Common pitfalls in human genomics and bioinformatics: ADMIXTURE, PCA, and the ‘Yamnaya’ ancestral component

invasion-from-the-steppe-yamnaya

Good timing for the publication of two interesting papers, that a lot of people should read very carefully:

ADMIXTURE

Open access A tutorial on how not to over-interpret STRUCTURE and ADMIXTURE bar plots, by Daniel J. Lawson, Lucy van Dorp & Daniel Falush, Nature Communications (2018).

Interesting excerpts (emphasis mine):

Experienced researchers, particularly those interested in population structure and historical inference, typically present STRUCTURE results alongside other methods that make different modelling assumptions. These include TreeMix, ADMIXTUREGRAPH, fineSTRUCTURE, GLOBETROTTER, f3 and D statistics, amongst many others. These models can be used both to probe whether assumptions of the model are likely to hold and to validate specific features of the results. Each also comes with its own pitfalls and difficulties of interpretation. It is not obvious that any single approach represents a direct replacement as a data summary tool. Here we build more directly on the results of STRUCTURE/ADMIXTURE by developing a new approach, badMIXTURE, to examine which features of the data are poorly fit by the model. Rather than intending to replace more specific or sophisticated analyses, we hope to encourage their use by making the limitations of the initial analysis clearer.

The default interpretation protocol

Most researchers are cautious but literal in their interpretation of STRUCTURE and ADMIXTURE results, as caricatured in Fig. 1, as it is difficult to interpret the results at all without making several of these assumptions. Here we use simulated and real data to illustrate how following this protocol can lead to inference of false histories, and how badMIXTURE can be used to examine model fit and avoid common pitfalls.

admixture-protocol
A protocol for interpreting admixture estimates, based on the assumption that the model underlying the inference is correct. If these assumptions are not validated, there is substantial danger of over-interpretation. The “Core protocol” describes the assumptions that are made by the admixture model itself (Protocol 1, 3, 4), and inference for estimating K (Protocol 2). The “Algorithm input” protocol describes choices that can further bias results, while the “Interpretation” protocol describes assumptions that can be made in interpreting the output that are not directly supported by model inference

Discussion

STRUCTURE and ADMIXTURE are popular because they give the user a broad-brush view of variation in genetic data, while allowing the possibility of zooming down on details about specific individuals or labelled groups. Unfortunately it is rarely the case that sampled data follows a simple history comprising a differentiation phase followed by a mixture phase, as assumed in an ADMIXTURE model and highlighted by case study 1. Naïve inferences based on this model (the Protocol of Fig. 1) can be misleading if sampling strategy or the inferred value of the number of populations K is inappropriate, or if recent bottlenecks or unobserved ancient structure appear in the data. It is therefore useful when interpreting the results obtained from real data to think of STRUCTURE and ADMIXTURE as algorithms that parsimoniously explain variation between individuals rather than as parametric models of divergence and admixture.

For example, if admixture events or genetic drift affect all members of the sample equally, then there is no variation between individuals for the model to explain. Non-African humans have a few percent Neanderthal ancestry, but this is invisible to STRUCTURE or ADMIXTURE since it does not result in differences in ancestry profiles between individuals. The same reasoning helps to explain why for most data sets—even in species such as humans where mixing is commonplace—each of the K populations is inferred by STRUCTURE/ADMIXTURE to have non-admixed representatives in the sample. If every individual in a group is in fact admixed, then (with some exceptions) the model simply shifts the allele frequencies of the inferred ancestral population to reflect the fraction of admixture that is shared by all individuals.

Several methods have been developed to estimate K, but for real data, the assumption that there is a true value is always incorrect; the question rather being whether the model is a good enough approximation to be practically useful. First, there may be close relatives in the sample which violates model assumptions. Second, there might be “isolation by distance”, meaning that there are no discrete populations at all. Third, population structure may be hierarchical, with subtle subdivisions nested within diverged groups. This kind of structure can be hard for the algorithms to detect and can lead to underestimation of K. Fourth, population structure may be fluid between historical epochs, with multiple events and structures leaving signals in the data. Many users examine the results of multiple K simultaneously but this makes interpretation more complex, especially because it makes it easier for users to find support for preconceptions about the data somewhere in the results.

In practice, the best that can be expected is that the algorithms choose the smallest number of ancestral populations that can explain the most salient variation in the data. Unless the demographic history of the sample is particularly simple, the value of K inferred according to any statistically sensible criterion is likely to be smaller than the number of distinct drift events that have practically impacted the sample. The algorithm uses variation in admixture proportions between individuals to approximately mimic the effect of more than K distinct drift events without estimating ancestral populations corresponding to each one. In other words, an admixture model is almost always “wrong” (Assumption 2 of the Core protocol, Fig. 1) and should not be interpreted without examining whether this lack of fit matters for a given question.

admixture-pitfalls
Three scenarios that give indistinguishable ADMIXTURE results. a Simplified schematic of each simulation scenario. b Inferred ADMIXTURE plots at K= 11. c CHROMOPAINTER inferred painting palettes.

Because STRUCTURE/ADMIXTURE accounts for the most salient variation, results are greatly affected by sample size in common with other methods. Specifically, groups that contain fewer samples or have undergone little population-specific drift of their own are likely to be fit as mixes of multiple drifted groups, rather than assigned to their own ancestral population. Indeed, if an ancient sample is put into a data set of modern individuals, the ancient sample is typically represented as an admixture of the modern populations (e.g., ref. 28,29), which can happen even if the individual sample is older than the split date of the modern populations and thus cannot be admixed.

This paper was already available as a preprint in bioRxiv (first published in 2016) and it is incredible that it needed to wait all this time to be published. I found it weird how reviewers focused on the “tone” of the paper. I think it is great to see files from the peer review process published, but we need to know who these reviewers were, to understand their whiny remarks… A lot of geneticists out there need to develop a thick skin, or else we are going to see more and more delays based on a perceived incorrect tone towards the field, which seems a rather subjective reason to force researchers to correct a paper.

PCA of SNP data

Open access Effective principal components analysis of SNP data, by Gauch, Qian, Piepho, Zhou, & Chen, bioRxiv (2018).

