Polygyny as a potential reason for Y-DNA bottlenecks among agropastoralists


Open access Greater wealth inequality, less polygyny: rethinking the polygyny threshold model by Ross et al. Journal of the Royal Society Interface (2018).

Interesting excerpts, from the discussion (emphasis mine):

We use cross-cultural data and a new mutual mate choice model to propose a resolution to the polygyny paradox. Following Oh et al. [17], we extend the standard polygyny threshold model to a mutual mate choice model that accounts for both female supply to, and male demand for, polygynous matchings, in the light of the importance of, and inequality in, rival and non-rival forms of wealth. The empirical results presented in figures 5 and 6 demonstrate two phenomena that are jointly sufficient to generate a transition to more frequent monogamy among populations with a co-occurring transition to a more unequal, highly stratified, class-based social structure. In such populations, fewer men can cross the wealth threshold required to obtain a second wife, and those who do may be fabulously wealthy, but—because of diminishing marginal fitness returns to increasing number of marriages—do not acquire wives in full proportion to their capacity to support them with rival wealth. Together, these effects reduce the population-level fraction of wives in polygynous marriages.

Our model demonstrates that a low population-level frequency of polygyny will be an equilibrium outcome among fitness maximizing males and females in a society characterized by a large class of wealth-poor peasants and a small class of exceptionally wealthy elite. Our mutual mate choice model thus provides an empirically plausible resolution to the polygyny paradox and the transition to monogamy which co-occurred with the rise of highly unequal agricultural populations.

(a) Mean frequency of married women who are married polygynously by production system (+2 s.e.) using the Standard Cross-Cultural Sample [30]. Rates of polygyny are measured with variable ]872, per cent of wives with co-wives. (b) Rates of monogamy and polygyny by production system are measured with variable ]861, the standard polygamy code. Data on subsistence come from variable ]858, categorized subsistence. In general, agricultural populations show reduced rates of polygyny and increased rates of monogamy relative to other subsistence systems. See electronic supplementary material for more information. (c) Gini of wealth by production system in our sample.

The reasons for this decrease in marginal fitness returns are explained as either a) a potential missing of important rival forms of wealth in the statistical model, or b) one or more of the following reasons:

  • [A] male’s time and attention are rival inputs to his own fitness (…) A single rich man will have to defend his 10 wives from nine unmarried men on average.”As the wealth ratio grows even more skewed, this situation could become increasingly difficult to manage (e.g. requiring the use of eunochs to defend harems [74]).
  • A related possibility is that a growing number of unmarried men could socially censure wealthy polygynous males, imposing costs on them that reduce male demand for and/or female supply to polygynous marriage [23,24]. (…)
  • A third possibility is that sexually transmitted infection (STI) burden [22,75] could diminish returns to polygyny, if polygyny enhances infection rates [76,77]. (…)
  • Finally, impediments to cooperation or even outright conflict among co-wives can be greater as the number of wives increases. Interference competition among co-wives could impose significant fitness costs in settings where effective child rearing benefits from cooperation [79,80].(…)
between the Gini coefficient on completed rival wealth and per cent completed female polygyny.

I have previously argued against some reasons traditionally given to explain the replacement of native male populations after migrations (i.e. polygyny, slavery, targeted male extermination, etc.), because I believe that a gradual successful expansion of patrilineal clans over some generations based on wealth alone is enough to explain the obvious Y-DNA bottlenecks that happened in many different prehistoric and historic cultures (especially among steppe pastoralists, including Indo-Europeans).

I realize that I haven’t really used any study to support my opinion, though, and data from modern and ancient pastoralists from different regions seem to contradict it, so maybe ancient DNA can show that Indo-Europeans had often children with more than one woman at the same time. I don’t remember seeing that kind of information in supplementary materials to date. From memory I can think of maybe two or three examples of agnate siblings published, but I doubt the archaeological age estimation (based on simple observation of skeletal remains) combined with radiocarbon age (usually given with broad CI) could be enough to prove a similar age of conception. Maybe a case of many siblings clearly of the same age and from many different mothers in the same burial could be a strong proof of this…

I recently read that theoretical models are actually trusted by no one except for the researchers who propose them, and experimental data are trusted by everyone except for the researchers who worked with them. I cannot agree more. However, we lack information about this question (as far as I know), so we may have to rely on indirect estimations, like the kind of models presented in the paper (or the one proposed for Post-Neolithic Y-chromosome bottlenecks).

The Late Proto-Indo-European word for bride comes from a root meaning ‘drive, lead’, hence literally ‘deportation’, so the bride was transferred from her father’s family to her husband’s house. Marriage was certainly an asymmetrical contract for its members, and the reconstructible word for ‘dowry’ further supports the weaker position of the wife in it. Also, ancient marriage could differ from a family agreement, because marriage by elopement, bride kidnapping or hostage was probably common (more or less socially regulated) for people belonging the same culture. Apart from this, I don’t know about reconstructed linguistic data pointing to polygyny, and I doubt archaeological data alone – without genetics – can help.


Wang et al. (2018) Suppl. data: R1b-M269 in Baltic Neolithic?


Looking for information on Novosvobodnaya samples from Wang et al. (2018) for my latest post, I stumbled upon this from the Supplementary Data 2 (download the Excel table):

Latvia_MN1.SG (ZVEJ26)

Skeletal element: petrous
Sample: Latvia_MN_dup.I4627.SG
Date: 4251-3976 calBCE
Location: Zvejnieki
mtDNA: U4a1
Y-DNA: R1b1a1a2
Coverage: 0.15
SNPs hit on autosomes: 167445

The data on Mathieson et al. (2018) is as follows:

I4627 (ZVEJ26)

Skeletal element: petrous
Origin: ThisStudy (New data; Individual first published in JonesNatureCommunications2017)
Sample: Latvia_MN
Date:4251-3976 calBCE (5280±55 BP, Ua-3639)
mtDNA: U4a1
Y-DNA: R1b1a1a(xR1b1a1a2)
Coverage: 1.77
SNPs hit on autosomes: 686273

Y-Chromosome derived SNPs: R1b1a1a:PF6475:17986687C->A; R1b1a1a:CTS3876:15239181G->C; R1b1a1a:CTS5577:16376495A->C; R1b1a1a:CTS9018:18617596C->T; R1b1a1a:FGC57:7759944G->A; R1b1a1a:L502:19020340G->C; R1b1a1a:PF6463:16183412C->A; R1b1a1a:PF6524:23452965T->C; R1b1a:A702:10038192G->A; R1b1a:FGC35:18407611C->T; R1b1a:FGC36:13822833G->T; R1b1a:L754:22889018G->A; R1b1a:L1345:21558298G->T; R1b1a:PF6249:8214827C->T; R1b1a:PF6263:21159055C->A; R1b1:CTS2134:14193384G->A; R1b1:CTS2229:14226692T->A; R1b1:L506:21995972T->A; R1b1:L822:7960019G->A; R1b1:L1349:22722580T->C; R1b:M343:2887824C->A; R1:CTS2565:14366723C->T; R1:CTS3123:14674176A->C; R1:CTS3321:14829196C->T; R1:CTS5611:16394489T->G; R1:L875:16742224A->G; R1:P238:7771131G->A; R1:P286:17716251C->T; R1:P294:7570822G->C; R:CTS207:2810583A->G; R:CTS2913:14561760A->G; R:CTS3622:15078469C->G; R:CTS7876:17722802G->A; R:CTS8311:17930099C->A; R:F33:6701239G->A; R:F63:7177189G->A; R:F82:7548900G->A; R:F154:8558505T->C; R:F370:16856357T->C; R:F459:18017528G->T; R:F652:23631629C->A; R:FGC1168:15667208G->C; R:L1225:22733758C->G; R:L1347:22818334C->T; R:M613:7133986G->C; R:M734:18066156C->T; R:P224:17285993C->T; R:P227:21409706G->C

Context of Latvia_MN1

The Middle Neolithic is known to mark the westward expansion of Comb Ware and related cultures in North-Eastern Europe.

Mathieson et al. (2017 and 2018) had this to say about the Middle Neolithic in the Baltic:

At Zvejnieki in Latvia, using 17 newly reported individuals and additional data for 5 previously reported34 individuals, we observe a transition in hunter-gatherer-related ancestry that is opposite to that seen in Ukraine. We find that Mesolithic and Early Neolithic individuals (labelled ‘Latvia_HG’) associated with the Kunda and Narva cultures have ancestry that is intermediate between WHG (approximately 70%) and EHG (approximately 30%), consistent with previous reports34–36(Supplementary Table 3). We also detect a shift in ancestry between Early Neolithic individuals and those associated with the Middle Neolithic Comb Ware complex (labelled ‘Latvia_MN’), who have more EHG-related ancestry; we estimate that the ancestry of Latvia_MN individuals comprises 65% EHG-related ancestry, but two of the four individuals appear to be 100% EHG in principal component space (Fig. 1b).

From Mathieson et al. (2018). Ancient individuals projected onto principal components defined by 777 presentday west Eurasians (shown in Extended Data Fig. 1); data include selected published individuals (faded circles, labelled) and newly reported individuals (other symbols, outliers enclosed in black circles). Coloured polygons cover individuals that had cluster memberships fixed at 100% for supervised ADMIXTURE analysis.

Other samples and errors on Y-SNP calls

The truth is, this is another sample (Latvia_MN_dup.I4627.SG) from the same individual ZVEJ26.

There is another sample used for the analysis of ZVEJ26, with the same data as in Mathieson et al. (2018), i.e. better coverage, and Y-DNA R1b1a1a(xR1b1a1a2).

Most samples in the tables from Wang et al. (2018) seem to be classified correctly, as in previous papers, but for:

  • Blätterhöhle Cave sample from Lipson et al. (2017), wrongly classified (again) as R1b1a1a2a1a2a1b2 (I am surprised no R1b-autochtonous-continuity-fan rushed to proclaim something based on this);
  • Mal’ta 1 sample from Raghavan et al. (2013) as R1b1a1a2;
  • Iron Gates HG, Schela Cladovey from Gonzalez Fortes (2017) as R1b1a1a2;
  • Oase1 from Fu (2015) as N1c1a;
  • samples from Skoglund et al. (2017) from Africa also wrongly classified as R1b1a1a2 and subclades.

It seems therefore that the poor coverage / SNPs hit on autosomes is the key common factor here for these Y-SNP calls, and so it is in the Zvejnieki MN1 duplicated sample. Anyway, if all Y-SNP calls come from the same software applied to all data, and this is going to be used in future papers, this seems to be a great improvement compared to Narasimhan et al. (2018)

EDIT (25 JUN 2018): I have been reviewing some more papers apart from Mathieson et al. (2018) and Olalde et al. (2018) to compare the reported haplogroups, and there seems to be many potential errors (or updated data, difficult to say sometimes, especially when the newly reported haplogroup is just one or two subclades below the reported one in ‘old’ papers), not only those listed above.

The sample accession number in the European Nucleotide Archive (ENA) is SAMEA45565168 (Latvia_MN1/ZVEJ26) (see here), in case anyone used to this kind of analysis wishes to repeat the Y-SNP calls on both samples.

EDIT (25 JUN 2018): Added that it is another sample with lesser coverage from the same ZVEJ26 individual.


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.

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).

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).


Ancient genomes from North Africa evidence Neolithic migrations to the Maghreb

BioRxiv preprint now published (behind paywall) Ancient genomes from North Africa evidence prehistoric migrations to the Maghreb from both the Levant and Europe, by Fregel et al., PNAS (2018).

NOTE. I think one of the important changes in this version compared to the preprint is the addition of the recent Iberomaurusian samples.

Abstract (emphasis mine):

The extent to which prehistoric migrations of farmers influenced the genetic pool of western North Africans remains unclear. Archaeological evidence suggests that the Neolithization process may have happened through the adoption of innovations by local Epipaleolithic communities or by demic diffusion from the Eastern Mediterranean shores or Iberia. Here, we present an analysis of individuals’ genome sequences from Early and Late Neolithic sites in Morocco and from Early Neolithic individuals from southern Iberia. We show that Early Neolithic Moroccans (∼5,000 BCE) are similar to Later Stone Age individuals from the same region and possess an endemic element retained in present-day Maghrebi populations, confirming a long-term genetic continuity in the region. This scenario is consistent with Early Neolithic traditions in North Africa deriving from Epipaleolithic communities that adopted certain agricultural techniques from neighboring populations. Among Eurasian ancient populations, Early Neolithic Moroccans are distantly related to Levantine Natufian hunter-gatherers (∼9,000 BCE) and Pre-Pottery Neolithic farmers (∼6,500 BCE). Late Neolithic (∼3,000 BCE) Moroccans, in contrast, share an Iberian component, supporting theories of trans-Gibraltar gene flow and indicating that Neolithization of North Africa involved both the movement of ideas and people. Lastly, the southern Iberian Early Neolithic samples share the same genetic composition as the Cardial Mediterranean Neolithic culture that reached Iberia ∼5,500 BCE. The cultural and genetic similarities between Iberian and North African Neolithic traditions further reinforce the model of an Iberian migration into the Maghreb.

Ancestry inference in ancient samples from North Africa and the Iberian Peninsula. PCA analysis using the Human Origins panel (European, Middle Eastern, and North African populations) and LASER projection of aDNA samples.

Relevant excerpts:

FST and outgroup-f3 distances indicate a high similarity between IAM and Taforalt. As observed for IAM, most Taforalt sample ancestry derives from Epipaleolithic populations from the Levant. However, van de Loosdrecht et al. (17) also reported that one third of Taforalt ancestry was of sub-Saharan African origin. To confirm whether IAM individuals show a sub-Saharan African component, we calculated f4(chimpanzee, African population; Natufian, IAM) in such a way that a positive result for f4 would indicate that IAM is composed both of Levantine and African ancestries. Consistent with the results observed for Taforalt, f4 values are significantly positive for West African populations, with the highest value observed for Gambian and Mandenka (Fig. 3 and SI Appendix, Supplementary Note 10). Together, these results indicate the presence of the same ancestral components in ∼15,000-y old and ∼7,000-y-old populations from Morocco, strongly suggesting a temporal continuity between Later Stone Age and Early Neolithic populations in the Maghreb. However, it is important to take into account that the number of ancient genomes available for comparison is still low and future sampling can provide further refinement in the evolutionary history of North Africa.

Genetic analyses have revealed that the population history of modern North Africans is quite complex (11). Based on our aDNA analysis, we identify an Early Neolithic Moroccan component that is (i) restricted to North Africa in present-day populations (11); (ii) the sole ancestry in IAM samples; and (iii) similar to the one observed in Later Stone Age samples from Morocco (17). We conclude that this component, distantly related to that of Epipaleolithic communities from the Levant, represents the autochthonous Maghrebi ancestry associated with Berber populations. Our data suggests that human populations were isolated in the Maghreb since Upper Paleolithic times. Our hypothesis is in agreement with archaeological research pointing to the first stage of the Neolithic expansion in Morocco as the result of a local population that adopted some technological innovations, such as pottery production or farming, from neighboring areas.

By 3,000 BCE, a continuity in the Neolithic spread brought Mediterranean-like ancestry to the Maghreb, most likely from Iberia. Other archaeological remains, such as African elephant ivory and ostrich eggs found in Iberian sites, confirm the existence of contacts and exchange networks through both sides of the Gibraltar strait at this time. Our analyses strongly support that at least some of the European ancestry observed today in North Africa is related to prehistoric migrations, and local Berber populations were already admixed with Europeans before the Roman conquest. Furthermore, additional European/ Iberian ancestry could have reached the Maghreb after KEB people; this scenario is supported by the presence of Iberian-like Bell-Beaker pottery in more recent stratigraphic layers of IAM and KEB caves. Future paleogenomic efforts in North Africa will further disentangle the complex history of migrations that forged the ancestry of the admixed populations we observe today.

Ancestry inference in ancient samples from North Africa and the Iberian Peninsula. (B) ADMIXTURE analysis using the Human Origins dataset (European, Middle Eastern, and North African populations) for modern and ancient samples (K = 8). (D) Detail of ADMIXTURE analysis using the Human Origins dataset (European, Middle Eastern, North African, and sub-Saharan African populations) for modern and ancient samples, including Taforalt.

Also, from the main author’s Twitter account:

I just realized that the paragraph with information on data availability is missing! Sequence data in the European Nucleotide Archive (PRJEB22699). Consensus mtDNA sequences are available at the National Center of Biotechnology Information (Accession Numbers MF991431-MF991448).

I find it hard to believe that this genetic continuity from Upper Palaeolithic to Late Neolithic could be representative of an autochthonous development of Afroasiatic. An important population movement – likely more than one – must be found in ancient DNA influencing North-Central and North-East Africa, probably during the time of the Green Sahara corridor.

See here:

Bayesian estimation of partial population continuity by using ancient DNA and spatially explicit simulations


Open access Bayesian estimation of partial population continuity by using ancient DNA and spatially explicit simulations, by Silva et al., Evolutionary Applications (2018).

Abstract (emphasis mine):

The retrieval of ancient DNA from osteological material provides direct evidence of human genetic diversity in the past. Ancient DNA samples are often used to investigate whether there was population continuity in the settlement history of an area. Methods based on the serial coalescent algorithm have been developed to test whether the population continuity hypothesis can be statistically rejected by analysing DNA samples from the same region but of different ages. Rejection of this hypothesis is indicative of a large genetic shift, possibly due to immigration occurring between two sampling times. However, this approach is only able to reject a model of full continuity model (a total absence of genetic input from outside), but admixture between local and immigrant populations may lead to partial continuity. We have recently developed a method to test for population continuity that explicitly considers the spatial and temporal dynamics of populations. Here we extended this approach to estimate the proportion of genetic continuity between two populations, by using ancient genetic samples. We applied our original approach to the question of the Neolithic transition in Central Europe. Our results confirmed the rejection of full continuity, but our approach represents an important step forward by estimating the relative contribution of immigrant farmers and of local hunter‐gatherers to the final Central European Neolithic genetic pool. Furthermore, we show that a substantial proportion of genes brought by the farmers in this region were assimilated from other hunter‐gatherer populations along the way from Anatolia, which was not detectable by previous continuity tests. Our approach is also able to jointly estimate demographic parameters, as we show here by finding both low density and low migration rate for pre‐Neolithic hunter‐gatherers. It provides a useful tool for the analysis of the numerous aDNA datasets that are currently being produced for many different species.

A) Different zones defined for computing proportions of ancestry in Central Europeans 4,500 BP. B) Schematic representation of various population contributions. C) Mean proportions of ancestry from the various PHG zones (A+B+C+D) in Central European populations from zone A at the end of the Neolithic transition 4,500 BP, computed for autosomal and mitochondrial markers.

Relevant excerpts:

Our results are in general accordance with two distinct ancestry components that have previously been detected at the continental scale by Lazaridis, Patterson et al. (2014): the “early European farmer” (EEF), which corresponds here to the NFA from Anatolia (zone C in Figure 3), and the “West European hunter-gatherer” (WHG), which corresponds here to the PHG from zones A and B in Figure 3. Notably, the contribution of an Ancient North Eurasians (ANE) component is not included in our model as we did not consider potential post-Neolithic immigration waves, which could have contributed to the modern European genetic pool, such as the wave that came from the Pontic steppes and was associated with the Yamnaya culture (Haak, Lazaridis et al. 2015). Without considering the ANE ancestry component, our estimate of the autosomal genetic contribution of Early farmers to the gene pool of Central European populations (25%) tends to be lower than the EEF ancestry estimated in most modern Western European populations, but is of the same order than the estimations in modern Estonians and in the ancient Late Neolithic genome “Karsdorf” from Germany (Lazaridis, Patterson et al. 2014, Haak, Lazaridis et al. 2015). Note that the contribution of hunter-gatherers to Neolithic communities appears to be variable in different regions of Europe (Skoglund, Malmstrom et al. 2012, Brandt, Haak et al. 2013, Lazaridis, Patterson et al. 2014), while we computed an average value for Central Europe. Moreover, we computed the ancestry of the two groups at the end of the Neolithic period while previous studies estimated it in modern times. Finally, previous studies used molecular information to directly estimate admixture proportions, while we use molecular information to estimate the model parameters and, then, we computed the expected genetic contributions of both groups using the best parameters, without using molecular information during this second step. Model assumptions may thus influence the inferences on the relative genetic contribution of both groups. In particular, we made the assumption of a uniform expansion of NFA with constant and similar assimilation of PHG over the whole continent but spatio-temporally heterogeneous environment, variable assimilation rate and long distance dispersal may have played an important role. The effects of those factors should be investigated in future studies.

Demographic history and genetic adaptation in the Himalayan region

Open access Demographic history and genetic adaptation in the Himalayan region inferred from genome-wide SNP genotypes of 49 populations, by Arciero et al. Mol. Biol. Evol (2018), accepted manuscript (msy094).

Abstract (emphasis mine):

We genotyped 738 individuals belonging to 49 populations from Nepal, Bhutan, North India or Tibet at over 500,000 SNPs, and analysed the genotypes in the context of available worldwide population data in order to investigate the demographic history of the region and the genetic adaptations to the harsh environment. The Himalayan populations resembled other South and East Asians, but in addition displayed their own specific ancestral component and showed strong population structure and genetic drift. We also found evidence for multiple admixture events involving Himalayan populations and South/East Asians between 200 and 2,000 years ago. In comparisons with available ancient genomes, the Himalayans, like other East and South Asian populations, showed similar genetic affinity to Eurasian hunter-gatherers (a 24,000-year-old Upper Palaeolithic Siberian), and the related Bronze Age Yamnaya. The high-altitude Himalayan populations all shared a specific ancestral component, suggesting that genetic adaptation to life at high altitude originated only once in this region and subsequently spread. Combining four approaches to identifying specific positively-selected loci, we confirmed that the strongest signals of high-altitude adaptation were located near the Endothelial PAS domain-containing protein 1 (EPAS1) and Egl-9 Family Hypoxia Inducible Factor 1 (EGLN1) loci, and discovered eight additional robust signals of high-altitude adaptation, five of which have strong biological functional links to such adaptation. In conclusion, the demographic history of Himalayan populations is complex, with strong local differentiation, reflecting both genetic and cultural factors; these populations also display evidence of multiple genetic adaptations to high-altitude environments.

Population samples analysed in this study. A. Map of South and East Asia, highlighting the four regions examined, and the colour assigned to each. B. Samples from the Tibetan Plateau. C.Samples from Nepal. D. Samples from Bhutan and India. The circle areas are proportional to the sample sizes. The three letter population codes in B-D are defined in supplementary table S1.

Relevant excerpts:

Genetic affinity to ancestral populations

We explored the genetic affinity between the Himalayan populations and five ancient genomes using f3-outgroup statistics. Himalayans show greater affinity to Eurasian hunter-gatherers (MA-1, a 24,000- year-old Upper Palaeolithic Siberian), and the related Bronze Age Yamnaya, than to European farmers (5,500-4,800 years ago; Fig. 5A) or to European hunter-gatherers (La Braña, 7,000 years ago; Fig. 5B), like other South and East Asian populations. We further explored the affinity of Himalayan populations by comparing them with the 45,000-year-old Upper Palaeolithic hunter-gatherer (Ust’-Ishim) and each of MA-1, La Braña, or Yamnaya. Himalayan individuals cluster together with other East Asian populations and show equal distance from Ust’-Ishim and the other ancient genomes, probably because Ust’-Ishim belongs to a much earlier period of time (supplementary fig. S15). We also explored genetic affinity between modern Himalayan populations and five ancient Himalayans (3,150 1,250 years old) from Nepal. The ancient individuals cluster together with modern Himalayan populations in a worldwide PCA (supplementary fig. S16), and the f3-outgroup statistics show modern high-altitude populations have the closest affinity with these ancient Himalayans, suggesting that these ancient individuals could represent a proxy for the first populations residing in the region (supplementary fig. S17 and supplementary table S4). Finally, we explored the genetic affinity of Himalayan samples with the archaic genomes of Denisovans and Neanderthals (Skoglund and Jakobsson 2011), and found that they show a similar sharing pattern with Denisovans and Neanderthals to the other South and East Asian populations. Individuals belonging to four Nepalese, one Cambodian, and three Chinese populations show the highest Denisovan sharing (after populations from Australia and Papua New Guinea) but these values are not significantly greater than other South and East Asian populations (supplementary figs. S18 and S19).

Genetic structure of the Himalayan region populations from analyses using unlinked SNPs. A. PCA of the Himalayan and HGDP-CEPH populations. Each dot represents a sample, coded by region as indicated. The Himalayan region samples lie between the HGDP-CEPH East Asian and South Asian samples on the right-hand side of the plot. B. PCA of the Himalayan populations alone. Each dot represents a sample, coded by country or region as indicated. Most samples lie on an arc between Bhutanese and Nepalese samples; Toto (India) are seen as extreme outlier in the bottom left corner, while Dhimal (Nepal) and Bodo (India) also form outliers.

NOTE. The variance explained in the PCA graphics seems to be too high. This happened recently also with the Damgaard et al. (2018) papers (see here the comment by Iosif Lazaridis).

Similarities and differences between high-altitude Himalayan

The most striking example is provided by the Toto from North India, an isolated tribal group with the lowest genetic diversity of the Himalayan populations examined here, indicated by the smallest long-term Ne (supplementary fig. S5), and a reported census size of 321 in 1951 (Mitra 1951), although their numbers have subsequently increased. Despite this extreme substructure, shared common ancestry among the high-altitude populations (Fig. 2C and Fig. 3) can be detected, and the Nepalese in general are distinguished from the Bhutanese and Tibetans (Fig. 2C) and they also cluster separately (Fig. 3). In a worldwide context, they share an ancestral component with South Asians (supplementary fig. S2). On the other hand, the Tibetans do not show detectable population substructure, probably due to a much more recent split in comparison with the other populations (Fig. 2C and supplementary fig. S6). The genetic similarity between the high-altitude populations, including Tibetans, Sherpa and Bhutanese, is also supported by their clustering together on the phylogenetic tree, the PCA generated from the co-ancestry matrix generated by fineSTRUCTURE (supplementary fig. S10 and S11), the lack of statistical significance for most of the D-statistics tests (Yoruba, Han; high-altitude Himalayan 1, high-altitude Himalayan 2), and the absence of correlation between the increased genetic affinity to lowland East Asians and the spatial location of the Himalayan populations (supplementary figs. S12 and S13). Together, these results suggest the presence of a single ancestral population carrying advantageous variants for high-altitude adaptation that separated from lowland East Asians, and then spread and diverged into different populations across the Himalayan region. (…)

Recent admixture events

Genetic structure of the Himalayan region populations from analyses using unlinked SNPs. C. ADMIXTURE (K values of 2 to 6, as indicated) analysis of the Himalayan samples. Note that most increases in the value of K result in single population being distinguished. Population codes in C are defined in supplementary table S1.

Himalayan populations show signatures of recent admixture events, mainly with South and East Asian populations as well as within the Himalayan region itself. Newar and Lhasa show the oldest signature of admixture, dated to between 2,000 and 1,000 years ago. Majhi and Dhimal display signatures of admixture within the last 1,000 years. Chetri and Bodo show the most recent admixture events, between 500 and 200 years ago (Fig. 4, supplementary tables S3). The comparison between the genetic tree and the linguistic association of each Himalayan population highlights the agreement between genetic and linguistic sub-divisions, in particular in the Bhutanese and Tibetan populations. Nepalese populations show more variability, with genetic sub-clusters of populations belonging to different linguistic affiliations (Fig. 3B). Modern high-altitude Himalayans show genetic affinity with ancient genomes from the same region (supplementary fig. S17), providing additional support for the idea of an ancient high-altitude population that spread across the Himalayan region and subsequently diverged into several of the present-day populations. Furthermore, Himalayan populations show a similar pattern of allele sharing with Denisovans as other South-East Asian populations (supplementary fig. S18 and S19). Overall, geographical isolation, genetic drift, admixture with neighbouring populations and linguistic subdivision played important roles in shaping the genetic variability we see in the Himalayan region today.


Ancient Patagonian genomes suggest origin and diversification of late maritime hunter-gatherers


Genomic insights into the origin and diversification of late maritime hunter-gatherers from the Chilean Patagonia, by de la Fuente et al. PNAS (2018) published ahead of print.

Abstract (emphasis mine):

Patagonia was the last region of the Americas reached by humans who entered the continent from Siberia ∼15,000–20,000 y ago. Despite recent genomic approaches to reconstruct the continental evolutionary history, regional characterization of ancient and modern genomes remains understudied. Exploring the genomic diversity within Patagonia is not just a valuable strategy to gain a better understanding of the history and diversification of human populations in the southernmost tip of the Americas, but it would also improve the representation of Native American diversity in global databases of human variation. Here, we present genome data from four modern populations from Central Southern Chile and Patagonia (n = 61) and four ancient maritime individuals from Patagonia (∼1,000 y old). Both the modern and ancient individuals studied in this work have a greater genetic affinity with other modern Native Americans than to any non-American population, showing within South America a clear structure between major geographical regions. Native Patagonian Kawéskar and Yámana showed the highest genetic affinity with the ancient individuals, indicating genetic continuity in the region during the past 1,000 y before present, together with an important agreement between the ethnic affiliation and historical distribution of both groups. Lastly, the ancient maritime individuals were genetically equidistant to a ∼200-y-old terrestrial hunter-gatherer from Tierra del Fuego, which supports a model with an initial separation of a common ancestral group to both maritime populations from a terrestrial population, with a later diversification of the maritime groups.

PCA of ancient and present-day South American populations. All of the ancient individuals were projected onto the first two PCs by using the lsq option from smartpca.


The origin and expansion of Pama–Nyungan languages across Australia

Yet another questionable paper by Nature, The origin and expansion of Pama–Nyungan languages across Australia, by Bouckaert, Bowern & Atkinson, Nat Ecol Evol (2018).


It remains a mystery how Pama–Nyungan, the world’s largest hunter-gatherer language family, came to dominate the Australian continent. Some argue that social or technological advantages allowed rapid language replacement from the Gulf Plains region during the mid-Holocene. Others have proposed expansions from refugia linked to climatic changes after the last ice age or, more controversially, during the initial colonization of Australia. Here, we combine basic vocabulary data from 306 Pama–Nyungan languages with Bayesian phylogeographic methods to explicitly model the expansion of the family across Australia and test between these origin scenarios. We find strong and robust support for a Pama–Nyungan origin in the Gulf Plains region during the mid-Holocene, implying rapid replacement of non-Pama–Nyungan languages. Concomitant changes in the archaeological record, together with a lack of strong genetic evidence for Holocene population expansion, suggests that Pama–Nyungan languages were carried as part of an expanding package of cultural innovations that probably facilitated the absorption and assimilation of existing hunter-gatherer groups.

“Diversification of the Pama–Nyungan language family. Maximum clade credibility tree showing the inferred timing and emergence of the major branches and their subsequent diversification.”

Even with my absolute lack of knowledge on Australian languages, I am not conviced. Not at all.

I have already expressed more than once my opinion on Glottochronology – and the improved method of this paper seems like the final twist of the screw for its strongest proponents.

Interestingly, this paper includes the same journal, author, and (mostly) method of the famous Language-tree divergence times support the Anatolian theory of Indo-European origin (2003).

And we have also seen how most suggested prehistorical cultural diffusion events were actually migrations, so it seems rather odd to dare publish this right now.

At a time of groundbreaking genomic papers being published on South-East Asian migrations, and probably expecting more on the region – including Australia – , this paper seems to me quite unnecessary.

It will especially not help Nature make forget its latest fiasco on Indo-European migrations.

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The demographic history and mutational load of African hunter-gatherers and farmers


Interesting new article (behind paywall), The demographic history and mutational load of African hunter-gatherers and farmers, Nat Ecol Evol (2018)

Abstract (emphasis mine):

Understanding how deleterious genetic variation is distributed across human populations is of key importance in evolutionary biology and medical genetics. However, the impact of population size changes and gene flow on the corresponding mutational load remains a controversial topic. Here, we report high-coverage exomes from 300 rainforest hunter-gatherers and farmers of central Africa, whose distinct subsistence strategies are expected to have impacted their demographic pasts. Detailed demographic inference indicates that hunter-gatherers and farmers recently experienced population collapses and expansions, respectively, accompanied by increased gene flow. We show that the distribution of deleterious alleles across these populations is compatible with a similar efficacy of selection to remove deleterious variants with additive effects, and predict with simulations that their present-day additive mutation load is almost identical. For recessive mutations, although an increased load is predicted for hunter-gatherers, this increase has probably been partially counteracted by strong gene flow from expanding farmers. Collectively, our predicted and empirical observations suggest that the impact of the recent population decline of African hunter-gatherers on their mutation load has been modest and more restrained than would be expected under a fully recessive model of dominance.

“Inferred demographic models of the studied populations. a, EUR-first branching model, in which ancestors of EUR (aEUR) diverged from African populations before the divergence of the ancestors of RHG (aRHG) and AGR (aAGR). b, RHG-first branching model, in which aRHG were the first to diverge from the other groups. c, AGR-first branching model, in which aAGR were the first to diverge from the other groups. We assumed an ancient change in the size of the ancestral population of all humans (ANC). We assumed that each subsequent divergence of populations was followed by an instantaneous change in the effective population size (Ne). We also assumed that there were two epochs of migration between the following population pairs: wAGR/aAGR and wRHG/aRHG, eAGR/aAGR and eRHG/aRHG, and EUR and eAGR/aAGR. The figure labels correspond to the parameters of the model estimated by maximum likelihood and the 95% confidence intervals assessed by bootstrapping by site 100 times (Supplementary Table 4). Vertical arrow corresponds to the direction of time, from past to present, with divergence times given on the left and expressed in thousand years ago(ka). Effective population sizes (N) are given within the diagram and expressed in thousands of individuals. Bold horizontal arrows indicate an estimated parameter for the effective strength of migration 2Nm > 1, while thin horizontal arrows indicate 2Nm ≤ 1.”

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The preferred northwest passage to Scandinavia

Pontus Skoglund writes (and shares publicly) his perspective on early postglacial migrations of hunter-gatherers into Scandinavia, in Northwest Passage to Scandinavia (Nat. Ecol. Evol.): an initial migration from the south and a second coastal migration north of the Scandinavian ice sheet.

He sums up the recently published Open Access paper Population genomics of Mesolithic Scandinavia: Investigating early postglacial migration routes and high-latitude adaptation, by Günther, Malmström , Svensson, Omrak, et al. PLoS Biol (2018) 16(1): e2003703, based on preprint at BioRxiv Genomics of Mesolithic Scandinavia reveal colonization routes and high-latitude adaptation (2017).


Scandinavia was one of the last geographic areas in Europe to become habitable for humans after the Last Glacial Maximum (LGM). However, the routes and genetic composition of these postglacial migrants remain unclear. We sequenced the genomes, up to 57× coverage, of seven hunter-gatherers excavated across Scandinavia and dated from 9,500–6,000 years before present (BP). Surprisingly, among the Scandinavian Mesolithic individuals, the genetic data display an east–west genetic gradient that opposes the pattern seen in other parts of Mesolithic Europe. Our results suggest two different early postglacial migrations into Scandinavia: initially from the south, and later, from the northeast. The latter followed the ice-free Norwegian north Atlantic coast, along which novel and advanced pressure-blade stone-tool techniques may have spread. These two groups met and mixed in Scandinavia, creating a genetically diverse population, which shows patterns of genetic adaptation to high latitude environments. These potential adaptations include high frequencies of low pigmentation variants and a gene region associated with physical performance, which shows strong continuity into modern-day northern Europeans.

The ice sheet distribution – which did not improve nuch for thousands of years – was clearly the greatest barrier for potential migrations in the region.

Baltischer Süßwassersee Vorläufer der Ostsee vor 12.000 Jahren, by Juschki and Koyos at Wikipedia

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