The Bronze age demographic transformation of Britain

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.

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


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∂i 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∂i, 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∂i 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.

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

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 resolve 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.

The logic of human destiny was inevitable 1 million years ago

Robert Wright’s best book, Nonzero: The Logic of Human Destiny, was published nearly 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.

Mouse fidelity comes down to the genes

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

Women hate going to India


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

Genetic variation in human populations and individuals


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.

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….

The future shall, and should, be sequenced

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.