Interesting excerpts:

A potential hindrance to our advice to upgrade from PCA graphs to PCA biplots is that the SNPs are often so numerous that they would obscure the Items if both were graphed together. One way to reduce clutter, which is used in several figures in this article, is to present a biplot in two side-by-side panels, one for Items and one for SNPs. Another stratagem is to focus on a manageable subset of SNPs of particular interest and show only them in a biplot in order to avoid obscuring the Items. A later section on causal exploration by current methods mentions several procedures for identifying particularly relevant SNPs.

One of several data transformations is ordinarily applied to SNP data prior to PCA computations, such as centering by SNPs. These transformations make a huge difference in the appearance of PCA graphs or biplots. A SNPs-by-Items data matrix constitutes a two-way factorial design, so analysis of variance (ANOVA) recognizes three sources of variation: SNP main effects, Item main effects, and SNP-by-Item (S×I) interaction effects. Double-Centered PCA (DC-PCA) removes both main effects in order to focus on the remaining S×I interaction effects. The resulting PCs are called interaction principal components (IPCs), and are denoted by IPC1, IPC2, and so on. By way of preview, a later section on PCA variants argues that DC-PCA is best for SNP data. Surprisingly, our literature survey did not encounter even a single analysis identified as DC-PCA.

The axes in PCA graphs or biplots are often scaled to obtain a convenient shape, but actually the axes should have the same scale for many reasons emphasized recently by Malik and Piepho [3]. However, our literature survey found a correct ratio of 1 in only 10% of the articles, a slightly faulty ratio of the larger scale over the shorter scale within 1.1 in 12%, and a substantially faulty ratio above 2 in 16% with the worst cases being ratios of 31 and 44. Especially when the scale along one PCA axis is stretched by a factor of 2 or more relative to the other axis, the relationships among various points or clusters of points are distorted and easily misinterpreted. Also, 7% of the articles failed to show the scale on one or both PCA axes, which leaves readers with an impressionistic graph that cannot be reproduced without effort. The contemporary literature on PCA of SNP data mostly violates the prohibition against stretching axes.

pca-how-to
DC-PCA biplot for oat data. The gradient in the CA-arranged matrix in Fig 13 is shown here for both lines and SNPs by the color scheme red, pink, black, light green, dark green.

The percentage of variation captured by each PC is often included in the axis labels of PCA graphs or biplots. In general this information is worth including, but there are two qualifications. First, these percentages need to be interpreted relative to the size of the data matrix because large datasets can capture a small percentage and yet still be effective. For example, for a large dataset with over 107,000 SNPs for over 6,000 persons, the first two components capture only 0.3693% and 0.117% of the variation, and yet the PCA graph shows clear structure (Fig 1A in [4]). Contrariwise, a PCA graph could capture a large percentage of the total variation, even 50% or more, but that would not guarantee that it will show evident structure in the data. Second, the interpretation of these percentages depends on exactly how the PCA analysis was conducted, as explained in a later section on PCA variants. Readers cannot meaningfully interpret the percentages of variation captured by PCA axes when authors fail to communicate which variant of PCA was used.

Conclusion

Five simple recommendations for effective PCA analysis of SNP data emerge from this investigation.

  1. Use the SNP coding 1 for the rare or minor allele and 0 for the common or major allele.
  2. Use DC-PCA; for any other PCA variant, examine its augmented ANOVA table.
  3. Report which SNP coding and PCA variant were selected, as required by contemporary standards in science for transparency and reproducibility, so that readers can interpret PCA results properly and reproduce PCA analyses reliably.
  4. Produce PCA biplots of both Items and SNPs, rather than merely PCA graphs of only Items, in order to display the joint structure of Items and SNPs and thereby to facilitate causal explanations. Be aware of the arch distortion when interpreting PCA graphs or biplots.
  5. Produce PCA biplots and graphs that have the same scale on every axis.

I read the referenced paper Biplots: Do Not Stretch Them!, by Malik and Piepho (2018), and even though it is not directly applicable to the most commonly available PCA graphs out there, it is a good reminder of the distorting effects of stretching. So for example quite recently in Krause-Kyora et al. (2018), where you can see Corded Ware and BBC samples from Central Europe clustering with samples from Yamna:

NOTE. This is related to a vertical distorsion (i.e. horizontal stretching), but possibly also to the addition of some distant outlier sample/s.

pca-cwc-yamna-bbc
Principal Component Analysis (PCA) of the human Karsdorf and Sorsum samples together with previously published ancient populations projected on 27 modern day West Eurasian populations (not shown) based on a set of 1.23 million SNPs (Mathieson et al., 2015). https://doi.org/10.7554/eLife.36666.006

The so-called ‘Yamnaya’ ancestry

Every time I read papers like these, I remember commenters who kept swearing that genetics was the ultimate science that would solve anthropological problems, where unscientific archaeology and linguistics could not. Well, it seems that, like radiocarbon analysis, these promising developing methods need still a lot of refinement to achieve something meaningful, and that they mean nothing without traditional linguistics and archaeology… But we already knew that.

Also, if this is happening in most peer-reviewed publications, made by professional geneticists, in journals of high impact factor, you can only wonder how many more errors and misinterpretations can be found in the obscure market of so many amateur geneticists out there. Because amateur geneticist is a commonly used misnomer for people who are not geneticists (since they don’t have the most basic education in genetics), and some of them are not even ‘amateurs’ (because they are selling the outputs of bioinformatic tools)… It’s like calling healers ‘amateur doctors’.

NOTE. While everyone involved in population genetics is interested in knowing the truth, and we all have our confirmation (and other kinds of) biases, for those who get paid to tell people what they want to hear, and who have sold lots of wrong interpretations already, the incentives of ‘being right’ – and thus getting involved in crooked and paranoid behaviour regarding different interpretations – are as strong as the money they can win or loose by promoting themselves and selling more ‘product’.

As a reminder of how badly these wrong interpretations of genetic results – and the influence of the so-called ‘amateurs’ – can reflect on research groups, yet another turn of the screw by the Copenhagen group, in the oral presentations at Languages and migrations in pre-historic Europe (7-12 Aug 2018), organized by the Copenhagen University. The common theme seems to be that Bell Beaker and thus R1b-L23 subclades do represent a direct expansion from Yamna now, as opposed to being derived from Corded Ware migrants, as they supported before.

