that the Yoruba are about ~5-10% Eurasian.
These sequences generate so much data that there are lots of potential models that might conform to them. Earlier work eliminates some possibilities and highlights others.
Ancient DNA has confirmed for many that non-Africans have Neanderthal ancestry. But there have been several debates about whether there are issues with the assumption that Africans have no Neanderthal ancestry, and how it skews statistics (e.g., if Africans have some Neanderthal one will underestimate the Neanderthal fraction). Though there are still details to be hashed out, looking at coalescence patterns of haplotypes the authors seem to be able to infer the presence of deeply diverged lineages in various populations without positing a prior model of which populations did not have the introgression as a baseline. Basically, Neanderthal and Denisovan ancestry is going to result in some “long branches” in the phylogenies of the genes within non-African populations which are lacking in Africans, and that is what they see.
These researchers also confirm the model presented by others that Neanderthal contribution seems to have been from a single admixture event (I do wonder if perhaps Neanderthals were not simply extremely homogeneous, so multiple close admixture events may not be differentiable). They also find that the “Denisovan” population structure was more complex, and there were several admixture events into eastern Eurasian and Oceanian populations.
Finally, there are attempts to adduce the nature of population differentiation, and times of separation. As noted in the text all of these sorts of analyses are sensitive to assumptions within models. They used a variety of methods which came to different results, but, one thing that seems clear is that Africa had a lot of deep structure for a long time, but gene flow between regional populations meant that genetic differentiation emerged gradually, rather than in a rapid fashion due to geographic separation. Over five years ago Iain Mathieson casually told me that he viewed much of the past 200,000 years as the collapse of deep population structure, and that does not seem to have been a crazy prediction if you read through this preprint (though the collapse may be increased rates of gene flow, rather than massive pulse admixtures).
But the separation and differentiation outside of Africa, and between the archaic lineages and Africans, seems to have exhibited more punctuation. For the past twenty years John Hawks has been emphasizing that we need to remember that during Pleistocene Africa likely had a much larger population than the rest of the world for hominins (with perhaps a caveat for lower latitude Asia). The relatively “clean” separation between the proto-modern African lineage and the Eurasian hominins, and then the quick separation between Neanderthals and the eastern group which became Denisovans, emphasizes perhaps the importance of particular geographic barriers (deserts in the Near East), as well as the lower carrying capacity in much of Eurasia. With lower population densities and patchy occupation patterns, gene flow would be sharply reduced. This would result in drift and sharply different lineages.
There are arguments out there about whether humans are a clinal species or not. These verbal descriptions really don’t tell us much. The combination of ancient DNA and whole genome data will allow us to specific at specific times and places the nature of population dynamics. If human population relationships can be thought of as a graph, a set of interconnected edges, in some areas the connections will be thicker (ergo, lots of continuous gene flow), and in other areas, the graphs will be easier to represent as diverging trees.
I think the last 10,000 years of the Holocene has brought to Eurasia a more African pattern, as deep structure comes crashing down due to rapid population expansion and mixing….
Many years ago, before I used ggplot, I did a little analysis of the genetics of the Tutsi. Actually, it was the genetics of a single Tutsi, or more precisely, someone who was 75% Tutsi ancestry (3 out of 4 grandparents).
I found that the Tutsi individual seemed quite distinct from the Bantu peoples in nearby Kenya. I suggested that it was likely that the Tutsi were then genetically distinct from the Hutu people amongst whom they lived. For many years this was part of the genetics section of the Wikipedia entry on the Tutsi, but recently the reference was removed and the page seems to have been re-edited.
That’s fine. I’m just a random blogger who had one sample. But as it happened recently about a dozen Diasporic Tutsis reached out to me. Over the last decade, the number of people who have been genotyped has increased greatly. So it wasn’t that difficult for interested parties to find these genotypes.
The mission they put before me is simple: “tell us about our genetics”. Over the next few weeks, I’ll do that. As there is no IRB, this won’t be published in a peer-reviewed journal (I am open to putting any researcher in contact with these Tutsis who reached out to me). I’m just going to put what I find out there so that Tutsis who do personalized genetic testing can make sense of what they’re finding out.
I received these genotypes today. A quick merge of samples I have reduced it down to 50,000 markers. I will work on creating a merge with a larger number of markers. But, I’ll report what I have found out so far as a first pass.
As you can see on the PCA plot above the Tutsi overlap almost perfectly with the Masai. Not with the Kenyan Bantu, or the Luo, who are more “African” shifted. But with the Masai. But, they are not as “Eurasian shifted” as the Somali.
Recently some British friends were asking about what we knew about South Asian historical genetics now. I explained that it does look like there was some migration in from the Central Asian steppe and West Asia into South Asia during the Holocene. To which one friend responded, “that’s obvious though, many Indians look like brown white people.” Setting aside the semantic paradox (if you are brown, you are literally not white), it is clear what he is getting at: due to shared ancestry the facial structure of many South Asians is not that different from West Eurasians.
The Bollywood actress Deepika Padukone is an example of someone who is rather brown-skinned (naturally), but whose facial features are such that if she went with 100% skin-bleaching she would pass as white without too much trouble. For the purposes of this post, I Googled Indian albino…and came up with this family. You can make your own judgments. I don’t know what to think of that!
Genetic variation in contemporary South Asian populations follows a northwest to southeast decreasing cline of shared West Eurasian ancestry. A growing body of ancient DNA evidence is being used to build increasingly more realistic models of demographic changes in the last few thousand years. Through high quality modern genomes, these models can be tested for gene and genome level deviations. Using local ancestry deconvolution and masking, we reconstructed population-specific surrogates of the two main ancestral components for more than 500 samples from 25 South Asian populations, and showed our approach to be robust via coalescent simulations.
Our f3 and f4 statistics based estimates reveal that the reconstructed haplotypes are good proxies for the source populations that admixed in the area and point to complex inter-population relationships within the West Eurasian component, compatible with multiple waves of arrival, as opposed to a simpler one wave scenario. Our approach also provides reliable local haplotypes for future downstream analyses. As one such example, the local ancestry deconvolution in South Asians reveals opposite selective pressures on two pigmentation genes (SLC45A2 and SLC24A5) that are common or fixed in West Eurasians, suggesting post-admixture purifying and positive selection signals, respectively.
The Munda languages of the northeastern quadrant of the Indian subcontinent are quite interesting because they are more closely related to the Austro-Asiatic languages of Southeast Asia than to the Indo-Aryan or Dravidian languages which are spoken by their neighbors. The Munda are usually classified as adivasi, which has connotations of being an ‘original inhabitant’ of the Indian subcontinent.
More concretely, the Munda have traditionally operated outside of the bounds of Sanskrit-influenced Hindu civilizations, occupying upland zones and governing themselves as tribal units, rather than being a caste population.
What the field of genetics tells us is that there are really no true aboriginal inhabitants of the Indian subcontinent in an unmixed form. That is, the vast majority of people in the Indian subcontinent have a substantial contribution of ancestry from the wave of migration out of Africa that occupied the southeast fringe of Eurasia beginning ~50-60,000 years ago. The modern adivasi generally are defined more by their social-cultural position within the landscape of Indian culture, as opposed to their long-term residence in the subcontinent.*
The term is a particular misnomer for the Munda because of the evidence that they are intrusive to the subcontinent from Southeast Asia. We have ancient DNA and archaeology which indicates that upland rice farmers, likely Austro-Asiatic, arrived in northern Vietnam ~4,000 years ago. This makes it unlikely to me that they were in India much earlier. The Y chromosomal data indicate that the paternal ancestry of the Munda derives from Southeast Asians, not the other way around.
Surrounded by speakers of Indo-European, Dravidian and Tibeto-Burman languages, around 11 million Munda (a branch of Austroasiatic language family) speakers live in the densely populated and genetically diverse South Asia. Their genetic makeup holds components characteristic of South Asians as well as Southeast Asians. The admixture time between these components has been previously estimated on the basis of archaeology, linguistics and uniparental markers. Using genome-wide genotype data of 102 Munda speakers and contextual data from South and Southeast Asia, we retrieved admixture dates between 2000–3800 years ago for different populations of Munda. The best modern proxies for the source populations for the admixture with proportions 0.29/0.71 are Lao people from Laos and Dravidian speakers from Kerala in India. The South Asian population(s), with whom the incoming Southeast Asians intermixed, had a smaller proportion of West Eurasian genetic component than contemporary proxies. Somewhat surprisingly Malaysian Peninsular tribes rather than the geographically closer Austroasiatic languages speakers like Vietnamese and Cambodians show highest sharing of IBD segments with the Munda. In addition, we affirmed that the grouping of the Munda speakers into North and South Munda based on linguistics is in concordance with genome-wide data.
The paper already came out as a preprint many months back, so I’ve already mentioned it. The big finding, to me, is that it uses genome-wide methods to estimate an admixture in the range of ~4,000 between the southern Munda Southeast Asian and South Asian ancestral components. It also confirms something that has been pretty evident for nearly ten years of genome-wide analysis of South Asian population genetics: the Munda have less West Eurasian ancestry even after you account for the Southeast Asian admixture than any mainland Indian population outside of the Tibeto-Burman fringe.
In Narasimhan et al. the authors present a model that fits the data where:
The proto-Munda mix with an “Ancient Ancestral South Indian” (AASI) population that has no West Eurasian admixture in India’s northeast
Then, mix more with an “Ancestral South Indian” (ASI) population that has some West Eurasian admixture
While introgression from Neanderthals and Denisovans has been well-documented in modern humans outside Africa, the contribution of archaic hominins to the genetic variation of present-day Africans remains poorly understood. Using 405 whole-genome sequences from four sub-Saharan African populations, we provide complementary lines of evidence for archaic introgression into these populations. Our analyses of site frequency spectra indicate that these populations derive 2-19% of their genetic ancestry from an archaic population that diverged prior to the split of Neanderthals and modern humans. Using a method that can identify segments of archaic ancestry without the need for reference archaic genomes, we built genome-wide maps of archaic ancestry in the Yoruba and the Mende populations that recover about 482 and 502 megabases of archaic sequence, respectively. Analyses of these maps reveal segments of archaic ancestry at high frequency in these populations that represent potential targets of adaptive introgression. Our results reveal the substantial contribution of archaic ancestry in shaping the gene pool of present-day African populations.
To get a sense of how much work went into this preprint, really do read the supplementary material. The step by step analysis convinced me pretty thoroughly that these results are not due to straightforward errors in the genotypes and classifications of the genotypes. Such things do happen, so it was nice to see them be very careful about that.
The key point is that the distribution of the conditional site frequency (CFS) spectrum in West Africans does not align with theoretical expectations. The condition here being the state in the archaic outgroup, generally the Vindijia Neanderthal. The authors ran a bunch of simulations and models and found a subset that could produce the CSF they see, the u-shaped distribution. It is represented by the graph you see at the top-right. Basically, a scenario where a diverged archaic lineage which diverged from the other human lineages before the Neanderthal-Denisovan lineage left Africa contributed to the ancestry of West Africans within the last ~100,000 years (the most likely time is ~50,000 years ago).
What has changed is that whole-genome sequencing, including high-quality sequences of ancient hominins, has allowed for a more robust exploration of the topic. The analysis of site frequencies was really not useful 20 years ago without genome-wide data. More data has allowed for more subtle methods.
Demographic estimates are presented for the Aurignacian techno-complex (~42,000 to 33,000 y calBP) and discussed in the context of socio-spatial organization of hunter-gatherer populations. Results of the analytical approach applied estimate a mean of 1,500 persons (upper limit: 3,300; lower limit: 800) for western and central Europe. The temporal and spatial analysis indicates an increase of the population during the Aurignacian as well as marked regional differences in population size and density. Demographic increase and patterns of socio-spatial organization continue during the subsequent early Gravettian period.
If you read The genetic history of Ice Age Europe you know the very first modern humans to arrive in Europe didn’t leave a genetic footprint in future populations. And the impact of both the later Gravettian and the Magdalenian seems to have been marginal. The primary “hunter-gatherer” contribution to modern Europeans is through a group which expanded after ~15,000 BC.
In any case, there are two things that I observe in relation to the population estimates above. First, they aren’t that unreasonable for a large mammal which isn’t much of a primary consumer of plants. Second, such a small and fragmented population indicates that extinction is always a possibility. You can take a standard conservation biological view and just assume statistically that small fragmented groups are likely to extinct over enough generations. Or, you can point out that genetically such small breeding populations (remember that the genetic breeding effective population is always smaller than the census population) are likely to build up deleterious alleles, and that’s probably going to result in a decrease of long term fitness.
In other words, I think localized mutational meltdowns would be possible in this scenario.
The small populations during this period are not surprising. Many of the Neanderthal, Denisovan, and hunter-gatherer (e.g., the first WHG sample) populations had small sizes that led to homogeneity genetically and inbreeding. You see it in the homozygosity data and the runs of homozygosity. Ultimately, it was the larger population sizes due to agriculture which changed things in a fundamental sense.
This makes me wonder what was so advantageous about these marginal modern humans which allowed them to overwhelm and absorb the older Eurasian hominins?
A lot has happened in the last few days in backchannel conversations and social media in relation to the piece in The New York Times Magazine which put the spotlight on ancient DNA, and David Reich, for the general audience. Unlike Carl Zimmer’s ancient DNA column in the science section of the paper, the people reading Gideon Lewis-Kraus’ 12,000-word piece are not going to be familiar with the field and will miss omissions and the context.
To “bullet” some of the issues with the piece, in order of simplicity and straightforwardness to me:
