New preprint papers on Finland’s population history and disease, skin pigmentation in Africa, and genetic variation in Thailand hunter-gatherers


New and interesting research these days in BioRxiv:

Haplotype sharing provides insights into fine-scale population history and disease in Finland, by Martín et al. (2017):

Finland provides unique opportunities to investigate population and medical genomics because of its adoption of unified national electronic health records, detailed historical and birth records, and serial population bottlenecks. We assemble a comprehensive view of recent population history (≤100 generations), the timespan during which most rare disease-causing alleles arose, by comparing pairwise haplotype sharing from 43,254 Finns to geographically and linguistically adjacent countries with different population histories, including 16,060 Swedes, Estonians, Russians, and Hungarians. We find much more extensive sharing in Finns, with at least one ≥ 5 cM tract on average between pairs of unrelated individuals. By coupling haplotype sharing with fine-scale birth records from over 25,000 individuals, we find that while haplotype sharing broadly decays with geographical distance, there are pockets of excess haplotype sharing; individuals from northeast Finland share several-fold more of their genome in identity-by-descent (IBD) segments than individuals from southwest regions containing the major cities of Helsinki and Turku. We estimate recent effective population size changes over time across regions of Finland and find significant differences between the Early and Late Settlement Regions as expected; however, our results indicate more continuous gene flow than previously indicated as Finns migrated towards the northernmost Lapland region. Lastly, we show that haplotype sharing is locally enriched among pairs of individuals sharing rare alleles by an order of magnitude, especially among pairs sharing rare disease causing variants. Our work provides a general framework for using haplotype sharing to reconstruct an integrative view of recent population history and gain insight into the evolutionary origins of rare variants contributing to disease.

Migration rates and haplotype sharing within Finland and between neighboring countries. A) Map of regional Finnish, Swedish, and Estonian birthplaces Purple triangle indicates St. Petersburg, Russia. Hungary not shown. 1 Finnish, Swedish, and Estonian region labels are shown in Table S3. B) Principal components analysis (PCA) of unrelated individuals, colored by birth region as shown in A) if available or country otherwise. C-D) Migration rates inferred with EEMS. Values and colors indicate inferred rates, for example with +1 (shades of blue) indicating an order of magnitude more migration at a given point on average, and shades of orange indicating migration barriers. C) Migration rates among municipalities in Finland. D) Migration rates within and between Finland, Sweden, Estonia, and St. Petersburg, Russia. Available under a CC-BY 4.0 International license.

Interesting to understand this paper is the whole research published by the Institute for Molecular Medicine Finland (FIMM): their website contains detailed research on Finland’s recent genetic history.

NOTE: The featured image of this article contains three figures from the FIMM (License CC-BY 4.0). Left: Position of the points represents the locations of 1042 Finnish individuals. By clustering the individuals into two groups based on genome data we see a split between eastern (blue) and western (red) parts. Individuals who show considerable relatedness to both groups have been colored with cyan. Both parents of each individual were born close to each other and based on the parents’ birth years we can infer that we are looking at the genetic structure present in Finland before 1950s. Center: An estimated borderline of the Treaty of Nöteborg on top of the map from the left. The border line is drawn between Jääski (28.92 N, 61.04 E) and Pyhäjoki (24.26 N, 64.46 E). Right: The settlement border divides Finland into the early settlement region (to west and south of the border) and the late settlement region (to east and north of the border) (Jutikkala 1933, s. 91). We see that Southern Savo (in south-eastern part of the early settlement) is among the only parts of the early settlement region that is dominated by the eastern genetic group. Information from Matti Pirinen and Sini Kerminen, 24.5.2017.

An Unexpectedly Complex Architecture for Skin Pigmentation in Africans, by Martin et al (2017):

Fewer than 15 genes have been directly associated with skin pigmentation variation in humans, leading to its characterization as a relatively simple trait. However, by assembling a global survey of quantitative skin pigmentation phenotypes, we demonstrate that pigmentation is more complex than previously assumed with genetic architecture varying by latitude. We investigate polygenicity in the Khoe and the San, populations indigenous to southern Africa, who have considerably lighter skin than equatorial Africans. We demonstrate that skin pigmentation is highly heritable, but that known pigmentation loci explain only a small fraction of the variance. Rather, baseline skin pigmentation is a complex, polygenic trait in the KhoeSan. Despite this, we identify canonical and non-canonical skin pigmentation loci, including near SLC24A5, TYRP1, SMARCA2/VLDLR, and SNX13 using a genome-wide association approach complemented by targeted resequencing. By considering diverse, under-studied African populations, we show how the architecture of skin pigmentation can vary across humans subject to different local evolutionary pressures.