NOTE. Yes, the “Yamna → Corded Ware → Únětice / Bell Beaker” migration model is still commonplace in the Copenhagen workgroup. Yes, in 2018. Guus Kroonen had already admitted they were wrong, and it was already changed in the graphic representation accompanying a recent interview to Willerslev. However, since there is still no official retraction by anyone, it seems that each member has to reject the previous model in their own way, and at their own pace. I don’t think we can expect anyone at this point to accept responsibility for their wrong statements.

So their lead archaeologist, Kristian Kristiansen, in The Indo-Europeanization of Europé (sic):

kristiansen-migrations
Kristiansen’s (2018) map of Indo-European migrations

I love the newly invented arrows of migration from Yamna to the north to distinguish among dialects attributed by them to CWC groups, and the intensive use of materials from Heyd’s publications in the presentation, which means they understand he was right – except for the fact that they are used to support a completely different theory, radically opposed to those defended in Heyd’s model

Now added to the Copenhagen’s unending proposals of language expansions, some pearls from the oral presentation:

  • Corded Ware north of the Carpathians of R1a lineages developed Germanic;
  • R1b borugh [?] Italo-Celtic;
  • the increase in steppe ancestry on north European Bell Beakers mean that they “were a continuation of the Yamnaya/Corded Ware expansion”;
  • Corded Ware groups [] stopped their expansion and took over the Bell Beaker package before migrating to England” [yep, it literally says that];
  • Italo-Celtic expanded to the UK and Iberia with Bell Beakers [I guess that included Lusitanian in Iberia, but not Messapian in Italy; or the opposite; or nothing like that, who knows];
  • 2nd millennium BC Bronze Age Atlantic trade systems expanded Proto-Celtic [yep, trade systems expanded the language]
  • 1st millennium BC expanded Gaulish with La Tène, including a “Gaulish version of Celtic to Ireland/UK” [hmmm, dat British Gaulish indeed].

You know, because, why the hell not? A logical, stable, consequential, no-nonsense approach to Indo-European migrations, as always.

Also, compare still more invented arrows of migrations, from Mikkel Nørtoft’s Introducing the Homeland Timeline Map, going against Kristiansen’s multiple arrows, and even against the own recent fantasy map series in showing Bell Beakers stem from Yamna instead of CWC (or not, you never truly know what arrows actually mean):

corded-ware-migrations
Nørtoft’s (2018) maps of Indo-European migrations.

I really, really loved that perennial arrow of migration from Volosovo, ca. 4000-800 BC (3000+ years, no less!), representing Uralic?, like that, without specifics – which is like saying, “somebody from the eastern forest zone, somehow, at some time, expanded something that was not Indo-European to Finland, and we couldn’t care less, except for the fact that they were certainly not R1a“.

This and Kristiansen’s arrows are the most comical invented migration routes of 2018; and that is saying something, given the dozens of similar maps that people publish in forums and blogs each week.

NOTE. You can read a more reasonable account of how haplogroup R1b-L51 and how R1-Z645 subclades expanded, and which dialects most likely expanded with them.

We don’t know where these scholars of the Danish workgroup stand at this moment, or if they ever had (or intended to have) a common position – beyond their persistent ideas of Yamnaya™ ancestral component = Indo-European and R1a must be Indo-European – , because each new publication changes some essential aspects without expressly stating so, and makes thus everything still messier.

It’s hard to accept that this is a series of presentations made by professional linguists, archaeologists, and geneticists, as stated by the official website, and still harder to imagine that they collaborate within the same professional workgroup, which includes experienced geneticists and academics.

I propose the following video to close future presentations introducing innovative ideas like those above, to help the audience find the appropriate mood:

Related

“Steppe people seem not to have penetrated South Asia”

indo-iranian-sintashta-uralic-migrations

Open access structured abstract for The first horse herders and the impact of early Bronze Age steppe expansions into Asia from Damgaard et al. Science (2018) 360(6396):eaar7711.

Abstract (emphasis mine):

The Eurasian steppes reach from the Ukraine in Europe to Mongolia and China. Over the past 5000 years, these flat grasslands were thought to be the route for the ebb and flow of migrant humans, their horses, and their languages. de Barros Damgaard et al. probed whole-genome sequences from the remains of 74 individuals found across this region. Although there is evidence for migration into Europe from the steppes, the details of human movements are complex and involve independent acquisitions of horse cultures. Furthermore, it appears that the Indo-European Hittite language derived from Anatolia, not the steppes. The steppe people seem not to have penetrated South Asia. Genetic evidence indicates an independent history involving western Eurasian admixture into ancient South Asian peoples.

INTRODUCTION
According to the commonly accepted “steppe hypothesis,” the initial spread of Indo-European (IE) languages into both Europe and Asia took place with migrations of Early Bronze Age Yamnaya pastoralists from the Pontic-Caspian steppe. This is believed to have been enabled by horse domestication, which revolutionized transport and warfare. Although in Europe there is much support for the steppe hypothesis, the impact of Early Bronze Age Western steppe pastoralists in Asia, including Anatolia and South Asia, remains less well understood, with limited archaeological evidence for their presence. Furthermore, the earliest secure evidence of horse husbandry comes from the Botai culture of Central Asia, whereas direct evidence for Yamnaya equestrianism remains elusive.

RATIONALE
We investigated the genetic impact of Early Bronze Age migrations into Asia and interpret our findings in relation to the steppe hypothesis and early spread of IE languages. We generated whole-genome shotgun sequence data (~1 to 25 X average coverage) for 74 ancient individuals from Inner Asia and Anatolia, as well as 41 high-coverage present-day genomes from 17 Central Asian ethnicities.

damgaard-south-asia
Model-based admixture proportions for selected ancient and present-day individuals, assuming K = 6, shown with their corresponding geographical locations. Ancient groups are represented by larger admixture plots, with those sequenced in the present work surrounded by black borders and others used for providing context with blue borders. Present-day South Asian groups are represented by smaller admixture plots with dark red borders.

RESULTS
We show that the population at Botai associated with the earliest evidence for horse husbandry derived from an ancient hunter-gatherer ancestry previously seen in the Upper Paleolithic Mal’ta (MA1) and was deeply diverged from the Western steppe pastoralists. They form part of a previously undescribed west-to-east cline of Holocene prehistoric steppe genetic ancestry in which Botai, Central Asians, and Baikal groups can be modeled with different amounts of Eastern hunter-gatherer (EHG) and Ancient East Asian genetic ancestry represented by Baikal_EN.

In Anatolia, Bronze Age samples, including from Hittite speaking settlements associated with the first written evidence of IE languages, show genetic continuity with preceding Anatolian Copper Age (CA) samples and have substantial Caucasian hunter-gatherer (CHG)–related ancestry but no evidence of direct steppe admixture.

In South Asia, we identified at least two distinct waves of admixture from the west, the first occurring from a source related to the Copper Age Namazga farming culture from the southern edge of the steppe, who exhibit both the Iranian and the EHG components found in many contemporary Pakistani and Indian groups from across the subcontinent. The second came from Late Bronze Age steppe sources, with a genetic impact that is more localized in the north and west.

CONCLUSION
Our findings reveal that the early spread of Yamnaya Bronze Age pastoralists had limited genetic impact in Anatolia as well as Central and South Asia. As such, the Asian story of Early Bronze Age expansions differs from that of Europe. Intriguingly, we find that direct descendants of Upper Paleolithic hunter-gatherers of Central Asia, now extinct as a separate lineage, survived well into the Bronze Age. These groups likely engaged in early horse domestication as a prey-route transition from hunting to herding, as otherwise seen for reindeer. Our findings further suggest that West Eurasian ancestry entered South Asia before and after, rather than during, the initial expansion of western steppe pastoralists, with the later event consistent with a Late Bronze Age entry of IE languages into South Asia. Finally, the lack of steppe ancestry in samples from Anatolia indicates that the spread of the earliest branch of IE languages into that region was not associated with a major population migration from the steppe.

I think the wording of the abstract is weird, but consequent with their samples and results, so probably just clickbait / citebait for Indian journalists and social networks, or maybe a new attempt to ‘show respect for the sensibilities of Indians’ related to the artificially magnified “AIT vs. OIT” controversy, that is only present in India.

However, everything is possible, since it is brought to you by the same Danish group who proposed the Yamnaya ancestral component™, the CHG = Indo-European (and simultaneously EHG in Maykop = Anatolian??), and now also the CWC/R1a = Indo-European & Volosovo = Uralic

Here is the reaction of Narasimhan: Narasimhan has deleted the Tweet, it basically questioned the sentence that steppe people did not penetrate South Asia.

Related

Cystic fibrosis probably spread with expanding Bell Beakers

indo-european-uralic-bell-beaker-corded-ware-migrations

New paper (behind paywall) Estimating the age of p.(Phe508del) with family studies of geographically distinct European populations and the early spread of cystic fibrosis, by Farrell et al., European Journal of Human Genetics (2018).

Interesting excerpts (emphasis mine):

Our results revealed tMRCA average values ranging from 4725 to 1175 years ago and support the estimates of Serre et al. (3000–6000 years ago) [11], rather than Morral et al. (52,000 years ago) [6], but the latter figure was challenged by Kaplan et al. [26] because of disagreement with assumptions used in their calculations. In addition, the tMRCA values from western European regions reported herein refine the results of Fichou et al. [7] from a study of Breton CF patients in which the Estiage analysis suggested that the most common recent ancestor lived 115 generations ago. That tMRCA value, however, may have underestimated the age of p.(Phe508del) in Brittany due to consideration of all the haplotypes, even those that were reconstructed with ambiguities, as well as a potential bias associated with consanguinity due to including both haplotypes in homozygous families. In the more stringent Estiage analyses reported herein, those potential biases were avoided for all populations, leading to estimates of the oldest tMCRA values corresponding to the Early Bronze Age in western Europe, which is generally agreed to begin around 3000 BCE. This finding extends our results from a direct investigation of aDNA in teeth from Iron Age burials near Vienna around 350 BCE and allow us to conclude that p.(Phe508del) was present in that region long before then. More specifically, in the Austrian families studied, the Estiage data revealed a mean tMCRA value of 3575 years ago, which converts to 1558 BCE (Middle Bronze Age) [22].

Perhaps most remarkably, the estimated ages of p.(Phe508del) in the three western European regions (France, Ireland, and Denmark) were similar with closely overlapping 95% CI values. This observation is also in line with previously documented spatial autocorrelograms expressing genetic and geographical distance for these populations [24]. Such data provide more insight about the ancient origin of CF in our judgment—both when and where—and lead us to propose that CFTR p.(Phe508del) is derived from ancestors who lived in western Europe during the Bronze Age, as early as 2700 BCE, and that its relatively rapid dissemination occurred because of human migrations around the northwestern Atlantic trading routes [21] and then towards central and eastern Europe [22]. Diffusion from northwestern to central Europe in approximately 1000 years is consistent with the prominent Bronze Age migrations evident in the archeological record [21, 22] and from genomic studies of aDNA [27]. On the other hand, we are assuming a discrete origin of the principal CF-causing variant, but it is possible that p.(Phe508del) arose more than once or earlier, and then reached western Europe subsequently through Neolithic migrations.

cystic-fibrosis

[About Bell Beakers] (…) More specifically, their distinctive Bell Beaker pottery appeared and spread across western and central Europe beginning around 3000–2750 BCE and then disappeared between 2200 and 1800 BCE [22, 29]. Their migrations are linked to the advent of western and central European metallurgy, as they manufactured and traded metal goods, especially weapons, while traveling over long distances [30]. Most relevant to our study is the evidence that they migrated in a direction and over a time period that fits well with the pattern of tMRCA data we found for the p.(Phe508del) variant. Olalde et al. [29] have shown that both migration and cultural transmission played a major role in diffusion of the “Beaker Complex” and led to a “profound demographic transformation” of Britain after 2400 BCE. Moreover, the cultural elements that unite the widely distributed Beaker folk are so obvious that some have considered them a distinct ethnicity of Bronze Age people [33].

From our results, we propose the novel concept that large scale, long term west-to-east migrations of the Bell Beaker Europeans [22, 28–30] during the Bronze Age, could explain the dissemination of p.(Phe508del) in Europe and its documented northwest-to-southeast gradient [4].In fact, our tMRCA data show a temporal gradient also.

As you can see from the references, they consulted with Barry Cunliffe (or people accepting his theory), who is obsessed with Bell Beakers expanding Celtic languages from the British Isles. He is like the British equivalent of Danish scholar Kristian Kristiansen, and his obsession with Corded Ware = Indo-European (and Germanic = CWC Denmark), immutable no matter what genetic results might show.

The funny thing is, the interpretation of the paper is probably right. From what we can see in the data, it is quite possible that the disease spread with expanding Bell Beakers…only it spread from the East group in Hungary, i.e. from east to west. The regional difference in TMRCA and apparent west—east cline would point to the different expansions of affected lineages in the corresponding regions, and not to an origin in the British Isles.

Related

The importance of fine-scale studies for integrating palaeogenomics and archaeology

eurasian-genomes-published

Short review (behind paywall) The importance of fine-scale studies for integrating paleogenomics and archaeology, by Krishna R. Veeramah, Current Opinion in Genetics & Development (2018) 53:83-89.

Abstract (emphasis mine):

There has been an undercurrent of intellectual tension between geneticists studying human population history and archaeologists for almost 40 years. The rapid development of paleogenomics, with geneticists working on the very material discovered by archaeologists, appears to have recently heightened this tension. The relationship between these two fields thus far has largely been of a multidisciplinary nature, with archaeologists providing the raw materials for sequencing, as well as a scaffold of hypotheses based on interpretation of archaeological cultures from which the geneticists can ground their inferences from the genomic data. Much of this work has taken place in the context of western Eurasia, which is acting as testing ground for the interaction between the disciplines. Perhaps the major finding has not been any particular historical episode, but rather the apparent pervasiveness of migration events, some apparently of substantial scale, over the past ∼5000 years, challenging the prevailing view of archaeology that largely dismissed migration as a driving force of cultural change in the 1960s. However, while the genetic evidence for ‘migration’ is generally statistically sound, the description of these events as structured behaviours is lacking, which, coupled with often over simplistic archaeological definitions, prevents the use of this information by archaeologists for studying the social processes they are interested in. In order to integrate paleogenomics and archaeology in a truly interdisciplinary manner, it will be necessary to focus less on grand narratives over space and time, and instead integrate genomic data with other form of archaeological information at the level of individual communities to understand the internal social dynamics, which can then be connected amongst communities to model migration at a regional level. A smattering of recent studies have begun to follow this approach, resulting in inferences that are not only helping ask questions that are currently relevant to archaeologists, but also potentially opening up new avenues of research.

Interesting excerpts (emphasis mine, reference numbers removed for clarity):

There are two major, somewhat intertwined, problems that currently exist.

First, archaeologists are not critiquing whether the migrations identified by paleogenomics using sophisticated population genetic machinery are actually occurring. Instead, the technical criticism arrives in terms of how these migrations are being ascribed to specific cultures. In many paleogenomic papers, there is a tendency (and often an analytical and technical need) to associate samples with particular archaeological cultures, for which all samples are then treated as possessing some kind homogenous and pervasive social identity that is bound in space and time. The major critiques of this thus far have been directed to those studies examining Corded-Ware and Bell-Beaker-related individuals and their potential relationship to the Yamnaya [Vander Linden (2016), Heyd (2017), Furholt (2017)], but are applicable to many other ‘migration’ scenarios described in the recent literature. This is compounded by the use of sometimes small numbers of samples to represent certain cultures from a particular geographic area as representatives of the entire culture at a supra-regional level. Yet often these archaeological cultures such as Corded-Ware and Bell-Beaker themselves show considerable variability in space and time, and even within cemeteries, which is not factored into the genetic analysis.

From a population geneticists point of view, this kind of simplification is somewhat understandable and will often likely have very little impact on the final analysis, given that the primary goal is usually to use ancient samples to better understand modern genetic variation. Though there may be a specific historical interest in some of these past events, I would argue that the aim for most population geneticists at a higher level is to try and fit modern patterns of genetic variation using the simplest models possible that take into account past demographic events (for example fitting f-statistics using the ADMIXTUREGRAPH approach), as this is how we are trained. Although sharing an archaeological culture may not mean that a set of individuals are part of the same homogeneous social group in reality, this approach may be a good enough heuristic to find broad genetic connections compared to another group represented by a different culture, which can then ultimately help understand and model modern human population structure. However, for an archaeologists interested in the ancient individuals themselves and their social identity, this lumping is unsatisfactory, where sophisticated narratives of the individual migrants and their ancient communities are the intended goal.

eurasian-genomes
From the paper. Barplot showing cumulative number of ancient Eurasian genomes published on a yearly basis up to 8th July 2018. Includes samples undergoing both whole genome shotgun and SNP capture sequencing.

The second related problem is that ‘migration’ in the sense used currently in the paleogenomics literature lacks sufficient detail to be of much use for an archaeologists attempting to disentangle the complex social dynamics within and between communities. To truly understand the role of migration as a social process and its contribution towards cultural changes, it is necessary to describe it as a structured behaviour, rather than treating it as an explanatory ‘black box’. Are the migrations occurring as a result of short range waves-of-advance movements, or as long-distance movements via leapfrogging models or stream migrations along established routes dependent on key kinship networks. Are there return migrants, and are some subset of individuals more predisposed to migration driving the signals? Although such models were implemented in past studies (even with classical markers [1]) and are part of the population genetics literature, they are lacking in the current paleogenomics literature when discussing migration. The finding that there is an increase of 12.3% of ancestry type X in population A compared to the preceding population B that is suggestive of a migration, is not particularly useful for examining these kind of models. It is also unclear to what degree standard population genetic parameters estimated from genomic data such as effective population size, Ne, and gene flow are relevant to models studied in archaeology, given they reflect (somewhat undefined) long-term population sizes and average rates of movements over time, rather than reflecting any kind of reality of census size and mobility in the ancient communities the archaeologists are actually attempting to study.

The text goes on to talk about ways of studying fine-grained social dynamics of local cultures, such as:

define levels of genetic relatedness, but also in terms of material culture, age, sex, stress and activity indicators, stable isotopes for diet reconstruction (nitrogen, d13C and d15N, carbon, 13C/12C) and strontium and oxygen isotopes for mobility (87Sr/86Sr, d18O). Where possible, sites should be examined over multiple generations. In addition it will be incredibly useful to characterize the impact of disease in these communities, which is also proving to be a highly fruitful realm for paleogenomics.

I would say that the main problem is not the obvious limitations of palaeogenomics in terms of identifying prehistoric ethnolinguistic communities and their evolution, which is why it is just another tool to complement archaeology and linguistics. The main problem is the narrow understanding that some people have of the inherent limitations of palaeogenomics – especially when it interests them – , when publicizing simplistic conclusions based on these tools and their results. And I am not referring only to amateurs.

Related

Y-chromosome mixture in the modern Corsican population shows different migration layers

mesolithic-europe

Open access Prehistoric migrations through the Mediterranean basin shaped Corsican Y-chromosome diversity, by Di Cristofaro et al. PLOS One (2018).

Interesting excerpts:

This study included 321 samples from men throughout Corsica; samples from Provence and Tuscany were added to the cohort. All samples were typed for 92 Y-SNPs, and Y-STRs were also analyzed.

Haplogroup R represented approximately half of the lineages in both Corsican and Tuscan samples (respectively 51.8% and 45.3%) whereas it reached 90% in Provence. Sub-clade R1b1a1a2a1a2b-U152 predominated in North Corsica whereas R1b1a1a2a1a1-U106 was present in South Corsica. Both SNPs display clinal distributions of frequency variation in Europe, the U152 branch being most frequent in Switzerland, Italy, France and Western Poland. Calibrated branch lengths from whole Y chromosome sequencing [44,45] and ancient DNA studies [46] both indicated that R1a and R1b diversification began relatively recently, about 5 Kya, consistent with Bronze Age and Copper Age demographic expansion. TMRCA estimations are concordant with such expansion in Corsica.

corsica-haplogroups
Spatial frequency maps for haplogroups with frequencies above 3%, their Y-STR based phylogenetic networks in Corsican populations (Blue: North, Green: West, Orange: South, Black: Center and Purple: East) and their TMRCA (in years, +/- SE).

Haplogroup G reached 21.7% in Corsica and 13.3% in Tuscany. Sub-clade G2a2a1a2-L91 accounted for 11.3% of all haplogroups in Corsica yet was not present in Provence or in Tuscany. Thirty-four out of the 37 G2a2a1a2-L91 displayed a unique Y-STR profile, illustrated by the star-like profile of STR networks (Fig 1). G2a2a1a2-L91 and G2a2a-PF3147(xL91xM286) show their highest frequency in present day Sardinia and southern Corsica compared to low levels from Caucasus to Southern Europe, encompassing the Near and Middle East [21,47–50]. Ancient DNA results from Early and Middle Neolithic samples reported the presence of haplogroup G2a-P15 [51–53], consistent with gene flow from the Mediterranean region during the Neolithic transition. Td expansion time estimated by STR for P15-affiliated chromosomes was estimated to be 15,082+/-2217 years ago [49]. Ötzi, the 5,300-year-old Alpine mummy, was derived for the L91 SNP [21]. A genetic relationship between G haplogroups from Corsica and Sardinia is further supported by DYS19 duplication, reported in North Sardinia [14], and observed in the southern part of the Corsica in 9 out of 37 G2a2a1a2-L91 chromosomes and in 4 out of 5 G2a2a-PF3147(xL91xM286) chromosomes, 3 of which displayed an identical STR profile (S4 Table).

This lineage has a reported coalescent age estimated by whole sequencing in Sardinian samples of about 9,000 years ago. This could reflect common ancestors coming from the Caucasus and moving westward during the Neolithic period [48], whereas their continental counterparts would have been replaced by rapidly expanding populations associated with the Bronze Age [46,54,55]. Estimated TMRCA for L91 lineage in Corsica is 4529 +/- 853 years. G-L497 showed high frequencies in Corsica compared to Provence and Tuscany, and this haplogroup was common in Europe, but rare in Greece, Anatolia and the Middle East. Fifteen out of the 17 Corsican G2a2b2a1a1b-L497 displayed a unique Y-STR profile (S4 Table) with an estimated TMRCA of 6867 +/- 1294 years. Haplogroup G2a2b1-M406, associated with Impressed Ware Neolithic markers, along with J2a1-DYS445 = 6 and J2a1b1-M92 [22,49], had very low levels in Corsica. Conversely, G2a2b2a-P303was highly represented and seemed to be independent of the G2a2b1-M406 marker. The 7 G2a2b2a-P303(xL497xM527) Corsican chromosomes displayed a unique Y-STR profile (S4 Table).

pca-corsica
First and second axes of the PCA based on 12 Y-chromosome haplogroup frequencies in 83 west Mediterranean populations.

Haplogroup J, mainly represented by J2a1b-M67(xM92), displayed intermediate frequencies in Corsica compared to Tuscany and Provence. J2a1b-M67(xM92) derived STR network analysis displayed a quite homogeneous profile across the island with an estimated TMRCA of 2381 +/- 449 years (Fig 1) and individuals displaying M67 were peripheral compared to Northwestern Italians (S2 Fig). The haplogroup J2a1-Page55(xM67xM530), characteristic of non-Greek Anatolia [22], was found in the north-west of Corsica. Haplogroup J2a1-DYS445 = 6 was found in the north-west with DYS391 = 10 repeats, and in the far south with DYS391 = 9 repeats, the former was associated with Anatolian Greek samples, whereas the second was found in central Anatolia [22]. The 7 J2b2a-M241 displayed a unique Y-STR profile (S4 Table), they were only detected in the Cap Corse region, this sub-haplogroup shows frequency peaks in both the southern Balkans and northern-central Italy [56] and is associated with expansion from the Near East to the Balkans during Neolithic period [57].

Haplogroup E, mainly represented by E1b1b1a1b1a-V13, displayed intermediate frequencies in Corsica compared to Tuscany and Provence. E1b1b1a1b1a-V13 was thought to have initiated a pan-Mediterranean expansion 7,000 years ago starting from the Balkans [52] and its dispersal to the northern shore of the Mediterranean basin is consistent with the Greek Anatolian expansion to the western Mediterranean [22], characteristic of the region surrounding Alaria, and consistent with the TMRCA estimated in Corsica for this haplogroup. A few E1b1a-V38 chromosomes are also observed in the same regions as V13.

Related:

Expansion of domesticated goat echoes expansion of early farmers

goat-neolithic

New paper (behind paywall) Ancient goat genomes reveal mosaic domestication in the Fertile Crescent, by Daly et al. Science (2018) 361(6397):85-88.

Interesting excerpts (emphasis mine):

Thus, our data favor a process of Near Eastern animal domestication that is dispersed in space and time, rather than radiating from a central core (3, 11). This resonates with archaeozoological evidence for disparate early management strategies from early Anatolian, Iranian, and Levantine Neolithic sites (12, 13). Interestingly, our finding of divergent goat genomes within the Neolithic echoes genetic investigation of early farmers. Northwestern Anatolian and Iranian human Neolithic genomes are also divergent (14–16), which suggests the sharing of techniques rather than large-scale migrations of populations across Southwest Asia in the period of early domestication. Several crop plants also show evidence of parallel domestication processes in the region (17).

PCA affinity (Fig. 2), supported by qpGraph and outgroup f3 analyses, suggests that modern European goats derive from a source close to the western Neolithic; Far Eastern goats derive from early eastern Neolithic domesticates; and African goats have a contribution from the Levant, but in this case with considerable admixture from the other sources (figs. S11, S16, and S17 and tables S26 and 27). The latter may be in part a result of admixture that is discernible in the same analyses extended to ancient genomes within the Fertile Crescent after the Neolithic (figs. S18 and S19 and tables S20, S27, and S31) when the spread of metallurgy and other developments likely resulted in an expansion of inter-regional trade networks and livestock movement.

goat-middle-east
Maximumlikelihood phylogeny and geographical distributions of ancient mtDNA haplogroups. (A) A phylogeny placing ancient whole mtDNA sequences in the context of known haplogroups. Symbols denoting individuals are colored by clade membership; shape indicates archaeological period (see key). Unlabeled nodes are modern bezoar and outgroup sequence (Nubian ibex) added for reference.We define haplogroup T as the sister branch to the West Caucasian tur (9). (B and C) Geographical distributions of haplogroups show early highly structured diversity in the Neolithic period (B) followed by collapse of structure in succeeding periods (C).We delineate the tiled maps at 7250 to 6950 BP, a period >bracketing both our earliest Chalcolithic sequence (24, Mianroud) and latest Neolithic (6, Aşağı Pınar). Numbered archaeological sites also include Direkli Cave (8), Abu Ghosh (9), ‘Ain Ghazal (10), and Hovk-1 Cave (11) (table S1) (9).

Our results imply a domestication process carried out by humans in dispersed, divergent, but communicating communities across the Fertile Crescent who selected animals in early millennia, including for pigmentation, the most visible of domestic traits.

Related

Shared ancestry of ancient Eurasian hepatitis B virus diversity linked to Bronze Age steppe

hepatitis-b-world

Ancient hepatitis B viruses from the Bronze Age to the Medieval period, by Mühlemann et al., Science (2018) 557:418–423.

NOTE. You can read the PDF at Dalia Pokutta’s Academia.edu account.

Abstract (emphasis):

Hepatitis B virus (HBV) is a major cause of human hepatitis. There is considerable uncertainty about the timescale of its evolution and its association with humans. Here we present 12 full or partial ancient HBV genomes that are between approximately 0.8 and 4.5 thousand years old. The ancient sequences group either within or in a sister relationship with extant human or other ape HBV clades. Generally, the genome properties follow those of modern HBV. The root of the HBV tree is projected to between 8.6 and 20.9 thousand years ago, and we estimate a substitution rate of 8.04 × 10−6–1.51 × 10−5 nucleotide substitutions per site per year. In several cases, the geographical locations of the ancient genotypes do not match present-day distributions. Genotypes that today are typical of Africa and Asia, and a subgenotype from India, are shown to have an early Eurasian presence. The geographical and temporal patterns that we observe in ancient and modern HBV genotypes are compatible with well-documented human migrations during the Bronze and Iron Ages1,2. We provide evidence for the creation of HBV genotype A via recombination, and for a long-term association of modern HBV genotypes with humans, including the discovery of a human genotype that is now extinct. These data expose a complexity of HBV evolution that is not evident when considering modern sequences alone.

hbv-genotypes-eurasia
Geographical distribution of analysed samples and modern genotypes. a (featured image), Distribution of modern human HBV genotypes. Genotypes relevant to this Letter are shown in colour. Coloured shapes indicate the locations of the HBV-positive samples included for further analysis. b (above this text), Locations of analysed Bronze Age samples are shown as circles and Iron Age and later samples are shown as triangles. Coloured markers indicate HBV-positive samples. Ancient genotype A samples are found in regions in which genotype D predominates today, and HBV-DA27 is of subgenotype D5 which today is found almost exclusively in India.

Interesting excerpts:

We find genotype A in south-western Russia by 4.3 ka (in samples RISE386 and RISE387) in individuals belonging to the Sintashta culture, and in a Hungarian sample (DA195) from the Scythian culture. The western Scythians are related to the Bronze Age cultures of western steppe populations2 and their shared ancestry suggests that the modern genotype A may descend from this ancient Eurasian diversity and not, as previously hypothesized, from African ancestors29,30. This is also consistent with the phylogeny (Fig. 2), as well as the fact that the three oldest ancient genotype A sequences (HBV-DA195, HBV-RISE386 and HBV-RISE387) lack the six-nucleotide insertion found in the youngest (HBV-DA119) and in all modern genotype A sequences. The ancestors of subgenotypes A1 and A3 could have been carried into Africa subsequently, via migration from western Eurasia31.

The ancient HBV genotype D sequences were all found in Central Asia. HBV-DA27, found in Kazakhstan and dated to 1.6 ka, falls basal to the modern subgenotype D5 sequences that today are found in the Paharia tribe from eastern India32. DA27 and the Paharia people in India are linked by their East Asian ancestry2,33.

hbv-genotype-tree
Dated maximum clade credibility tree of HBV. A log-normal relaxed clock and coalescent exponential population prior were used. Grey horizontal bars indicate the 95% HPD interval of the age of the node. Larger numbers on the nodes indicate the median age and 95% HPD interval of the age (in parentheses) under a strict clock and Bayesian skyline tree prior. Clades of genotypes C (except clade C4), E, F, G and H are collapsed and shown as dots. The figure includes a possible tenth genotype, J, based on a single human isolate. Taxon names for ancient samples indicate era (BA, Bronze Age; IA, Iron Age or later), sample name, sample age in years, ISO 3166 three-letter abbreviation of country of sequence origin, and region of sequence origin. Taxon names for modern samples indicate human genotype or subgenotype or host species if non-human, GenBank accession number, sample age in years, ISO 3166 three-letter abbreviation of country of sequence origin, and region of sequence origin.

(…)Despite the age of the samples and the imperfect diagnostic test, our dataset contained a high proportion of HBV-positive individuals. The actual ancient prevalence during the Bronze Age and thereafter might have been higher, reaching or exceeding the prevalence typically found in contemporary indigenous populations5. This clearly establishes the potential of HBV as powerful proxy tool for research into human spread and interactions. The data from ancient genomes reveal aspects of complexity in HBV evolution that are not apparent when only modern sequences are considered. They show the existence of ancient HBV genotypes in locations incongruent with their present-day distribution, contradicting previously suggested geographical or temporal origins of genotypes or sub-genotypes; evidence for the creation of genotype A via recombination and the emergence of the genotype outside Africa; at least one now-extinct human genotype; ancient genotype-level localized diversity; and demonstrate that the viral substitution rate obtained from modern heterochronously sampled sequences is probably misleading. Together, these findings suggest that the difficulty in formulating a coherent theory for the origin and spread of HBV may be due to genetic evidence of an earlier evolutionary scenario being overwritten by relatively recent alterations, as has previously been suggested in the context of recombination24

See also:

Male-biased expansions and migrations also observed in Northwestern Amazonia

Open access preprint Cultural Innovations influence patterns of genetic diversity in Northwestern Amazonia, by Arias et al., bioRxiv (2018).

Abstract (emphasis mine):

Human populations often exhibit contrasting patterns of genetic diversity in the mtDNA and the non-recombining portion of the Y-chromosome (NRY), which reflect sex-specific cultural behaviors and population histories. Here, we sequenced 2.3 Mb of the NRY from 284 individuals representing more than 30 Native-American groups from Northwestern Amazonia (NWA) and compared these data to previously generated mtDNA genomes from the same groups, to investigate the impact of cultural practices on genetic diversity and gain new insights about NWA population history. Relevant cultural practices in NWA include postmarital residential rules and linguistic-exogamy, a marital practice in which men are required to marry women speaking a different language. We identified 2,969 SNPs in the NRY sequences; only 925 SNPs were previously described. The NRY and mtDNA data showed that males and females experienced different demographic histories: the female effective population size has been larger than that of males through time, and both markers show an increase in lineage diversification beginning ~5,000 years ago, with a male-specific expansion occurring ~3,500 years ago. These dates are too recent to be associated with agriculture, therefore we propose that they reflect technological innovations and the expansion of regional trade networks documented in the archaeological evidence. Furthermore, our study provides evidence of the impact of postmarital residence rules and linguistic exogamy on genetic diversity patterns. Finally, we highlight the importance of analyzing high-resolution mtDNA and NRY sequences to reconstruct demographic history, since this can differ considerably between males and females.

y-dna-mtdna-amazonia
MDS plots for mtDNA and NRY. Stress values (within parentheses) are indicated in percentages.

Looking more precisely at the different groups (even with the resampling approach), there are no significant differences between matrilocal and patrilocal groups. At best, as the study proposes, “this is just one of the factors at play in structuring the observed genetic variation”.

Interesting excerpts:

(…) we found evidence that the patterns of genetic differentiation depend on the geographical scale of the study. The magnitude of between-population differentiation in the NRY compared to the mtDNA is smaller when looking at the continental scale than in NWA (Figure 6). This is in agreement with the findings of Wilkins and Marlowe (2006), who showed that the excess of between-population differentiation for the NRY in comparison to the mtDNA decreases when comparing more geographically distant populations. Heyer et al. (2012) and Wilkins and Marlowe (2006) have proposed that at a local scale the patterns of genetic diversity reflect cultural practices over a relatively small number of generations, whereas at a larger geographic scale the genetic diversity reflects old migration and/or old common ancestry patterns(Heyer et al. 2012; Wilkins and Marlowe 2006).

y-dna-mtdna-amazon
BSPs for the mtDNA and NRY sequences from NWA. The dotted lines indicate the 95% HPD intervals. Ne was corrected for generation time according to (Fenner 2005), using 26 years for mtDNA and 31 years for NRY.

The BSP plots and the diversity statistics indicate that overall the Ne of males has been smaller than that of females. One tentative explanation for this difference is that it reflects larger differences in reproductive success among males than among females. Some support for this explanation comes from the shape of the phylogenies (Supplementary Figures 1 and 6), since differences in reproductive success and the cultural transmission of fertility lead to imbalance phylogenies (Blum et al. 2006; Heyer et al. 2015). We estimated a common index of tree imbalance (Colless index) and calculated whether the mtDNA and NRY trees were more unbalanced than 1000 simulated trees generated under a Yule process (Bortolussi et al. 2006) (i.e. a simple pure birth process that assumes that the birth rate of new lineages is the same along the tree). We found that the NRY tree is more unbalanced than predicted by the Yule model (p-value=0.001), whereas the mtDNA tree is not significantly different from trees generated by the Yule model (p-value=0.628). It has been suggested that highly mobile hunter-gatherer societies, such as those typical of most of human prehistory, were polygynous bands (Dupanloup et al. 2003); similarly, nomadic horticulturalist Amazonian societies exhibit strong differences in reproductive success due to the common practice of polygyny, especially among community chiefs, whose offspring also enjoy a high fertility (Neel 1970; 1980; Neel and Weiss 1975).

Furthermore, a more recent expansion can be observed in the BSP based on the NRY, but not in the mtDNA BSP (Figure 5), indicating an expansion specifically in the paternal line. The reasons behind this recent male-biased population expansion, which starts ~3.5 kya, are as yet unclear. However, similar male-biased expansions have been observed in other studies using high-resolution NRY sequences (Batini et al. 2017; Karmin et al. 2015).

Related: