New pre-print papers on ancient and modern population genetics

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Two pre-print papers reposted or published recently, interesting for the genetic analysis of ancient and modern populations (emphasis mine):

Assessing the relationship of ancient and modern populations, by Joshua G Schraiber (2017) Abstract:

Genetic material sequenced from ancient samples is revolutionizing our understanding of the recent evolutionary past. However, ancient DNA is often degraded, resulting in low coverage, error-prone sequencing. Several solutions exist to this problem, ranging from simple approach such as selecting a read at random for each site to more complicated approaches involving genotype likelihoods. In this work, we present a novel method for assessing the relationship of an ancient sample with a modern population while accounting for sequencing error by analyzing raw read from multiple ancient individuals simultaneously. We show that when analyzing SNP data, it is better to sequencing more ancient samples to low coverage: two samples sequenced to 0.5x coverage provide better resolution than a single sample sequenced to 2x coverage. We also examined the power to detect whether an ancient sample is directly ancestral to a modern population, finding that with even a few high coverage individuals, even ancient samples that are very slightly diverged from the modern population can be detected with ease. When we applied our approach to European samples, we found that no ancient samples represent direct ancestors of modern Europeans. We also found that, as shown previously, the most ancient Europeans appear to have had the smallest effective population sizes, indicating a role for agriculture in modern population growth.

Polygenic Adaptation has Impacted Multiple Anthropometric Traits, by Jeremy J Berg, Xinjun Zhang, and Graham Coop (2017). Abstract:

Most of our understanding of the genetic basis of human adaptation is biased toward loci of large phenotypic effect. Genome wide association studies (GWAS) now enable the study of genetic adaptation in highly polygenic phenotypes. Here we test for polygenic adaptation among 187 world- wide human populations using polygenic scores constructed from GWAS of 34 complex traits. By comparing these polygenic scores to a null distribution under genetic drift, we identify strong signals of selection for a suite of anthropometric traits including height, infant head circumference (IHC), hip circumference (HIP) and waist-to-hip ratio (WHR), as well as type 2 diabetes (T2D). In addition to the known north-south gradient of polygenic height scores within Europe, we find that natural selection has contributed to a gradient of decreasing polygenic height scores from West to East across Eurasia, and that this gradient is consistent with selection on height in ancient populations who have contributed ancestry broadly across Eurasia. We find that the signal of selection on HIP can largely be explained as a correlated response to selection on height. However, our signals in IHC and WC/WHR cannot, suggesting a response to selection along multiple axes of body shape variation. Our observation that IHC, WC, and WHR polygenic scores follow a strong latitudinal cline in Western Eurasia support the role of natural selection in establishing Bergmann’s Rule in humans, and are consistent with thermoregulatory adaptation in response to latitudinal temperature variation.

Featured image from the second article: Polygenic Height Scores for 187 population samples (combined Human origin panel and 1000 genomes datasets), plotted on geographic coordinates. Blue corresponds to populations with the “tallest” polygenic height scores, and yellow the “shortest”.

Indo-European and Central Asian admixture in Indian population, dependent on ethnolinguistic and geodemographic divisions

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Preprint paper at BioRxiv, Dissecting Population Substructure in India via Correlation Optimization of Genetics and Geodemographics, by Bose et al. (2017), a mixed group from Purdue University and IBM TJ Watson Research Center. A rather simple paper, which is nevertheless interesting in its approach to the known multiple Indian demographic divisions, and in its short reported methods and results.

Abstract:

India represents an intricate tapestry of population substructure shaped by geography, language, culture and social stratification operating in concert. To date, no study has attempted to model and evaluate how these evolutionary forces have interacted to shape the patterns of genetic diversity within India. Geography has been shown to closely correlate with genetic structure in other parts of the world. However, the strict endogamy imposed by the Indian caste system, and the large number of spoken languages add further levels of complexity. We merged all publicly available data from the Indian subcontinent into a data set of 835 individuals across 48,373 SNPs from 84 well-defined groups. Bringing together geography, sociolinguistics and genetics, we developed COGG (Correlation Optimization of Genetics and Geodemographics) in order to build a model that optimally explains the observed population genetic sub-structure. We find that shared language rather than geography or social structure has been the most powerful force in creating paths of gene flow within India. Further investigating the origins of Indian substructure, we create population genetic networks across Eurasia. We observe two major corridors towards mainland India; one through the Northwestern and another through the Northeastern frontier with the Uygur population acting as a bridge across the two routes. Importantly, network, ADMIXTURE analysis and f3 statistics support a far northern path connecting Europe to Siberia and gene flow from Siberia and Mongolia towards Central Asia and India.

Among the most interesting results (emphasis mine):

Our meta-analysis of the ADMIXTURE output shows that the IE and DR populations across castes shared very high ancestry, indicating the autochthonous origin of the caste system in India (Figure 2). f3 statistics show that most of the castes and tribes in India are admixed, with contributions from other castes and/or tribes, across languages affiliations (Supplementary Table 4 and Supplementary Note). The geographically isolated Tibeto-Burman tribes and the Dravidian speaking tribes appear to be the most isolated in India. Linear Discriminant Analysis on the normalized data set clearly supports genetic strati cation by castes and languages in the Indian sub-continent

(…)

Our meta-analysis of the ADMIXTURE plot in Figure 4A quantifies the ADMIXTURE results (darker colors indicate higher pairwise shared ancestry). Indian populations show a greater proportion of shared ancestry with the so-called Indian Northwestern Frontier populations, namely the tribal populations spanning Afghanistan and Pakistan. Central Asian populations share higher degrees of ancestry with IE and DR Froward castes. Uygurs share high degrees of ancestry with Indian populations.

(…)

f3 statistics (all negative Z-scores are shown) indicate Chinese and Siberian ancestry contributing to the Tibeto-Burman tribal speakers. On the other hand, the Mongols and the Europeans have contributed significant amounts of ancestry to the Indo-European and Tibeto-Burman forward castes. F3 statistics also show that the Central Asians are an admixed population with signs of admixture from Caucasus and other parts of Europe.

Among the results for proportions of shared ancestry between Indians and Eurasians (FIG. 4), there is an obvious influence of European admixture (Caucasus, and Southern, Central, and Northern EU), potentially from the Yamna-Corded Ware expansion, in IE_ForwardCaste, which is lessened in IE_BackwardCaste and also in IE_Tribal, while DR_ForwardCaste shows again more admixture than IE_Tribal, but diminishing with lower castes and quite low in DR_Tribal.

Ancestry from Central Asia is strong with a similar pattern, which hints at the influence of Sintashta, Andronovo, and BMAC influence in the expansion of the Steppe component, even more than a later Turkic component.

On the other hand, the influence from Turkey is difficult to assess, given the complex genetic history of Anatolia, but the map contained in Fig. 6 doesn’t feel right, not only from a genetic viewpoint, but also from linguistic and archaeological points of view. This is the typical map created with admixture analyses that is wrong because of not taking into account anthropological theories.

Quite interesting is then the influence of admixture in these different ethnolinguistic groups, Indo-European and Dravidic, which points to an initially greater expansion of Indo-European speakers, and later resurge of Dravidian languages.

Featured image contains simplified origin and data of samples studied, from the article.

Related:

My European Family: The First 54,000 years, by Karin Bojs

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I have recently read the book My European Family: The First 54,000 years (2015), by Karin Bojs, a known Swedish scientific journalist, former science editor of the Dagens Nyheter.

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My European Family: The First 54,000 Years
It is written in a fresh, dynamic style, and contains general introductory knowledge to Genetics, Archaeology, and their relation to language, and is written in a time of great change (2015) for the disciplines involved.

The book is informed, it shows a balanced exercise between responsible science journalism and entertaining content, and it is at times nuanced, going beyond the limits of popular science books. It is not written for scholars, although you might learn – as I did – interesting details about researchers and institutions of the anthropological disciplines involved. It contains, for example, interviews with known academics, which she uses to share details about their personalities and careers, which give – in my opinion – a much needed context to some of their publications.

Since I am clearly biased against some of the findings and research papers which are nevertheless considered mainstream in the field (like the identification of haplogroup R1a with the Proto-Indo-European expansion, or the concept of steppe admixture), I asked my wife (who knew almost nothing about genetics, or Indo-European studies) to read it and write a summary, if she liked it. She did. So much, that I have convinced her to read The Horse, the Wheel, and Language: How Bronze-Age Riders from the Eurasian Steppes Shaped the Modern World (2007), by David Anthony.

Here is her summary of the book, translated from Spanish:

The book is divided in three main parts: The Hunters, The Farmers, and The Indo-Europeans, and each has in turn chapters which introduce and break down information in an entertaining way, mixing them with recounts of her interactions and personal genealogical quest.

Part one, The Hunters, offers intriguing accounts about the direct role music had in the development of the first civilizations, the first mtDNA analyses of dogs (Savolainen), and the discovery of the author’s Saami roots. Explanations about the first DNA studies and their value for archaeological studies are clear and comprehensible for any non-specialized reader. Interviews help give a close view of investigations, like that of Frederic Plassard’s in Les Combarelles cave.

Part two, The Farmers, begins with her travel to Cyprus, and arouses the interest of the reader with her description of the circular houses, her notes on the Basque language, the new papers and theories related to DNA analyses, the theory of the decision of cats to live with humans, the first beers, and the houses built over graves. Karin Bojs analyses the subgroup H1g1 of her grandmother Hilda, and how it belonged to the first migratory wave into Central Europe. This interest in her grandmother’s origins lead her to a conference in Pilsen about the first farmers in Europe, where she knows firsthand of the results of studies by János Jakucs, and studies of nuclear DNA. Later on she interviews Guido Brandt and Joachim Burguer, with whom she talks about haplogroups U, H, and J.

The chapter on Ötzi and the South Tyrol Museum of Archaeology (Bolzano) introduces the reader to the first prehistoric individual whose DNA was analysed, belonging to haplogroup G2a4, but also revealing other information on the Iceman, such as his lactose intolerance.

Part three, dealing with the origin of Indo-Europeans, begins with the difficulties that researchers have in locating the origin of horse domestication (which probably happened in western Kazakhstan, in the Russian steppe between the rivers Volga and Don). She mentions studies by David Anthony and on the Yamna culture, and its likely role in the diffusion of Proto-Indo-European. In an interview with Mallory in Belfast, she recalls the potential interest of far-right extremists in genetic studies (and early links of the Journal of Indo-European Studies to certain ideology), as well as controversial statements of Gimbutas, and her potentially biased vision as a refugee from communist Europe. During the interview, Mallory had a copy of the latest genetic paper sent to Nature Magazine by Haak et al., not yet published, for review, but he didn’t share it.

Then haplogroups R1a and R1b are introduced as the most common in Europe. She visits the Halle State Museum of Prehistory (where the Nebra sky disk is exhibited), and later Krakow, where she interviews Slawomir Kadrow, dealing with the potential creation of the Corded Ware culture from a mix of Funnelbeaker and Globular Amphorae cultures. New studies of ancient DNA samples, published in the meantime, are showing that admixture analyses between Yamna and Corded Ware correlate in about 75%.

In the following chapters there is a broad review of all studies published to date, as well as individuals studied in different parts of Europe, stressing the importance of ships for the expansion of R1b lineages (Hjortspring boat).

The concluding chapter is dedicated to vikings, and is used to demystify them as aggressive warmongers, sketching their relevance as founders of the Russian state.

To sum up, it is a highly documented book, written in a clear style, and is capable of awakening the reader’s interest in genetic and anthropological research. The author enthusiastically looks for new publications and information from researchers, but is at the same time critic with them, showing often her own personal reactions to new discoveries, all of which offers a complex personal dynamic often shared by the reader, engaged with her first-person account the full length of the book.

Mayte Batalla (July 2017)

DISCLAIMER: The author sent me a copy of the book (a translation into Spanish), so there is a potential conflict of interest in this review. She didn’t ask for a review, though, and it was my wife who did it.

Effective migration in Western Eurasia reveals fine-scale migration surface features

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Interesting poster from SMBE 2017, Maps of effective migration as a summary of global human genetic diversity, by Benjamin Peter, Desislava Petkova, Matthew Stephens & John Novembre, of the JNPopGen group of the University of Chicago.

You can read the full poster in the original PDF, or in compressed image. The following are important excerpts:

Aim: To answer the following questions:

  • Which regions have high/low effective migration?
  • How well is human genetic diversity explained by this pure isolation-by-distance model?
  • How does the explanatory performance of EEMS compare to PCA?

Method: It uses the method proposed by Petkova et al. (2016) to fit a map of time-averaged (effective) migration rates to geographically referenced samples, and merges data from 24 different studies (8740 individuals from 469 populations) to assess human genetic diversity on global and continental scale.

  1. Basic workflow:
    • Merge data, remove duplicated & related individuals.
    • Remove Hunter-Gatherer and recently admixed populations. Their locations are still indicated with (H) and (X), respectively
  2. EEMS analysis
    • Calculate genetic distance matrix between all individuals.
    • Fit migration map to data using EEMS MCMC algorithm
  3. Comparison to PCA: Standard PCA using flashpca (Abraham & Inouye 2014) was used, they compare correlation of genetic distance induced from first ten PCs with the fitted EEMS distance

Interpretation: A continuous habitat is approximated by a discrete grid (light gray). A Bayesian model is used to infer the most likely migration rates, which are given on a log scale compared to the Average (BLUE= 100x higher, BROWN=100x lower

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Map of effective migrations in Europe

Results (see maps):

  1. Global diversity patterns correlate with topographical features
  2. In Western Eurasia, EEMS reveals fine-scale migration surface features

Discussion: EEMS Maps are intuitive and direct way to visualize geographically referenced genetic data.

Dense sampling (WEstern Eurasian panel) in particular yields high resolution and accuracy, but the method works well at a global scale (FST=0.06) and just in Western Eurasia (FST=0.01).

EEMS-maps are able to reasonably well predict genetic differences, but hunter-gatherer populations and admixed populations were a priori excluded.

Discovered via Eurogenes. Full image via Reddit.

The over-simplistic “Kossinian Model”: homogeneous peoples speaking a common language within clearly delimited cultures

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There seems to be a growing trend to over-simplistic assumptions in archaeology and linguistics, led by amateur and professional geneticists alike, due to the recent (only partially deserved) popularity of Human Evolutionary Biology.

These studies are offering ancient DNA samples, whose Y-DNA and mtDNA haplogroups and admixture analyses are showing some new valuable information on ancient cultures and peoples. However, their authors are constantly giving uninformed conclusions.

I have read a good, simple description of the Kossinnian model in the book Balkan Dialogues (Routledge, 2017), which has been shared to be fully read online by co-editor Maria Ivanova.

Chapter 3, The transitions between Neolithic and Early Bronze Age in Greece, and the “Indo-European problem”, by Jean-Paul Demoule, offers a clear account of the difficulties found in tracing the arrival of Proto-Greek speakers to Greece or the “Coming of the Greeks”. The identifications of cultural breaks most commonly supported by academics as potentially signaling the arrival of Proto-Greeks are cited, including the Early Helladic III period ca. 2300 BC (with the diffusion of Mynian ware), or the Middle Helladic period ca. 2000 BC. The problem of finding a clear cultural break before the emergence of Mycenaean Greece (which obviously spoke an early Greek dialect) has led some to adopt a “Palaeolithic autochthonous theory” (Giannopoulos 2012), which offers still more problems than it solves.

Of interest is his reference to Kossina in light of the recent popularity in resorting to DNA to answer all problems. It is mandatory for the field of Indo-European studies – regardless of what renown labs and journals of high impact factor are publishing – to avoid carrying on “in the steps of race based cranial measurement which enjoyed its floruit in the 19th century before fading into oblivion.”

This is why, without denying the relationship between Indo-European languages, we need to question the validity of the overall model itself, which has shown itself to be over-simplistic in assuming the movement of permanent and long-lasting homogeneous “peoples”. More precisely, we have to criticize in details the “Kossinnian Model” underlying all those assumptions – “Kossinnian”, because of the German archaeologist Gustaf Kossina (1858–1931), well known for the famous sentence: “Cultural provinces, which are clearly delimited on the basis of archaeology, correspond in every era to specific peoples or tribes” (“Scharf umgrenzte archäologische Kultur-provinzen decken sich zu allen Zeiten mit ganz bestimmten Völkern und Völkerstämmen”). Four basic assumptions arise from this central idea:

  1. Changes in languages are due to population movements, usually involving conquest, and every migration implies a linguistic change.
  2. Archaeological “cultures” are homogenous ethnic groups, with defined frontiers, based on the model of 19th- and 20th-century nation-states and equally on the model of biological entities that reproduce by parthenogenesis.
  3. There is coincidence between language and material culture.
  4. Finally, languages are also homogenous biological entities which are autonomous and clearly delimited, and which can reproduce by parthenogenesis or by scissiparity.

Unfortunately, none of these points is self-evident and each can be countered by a number of historical examples (Demoule 2014: 553–592).

While I agree with the first part of the first statement attributed to the “Kossinnian model”, i.e. that languages are usually the product of population movements (either involving conquest or not), the other statements are obviously and demonstrably false, and are frequently assumed in comments, blog posts, forums, and even research articles – particularly in those based on genetic studies -, and this trend seems to be increasing lately.

Preprint paper: Estimating genetic kin relationships in prehistoric populations, by Monroy Kuhn, Jakobsson, and Günther

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A new preprint paper appeared some days ago in BioRxiv, Estimating genetic kin relationships in prehistoric populations, by researchers of the Uppsala University Jose Manuel Monroy Kuhn, Mattias Jakobsson, and Torsten Günther. Jakobsson and Günther. You might remember the last two from their work Ancient X chromosomes reveal contrasting sex bias in Neolithic and Bronze Age Eurasian migrations, whose results were said not to be replicable by Lazaridis and Reich (PNAS), something they denied pointing to the limitations of the current aDNA data (PNAS).

They propose a new, more conservative method to infer close relationships (in contrast with available methods, suitable for modern samples). They have implemented the method as a software program, called READ, which should work better with degraded samples (typical of ancient DNA) by reducing false positives – and having therefore more false negatives. Abstract:

Archaeogenomic research has proven to be a valuable tool to trace migrations of historic and prehistoric individuals and groups, whereas relationships within a group or burial site have not been investigated to a large extent. Knowing the genetic kinship of historic and prehistoric individuals would give important insights into social structures of ancient and historic cultures. Most archaeogenetic research concerning kinship has been restricted to uniparental markers, while studies using genome-wide information were mainly focused on comparisons between populations. Applications which infer the degree of relationship based on modern-day DNA information typically require diploid genotype data. Low concentration of endogenous DNA, fragmentation and other post-mortem damage to ancient DNA (aDNA) makes the application of such tools unfeasible for most archaeological samples. To infer family relationships for degraded samples, we developed the software READ (Relationship Estimation from Ancient DNA). We show that our heuristic approach can successfully infer up to second degree relationships with as little as 0.1x shotgun coverage per genome for pairs of individuals. We uncover previously unknown relationships among prehistoric individuals by applying READ to published aDNA data from several human remains excavated from different cultural contexts. In particular, we find a group of five closely related males from the same Corded Ware culture site in modern-day Germany, suggesting patrilocality, which highlights the possibility to uncover social structures of ancient populations by applying READ to genome-wide aDNA data.

The software READ applied to the 230 ancient European DNA data from Mathieson et al. (2015) was studied, with certain interesting results. For starters, this paper already supports the idea that the five German Corded Ware samples from Esperstedt were all related, thus further supporting to a certain extent the culture’s patrilocality and female exogamy practices:

Of particular interest was a group of five males from Esperstedt in Germany who were associated with the Corded Ware culture {a culture that arose after large scale migrations of males from the east. Around 50 Corded Ware burials, six of them stone cists, were excavated near Esperstedt in the context of road constructions in 2005. Characteristic Corded Ware pottery was found in the graves and all male individuals had been buried on their right hand site. Interestingly, the central individual of the group of related individuals (I1541) was buried in a stone cist approximately 700 meters from the graves of the other four individuals which were all close to each other. The close relationship of this group of only male individuals from the same location suggest patrilocality and female exogamy, a pattern which has also been found from Strontium isotopes at another Corded Ware site just 30 kilometers from Esperstedt and suggested for the Corded Ware culture in general. This represents just one example of how the genetic analysis of relationships can be used to uncover and understand social structures in ancient populations.

It is to be expected that improvement in such methods can help more accurately define certain samples, by inferring their precise subclades. For example, in the case of those relatives from Esperstedt – classified variously as R(xR1b), R1a, or R1a1 – one would be able to classify those related patrilineally to the most precise subclade: in this case, that of the sample I0104 (ca.2473-2348 BC), of subclade R1a1a1-M417.

However, errors are dependent on the quality of the ancient DNA recovered:

READ does not explicitly model aDNA damage and it only considers one allele at heterozygous sites. This implies that a careful curation of the data is required to avoid errors due to low coverage, short sequence fragments, deamination damage, sequencing errors and potential contamination. We recommend a number of well established filtering steps when working with low coverage aDNA data