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July 17, 2017

Castes are not just of mind

Filed under: Caste,Human Genetics,India — Razib Khan @ 8:31 pm

Before Nicholas Dirks was a controversial chancellor of UC Berkeley, he was a well regarded historian of South Asia. He wrote Castes of Mind: Colonialism and the Making of Modern India. I read it, along with other books on the topic in the middle 2000s.

Here is Amazon summary from Library Journal:

Is India’s caste system the remnant of ancient India’s social practices or the result of the historical relationship between India and British colonial rule? Dirks (history and anthropology, Columbia Univ.) elects to support the latter view. Adhering to the school of Orientalist thought promulgated by Edward Said and Bernard Cohn, Dirks argues that British colonial control of India for 200 years pivoted on its manipulation of the caste system. He hypothesizes that caste was used to organize India’s diverse social groups for the benefit of British control. His thesis embraces substantial and powerfully argued evidence. It suffers, however, from its restricted focus to mainly southern India and its near polemic and obsessive assertions. Authors with differing views on India’s ethnology suffer near-peremptory dismissal. Nevertheless, this groundbreaking work of interpretation demands a careful scholarly reading and response.

The condensation is too reductive. Dirks does not assert that caste structures (and jati) date to the British period, but the thrust of the book clearly leaves the impression that this particular identity’s formative shape on the modern landscape derives from the colonial experience. The British did not invent caste, but the modern relevance seems to date to the British period.

This is in keeping with a mode of thought flourishing today under the rubric of postcolonialism, with roots back to Edward Said’s Orientalism. As a scholar of literature Said’s historical analysis suffered from the lack of deep knowledge. A cursory reading of Orientalism picks up all sorts of errors of fact. But compared to his heirs Said was actually a paragon of analytical rigor. I say this after reading some contemporary postcolonial works, and going back and re-reading Orientalism.

To not put too fine a point on it postcolonialism is more about a rhetorical posture which aims to destroy what it perceives as Western hegemonic culture. In the process it transforms the modern West into the causal root of almost all social and cultural phenomenon, especially those that are not egalitarian. Anyone with a casual grasp of world history can see this, which basically means very few can, since so few actually care about details of fact.

Castes of Mind is an interesting book, and a denser piece of scholarship than Orientalism. Its perspective is clear, and though it is not without qualification, many people read it to mean that caste was socially constructed by the British.

This seems false. It has become quite evident that even the classical varna categories seem to correlate with genome-wide patterns of relatedness. And the Indian jatis have been endogamous for on the order of two thousand. From The New York Times, In South Asian Social Castes, a Living Lab for Genetic Disease:

The Vysya may have other medical predispositions that have yet to be characterized — as may hundreds of other subpopulations across South Asia, according to a study published in Nature Genetics on Monday. The researchers suspect that many such medical conditions are related to how these groups have stayed genetically separate while living side by side for thousands of years.

This is not really a new finding. It was clear in 2009’s Reconstructing Indian Population History. It’s more clear now in The promise of disease gene discovery in South Asia.

Unfortunately though science is not well known in any depth in the general public. The ascendency of social constructionism is such that a garbled and debased view that “caste was invented by the British” will continue to be the “smart” and fashionable view among many elites.

July 10, 2017

The great Bantu expansion was massive

Filed under: History,Human Genetics,Punt — Razib Khan @ 12:01 am

Lots of stuff at SMBE of interest to me. I went to the Evolution meeting last year, and it was a little thin on genetics for me. And I go to ASHG pretty much every year, but there’s a lot of medical stuff that is not to my taste. SMBE was really pretty much my style.

In any case one of the more interesting talks was given by Pontus Skoglund (soon of the Crick Institute). He had several novel African genomes to talk about, in particular from Malawi hunter-gatherers (I believe dated to 3,000 years before the present), and one from a pre-Bantu pastoralist.

At one point Skoglund presented a plot showing what looked like an isolation by distance dynamic between the ancient Ethiopian Mota genome and a modern day Khoisan sample, with the Malawi population about $\frac{2}{3}$ of the way toward the Khoisan from the Ethiopian sample. Some of my friends from a non-human genetics background were at the talk and were getting quite excited at this point, because there is a general feeling that the Reich lab emphasizes the stylized pulse admixture model a bit too much. Rather than expansion of proto-Ethiopian-like populations and proto-Khoisan-like populations they interpreted this as evidence of a continuum or cline across East Africa. I’m not sure if this is the right interpretation of the plot presented, but it’s a reasonable one.

Malawi is considerably to the north of modern Khoisan populations. This is not surprising. From what I have read Khoisan archaeological remains seem to be found as far north as Zimbabwe, while others have long suggested a presence as far afield as Kenya. Perhaps more curiously: the Malawi hunter-gatherers exhibit not evidence of having contributed genes to modern Bantu residents of Malawi.

Surprising, but not really. If you look at a PCA plot of Bantu genetic variation it really starts showing evidence of local substrate (Khoisan) in South Africa. From Cameroon to Mozambique it looks like the Bantu simply overwhelmed local populations, they are clustered so tight. Though it is true that African populations harbor a lot of diversity, that diversity is not necessarily partition between the populations. The Bantu expansion is why.

Of more interest from the perspective of non-African history is the Tanzanian pastoralist. This individual is about 38% West Eurasian, and that ancestry has the strongest affinities with Levantine Neolithic farmers. Specifically, the PPN, which dates to between 8500-5500 BCE. More precisely, this individual was exclusively “western farmer” in the Lazaridis et al. formulation. Additionally, Skoglund also told me that the Cushitic (and presumably Semitic) peoples to the north and east had some “eastern farmer.” I immediately thought back to Hogdson et al. Early Back-to-Africa Migration into the Horn of Africa, which suggested multiple layers. Finally, 2012 Pagani et al. suggested that admixture in the Ethiopian plateau occurred on the order of ~3,000 years ago.

Bringing all of this together it suggests to me two things

  1. The migration back from Eurasia occurred multiple times, with an early wave arriving well before the Copper/Bronze Age east-west and west-east gene flow in the Near East (also, there was backflow to West Africa, but that’s a different post….).
  2. The migration was patchy; the Mota sample dates to 4,500 years ago, and lacks any Eurasian ancestry, despite the likelihood that the first Eurasian backflow was already occurring.

Skoglund will soon have the preprint out.

July 9, 2017

SLC24A5 is very important, but we don’t know why


The golden of pigmentation genetics started in 2005 with SLC24A5, a putative cation exchanger, affects pigmentation in zebrafish and humans. Prior to that pigmentation genetics was really to a great extent coat color genetics, done in mice and other organisms which have a lot of pelage variation.

Of course there was work on humans, mostly related to melanocortin 1. But more interesting were classical pedigree studies which indicated that the number of loci controlling variation in pigmentation was not that high. This, it was a mildly polygenic trait insofar as some large effect quantitative trait loci could be discerned in the inheritance patterns.

From The Genetics of Human Populations, written in the 1960s, but still useful today because of its comprehensive survey of the classical period:

Depending on what study samples you use variance on a locus of SLC24A5 explains less than 10% or more than 30% of the total variance. But it is probably the biggest effect locus on the whole in human populations when you pool them altogether (obviously it explains little variance in Africans or eastern non-Africans since it is homozygous ancestral by and large in both groups).

One aspect of the derived SNP in this locus is that it seems to be under strong selection. In a European 1000 Genomes sample there are 1003 SNPs of the derived variant, and 3 of the ancestral. Curiously this allele was absent in Western European Mesolithic European hunter-gatherers, though it was present in hunter-gatherers on the northern and eastern fringes of the continent. It was also present in Caucasian hunter-gatherers and farmers from the Middle East who migrated to Europe. It seems very likely that these sorts of high frequencies are due to selection in Europe.

The variant is also present in appreciably frequencies in many South Asian populations, and there seems to have been in situ selection there too, as well as the Near East. In Ethiopia it also seems to be under selection.

It could be something due to radiation…but the Near East and South Asia are quite high intensity in that regard. As are the highlands of Ethiopia. About seven years ago I suggested that rather that UV radiation as such the depigmentation that has occurred across the Holocene might be due to agriculture and changes in diet.

But a new result from southern Africa presented at the SMBE meeting this year suggests that this can not be a comprehensive answer. Meng Lin in Brenna Henn’s lab uses a broad panel of KhoeSan populations to find that the derived allele on SLC24A5 reaches ~40% frequency. Probably a high fraction of West Eurasian admixture in these groups is around ~10% being generous. Where did this allele come from? The results from Joe Pickrell a few years back are sufficient to explain: there was a movement of pastoralists with distant West Eurasian ancestry who brought cattle to southern Africa, and so resulted in the ethnogenesis of groups such as the Nama people (there is also Y chromosomal work by Henn on this).

Sad human with two derived alleles of SNP of interest

Lin reports that the haplotype around SLC24A5 is the same one as in Western Eurasia. Iain Mathieson (who is now at Penn if anyone is looking for something to do in grad school or a post-doc) has told me that the haplotype in the Motala Mesolithic hunter-gatherers and in the hunter-gatherers from the Caucasus are the same. It seems that this haplotype was widespread early in the Holocene. Curiously, the Motala hunter-gatherers also carry the East Asian haplotype around their derived EDAR variant.

I don’t know what to make of this. My intuition is that if a haplotype like this is so widespread nearly ~10,000 years ago recombination would have broken it apart into smaller pieces so that haplotype structure would be easier to discern. As it is that doesn’t seem to be the case.

And we also don’t know what’s going on withSLC24A5. Obviously it impacts skin color. It has been shown to do so in admixed populations. But it is hard to believe that that is the sole target of natural selection here.

June 14, 2017

The fad for dietary adaptations is not going away

Filed under: Diet,FADS,Genetics,Human Genetics — Razib Khan @ 7:21 pm


Food is a big deal for humans. Without it we die. Unlike some animals (here’s looking at you pandas) we’re omnivorous. We eat fruit, nuts, greens, meat, fish, and even fungus. Some of us even eat things which give off signals of being dangerous or unpalatable, whether it be hot sauce or lutefisk.

This ability to eat a wide variety of items is a human talent. Those who have put their cats on vegetarian diets know this. After a million or so years of being hunters and gatherers with a presumably varied diet for thousands and thousands of years most humans at any given time ate some form of grain based gruel. Though I am sympathetic to the argument that in terms of quality of life this was a detriment to median human well being, agriculture allowed our species to extract orders of magnitude more calories from a unit of land, though there were exceptions, such as in marine environments (more on this later).

Ergo, some scholars, most prominently Peter Bellwood, have argued that farming did not spread through cultural diffusion. Rather, farmers simply reproduced at much higher rates because of the efficiency of their lifestyle in comparison to that of hunter-gatherers. The latest research, using ancient DNA, broadly confirms this hypothesis. More precisely, it seems that cultural revolutions in the Holocene have shaped most of the genetic variation we see around us.

But genetic variation is not just a matter of genealogy. That is, the pattern of relationships, ancestor to descendent, and the extent of admixtures across lineages. Selection is also another parameter in evolutionary genetics. This can even have genome-wide impacts. It seems quite possible that current levels of Neanderthal ancestry are lower than might otherwise have been the case due to selection against functional variants derived from Neanderthals, which are less fitness against a modern human genetic background.

The importance of selection has long been known and explored. Sickle-cell anemia only exists because of balancing selection. Ancient DNA has revealed that many of the salient traits we associate with a given population, e.g., lactose tolerance or blue eyes, have undergone massive changes in population wide frequency over the last 10,000 years. Some of this is due to population replacement or admixture. But some of it is due to selection after the demographic events. To give a concrete example, the frequency of variants associated with blue eyes in modern Europeans dropped rapidly with the expansion of farmers from the Near East ~10,000 years ago, but has gradually increased over time until it is the modal allele in much of Northern Europe. Lactase persistence in contrast is not an ancient characteristic which has had its ups and downs, but something new that evolved due to the cultural shock of the adoption of dairy consumption by humans as adults. The region around lactase is one of the strongest signals of natural selection in the European genome, and ancient DNA confirms that the ubiquity of the lactase persistent allele is a very recent phenomenon.

But obviously lactase is not going to be the only target of selection in the human genome. Not only can humans eat many different things, but we change our portfolio of proportions rather quickly. In a Farewell to Alms the economic historian Gregory Clark observed that English peasants ate very differently before and after the Black Death. As any ecologist knows populations are resource constrained when they are near the carrying capacity, and England during the High Medieval period there was massive population growth due to gains in productivity (e.g., the moldboard plough) as well as intensification of farming and utilization of all the marginal land.

After the Black Death (which came in waves repeatedly) there was a massive population decline across much of Europe. Because institutions and practices were optimized toward maintaining a much higher population, European peasants lived a much better lifestyle after the population crash because the pie was being cut into far fewer pieces. In other words, centuries of life on the margins just scraping by did not mean that English peasants couldn’t live large when the times allowed for it. We were somewhat pre-adapted.

Our ability to eat a variety of items, and the constant varying of the proportions and kind of elements which go into our diet, mean that sciences like nutrition are very difficult. And, it also means that attempts to construct simple stories of adaptation and functional patterns from regions of the genome implicated in diet often fail. But with better analytic technologies (whole genome sequencing, large sample sizes) and some elbow grease some scientists are starting to get a better understanding.

A group of researchers at Cornell has been taking a closer look at the FADS genes over the past few years (as well as others at CTEG). These are three nearby genes, FADS1FADS2, and FADS3 (they probably underwent duplication). These genes are involved in the metabolization of fatty acids, and dietary regime turns out to have a major impact on variation around these loci.

The most recent paper out of the Cornell group, Dietary adaptation of FADS genes in Europe varied across time and geography:

Fatty acid desaturase (FADS) genes encode rate-limiting enzymes for the biosynthesis of omega-6 and omega-3 long-chain polyunsaturated fatty acids (LCPUFAs). This biosynthesis is essential for individuals subsisting on LCPUFA-poor diets (for example, plant-based). Positive selection on FADS genes has been reported in multiple populations, but its cause and pattern in Europeans remain unknown. Here we demonstrate, using ancient and modern DNA, that positive selection acted on the same FADS variants both before and after the advent of farming in Europe, but on opposite (that is, alternative) alleles. Recent selection in farmers also varied geographically, with the strongest signal in southern Europe. These varying selection patterns concur with anthropological evidence of varying diets, and with the association of farming-adaptive alleles with higher FADS1 expression and thus enhanced LCPUFA biosynthesis. Genome-wide association studies reveal that farming-adaptive alleles not only increase LCPUFAs, but also affect other lipid levels and protect against several inflammatory diseases.

The paper itself can be difficult to follow because they’re juggling many things in the air. First, they’re not just looking at variants (e.g., SNPs, indels, etc.), but also the haplotypes that the variants are embedded in. That is, the sequence of markers which define an association of variants which indicate descent from common genealogical ancestors. Because recombination can break apart associations one has to engage with care in historical reconstruction of the arc of selection due to a causal variant embedded in different haplotypes.

But the great thing about this paper is that in the case of Europe they can access ancient DNA. So they perform inferences utilizing whole genomes from many extant human populations, but also inspect change in allele frequency trajectories over time because of the density of the temporal transect. The figure to the left shows variants in both an empirical and modeling framework, and how they change in frequency over time.

In short, variants associated with higher LCPUFA synthesis actually decreased over time in Pleistocene Europe. This is similar to the dynamic you see in the Greenland Inuit. With the arrival of farmers the dynamic changes. Some of this is due to admixture/replacement, but some of it can not be accounted for admixture and replacement. In other words, there was selection for the variants which synthesize more LCPUFA.

This is not just limited to Europe. The authors refer to other publications which show that the frequency of alleles associated with LCPUFA production are high in places like South Asia, notable for a culture of preference for plant-based diets, as well as enforced by the reality that animal protein was in very short supply. In Europe they can look at ancient DNA because we have it, but the lesson here is probably general: alternative allelic variants are being whipsawed in frequency by protean shifts in human cultural modes of production.

In War Before Civilization Lawrence Keeley observed that after the arrival of agriculture in Northern Europe in a broad zone to the northwest of the continent, facing the Atlantic and North Sea, farming halted rather abruptly for centuries. Keeley then recounts evidence of organized conflict in between two populations across a “no man’s land.”

But why didn’t the farmers just roll over the old populations as they had elsewhere? Probably because they couldn’t. It is well known that marine regions can often support very high densities of humans engaged in a gathering lifestyle. Though not farmers, these peoples are often also not nomadic, and occupy areas as high density. The tribes of the Pacific Northwest, dependent upon salmon fisheries, are classic examples. Even today much of the Northern European maritime fringe relies on the sea. High density means they had enough numbers to resist the human wave of advance of farmers. At least for a time.

Just as cultural forms wane and wax, so do some of the underlying genetic variants. If you dig into the guts of this paper you see much of the variation dates to the out of Africa period. There were no great sweeps which expunged all variation (at least in general). Rather, just as our omnivorous tastes are protean and changeable, so the genetic variation changes over time and space in a difficult to reduce manner. The flux of lifestyle change is probably usually faster than biological evolution can respond, so variation reducing optimization can never complete its work.

The modern age of the study of natural selection in the human genome began around when A Map of Recent Positive Selection In the Human Genome was published. And it continues with methods like SDS, which indicate that selection operates to this day. Not a great surprise, but solidifying our intuitions. In the supplements to the above paper the authors indicate that the focal alleles that they are interrogating exhibit coefficients of selection around ~0.5% or so. This is rather appreciable. The fact that fixation has not occurred indicates in part that selection has reversed or halted, as they noted. But another aspect is that there are correlated responses; the FADS genes are implicated in many things, as the authors note in relation to inflammatory diseases. But I’m not sure that the selection effects of these are really large in any case. I bet there are more important things going on that we haven’t discovered or understood.

Obviously genome-wide analyses are going to continue for the foreseeable future. Ten years ago my late friend Mike McKweon predicted that at some point genomics was going to have be complemented by detailed follow up through bench-work. I’m not sure if we’re there yet, but there are only so many populations you can sequence, and only to a particular coverage to obtain any more information. Some selection sweeps will be simple stories with simple insights. But I suspect many more like FADS will be more complex, with the threads of the broader explanatory tapestry assembled publications by publication over time.

Citation: Ye, K., Gao, F., Wang, D., Bar-Yosef, O. & Keinan, A. Dietary adaptation of FADS genes in Europe varied across time and geography. Nat. Ecol. Evol. 1, 0167 (2017).

May 10, 2017

The Bronze age demographic transformation of Britiain

Filed under: Bell Beaker,Britain,Evolution,History,Human Genetics,Human Genomics — Razib Khan @ 8:52 am

In Norman Davies’ the excellent The Isles: A History, he mentions offhand that unlike the Irish the British to a great extent have forgotten their own mythology. This is one reason that J. R. R. Tolkien created Middle Earth, they gave the Anglo-Saxons the same sort of mythos that the Irish and Norse had.

But to some extent I think we can update our assessments. Science is bringing myth to life. The legendary “Bell Beaker paper” is now available in preprint form, The Beaker Phenomenon And The Genomic Transformation Of Northwest Europe. The methods are not too abstruse if you have read earlier works on this vein (i.e., no Nick Patterson authored methodological supplement that I saw). And the results are straightforward.

And what are those results?

First, the Bell Beaker phenomenon was both cultural and demographic. Cultural in that it began in the Iberian peninsula, and was transmitted to Central Europe, without much gene flow from what they can see. Demographic in that its push west into what is today the Low Countries and France and the British Isles was accompanied by massive gene flow.

In their British samples they conclude that 90% of the ancestry of early Bronze Age populations derive from migrants from Central Europe with some steppe-like ancestry. In over words, in a few hundred years there was a 90% turnover of ancestry. The preponderance of the male European R1b lineage also dates to this period. It went from ~0% to ~75-90% in Britain over a few hundred years.

If most of the genetic-demographic character of modern Britain was established during the Bronze Age*, then there has been significant selection since the Bronze Age. The figure to the left shows ancient (Neolithic/Bronze age) frequencies of selected SNPs, with modern frequencies in the British in dashed read. The top-left SNP is for HERC2-OCA2, the region related to brown vs. blue eye color, and also associated with some more general depigmentation. The top-right SNP is in SLC45A2, the second largest effect skin color locus in Europeans. The bottom SNP is for a mutation on LCT, which allows for the digestion of milk sugar as adults.

The vast majority of the allele frequency change in Britons for digestion of milk sugar post-dates the demographic turnover. In other words, the modern allele frequency is a function of post-Bronze Age selection. This is not surprising, as it supports the result in Eight thousand years of natural selection.

1000 Genomes derived SLC45A2 SNP frequency

At least as interesting are the pigmentation loci. The fact that the derived frequency in HERC2-OCA2 is lower in both British and Central European Beaker people samples indicates that the lower proportion is not an artifact of sampling. Britons have gotten more blue-eyed over the last 4,000 years. Second, SLC45A2 is at shocking low proportions for modern European populations.

HGDP derived SLC45A2 SNP frequency

In the 1000 Genomes the 4% ancestral allele frequency is almost certainly a function of the Siberian (non-European) ancestry. In modern Iberians the ancestral frequency is 18% (and it is even higher in Sardinians last I checked), but in Tuscans it is ~2%. Though not diagnostic of Europeans in the way the derived SNP at SLC24A2 is, SLC452 derived variants are much more constrained to Europe. Individuals who are homozygote ancestral for SNPs atSLC45A2 rare in modern Northern Europeans (pretty much nonexistent actually). But even as late as the Bronze Age they would have been present at low but appreciable frequencies.

This particular result convinces me that the method in Field et al. which detected lots of recent (last 2,000 years) selection on pigmentation in British populations is not just a statistical artifact. Though these papers are solving much of European prehistory, they are also going to be essential windows into the trajectory of natural selection in human populations over the last 5,000 years.

* In the context of this paper the Anglo-Saxon migrations tackled by the PoBI paper are minor affairs because the two populations were already genetically rather close. Additionally, the PoBI paper found that the German migrations were significant demographic events, but most of the ancestry across Britain does date to the previous period.

May 1, 2017

So what’s point of demographic models which leave you scratching your head

Filed under: Genomics,History,Human Genetics,Selection,Tibetans — Razib Khan @ 10:45 pm


There’s a new paper on Tibetan adaptation to high altitudes, Evolutionary history of Tibetans inferred from whole-genome sequencing. The focus of the paper is on the fact that more genes than have previously been analyzed seem to be the targets of natural selection. And I buy most of their analyses (not sure about the estimate of Denisovan ancestry being 0.4%…these sorts of things can be tricky).

But they fancy it up with a ∂a∂ model of population history, as well as using MSMC to account for gene flow. I don’t understand why they didn’t use something simpler like TreeMix, which can also handle more complex models. I guess because they wanted to focus on only a few populations?

Years ago I asked the developer of MSMC, Stephan Schiffels, if assuming an admixed population is not admixed might cause weird inferences. Why yes, it would. For example, admixed populations might show higher effective population since they’re pooling the histories of two separate populations. As for ∂a∂, the model above leaves me literally scratching my head.

…predicted that the initial divergence between Han and Tibetan was much earlier, at 54kya (bootstrap 95% C.I 44 kya to 58 kya). However, for the first 45ky, the two populations maintained substantial gene flow (6.8×10-4 and 9.0×10-4 per generation per chromosome). After 9.4 kya (bootstrap 95% C.I 8.6 kya to 11.2 kya), the gene flow rate dramatically dropped (1.3×10-11 and 4×10-7 per generation per chromosome), which is consistent with the estimate from MSMC.

Mystifying. The separation between Chinese and Tibetans is pretty much immediately after modern humans arrive in East Asia. Then there’s a lot of reciprocal gene flow…which ends during the Holocene.

We’re being told here that there are two populations which persisted in some form for ~45,000 years. Is this believable? That these two populations maintained some sort of continuity, and, remained in close proximity to engage in gene flow. And then ~10,000 years ago the ancestors of the Tibetans separated from the ancestors of the modern Han Chinese.

The latter scenario I can imagine. It’s this ~45,000 year dance I’m confused by. If there is substantial gene flow between the two groups why did they keep enough distinctive drift to be separate populations?

With what we know about ancient DNA from Europe if we posited such a model for that continent we’d be way off. There’s been too many population turnovers. Is East Asia different? I’m moderately skeptical of that. I think perhaps researchers should be very aware of the limitations of ∂a∂ when it comes to fine-grained population genomic analyses.

Note: This is a cool paper, and this small section is not entirely relevant. Which is why I’m confused about it since it seems the weakest part of the analysis in terms of originality, and the least believable.

April 28, 2017

Beyond “Out of Africa” and multiregionalism: a new synthesis?

Filed under: Africa,Evolution,Genetics,Genomics,Human Evolution,Human Genetics — Razib Khan @ 4:14 pm

For several decades before the present era there have been debates between proponents of the recent African origin of modern humans, and the multiregionalist model. Though molecular methods in a genetic framework have come of the fore of late these were originally paleontological theories, with Chris Stringer and Milford Wolpoff being the two most prominent public exponents of the respective paradigms.

Oftentimes the debate got quite heated. If you read books from the 1990s, when multiregionalism in particular was on the defensive, there were arguments that the recent out of Africa model was more inspirational in regards to our common humanity. As a riposte the multiregionalists asserted that those suggesting recent African origins with total replacement was saying that our species came into being through genocide.

Though some had long warned against this, the dominant perception outside of population genetics was that results such the “mitochondrial Eve” had given strong support to the recent African origin of modern humans, to the exclusion of other ancestry. 2002’s Dawn of Human Culture took it for granted that the recent African origin of modern humans to the total exclusion of other hominin lineages was established fact.

In 2008 I went to a talk where Svante Paabo presented some recent Neanderthal ancient mtDNA work. It was rather ho-hum, as Paabo showed that the Neanderthal lineages were highly diverged from modern ones, and did not leave any descendants. Though of course most modern human lineages did not leave any descendants from that period, Paabo took this evidence supporting the proposition that Neanderthals did not contribute to the modern human gene pool.

When his lab reported autosomal Neanderthal admixture in 2010, it was after initial skepticism and shock internally. I know Milford Wolpoff felt vindicated, while Chris Stringer began to emphasize that the recent African origin of modern humanity also was defined by regional assimilation of other lineages. The data have ultimately converged to a position somewhere between the extreme models of total replacement or balanced and symmetrical gene flow.

This is not surprising. Extreme positions are often rhetorically useful and popular when there’s no data. But reality does not usually conform to our prejudices, so ultimately one has to come down at some point.

The data for non-Africans is rather unequivocal. The vast majority of (>90%) of the ancestry of non-Africans seems to go back to a small number of common ancestors ~60,000 years ago. Perhaps in the range of ~1,000 individuals. These individuals seem to be a node within a phylogenetic tree where all the other branches are occupied by African populations. Between this period and ~15,000 years ago these non-Africans underwent a massive range expansion, until modern humans were present on all continents except Antarctica. Additionally, after the Holocene some of these non-African groups also experienced huge population growth due to intensive agricultural practice.

To give a sense of what I’m getting at, the bottleneck and common ancestry of non-Africans goes back ~60,000 years, but the shared ancestry of Khoisan peoples and non-Khoisan peoples goes back ~150,000-200,000 years. A major lacunae of the current discussion is that often the dynamics which characterize non-Africans are assumed to be applicable to Africans. But they are not.

A 2014 paper illustrates one major difference by inferring effective population from whole genomes: African populations have not gone through the major bottleneck which is imprinted on the genomes of all non-African populations. The Khoisan peoples, the most famous of which are the Bushmen of the Kalahari, have the largest long term effective populations of any human group. The Yoruba people of Nigeria have a history where they were subject to some population decline, but not to the same extent as non-Africans.

What do we take away from this?

One thing is that we have to consider that the assimilationist model which seems to be necessary for non-Africans, also applies to Africans. For years some geneticists have been arguing that some proportion of African ancestry as well is derived from lineages outside of the main line leading up to anatomically modern humans. Without the smoking gun of ancient genomes this will probably remain a speculative hypothesis. I hope that Lee Berger’s recent assertion that they’ve now dated Homo naledi to ~250,000 years before the present may offer up the possibility that ancient DNA will help resolver the question of African archaic admixture (i.e., if naledi is related to the “ghost population”?).

The second dynamic is that the bottleneck-then-range-expansion which is so important in defining the recent prehistory of non-Africans is not as relevant to Africans during the Pleistocene. The very deep split dates being inferred from whole genome analysis of African populations makes me wonder if multiregional evolution is actually much more important within Africa in the development of modern humans in the last few hundred thousand years. Basically, the deep split dates may highlight that there was recurrent gene flow over hundreds of thousands of years between different closely related hominin populations in Africa.

Ultimately, it doesn’t seem entirely surprising that the “Out of Africa” model does not quite apply within Africa.

Addendum: Over the past ~5,000 years we have seen the massive expansion of agricultural populations within the continent. The “deep structure” therefore may have been erased to a great extent, with Pygmies, Khoisan, and Hadza, being the tip of the iceberg in terms of the genetic variation which had characterized the Africa during the Pleistocene.

April 23, 2017

The logic of human destiny was inevitable 1 million years ago

Filed under: Evolution,Genetics,Genomics,Human Evolution,Human Genetics — Razib Khan @ 1:11 pm

Robert Wright’s best book, Nonzero: The Logic of Human Destiny, was published near 20 years ago. At the time I was moderately skeptical of his thesis. It was too teleological for my tastes. And, it does pander to a bias in human psychology whereby we look to find meaning in the universe.

But this is 2017, and I have somewhat different views.

In the year 2000 I broadly accepted the thesis outlined a few years later in The Dawn of Human Culture. That our species, our humanity, evolved and emerged in rapid sequence, likely due to biological changes of a radical kind, ~50,000 years ago. This is the thesis of the “great leap forward” of behavioral modernity.

Today I have come closer to models proposed by Michael Tomasello in The Cultural Origins of Human Cognition and Terrence Deacon in The Symbolic Species: The Co-evolution of Language and the Brain. Rather than a punctuated event, an instance in geological time, humanity as we understand it was a gradual process, driven by general dynamics and evolutionary feedback loops.

The conceit at the heart of Robert J. Sawyer’s often overly preachy Neanderthal Parallax series, that if our own lineage went extinct but theirs did not they would have created a technological civilization, is I think in the main correct. It may not be entirely coincidental that the hyper-drive cultural flexibility of African modern humans evolved in African modern humans first. There may have been sufficient biological differences to enable this to be likely. But I believe that if African modern humans were removed from the picture Neanderthals would have “caught up” and been positioned to begin the trajectory we find ourselves in during the current Holocene inter-glacial.

Luke Jostins’ figure showing across board encephalization

The data indicate that all human lineages were subject to increased encephalization. That process trailed off ~200,000 years ago, but it illustrates the general evolutionary pressures, ratchets, or evolutionary “logic”, that applied to all of them. Overall there were some general trends in the hominin lineage that began to characterized us about a million years ago. We pushed into new territory. Our rate of cultural change seems to gradually increased across our whole range.

One of the major holy grails I see now and then in human evolutionary genetics is to find “the gene that made us human.” The scramble is definitely on now that more and more whole genome sequences from ancient hominins are coming online. But I don’t think there will be such gene ever found. There isn’t “a gene,” but a broad set of genes which were gradually selected upon in the process of making us human.

In the lingo, it wasn’t just a hard sweep from a de novo mutation. It was as much, or even more, soft sweeps from standing variation.

April 19, 2017

Mouse fidelity comes down to the genes

Filed under: Genetics,Genomics,Human Genetics — Razib Khan @ 10:02 pm

While birds tend to be at least nominally monogamous, this is not the case with mammals. This strikes some people as strange because humans seem to be monogamous, at least socially, and often we take ourselves to be typically mammalian. But of course we’re not. Like many primates we’re visual creatures, rather than relying in smell and hearing. Obviously we’re also bipedal, which is not typical for mammals. And, our sociality scales up to massive agglomerations of individuals.

How monogamous we are is up for debate. Desmond Morris, who is well known to many from his roles in television documentaries, has been a major promoter of the idea that humans are monogamous, with a focus on pair-bonds. In contrast, other researchers have highlighted our polygamous tendencies. In The Mating Mind Geoffrey Miller argues for polygamy, and suggests that pair-bonds in a pre-modern environment were often temporary, rather than lifetime (Miller is now writing a book on polyamory).

The fact that in many societies high status males seem to engage in polygamy, despite monogamy being more common, is one phenomenon which confounds attempts to quickly generalize about the disposition of our species. What is preferred may not always be what is practiced, and the external social adherence to norms may be quite violated in private.

Adducing behavior is simpler in many other organisms, because their range of behavior is more delimited. When it comes to studying mating patterns in mammals voles have long been of interest as a model. There are vole species which are monogamous, and others which are not. Comparing the diverged lineages could presumably give insight as to the evolutionary genetic pathways relevant to the differences.

But North American deer mice, Peromyscus, may turn to be an even better bet: there are two lineages which exhibit different mating patterns which are phylogenetically close enough to the point where they can interbreed. That is crucial, because it allows one to generate crosses and see how the characteristics distribute themselves across subsequent generations. Basically, it allows for genetic analysis.

And that’s what a new paper in Nature does, The genetic basis of parental care evolution in monogamous mice. In figure 3 you can see the distribution of behaviors in parental generations, F1 hybrids, and the F2, which is a cross of F1 individuals. The widespread distribution of F2 individuals is likely indicative of a polygenic architecture of the traits. Additionally, they found that some traits are correlated with each other in the F2 generation (probably due to pleiotropy, the same gene having multiple effects), while others were independent.

With the F2 generation they ran a genetic analysis which looked for associations between traits and regions of the genome. They found 12 quantitative trait loci (QTLs), basically zones of the genome associated with variation on one or more of the six traits. From this analysis they immediately realized there was sexual dimorphism in terms of the genetic architecture; the same locus might have a different effect in the opposite sex. This is evolutionarily interesting.

Because the QTLs are rather large in terms of physical genomic units the authors looked to see which were plausible candidates in terms of function. One of their hits was vasopressin, which should be familiar to many from vole work, as well as some human studies. Though the QTL work as well as their pup-switching experiment (which I did not describe) is persuasive, the fact that a gene you’d expect shows up as a candidate really makes it an open and shut case.

The extent of the variation explained by any given QTL seems modest. In the extended figures you can see it’s mostly in the 1 to 5 percent range. In Carl Zimmer’s excellent write up he ends:

But Dr. Bendesky cautioned that the vasopressin gene would probably turn out to be just one of many that influence oldfield mice. Though it is strongly linked to parental behavior, the vasopressin gene accounts for 6.7 percent of the variation in nest building among males, and only 2.9 percent among females.

The genetic landscape of human parenting will turn out to be even more rugged, Dr. Bendesky predicted.

“You cannot do a 23andMe test and find out if your partner is going to be a good father,” he said.

Sort of. The genetic architecture above is polygenic…but not incredibly diffuse. The proportion of variation explained by the largest effect allele is more than for height, and far more than for education. If human research follows up on this, I wouldn’t be surprised if you could develop a polygenic risk score.

But I don’t have a good intuition on how much variation in humans there really is for these sorts of traits that are heritable. I assume some. But I don’t know how much. And how much of the variance in behavior might be explained by human QTLs? Humans don’t lick or build nests, or retrieve pups. Also, as one knows from Genetics and Analysis of Quantitative Traits sexually dimorphic traits take a long time to evolve. These are two deer mice species. Within humans there may not have been enough time for this sort of heritable complexity of behavior to evolve.

There are a lot of philosophical issues here about translating to a human context.

Nevertheless, this research shows that ingenious animal models can powerfully elucidate the biological basis of behavior.

Citation: The genetic basis of parental care evolution in monogamous mice. Nature (2017) doi:10.1038/nature22074

April 18, 2017

Women hate going to India

Filed under: Anthroplogy,Genetics,Human Genetics,India,Parsi — Razib Khan @ 9:11 pm


For some reason women do not seem to migrate much into South Asia. In the late 2000s I, along with others, noticed a strange discrepancy in the Y and mtDNA lineages which trace one’s direct male and female lines: in South Asia the male lineages were likely to cluster with populations to the north an west, while the females lines did not. South Asia’s females lines in fact had a closer relationship to the mtDNA lineages of Southeast and East Asia, albeit distantly.

One solution which presented itself was to contend there was no paradox at all. That the Y chromosomal lineages found in South Asia were basal to those to the west and north. In particular, there were some papers suggesting that perhaps R1a1a originated in South Asia at the end of the last Pleistocene. Whole genome sequencing of Y chromosomes does not bear this out though. R1a1a went through rapid expansion recently, and ancient DNA has found it in Russia first. But in 2009 David Reich came out with Reconstructing Indian population history, which offered up somewhat of a possible solution.

What Reich and his coworkers found that South Asia seems to be characterized by the mixture of two very different types of populations. One set, ANI (Ancestral North Indian), are basically another western or northwestern Eurasian group. ASI (Ancestral South Indian), are indigenous, and exhibit distant affinities to the Andaman Islanders. The India-specific mtDNA then were from ASI, while the Y chromosomes with affinities to people to the north and west were from ANI. In other words, the ANI mixture into South Asia was probably through a mass migration of males.

But it’s not just Y and mtDNA in this case only. A minority of South Asians speak Austro-Asiatic languages. The most interesting of these populations are the Munda, who tend to occupy uplands in east-central India. Older books on India history often suggest that the Munda are the earliest aboriginals of the subcontinent, but that has to confront the fact that most Austro-Asiatic language are spoken in Southeast Asia. There was no true consensus where they were present first.

Genetics seems to have solved this question. The evidence is building up that Austro-Asiatic languages arrived with rice farmers from Southeast Asia. Though most of the ancestry of the Munda is of ANI-ASI mix, a small fraction is clearly East Asian. And interestingly, though they carry no East Asian mtDNA, they do carry East Asian Y. Again, gene flow mediated by males.

The same is true of India’s Bene Israel Jewish community.

A new preprint on biorxiv confirms that the Parsis are another instance of the same dynamic: The genetic legacy of Zoroastrianism in Iran and India: Insights into population structure, gene flow and selection:

Zoroastrianism is one of the oldest extant religions in the world, originating in Persia (present-day Iran) during the second millennium BCE. Historical records indicate that migrants from Persia brought Zoroastrianism to India, but there is debate over the timing of these migrations. Here we present novel genome-wide autosomal, Y-chromosome and mitochondrial data from Iranian and Indian Zoroastrians and neighbouring modern-day Indian and Iranian populations to conduct the first genome-wide genetic analysis in these groups. Using powerful haplotype-based techniques, we show that Zoroastrians in Iran and India show increased genetic homogeneity relative to other sampled groups in their respective countries, consistent with their current practices of endogamy. Despite this, we show that Indian Zoroastrians (Parsis) intermixed with local groups sometime after their arrival in India, dating this mixture to 690-1390 CE and providing strong evidence that the migrating group was largely comprised of Zoroastrian males. By exploiting the rich information in DNA from ancient human remains, we also highlight admixture in the ancestors of Iranian Zoroastrians dated to 570 BCE-746 CE, older than admixture seen in any other sampled Iranian group, consistent with a long-standing isolation of Zoroastrians from outside groups. Finally, we report genomic regions showing signatures of positive selection in present-day Zoroastrians that might correlate to the prevalence of particular diseases amongst these communities.

The paper uses lots of fancy ChromoPainter methodologies which look at the distributions of haplotypes across populations. But some of the primary results are obvious using much simpler methods.

1) About 2/3 of the ancestry of Indian Parsis derives from an Iranian population
2) About 1/3 of the ancestry of Indian Parsis derives from an Indian popuation
3) Almost all the Y chromosomes of Indian Parsis can be accounted for by Iranian ancestry
4) Almost all the mtDNA haplogroups of Indian Parsis can be accounted for by Indian ancestry
5) Iranian Zoroastrians are mostly endogamous
6) Genetic isolation has resulted in drift and selection on Zoroastrians

The fact that the ancestry proportion is clearly more than 50% Iranian for Parsis indicates that there was more than one generation of males who migrated. They did not contribute mtDNA, but they did contribute genome-wide to Iranian ancestry. There are wide intervals on the dating of this admixture event, but they are consonant oral history that was later written down by the Parsis.

So there you have it. Another example of a population formed from admixture because women hate going to India.

Citation: The genetic legacy of Zoroastrianism in Iran and India: Insights into population structure, gene flow and selection.
Saioa Lopez, Mark G Thomas, Lucy van Dorp, Naser Ansari-Pour, Sarah Stewart, Abigail L Jones, Erik Jelinek, Lounes Chikhi, Tudor Parfitt, Neil Bradman, Michael E Weale, Garrett Hellenthal
bioRxiv 128272; doi: https://doi.org/10.1101/128272

April 15, 2017

Genetic variation in human populations and individuals

Filed under: Genetics,Genomics,Human Genetics,Polymorphisms,SNPs — Razib Khan @ 9:25 pm


I’m old enough to remember when we didn’t have a good sense of how many genes humans had. I vaguely recall numbers around 100,000 at first, which in hindsight seems rather like a round and large number. A guess. Then it went to 40,000 in the early 2000s and then further until it converged to some number just below 20,000.

But perhaps more fascinating is that we have a much better catalog of the variation across the whole human genome now. Often friends ask me questions of the form: “so DTC genomic company X has about 800,000 SNPs, is that enough to do much?” To answer such a question you need some basic numbers in your head, as well as what you want to “do.”

First, the human genome has about 3 billion base pairs (3 Gb). That’s a lot. But most of the genome famously doesn’t code for proteins. The exome, the proportion of the genome where bases directly translate into a protein accounts for 1% of the whole genome. That’s 30 million bases (30 Mb). But this small region of the genome is very important, as the vast majority of major disease mutations are found in the exome.

When it comes to a standard 800K SNP chip, which samples 800,000 positions across the 3 Gb genome, it is likely that the designers enriched the marker set for functional positions relevant to diseases. Not all marker positions are created equal. Though even outside of those functional positions there are often nearby SNPs that can “tag” them, so you can infer one from the state of the other.

But are 800,000 positions enough to make good ancestry inference? (to give one example) Yes. 800,000 is actually a substantial proportion of the polymorphism in any given genome. There have been some papers which improved on the numbers in 2015’s A global reference for human genetic variation, but it’s still a good comprehensive review to get an order-of-magnitude sense. The table below gives you a sense of individual variation:

Median autosomal variant sites per genome

When it comes to single nucleotide polymorphisms (SNPs), what SNP chips are getting at, an 800K array should get a substantial proportion of your genome-wide variation. More than enough for ancestry inference or forensics. The singleton column shows mutations specific to the individual.  When focusing on new mutations specific to an individual that might cause disease, singleton large deletions and nonsynonymous SNPs is really where I’d look.

But what about whole populations? The plot to the left shows the count of variants as a function of alternative allele frequency. When we say “SNP”, you really mean variants which exhibit polymorphism at a particular cut-off frequency for the minor allele (often 1%). It is clear that as the minor allele frequency increases in relation to the human reference genome the number of variants decreases.

From the paper:

The majority of variants in the data set are rare: ~64 million autosomal variants have a frequency <0.5%, ~12 million have a frequency between 0.5% and 5%, and only ~8 million have a frequency >5% (Extended Data Fig. 3a). Nevertheless, the majority of variants observed in a single genome are common: just 40,000 to 200,000 of the variants in a typical genome (1–4%) have a frequency <0.5% (Fig. 1c and Extended Data Fig. 3b). As such, we estimate that improved rare variant discovery by deep sequencing our entire sample would at least double the total number of variants in our sample but increase the number of variants in a typical genome by only ~20,000 to 60,000.

An 800K SNP chip will be biased toward the 8 million or so variants with a frequency of 5%. This number gives you a sense of the limited scope of variation in the human genome. 0.27% of the genome captures a lot of the polymorphism.

Citation: 1000 Genomes Project Consortium. “A global reference for human genetic variation.” Nature 526.7571 (2015): 68-74.

April 8, 2017

Why only one migrant per generation keeps divergence at bay

The best thing about population genetics is that because it’s a way of thinking and modeling the world it can be quite versatile. If Thinking Like An Economist is a way to analyze the world rationally, thinking like a population geneticist allows you to have the big picture on the past, present, and future, of life.

I have some personal knowledge of this as a transformative experience. My own background was in biochemistry before I became interested in population genetics as an outgrowth of my lifelong fascination with evolutionary biology. It’s not exactly useless knowing all the steps of the Krebs cycle, but it lacks in generality. In his autobiography I recall Isaac Asimov stating that one of the main benefits of his background as a biochemist was that he could rattle off the names on medicine bottles with fluency. Unless you are an active researcher in biochemistry your specialized research is quite abstruse. Population genetics tends to be more applicable to general phenomena.

In a post below I made a comment about how one migrant per generation or so is sufficient to prevent divergence between two populations. This is an old heuristic which goes back to Sewall Wright, and is encapsulated in the formalism to the left. Basically the divergence, as measured by Fst, is proportional to the inverse of 4 time the proportion of migrants times the total population + 1. The mN is equivalent to the number of migrants per generation (proportion times the total population). As the mN become very large, the Fst converges to zero.

The intuition is pretty simple. Image you have two populations which separate at a specific time. For example, sea level rise, so now you have a mainland and island population. Since before sea level rise the two populations were one random mating population their initial allele frequencies are the same at t = 0. But once they are separated random drift should begin to subject them to divergence, so that more and more of their genes exhibit differences in allele frequencies (ergo, Fst, the between population proportion of genetic variation, increases from 0).

Now add to this the parameter of migration. Why is one migrant per generation sufficient to keep divergence low? The two extreme scenarios are like so:

  1. Large populations change allele frequency very slowly due to drift, so only a small proportion of migration is needed to prevent them from diverging
  2. Small populations change allele frequency very fast due to drift, so a larger proportion of migration is needed to prevent them from drifting

Within a large population one migrant is a small proportion, but drift is occurring very slowly. Within a small population drift is occurring fast, but one migrant is a relatively large proportion of a small population.

Obviously this is a stylized fact with many details which need elaborating. Some conservation geneticists believe that the focus on one migrant is wrongheaded, and the number should be set closer to 10 migrants.

But it still gets at a major intuition: gene flow is extremely powerful and effective at reducing differences between groups. This is why most geneticists are skeptical of sympatric speciation. Though the focus above is on drift, the same intuition applies to selective divergence. Gene flow between populations work at cross-purposes with selection which drives two groups toward different equilibrium frequencies.

This is why it was surprising when results showed that Mesolithic hunter-gatherers and farmers in Europe were extremely genetically distinct in close proximity for on the order of 1,000 years. That being said, strong genetic differentiation persists between Pygmy peoples and their agriculturalist neighbors, despite a long history of living nearby each other (Pygmies do not have their own indigenous languages, but speak the tongue of their farmer neighbors). In the context of animals physical separation is often necessary for divergence, but for humans cultural differences can enforce surprisingly strong taboos. Culture is as strong a phenomenon as mountains or rivers….

April 2, 2017

The future shall, and should, be sequenced

Filed under: Genomics,GWAS,Human Genetics — Razib Khan @ 10:32 pm

Last fall I talked about a preprint, Human demographic history impacts genetic risk prediction across diverse populations. It’s now published in AJHG, with the same informative title, Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations. Even though talked about this before, I thought it would be useful to highlight again.

To recap, GWAS is a pretty big deal, but only in the last 15 years or so. With genome-wide data researchers began to explore associations between diseases and population genetic variation. In some cases they discovered strong associations between characteristics and genetic variants, but in many casese it turned out that though a trait is highly heritable (e.g., schizophrenia) the causal variants are either not common or do not explain much of the variation in the poplation (or both).

But as the second decade of GWAS proceeds the sample sizes are getting larger, and researchers are moving from SNP-chips, with their various biases, to high quality whole-genome sequences. One of the major sorts of low hanging fruit in the minds of many people are rare variants. Basically SNP-chips are geared toward finding common variations within large populations, since they have a finite number of markers they are going to interrogate. Sequencing though is a comprehensive catalog of the genome in a relative sense. If you have high coverage (so you sample the site many times) you can easily discover rare mutations within an individual genome that makes them distinctive from almost the rest of the human race (these may be de novo mutations, or, they could be mutations private to their extended pedigree).

But context matters. Martin et al. find that confirmed GWAS hits in Europeans tend to exhibit decreased portability as a function of genetic distance. This isn’t entirely surprising, especially if rarer variants are part of the explanation. Rare variants usually emerged later in history, after the differentiation between geographic races.

A solution would be to have a diverse panel of populations in your studies. For many reasons this was not to be. Northwest Europeans are enormously enriched in current data sets. Martin et al. observe that recent this has diminished somewhat, from 95% European to less than 80%. But they observe that this is mostly due to the inclusion of “Asian” samples, as opposed to African and Native Americans, who remain as undererpresented as they did several years ago.

The African and Native American samples present somewhat different problems. The Native American groups are quite drifted due to bottlenecks. Likely they have their own variants due to the combined affects of mutation and selection through 15 to 20,000 years of isolation from other human populations. In contrast, the African groups have lots of diversity with a high time depth due to their ancestral histories, which are less subject to bottleneck effects. The prediction ability into Africans of current GWAS looks to be rather pathetic. This is reasonable because their diversity is poorly captured in Eurocentric study designs, and, they are more genetically diverged from Europeans than Asians are.

Ultimatley I think, and hope, this portability question will be of short term utility. As sequencing gets cheap, and studies become more numerous, we’ll fill in the gaps of understudied populations. Finally, ethics is above my paygrade, but I do hope those who demand a strenuous bar on consent keep in mind that that will result in slower growth of these study populations. Academics want to do a good job, but they also want to stay on the good side of IRB.

Citation: Martin, Alicia R., et al. “Human demographic history impacts genetic risk prediction across diverse populations.” bioRxiv (2016): 070797.

January 5, 2013

Why the future won’t be genetically homogeneous

While reading The Founders of Evolutionary Genetics I encountered a chapter where the late James F. Crow admitted that he had a new insight every time he reread R. A. Fisher’s The Genetical Theory of Natural Selection. This prompted me to put down The Founders of Evolutionary Genetics after finishing Crow’s chapter and pick up my copy of The Genetical Theory of Natural Selection. I’ve read it before, but this is as good a time as any to give it another crack.

Almost immediately Fisher aims at one of the major conundrums of 19th century theory of Darwinian evolution: how was variation maintained? The logic and conclusions strike you like a hammer. Charles Darwin and most of his contemporaries held to a blending model of inheritance, where offspring reflect a synthesis of their parental values. As it happens this aligns well with human intuition. Across their traits offspring are a synthesis of their parents. But blending presents a major problem for Darwin’s theory of adaptation via natural selection, because it erodes the variation which is the raw material upon which selection must act. It is a famously peculiar fact that the abstraction of the gene was formulated over 50 years before the concrete physical embodiment of the gene, DNA, was ascertained with any confidence. In the first chapter of The Genetical Theory R. A. Fisher suggests that the logical reality of persistent copious heritable variation all around us should have forced scholars to the inference that inheritance proceeded via particulate and discrete means, as these processes do not diminish variation indefinitely in the manner which is entailed by blending.

More formally the genetic variance decreases by a factor of 1/2 every generation in a blending model. This is easy enough to understand. But I wanted to illustrate it myself, so I slapped together a short simulation script. The specifications are as follows:

1) Fixed population size, in this case 100 individuals

2) 100 generations

3) All individuals have 2 offspring, and mating is random (no consideration of sex)

4) The offspring trait value is the mid-parent value of the parents, though I also including a “noise” parameter in some of the runs, so that the outcome is deviated somewhat in a random fashion from expected parental values

In terms of the data structure the ultimate outcome is a 100 ✕ 100 matrix, with rows corresponding to generations, and each cell an individual in that generation. The values in each cell span the range from 0 to 1. In the first generation I imagine the combining of two populations with totally different phenotypic values; 50 individuals coded 1 and 50 individuals coded 0. If a 1 and 1 mate, the produce only 1′s. Likewise with 0′s. On the other hand a 0 and a 1 produce a 0.5. And so forth. The mating is random in each generation.

The figure to the left illustrates the decay in the variance of the trait value over generation time in different models. The red line is the idealized decay: 1/2 decrease in variance per generation. The blue line is one simulation. It roughly follows the decay pattern, though it is deviated somewhat because it seems that there was some assortative mating randomly (presumably if I used many more individuals it would converge upon the analytic curve). Finally you see one line which follows the trajectory of a simulation with noise. Though this population follows the theoretical decay more closely initially, it converges upon a different equilibrium value, one where some variance remains. That’s because the noise parameter continues to inject this every generation. The relevant point is that most of the variation disappears < 5 generations, and it is basically gone by the 10th generation. To maintain variation in a blending inheritance model requires a great deal of mutation, the extent of which is just not plausible.

To get a different sense of what occurred in these two particular simulations, here are heat maps. The interval 0 and  1 now have shading in each sell. I am displaying only 50 generations here. The top panel is one without noise, while the bottom panel has the noise parameter.

The contrast with a Mendelian model is striking. Imagine that 0 and 1 are now coded by two homozygote genotypes, with heterozygotes exhibiting a value of 0.5. If all the variation is controlled by the genotypes, then you have three genotypes, and three trait values. If I change the scenario above to a Mendelian one than variance will initially decrease, but the equilibrium will be maintained at a much higher level, as 50% of the population will be heterozygotes (0.5), and 50% homozygotes of each variety (0 and 1). With the persistence of heritable variation natural selection can operate to change the allele frequencies over time without the worry that the trait values within a breeding population will converge upon each other too rapidly. This is true even in cases of polygenic traits. Height and I.Q. remain variant, because they are fundamentally heritable through discrete and digital processes.

All this is of course why the “blond gene” won’t disappear, redheads won’t go extinct, nor will humans converge upon a uniform olive shade in a panmictic future. A child is a genetic cross between parents, but only between 50% of each parent’s genetic makeup. And that is one reason they are not simply an “averaging” of parental trait values.

January 4, 2013

Mitochondrial Eve: a de facto deception?

The above image, and the one to the left, are screenshots from my father’s 23andMe profile. Interestingly, his mtDNA haplogroup is not particularly common among ethnic Bengalis, who are more than ~80% on a branch of M. This reality is clear in the map above which illustrates the Central Asian distribution my father’s mtDNA lineage. In contrast, his whole genome is predominantly South Asianform, as is evident in the estimate that 23andMe provided via their ancestry composition feature, which utilizes the broader genome. The key takeaway here is that the mtDNA is informative, but it should not be considered to be representative, or anything like the last word on one’s ancestry in this day and age.


As a matter of historical record mtDNA looms large in human population genetics and phylogeography for understandable reasons. Mitchondria produce more genetic material than is found in the nucleus, and so were the lowest hanging fruit in the pre-PCR era. Additionally, because mtDNA lineages do not recombine they are well suited to a coalescent framework, where an idealized inverted treelike phylogeny converges upon a common ancestor. Finally, mtDNA was presumed to be neutral, so reflective of demographic events unperturbed by adaptation, and characterized by a high mutation rate, yielding a great amount of variation with which to differentiate the branches of the human family tree.

Many of these assumptions are are now disputable. But that’s not the point of this post. In the age of dense 1 million marker SNP-chips why are we still focusing on the history of one particular genetic region? In a word: myth. Eve, the primal woman. The “mother of us all,” who even makes cameos in science fiction finales!

In 1987 a paper was published which found that Africans harbored the greatest proportion of mtDNA variation among human populations. Additionally, these lineages coalesced back to a common ancestor on the order of 150,000 years ago. Since mtDNA is present in humans, there was a human alive 150,000 years ago who carried this ancestral lineage, from which all modern lineages derive. Mitochondrial DNA is passed from mothers to their offspring, so this individual must have been a woman. In the press she was labeled Eve, for obvious reasons. The scientific publicity resulted in a rather strange popular reaction, culminating in a Newsweek cover where Adam and Eve are depicted as naked extras from Eddie Murphy’s Coming to America film.

The problem is that people routinely believe that mtDNA Eve was the only ancestress of all modern humans from the period in which she lived. Why they believe this is common sense, and requires no great consideration. The reality is that the story being told by science is the story of mtDNA, with inferences about the populations which serve as hosts for mtDNA being incidental. These inferences need to be made cautiously and with care. It is basic logic that a phylogeny will coalesce back to a common ancestor at some point. Genetic lineages over time go extinct, and so most mtDNA lineages from the time of Eve went extinct. There were many woman who were alive during the same time as Eve, who contributed at least as much, perhaps more, to the genetic character of modern humans today. All we can say definitively is that their mtDNA lineage is no longer present. As mtDNA is passed from mother to daughter (males obviously have mtDNA, but we are dead ends, and pass it to no one), all one needs for a woman’s mtDNA lineage to go extinct is for her to have only sons. Though she leaves no imprint on the mtDNA phylogeny, obviously her sons may contribute genes to future generations.

Prior to ancient DNA and the proliferation of dense SNP data sets scholars were a bit too ambitious about what they believed they could infer from mtDNA and Y lineages (e.g., The Real Eve: Modern Man’s Journey Out of Africa). We are in a different time now, inferences made about the past rest on more than one leg. But the legend of Eve of the mtDNA persists, not because of its compelling scientific nature, but because this is a case where science piggy-backs upon prior conceptual furniture. This yields storytelling power, but a story which is based on a thin basis of fact becomes just another tall tale.

All this is on my mind because one of the scientists involved with Britain’s DNA, Jim Wilson, has penned a response to Vincent Plagnol’s Exaggerations and errors in the promotion of genetic ancestry testing (see here for more on this controversy). Overall I don’t find Wilson’s rebuttal too persuasive. It is well written, but it has the air of sophistry and lawyerly precision. I have appreciated Wilson’s science before, so I am not casting aspersions at his professional competence. Rather, some of the more enthusiastic and uninformed spokespersons for his firm have placed him in a delicate and indefensible situation, and he is gamely attempting to salvage the best of a bad hand. Importantly, he does not reassure me in the least that his firm did not use Britain’s atrocious libel laws as a threat to mute forceful criticism of their business model on scientific grounds. A more general issue here is that Wilson is in a situation where he must not damage the prospects of his firm, all the while maintaining his integrity as a scientist. From what I have seen once science becomes a business one must abandon the pretense of being a scientist first and foremost, no matter how profitable that aura of objectivity may be. The nature of marketing is such that the necessary caution and qualification essential for science becomes a major liability in the processing of communicating. It’s about selling, not convincing.

Going back to Eve, Wilson marshals a very strange argument:

“The claim that Adam and Eve really existed, as you suggest, refers to the most recent common ancestors of the mtDNA and non-recombining part of the Y chromosome. I don’t agree that there is nothing special about these individuals: there must have been a reason why mitochondrial Eve was on the front cover of Time magazine in the late 80s!….

A minor quibble, but I suspect he means the Newsweek cover. More seriously, this line of argumentation is bizarre on scientific grounds. Rather, it is a tack which is more rational when aiming toward a general audience which might purchase a kit which they believe might tell them of their relationship to “Eve.”

In the wake of the discussion at Genomes Unzipped I participated in further exchanges with Graham Coop and Aylwyn Scally on Twitter, and decided to spend 20 minutes this afternoon asking people what they thought about mitochondrial Eve. By “people,” I mean individuals who are pursuing graduate educations in fields such as genetics and forensics. My cursory “field research” left me very alarmed. Naturally these were individuals who did not make elementary mistakes in regards to the concept, but there was great confusion. I can only wonder what’s going through the minds of the public.

Analogies, allusions, and equivalences are useful when they leverage categories and concepts which we are solidly rooted in, and transpose them upon a foreign cognitive landscape. By pointing to similarities of structure and relation one can understand more fully the novel ground which one is exploring. Saying that the president of India is analogous to the queen of England is an informative analogy. These are both positions where the individual is a largely ceremonial head of state. In contrast, the president of the United States and the queen of England are very different figures, because the American executive is not ceremonial at all. This is not a useful analogy, even though superficially it sees no lexical shift.

Who was Eve? A plain reading is that she is the ancestor of all humans, and more importantly, the singular ancestress of all humans back to the dawn of time. This is a concept which the public grasps intuitively. Who is mtDNA Eve? A woman who flourished 150,000 years ago, who happened to carry the mtDNA lineage which would drift to fixation in the ancestors of modern humans. I think this is a very different thing indeed. For purposes of poetry and marketing the utilization of the name Eve is justifiable. But on scientific grounds all it does is confuse, obfuscate, and mislead.

The fiasco that Vincent Plagnol stumbled upon is just a symptom of a broader problem. Scientists need to engage in massive conceptual clean up, as catchy phrases such as “mitochondrial Eve” and “Y Adam” permeated the culture over the past generation, and mislead many sincere and engaged seekers of truth. This is of the essence because personal genomics, and the scientific understanding of genealogy, are now moving out of the ghetto of hobbyists, enthusiasts, and researchers. Though I doubt this industry will be massive, it will be ubiquitous, and a seamless part of our information portfolio. If people still have ideas like mitochondrial Eve in their head it is likely to cloud their perception of the utility of the tools at hand, and their broader significance.

December 18, 2012

Buddy, can you spare some ascertainment?

The above map shows the population coverage for the Geno 2.0 SNP-chip, put out by the Genographic Project. Their paper outlining the utility and rationale by the chip is now out on arXiv. I saw this map last summer, when Spencer Wells hosted a webinar on the launch of Geno 2.0, and it was the aspect which really jumped out at me. The number of markers that they have on this chip is modest, only >100,000 on the autosome, with a few tens of thousands more on the X, Y, and mtDNA. In contrast, the Axiom® Genome-Wide Human Origins 1 Array Plate being used by Patterson et al. has ~600,000 SNPs. But as is clear by the map above Geno 2.0 is ascertained in many more populations that the other comparable chips (Human Origins 1 Array uses 12 populations). It’s obvious that if you are only catching variation on a few populations, all the extra million markers may not give you much bang for the buck (not to mention the biases that that may introduce in your population genetic and phylogenetic inferences).


To the left are the list of populations against which the Human Origins 1 Array was ascertained, and they look rather comprehensive to me. In contrast, for Geno 2.0 ‘ancestrally informative markers’ were ascertained on 450 populations. The ultimate question for me is this: is all the extra ascertainment on diverse and obscure groups worth it? On first inspection Geno 2.0′s number of SNPs looks modest as I stated, but in my experience when you quality control and merge different panels together you are often left with only a few hundred thousand SNPs in any case. 100-200,000 SNPs is also sufficient to elucidate relationships even in genetically homogeneous regions such as Europe in my experience (it’s more than enough for model-based clustering, and seems to be overkill for MDS or PCA). One issue that jumps out at me about the Affymetrix chip is that it is ascertained toward the antipodes. In contrast, Geno 2.0 takes into account the Eurasian heartland. I suspect, for example, that Geno 2.0 would be better for population or ancestry assignment for South Asians because it would have more informative markers for those populations.

Ultimately I can’t really say much more until I use both marker sets in different and similar contexts. Since Geno 2.0 consciously excludes many functional and medically relevant SNPs its utility is primarily in the domain of demographics and history. If the populations in question are well covered by the Human Origins 1 Array, I see no reason why one shouldn’t go with it. Not only does it have more information about biological function, but the number of markers are many fold greater. On the other hand, Geno 2.0 may be more useful on the “blank zones” of the Affy chip. Hopefully the Genographic Project results paper for Geno 2.0 will come out soon and I can pull down their data set and play with it.

Cite: arXiv:1212.4116

Unveiling the genealogical lattice

To understand nature in all its complexity we have to cut down the riotous variety down to size. For ease of comprehension we formalize with math, verbalize with analogies, and visualize with representations. These approximations of reality are not reality, but when we look through the glass darkly they give us filaments of essential insight. Dalton’s model of the atom is false in important details (e.g., fundamental particles turn out to be divisible into quarks), but it still has conceptual utility.

Likewise, the phylogenetic trees popularized by L. L. Cavalli-Sforza in The History and Geography of Human Genes are still useful in understanding the shape of the human demographic past. But it seems that the bifurcating model of the tree must now be strongly tinted by the shades of reticulation. In a stylized sense inter-specific phylogenies, which assume the approximate truth of the biological species concept (i.e., little gene flow across lineages), mislead us when we think of the phylogeny of species on the microevolutionary scale of population genetics. On an intra-specific scale gene flow is not just a nuisance parameter in the model, it is an essential phenomenon which must be accommodated into the framework.


This is on my mind because of the emergence of packages such as TreeMix and AdmixTools. Using software such as these on the numerous public data sets allows one to perceive the reality of admixture, and overlay lateral gene flow upon the tree as a natural expectation. But perhaps a deeper result is the character of the tree itself is torn asunder. The figure above is from a new paper, Efficient moment-based inference of admixture parameters and sources of gene flow, which debuts MixMapper. The authors bring a lot of mathematical heft to their exposition, and I can’t say I follow all of it (though some of the details are very similar to Pickrell et al.’s). But in short it seems that in comparison to TreeMix MixMapper allows for more powerful inference of a narrower set of populations, selected for exploring very specific questions. In contrast, TreeMix explores the whole landscape with minimal supervision. Having used the latter I can testify that that is true.

The big result from MixMapper is that it extends the result of Patterson et al., and confirms that modern Europeans seem to be an admixture between a “north Eurasian” population, and a vague “west Eurasian” population. Importantly, they find evidence of admixture in Sardinians, which implies that Patterson et al.’s original were not sensitive to admixture in putative reference populations (note that Patterson is a coauthor on this paper as well). The rub, as noted in the paper, is that it is difficult to estimate admixture when you don’t have “pure” ancestral reference populations. And yet here the takeaway for me is that we may need to rethink our whole conception of pure ancestral populations, and imagine a human phylogenetic tree as a series of lattices in eternal flux, with admixed nodes periodically expanding so as to generate the artifice of a diversifying tree. The closer we look, the more likely that it seems that most of the populations which have undergone demographic expansion in the past 10,000 years are also the products of admixture. Any story of the past 10,000 years, and likely the past 100,000 years, must give space at the center of the narrative arc lateral gene flow across populations.

Cite: arXiv:1212.2555 [q-bio.PE]

December 13, 2012

We are Nature

Filed under: Genetics,Genomics,Human Genetics,Human Genomics — Razib Khan @ 8:03 am

There’s an interesting piece in Slate, The Great Schism in the Environmental Movement, which seems to be a distillation of trends which have been bubbling within the modern environmentalist movement for a generation now (I’ve read earlier manifestos in a similar vein). I can’t assess the magnitude of the shift, but here’s the top-line:

But that is a false construct that scientists and scholars have been demolishing the past few decades. Besides, there’s a growing scientific consensus that the contemporary human footprint—our cities, suburban sprawl, dams, agriculture, greenhouse gases, etc.—has so massively transformed the planet as to usher in a new geological epoch. It’s called the Anthropocene.

Modernist greens don’t dispute the ecological tumult associated with the Anthropocene. But this is the world as it is, they say, so we might as well reconcile the needs of people with the needs of nature. To this end, Kareiva advises conservationists to craft “a new vision of a planet in which nature—forests, wetlands, diverse species, and other ancient ecosystems—exists amid a wide variety of modern, human landscapes.”


Let’s take this debate as a given. It is fundamentally normative. That is, it is about values. We we need to tread carefully before projecting values across disputants. Far too often in this domain people seem to presume normative alignments, and therefore confuse ideological disagreement for rejection of factual truths. But, one thing to consider is that it is probable that human beings have already radically reshaped the ecological character of the world over the past 100,000 years. The implicit model that many older environmental activists seem to present is a framework pitting man & the machine vs. nature (the Shire vs. Mordor). But it is just not a useful dichotomy for many.

It is possible that there was, and is, no “pristine” nature. These disparate perspectives come to the fore in particular in post-colonial landscapes settled by Europeans. There is a long tradition in these areas of transforming ‘natives’ into ‘Noble Savages,’ who have attained some idealized harmony with Nature. The reality is that it is not harmony that was attained, but equilibrium. The arrival of anatomically modern humans to Australia and the New World resulted in a ‘shock’ to the ecological system, as megafauna went extinct due to the new variable of human predation. Even if H. sapiens were not the sufficient condition for these extinctions (populations naturally go through cycles), it is likely they were necessary (i.e., humans might extirpate species during times of low census size). But it is not just the initial impact in terms of species turnover. Australian and Amerindian populations seem to have reshaped the long term character of the landscape through fireCharles C. Mann argues in 1491 that  the vast forests which colonial and early American settlers cleared were in fact second growth, which emerged in the wake of massive die-offs of indigenous peoples due to Old World disease.

All of this is fundamentally complicated. Instead of a decision tree with two options, ‘Civilization’ vs. ‘Nature,’ there is actually a space populated with a multitude of positions. As someone touched by a moderate amount of biophilia my vision for the future is one of arcology based urbanism, massively scaled up algaculture, and megafaunal rewilding through genetic engineering and ancient DNA. Rather than idealize a mythic past we should endeavor to forge a new future. So it was, and so shall it ever be.

December 12, 2012

A lighter shade of brown: Dan MacArthur, look east or south!

Filed under: Genetics,Genomics,Human Genetics,Human Genomics — Razib Khan @ 2:58 pm

South Indian Udupi cuisine

In the post below I offered up my supposition that Dan MacArthur’s ancestry is unlikely to be Northwest Indian, which precludes a Romani origin for his South Asian ancestry. Indeed this is almost certainly so, Dienekes Pontikos followed up my crude analyses with IBD-sharing calculations (IBD = ‘identity by descent,’ which is basically what you would think it is). The South Asian population which MacArthur has the closest affinity to is from Karnataka, which is one of the Dravidian speaking states of the South. This does not necessarily refute my earlier contention, as aside from Brahmins most Bengalis seem to have broad South Indian affinities, except for the fact that they often have more East Asian ancestry.


Now, I may seem a touch obsessive on this issue at this point. There are several things motivating me. First, this was laying around in plain sight, but we missed it for years! Second, I’ve known Dan for a while, so this is very amusing on a personal level. Third, Dienekes’ has been pushing me to continue my exploration in a friendly competition. None of this is very difficult, and I’ve been going at it in the early hours of the day before work, or right before I go to sleep. In short, I’m doing this in part to show that you don’t need to just talk genomics, you too can do genomics. Ironically the age of “Big Data” is also the age of distributed data.

A lighter shade of brown: the Dan MacArthur chronicles, not a Romani

Filed under: Anthroplogy,Daniel MacArthur,Human Genetics,Human Genomics — Razib Khan @ 9:25 am

Pakistani honor guard

A few days ago I suggested that Dr. Daniel MacArthur might have South Asian ancestry. Now, when confronted with surprise the best option is to stick with your prior assumption, unless that surprise is powerful enough for you to “update” your model. After a few days of further analysis I will update: I do think Dan MacArthur has South Asian ancestry. Dienekes dug further, and noticed that there are hallmarks of “Ancestral South Indian” ancestry along the first 2/3 or so of chromosome 10. Now, you do have to remember that this genomic region is only half South Asian. The other half is European.

But in any case, one question that some people brought up: perhaps MacArthur has Romani heritage? I’m skeptical of this partly because:

1) there weren’t that many Romani in Britain in the 19th century

2) The British Romani are already very highly admixed

Another friend, who is a population genomicist himself, expressed some skepticism that such a long segment wasn’t broken up by recombination over the generations. My only moderately informed answer is this: we’d only notice the long segments, because if a very small region of ‘exotic’ ancestry was embedded within the dominant ancestral component it probably would not show up on some of these tests (or, we’d assume it was noise). Dan has another segment of South Asian ancestry, but much smaller in size. It may be there are other regions which we could find if we used better reference populations.

Here’s what I tentatively want to do with Dan’s data now. First, take the 80 MB or so which has South Asian ancestry, and phase it. That way I’d have a South Asian chromosome and a European one, and we could look for matches for only the South Asian one. But being busy I didn’t have time to do this. What I did have time to do was reduce the chromosomal region under consideration, and then run an IBS distance analysis in a private data set I have. This is a crude, but not always uninformative analysis. But by looking at the relationships I can now conclude that Dan MacArthur probably does not have Romani ancestry. Why? Because the Romani are of Northwest Indian heritage, and MacArthur’s match pattern using the diploid genotype (so South Asian + European) does not match what I expect would emerge from such a combination.

The full table is below, but to me the fact that he has so many matches with Northwest Indian populations is evidence that his ancestry was not Northwest Indian. Otherwise, he would be matching more Utah white (CEU samples) more often. Rather, someone with a mix of more conventional South Asian ancestry and European ancestry often resembles some of the less South Asian populations of South Asia (e.g., Brahui) in these crude measures. In fact, one of the closest matches to Dan’s IBS profile’s is that of my own mother. She is a rather vanilla ethnic Bengali, so I think there is a strong chance that his Indian ancestry is similar. This weak genetic data isn’t really the primary reason. The British East India company operated out of Bengal for much of its history, and there are simply a lot of Bengalis.

There’s a lot more that can be done here. Since I don’t have time, here’s the pedigree file if anyone wants to play with them (Dan is DGM001).

Population Genetic distance from Dan Standardized distance
Brahui 0.253 81.268
Burusho 0.257 82.736
Razib’s Mother 0.258 82.783
CEU 0.258 82.993
Burusho 0.258 83.024
CEU 0.26 83.547
Sakilli 0.26 83.555
Brahui 0.261 83.831
Brahui 0.261 83.857
GIH 0.261 83.955
CEU 0.261 83.972
CEU 0.261 83.985
CEU 0.262 84.043
North Kannadi 0.262 84.169
CEU 0.262 84.207
CEU 0.262 84.318
CEU 0.262 84.33
CEU 0.263 84.391
Paniya 0.263 84.408
CEU 0.263 84.437
CEU 0.263 84.445
CEU 0.263 84.488
CEU 0.263 84.606
CEU 0.263 84.609
CEU 0.264 84.691
Brahui 0.264 84.709
CEU 0.264 84.752
CEU 0.264 84.764
Brahui 0.264 84.822
GIH 0.264 84.826
Burusho 0.264 84.841
CEU 0.264 84.898
CEU 0.264 84.975
North Kannadi 0.264 84.992
CEU 0.265 85.087
Paniya 0.265 85.212
CEU 0.265 85.226
CEU 0.265 85.25
CEU 0.265 85.25
CEU 0.265 85.278
CEU 0.265 85.299
North Kannadi 0.265 85.3
Burusho 0.265 85.309
Burusho 0.266 85.328
CEU 0.266 85.363
CEU 0.266 85.409
North Kannadi 0.266 85.412
CEU 0.266 85.436
Burusho 0.266 85.446
Bene Israel 0.266 85.508
CEU 0.266 85.521
GIH 0.266 85.618
GIH 0.267 85.661
CEU 0.267 85.696
CEU 0.267 85.722
CEU 0.267 85.732
Brahui 0.267 85.777
GIH 0.267 85.793
CEU 0.267 85.799
CEU 0.267 85.816
Cochin Jews 0.267 85.85
CEU 0.267 85.943
Brahui 0.268 85.996
CEU 0.268 86.005
Cochin Jews 0.268 86.011
CEU 0.268 86.08
CEU 0.268 86.115
CEU 0.268 86.18
GIH 0.268 86.229
Cochin Jews 0.268 86.234
CEU 0.268 86.244
Burusho 0.268 86.265
CEU 0.268 86.277
CEU 0.268 86.278
CEU 0.269 86.288
CEU 0.269 86.291
CEU 0.269 86.318
CEU 0.269 86.325
CEU 0.269 86.326
GIH 0.269 86.327
CEU 0.269 86.329
CEU 0.269 86.354
CEU 0.269 86.387
CEU 0.269 86.463
CEU 0.269 86.515
CEU 0.269 86.517
CEU 0.269 86.55
CEU 0.27 86.609
Paniya 0.27 86.682
CEU 0.27 86.687
CEU 0.27 86.696
CEU 0.27 86.717
CEU 0.27 86.733
Sakilli 0.27 86.74
CEU 0.27 86.866
Malayan 0.27 86.879
North Kannadi 0.27 86.883
CEU 0.271 86.937
Brahui 0.271 86.952
Burusho 0.271 86.956
CEU 0.271 86.957
CEU 0.271 86.977
North Kannadi 0.271 86.995
GIH 0.271 87.018
CEU 0.271 87.042
CEU 0.271 87.066
CEU 0.271 87.07
Brahui 0.271 87.09
Bene Israel 0.271 87.094
Sakilli 0.271 87.141
CEU 0.271 87.2
CEU 0.271 87.24
North Kannadi 0.272 87.253
CEU 0.272 87.297
Burusho 0.272 87.307
CEU 0.272 87.327
GIH 0.272 87.353
CEU 0.272 87.355
Cochin Jews 0.272 87.381
CEU 0.272 87.384
CEU 0.272 87.5
CEU 0.272 87.535
CEU 0.273 87.594
Malayan 0.273 87.676
CEU 0.273 87.702
CEU 0.273 87.741
Burusho 0.273 87.806
CEU 0.273 87.846
Cambodians 0.274 87.932
North Kannadi 0.274 87.951
CEU 0.274 87.951
Burusho 0.274 88.03
CEU 0.274 88.047
CEU 0.274 88.081
CEU 0.274 88.089
CEU 0.274 88.101
CEU 0.274 88.179
CEU 0.274 88.19
North Kannadi 0.275 88.243
CEU 0.275 88.32
GIH 0.275 88.325
CEU 0.275 88.349
Brahui 0.275 88.393
CEU 0.275 88.402
CEU 0.275 88.457
Bene Israel 0.276 88.552
CEU 0.276 88.577
CEU 0.276 88.603
CEU 0.276 88.647
CEU 0.276 88.7
CEU 0.276 88.729
CEU 0.276 88.814
CEU 0.276 88.85
Brahui 0.276 88.855
CEU 0.277 88.923
GIH 0.277 88.99
Paniya 0.277 89.082
CEU 0.277 89.118
CEU 0.277 89.15
CEU 0.277 89.151
CEU 0.277 89.17
CEU 0.278 89.184
Cambodians 0.278 89.208
Cambodians 0.278 89.233
Cambodians 0.278 89.383
CEU 0.278 89.45
CEU 0.278 89.493
Cambodians 0.279 89.522
CEU 0.279 89.595
CEU 0.279 89.679
CEU 0.279 89.753
CEU 0.279 89.762
CEU 0.279 89.807
Cambodians 0.28 89.942
GIH 0.28 90.085
CEU 0.281 90.178
Brahui 0.281 90.364
Cambodians 0.282 90.543
Cambodians 0.282 90.559
Cambodians 0.282 90.77
Cambodians 0.283 90.898
CEU 0.283 90.956
CEU 0.284 91.316
CHD 0.289 92.952
Sakilli 0.29 93.103
Bene Israel 0.29 93.122
CHD 0.291 93.619
CHD 0.291 93.663
CHD 0.293 94.125
CHD 0.293 94.248
CHD 0.294 94.451
CHD 0.294 94.629
CHD 0.296 94.965
CHD 0.296 95.279
Yorubas 0.297 95.298
CHD 0.297 95.368
CHD 0.297 95.438
CHD 0.297 95.441
Yorubas 0.297 95.567
CHD 0.298 95.678
CHD 0.298 95.828
CHD 0.299 96.032
CHD 0.299 96.127
CHD 0.3 96.349
CHD 0.3 96.403
CHD 0.3 96.443
CHD 0.3 96.508
CHD 0.3 96.523
CHD 0.3 96.533
CHD 0.301 96.575
CHD 0.301 96.598
CHD 0.301 96.624
CHD 0.301 96.625
CHD 0.301 96.738
CHD 0.301 96.758
CHD 0.301 96.869
Yorubas 0.302 97.106
CHD 0.303 97.37
CHD 0.303 97.41
Yorubas 0.304 97.681
CHD 0.304 97.713
CHD 0.304 97.747
Yorubas 0.304 97.829
CHD 0.304 97.838
CHD 0.305 98.106
CHD 0.306 98.309
Yorubas 0.307 98.499
CHD 0.307 98.546
CHD 0.307 98.547
CHD 0.307 98.606
CHD 0.307 98.764
CHD 0.307 98.78
CHD 0.307 98.803
Yorubas 0.308 98.947
Yorubas 0.308 99.03
Yorubas 0.309 99.411
Yorubas 0.309 99.417
CHD 0.309 99.452
CHD 0.31 99.624
Yorubas 0.311 100
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