Contrasting maternal and paternal genetic variation of hunter-gatherer groups in Thailand, by Kutanan et al. (2017):

The Maniq and Mlabri are the only recorded nomadic hunter-gatherer groups in Thailand. Here, we sequenced complete mitochondrial (mt) DNA genomes and ~2.364 Mbp of non-recombining Y chromosome (NRY) to learn more about the origins of these two enigmatic populations. Both groups exhibited low genetic diversity compared to other Thai populations, and contrasting patterns of mtDNA and NRY diversity: there was greater mtDNA diversity in the Maniq than in the Mlabri, while the converse was true for the NRY. We found basal uniparental lineages in the Maniq, namely mtDNA haplogroups M21a, R21 and M17a, and NRY haplogroup K. Overall, the Maniq are genetically similar to other negrito groups in Southeast Asia. By contrast, the Mlabri haplogroups (B5a1b1 for mtDNA and O1b1a1a1b and O1b1a1a1b1a1 for the NRY) are common lineages in Southeast Asian non-negrito groups, and overall the Mlabri are genetically similar to their linguistic relatives (Htin and Khmu) and other groups from northeastern Thailand. In agreement with previous studies of the Mlabri, our results indicate that the Malbri do not directly descend from the indigenous negritos. Instead, they likely have a recent origin (within the past 1,000 years) by an extreme founder event (involving just one maternal and two paternal lineages) from an agricultural group, most likely the Htin or a closely-related group.


The concept of “outlier” in studies of Human Ancestry, and the Corded Ware outlier from Esperstedt


While writing the third version of the Indo-European demic diffusion model, I noticed that one Corded Ware sample (labelled I0104) clusters quite closely with steppe samples (i.e. Yamna, Afanasevo, and Potapovka). The other Corded Ware samples cluster, as expected, closely with east-central European samples, which include related cultures such as the Swedish Battle Axe, and later Sintashta, or Potapovka (cultures that are from the steppe proper, but are derived from Corded Ware).

I also noticed after publishing the draft that I had used the wording “Corded Ware outlier” at least once. I certainly had that term in mind when developing the third version, but I did not intend to write it down formally. Nevertheless, I think it is the right name to use.

PCA of dataset including Minoans and Mycenaeans, and Scythians and Sarmatians. The graphic has been arranged so that ancestries and samples are located in geographically friendly axes similar to north-south (Y), east-west(X). Symbols are used, in a simplified manner, in accordance with symbols for Y-DNA haplogroups used in the maps. Labels have been used for simplification of important components. Areas are drawn surrounding Yamna, Poltavka, Afanasevo, Corded Ware (including samples from Estonia, Battle Axe, and Poltavka outlier), and succeeding Sintashta and Potapovka cultures, as well as Bell Beaker. Corded Ware sample I0104, from Esperstedt, has also been labelled.

Outlier in Statistics, as you can infer from the name, is a sample (more precisely an observation) that lies distant to others. It is a slippery concept in Human Evolutionary Biology, because it has no clear definition, and it is thus dependent on a certain degree of subjective evaluation. It seems to be mainly based on a combination of PCA and ADMIXTURE analyses, but should obviously be dependent on the number of samples available for a certain culture, and the regional distribution of the samples available.

We have thus certain clear cases, like the Poltavka outlier, of R1a-M417 lineage, clustering close to Corded Ware (and Sintashta, and Potapovka) samples, but far from other R1b-L23 samples from Poltavka or Yamna cultures, from neighbouring regions in the steppe.

We have also less clear observations, like Balkan Chalcolithic samples, which may or may not have been part of different cultural groups (say, related to the Suvorovo-Novodanilovka expansion, or not), which may justify their differences in ancestral components in ADMIXTURE, and in their position in PCA.

And we have a Yamna sample from western Ukraine, which – unlike the other two available samples – clusters “to the south” of east Yamna samples. Taking into account the Yamna sample from Bulgaria, clustering closely with south-eastern European samples, could you really call this an outlier? Two outliers out of four western Yamna samples? Well, maybe. If you take east and west Yamna from the steppe as a whole, and exclude the Yamna sample from Bulgaria, of course you can. Whether that classification is useful, or actually hinders a proper interpretation of western Yamna samples, and of the “Yamna component” seen in them, is a different story…

PCA for European samples of Mathieson et al. (2017)

But what then about the Corded Ware male from Esperstedt, labelled I0104, dated ca. 2430 BC, which clusters among contemporaneous steppe (Poltavka) samples, and has the greatest proportion of ‘Yamna component’ in ADMIXTURE? After all, it is different in both respects from any other Corded Ware individual – including the oldest samples available, from Latvia (ca. 2885 BC) and Tiefbrunn (ca. 2755 BC).

This sample is one of the direct links between the steppe and Corded Ware in late times, and has been the main reason for the confusion a lot of people seem to have about the “Yamna component” in Corded Ware, with some supporting a direct migration from one into the other, and a few even daring to say that “Corded Ware is indistinguishable from Yamna”(!?).

His family members – all males of haplogroup R1a-M417 (like I0104 and most males from the Corded Ware culture) -, few generations later, show a decreased Yamna component, which clearly indicates that this individual’s admixture came directly from the steppe, and most likely from one or multiple female ancestors. That is compatible with the nomadic nature of the Corded Ware culture (and its known exogamy practices), which connected central Europe with the steppes, up to the North Caspian region.

If labelling other samples as outliers may be interesting to improve the conclusions one can obtain from genetic research, labelling this sample is, in my opinion, essential, to avoid certain strong misconceptions about the origin of the Corded Ware culture.


Indo-European demic diffusion model, 3rd edition


I have just uploaded the working draft of the third version of the Indo-European demic diffusion model. Unlike the previous two versions, which were published as essays (fully developed papers), this new version adds more information on human admixture, and probably needs important corrections before a definitive edition can be published.

The third version is available right now on ResearchGate and I will post the PDF at Academia Prisca, as soon as possible:

Map overlaid by PCA including Yamna, Corded Ware, Bell Beaker, and other samples

Feel free to comment on the paper here, or (preferably) in our forum.

A working version (needing some corrections) divided by sections, illustrated with up-to-date, high resolution maps, can be found (as always) at the official collaborative Wiki website

Human ancestry solves language questions? New admixture citebait


A paper at Scientific Reports, Human ancestry correlates with language and reveals that race is not an objective genomic classifier, by Baker, Rotimi, and Shriner (2017).

Abstract (emphasis mine):

Genetic and archaeological studies have established a sub-Saharan African origin for anatomically modern humans with subsequent migrations out of Africa. Using the largest multi-locus data set known to date, we investigated genetic differentiation of early modern humans, human admixture and migration events, and relationships among ancestries and language groups. We compiled publicly available genome-wide genotype data on 5,966 individuals from 282 global samples, representing 30 primary language families. The best evidence supports 21 ancestries that delineate genetic structure of present-day human populations. Independent of self-identified ethno-linguistic labels, the vast majority (97.3%) of individuals have mixed ancestry, with evidence of multiple ancestries in 96.8% of samples and on all continents. The data indicate that continents, ethno-linguistic groups, races, ethnicities, and individuals all show substantial ancestral heterogeneity. We estimated correlation coefficients ranging from 0.522 to 0.962 between ancestries and language families or branches. Ancestry data support the grouping of Kwadi-Khoe, Kx’a, and Tuu languages, support the exclusion of Omotic languages from the Afroasiatic language family, and do not support the proposed Dené-Yeniseian language family as a genetically valid grouping. Ancestry data yield insight into a deeper past than linguistic data can, while linguistic data provide clarity to ancestry data.

Regarding European ancestry:

Southern European ancestry correlates with both Italic and Basque speakers (r = 0.764, p = 6.34 × 10−49). Northern European ancestry correlates with Germanic and Balto-Slavic branches of the Indo-European language family as well as Finno-Ugric and Mordvinic languages of the Uralic family (r = 0.672, p = 4.67 × 10−34). Italic, Germanic, and Balto-Slavic are all branches of the Indo-European language family, while the correlation with languages of the Uralic family is consistent with an ancient migration event from Northern Asia into Northern Europe. Kalash ancestry is widely spread but is the majority ancestry only in the Kalash people (Table S3). The Kalasha language is classified within the Indo-Iranian branch of the Indo-European language family.

Sure, admixture analysis came to save the day. Yet again. Now it’s not just Archaeology related to language anymore, it’s Linguistics; all modern languages and their classification, no less. Because why the hell not? Why would anyone study languages, history, archaeology, etc. when you can run certain algorithms on free datasets of modern populations to explain everything?

What I am criticising here, as always, is not the study per se, its methods (PCA, the use of Admixture or any other tools), or its results, which might be quite interesting – even regarding the origin or position of certain languages (or more precisely their speakers) within their linguistic groups; it’s the many broad, unsupported, striking conclusions (read the article if you want to see more wishful thinking).

This is obviously simplistic citebait – that benefits only journals and authors, and it is therefore tacitly encouraged -, but not knowledge, because it is not supported by any linguistic or archaeological data or expertise.

Is anyone with a minimum knowledge of languages, or general anthropology, actually reviewing these articles?


Featured image: Ancestry analysis of the global data set, from the article.

Iberian Peninsula: Discontinuity in mtDNA between hunter-gatherers and farmers, not so much during the Chalcolithic and EBA


A new preprint paper at BioRxiv, The maternal genetic make-up of the Iberian Peninsula between the Neolithic and the Early Bronze Age, by Szécsényi-Nagy et al. (2017).


Agriculture first reached the Iberian Peninsula around 5700 BCE. However, little is known about the genetic structure and changes of prehistoric populations in different geographic areas of Iberia. In our study, we focused on the maternal genetic makeup of the Neolithic (~ 5500-3000 BCE), Chalcolithic (~ 3000-2200 BCE) and Early Bronze Age (~ 2200-1500 BCE). We report ancient mitochondrial DNA results of 213 individuals (151 HVS-I sequences) from the northeast, central, southeast and southwest regions and thus on the largest archaeogenetic dataset from the Peninsula to date. Similar to other parts of Europe, we observe a discontinuity between hunter-gatherers and the first farmers of the Neolithic. During the subsequent periods, we detect regional continuity of Early Neolithic lineages across Iberia, however the genetic contribution of hunter-gatherers is generally higher than in other parts of Europe and varies regionally. In contrast to ancient DNA findings from Central Europe, we do not observe a major turnover in the mtDNA record of the Iberian Late Chalcolithic and Early Bronze Age, suggesting that the population history of the Iberian Peninsula is distinct in character.

Iberian mtDNA samples

Detailed conclusions of their work,

The present study, based on 213 new and 125 published mtDNA data of prehistoric Iberian individuals suggests a more complex mode of interaction between local hunter-gatherers and incoming early farmers during the Early and Middle Neolithic of the Iberian Peninsula, as compared to Central Europe. A characteristic of Iberian population dynamics is the proportion of autochthonous hunter-gatherer haplogroups, which increased in relation to the distance to the Mediterranean coast. In contrast, the early farmers in Central Europe showed comparatively little admixture of contemporaneous hunter-gatherer groups. Already during the first centuries of Neolithic transition in Iberia, we observe a mix of female DNA lineages of different origins. Earlier hunter-gatherer haplogroups were found together with a variety of new lineages, which ultimately derive from Near Eastern farming groups. On the other hand, some early Neolithic sites in northeast Iberia, especially the early group from the cave site of Els Trocs in the central Pyrenees, seem to exhibit affinities to Central European LBK communities. The diversity of female lineages in the Iberian communities continued even during the Chalcolithic, when populations became more homogeneous, indicating higher mobility and admixture across different geographic regions. Even though the sample size available for Early Bronze Age populations is still limited, especially with regards to El Argar groups, we observe no significant changes to the mitochondrial DNA pool until the end of our time transect (1500 BCE). The expansion of groups from the eastern steppe, which profoundly impacted Late Neolithic and EBA groups of Central and North Europe, cannot (yet) be seen in the contemporaneous population substrate of the Iberian Peninsula at the present level of genetic resolution. This highlights the distinct character of the Neolithic transition both in the Iberian Peninsula and elsewhere and emphasizes the need for further in depth archaeogenetic studies for reconstructing the close reciprocal relationship of genetic and cultural processes on the population level.

So it seems more and more likely that the North-West Indo-European invasion during the Copper Age (signaled by changes in Y-DNA lineages) was not, as in central Europe, accompanied by much mtDNA turnover. What that means – either a male-dominated invasion, or a longer internal evolution of invasive Y-DNA subclades – remains to bee seen, but I am still more inclined to see the former as the most likely interpretation, in spite of admixture results.


Featured images: from the article, licensed BY-NC-ND.

Spread of Indo-European folktale traditions related to cultural and demic diffusion (using genomic data)


New article at PNAS, Inferring patterns of folktale diffusion using genomic data, by Bortoloni et al. (2017).


Observable patterns of cultural variation are consistently intertwined with demic movements, cultural diffusion, and adaptation to different ecological contexts [Cavalli-Sforza and Feldman (1981) Cultural Transmission and Evolution: A Quantitative Approach; Boyd and Richerson (1985) Culture and the Evolutionary Process]. The quantitative study of gene–culture coevolution has focused in particular on the mechanisms responsible for change in frequency and attributes of cultural traits, the spread of cultural information through demic and cultural diffusion, and detecting relationships between genetic and cultural lineages. Here, we make use of worldwide whole-genome sequences [Pagani et al. (2016) Nature 538:238–242] to assess the impact of processes involving population movement and replacement on cultural diversity, focusing on the variability observed in folktale traditions (n = 596) [Uther (2004) The Types of International Folktales: A Classification and Bibliography. Based on the System of Antti Aarne and Stith Thompson] in Eurasia. We find that a model of cultural diffusion predicted by isolation-by-distance alone is not sufficient to explain the observed patterns, especially at small spatial scales (up to ~4,000 km). We also provide an empirical approach to infer presence and impact of ethnolinguistic barriers preventing the unbiased transmission of both genetic and cultural information. After correcting for the effect of ethnolinguistic boundaries, we show that, of the alternative models that we propose, the one entailing cultural diffusion biased by linguistic differences is the most plausible. Additionally, we identify 15 tales that are more likely to be predominantly transmitted through population movement and replacement and locate putative focal areas for a set of tales that are spread worldwide.

I am very interested in folktales and their origins within Proto-Indo-European culture, so the title alone was an immediate click-bait for me. It did, as always, disappoint in its methods and conclusions, but just the idea it proposes is of great interest for future studies.

There are gross limitations in assessing folktales using simply the Aarne-Thompson-Uther Classification without further analysis or explanation, apart from a summary of tales in the supplementary materials.

But their maps and simplistic hypothesized waves of diffusion (‘African origin’, ‘northern Eurasian’, ‘Eastern European’, or ‘Middle-Eastern/Caucasian’) seem to me as if they try to swim with the tide of the current literature regarding the identification of Proto-Indo-European demic diffusion with “steppe admixture” distribution (and ancient language family diffusion in general through admixture), and as such it can only be wrong.

If you just look at actual folktale distribution (black dots) and compare them with prehistoric cultures and ancient Y-DNA distribution, you realize their maps don’t make much sense, and more complex methods (and a clearer idea of what admixture represents) are needed.

If their intention was to get published in a journal of high impact factor, they succeeded, so good for them. I am glad this subject gets more attention. Of course, their conclusions are kept formally in line with the many limitations of their methods, and are the most interesting aspect of the article:

By correcting for the presence of ethnolinguistic barriers, we find that the null model of cultural diffusion predicted by IBD alone cannot explain the observed distribution of folktales across Eurasia. Instead, beyond ~4,000 km, cultural diffusion biased by linguistic barriers exhibits the highest correlation at all geographic bins. At small geographic bins (<4,000 km), population movements and linguistic barriers may be more relevant than geographic proximity, pointing once again at the possible importance of small-scale processes of cultural transmission for testing more specific hypotheses when using genetic evidence. In addition, processes other than simple cultural diffusion may be more relevant for a smaller group of tales shared by pairs of populations that are genetically closer than populations not exhibiting those tales. Looking for smaller packages of tales or individual tales and their variants can be useful to shed light on the formation process of this vast body of popular knowledge. The long-range patterns detected by our analyses may complement this picture by suggesting a more ancient origin of some of these folktales (SI Appendix). On a broader level, these results can be used in the future to infer directional trends of cultural dispersal as well as to test for the emergence of systematic social biases [such as prestige bias, conformism/anticonformism, heterophily, and content-dependent biases] or cultural barriers different from linguistic ones, which have a chronology that may be independently ascertained.

If you are interested in studies about folktales, and especially those related to Indo-European traditions, you can check out the following articles I found interesting in the past:


Featured image (featured also in the article): Possible focal area and dispersion pattern for tale ATU313 “The Magic Flight,” one the most popular folktales in this dataset, which may have been additionally spread through population movement and replacement. It is interesting to note how this tale reached locations that are far from its putative origin (such as Japan and southeastern Africa), whereas it was not retained by many populations located in between (gray dots).