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Thread: Massive paper on Stone age Europe and Eurasia. New HG ancestry in Steppe herders

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  1. #1
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    stone age eurasia

    STONE AGE EURASIA


    https://www.biorxiv.org/content/10.1...05.04.490594v1

    Allenthoft & Willerslev et. al. 2022, Biorxiv.

    Abstract:

    The transitions from foraging to farming and later to pastoralism in Stone Age Eurasia (c. 11-3 thousand years before present, BP) represent some of the most dramatic lifestyle changes in human evolution. We sequenced 317 genomes of primarily Mesolithic and Neolithic individuals from across Eurasia combined with radiocarbon dates, stable isotope data, and pollen records. Genome imputation and co-analysis with previously published shotgun sequencing data resulted in >1600 complete ancient genome sequences offering fine-grained resolution into the Stone Age populations. We observe that: 1) Hunter-gatherer groups were more genetically diverse than previously known, and deeply divergent between western and eastern Eurasia. 2) We identify hitherto genetically undescribed hunter-gatherers from the Middle Don region that contributed ancestry to the later Yamnaya steppe pastoralists; 3) The genetic impact of the Neolithic transition was highly distinct, east and west of a boundary zone extending from the Black Sea to the Baltic. Large-scale shifts in genetic ancestry occurred to the west of this "Great Divide", including an almost complete replacement of hunter-gatherers in Denmark, while no substantial ancestry shifts took place during the same period to the east. This difference is also reflected in genetic relatedness within the populations, decreasing substantially in the west but not in the east where it remained high until c. 4,000 BP; 4) The second major genetic transformation around 5,000 BP happened at a much faster pace with Steppe-related ancestry reaching most parts of Europe within 1,000-years. Local Neolithic farmers admixed with incoming pastoralists in eastern, western, and southern Europe whereas Scandinavia experienced another near-complete population replacement. Similar dramatic turnover-patterns are evident in western Siberia; 5) Extensive regional differences in the ancestry components involved in these early events remain visible to this day, even within countries. Neolithic farmer ancestry is highest in southern and eastern England while Steppe-related ancestry is highest in the Celtic populations of Scotland, Wales, and Cornwall (this research has been conducted using the UK Biobank resource); 6) Shifts in diet, lifestyle and environment introduced new selection pressures involving at least 21 genomic regions. Most such variants were not universally selected across populations but were only advantageous in particular ancestral backgrounds. Contrary to previous claims, we find that selection on the FADS regions, associated with fatty acid metabolism, began before the Neolithisation of Europe. Similarly, the lactase persistence allele started increasing in frequency before the expansion of Steppe-related groups into Europe and has continued to increase up to the present. Along the genetic cline separating Mesolithic hunter-gatherers from Neolithic farmers, we find significant correlations with trait associations related to skin disorders, diet and lifestyle and mental health status, suggesting marked phenotypic differences between these groups with very different lifestyles. This work provides new insights into major transformations in recent human evolution, elucidating the complex interplay between selection and admixture that shaped patterns of genetic variation in modern populations.


    Our study comprises the largest genomic dataset on European hunter-gatherers to date, including
    329 113 imputed hunter-gatherer genomes of which 79 were sequenced in this study. Among them, we
    330 report a 0.83X genome of an Upper Palaeolithic (UP) skeleton from Kotias Klde Cave in Georgia,
    331 Caucasus (NEO283), directly dated to 26,052 - 25,323 cal BP (95%)
    . In the PCA of all non-African
    332 individuals, it occupies a position distinct from other previously sequenced UP individuals, shifted
    333 towards west Eurasians along PC1 (Supplementary Note 3d). Using admixture graph modelling, we
    334 find that this Caucasus UP lineage derives from a mixture of predominantly West Eurasian UP
    335 hunter-gatherer ancestry (76%) with ~24% contribution from a “basal Eurasian” ghost population,
    first observed in West Asian Neolithic individuals29 336 (Extended Data Fig. 5A).
    Models attempting to
    337 reconstruct major post-LGM clusters such as European hunter-gatherers and Anatolian farmers
    338 without contributions from this Caucasus UP lineage provided poor admixture graph fits or were
    339 rejected in qpAdm analyses (Extended Data Fig. 5B,C). These results thus suggest a central role of
    340 the descendants related to this Caucasus UP lineage in the formation of later West Eurasian
    341 populations, consistent with recent genetic data from the nearby Dzudzuana Cave, also in
    Georgia30 342
    .
    We replicate previous results of broadscale genetic structure correlated to geography in European hunter-gatherers after the LGM17 347 , while
    348 also revealing novel insights into their fine-scale structure. Ancestry related to southern European
    349 hunter-gatherers (source: Italy_15000BP_9000 BP) predominates in western Europe.
    This includes
    350 Denmark, where our 28 sequenced and imputed hunter-gatherer genomes derive almost exclusively
    351 from this cluster, with remarkable homogeneity across a 5,000 year transect (Fig. 3A). In contrast,
    352 hunter-gatherer individuals from the eastern and far northern reaches of Europe show the highest
    353 proportions of Russian hunter-gatherer ancestry (source: RussiaNW_11000BP_8000BP; Fig. 2B,
    354 D), with genetic continuity until ~5,000 BP in Russia.
    Ancestry related to Mesolithic hunter355 gatherer populations from Ukraine (source: Ukraine_10000BP_4000BP) is carried in highest
    356 proportions in hunter-gatherers from a geographic corridor extending from south-eastern Europe
    357 towards the Baltic and southern Scandinavia.
    Swedish Mesolithic individuals derive up to 60% of
    358 their ancestry from that source (Fig. 2C). Our results thus indicate northwards migrations of at least
    359 three distinct waves of hunter-gatherer ancestry into Scandinavia: a predominantly southern
    360 European source into Denmark; a source related to Ukrainian and south-eastern European hunter361 gatherers into the Baltic and southern Sweden; and a northwest Russian source into the far north,
    before venturing south along the Atlantic coast of Norway31 362 (Fig. 2).
    These movements are likely to
    represent post glacial expansions from refugia areas shared with many plant and animal species32,33 363 .



    Interestingly, two herein reported ~7,300-year-old imputed
    383 genomes from the Middle Don River region in the Pontic-Caspian steppe (Golubaya Krinitsa,
    384 NEO113 & NEO212) derive ~20-30% of their ancestry from a source cluster of hunter-gatherers
    385 from the Caucasus (Caucasus_13000BP_10000BP) (Fig. 3). Additional lower coverage (non386 imputed) genomes from the same site project in the same PCA space (Fig. 1D), shifted away from
    387 the European hunter-gatherer cline towards Iran and the Caucasus.
    Our results thus document
    388 genetic contact between populations from the Caucasus and the Steppe region as early as 7,300
    389 years ago...




    From approximately 5,000 BP, an ancestry component appears on the eastern European plains in
    425 Early Bronze Age Steppe pastoralists associated with the Yamnaya culture and it rapidly spreads
    across Europe through the expansion of the Corded Ware complex (CWC) and related cultures20,21 426 .
    427 We demonstrate that this “steppe” ancestry (Steppe_5000BP_4300BP) can be modelled as a
    428 mixture of ~65% ancestry related to herein reported hunter-gatherer genomes from the Middle Don
    429 River region (MiddleDon_7500BP) and ~35% ancestry related to hunter-gatherers from Caucasus
    430 (Caucasus_13000BP_10000BP) (Extended Data Fig. 4).
    Thus, Middle Don hunter-gatherers, who
    431 already carry ancestry related to Caucasus hunter-gatherers (Fig. 2), serve as a hitherto unknown
    432 proximal source
    for the majority ancestry contribution into Yamnaya genomes. The individuals in
    433 question derive from the burial ground Golubaya Krinitsa (Supplementary Note 3). Material culture
    434 and burial practices at this site are similar to the Mariupol-type graves, which are widely found in
    435 neighbouring regions of Ukraine, for instance along the Dnepr River. They belong to the group of
    436 complex pottery-using hunter-gatherers mentioned above, but the genetic composition at Golubaya
    437 Krinitsa is different from the remaining Ukrainian sites (Fig 2A, Extended Data Fig. 4).




    Individuals associated with Neolithic
    470 farming cultures from Denmark show some of the highest overall hunter-gatherer ancestry
    471 proportions (up to ~25%), mostly derived from Western European-related hunter-gatherers
    472 (EuropeW_13500BP_8000BP) supplemented with marginal contribution from local Danish groups
    473 in some individuals (Extended Data Fig. 7D; Supplementary Note 3f). We estimated the timing of
    the admixture using the linkage-disequilibrium-based method DATES48 474 at ~6,000 BP. Both lines of
    475 evidence thus suggest that a significant part of the hunter-gatherer admixture observed in Danish
    476 Neolithic individuals occurred already before the arrival of the incoming Neolithic people in the
    477 region (Extended Data Fig. 7), and further imply Central Europe as a key region in the resurgence
    478 of HG ancestry.



    The second continental-wide and CWC-mediated transition from Neolithic farmer ancestry to
    497 Steppe-related ancestry was found to differ markedly between geographic regions. The contribution
    498 of local Neolithic farmer ancestry to the incoming groups was high in eastern, western and southern
    Europe, reaching >50% on the Iberian Peninsula (“postNeol” set; Extended Data Fig. 4, 6B, C)34 499 .

    500 Scandinavia, however, portrays a dramatically different picture, with a near-complete replacement
    501 of the local Neolithic farmer population inferred across all sampled individuals (Extended Data Fig.
    502 7B, C). Following the second transition, Neolithic Anatolian-related farmer ancestry remains in
    503 Scandinavia, but the source is now different. It can be modelled as deriving almost exclusively from
    504 a genetic cluster associated with the Late Neolithic Globular Amphora Culture (GAC)
    505 (Poland_5000BP_4700BP; Extended Data Fig. 4).
    Strikingly, after the Steppe-related ancestry was
    506 first introduced into Europe (Steppe_5000BP_4300BP), it expanded together with GAC-related
    507 ancestry across all sampled European regions (Extended Data Fig. 7I).
    This suggests that the spread
    508 of steppe-related ancestry throughout Europe was predominantly mediated through groups that were
    509 already admixed with GAC-related farmer groups of the eastern European plains.




    The Neolithic transition also
    610 marks a considerable rise in frequency of major effect alleles associated with light hair
    pigmentation79 611
    , whereas polygenic score predictions for height are generally low throughout the
    612 first millennium of the Neolithic (Funnel Beaker epoch), echoing previous findings based on a
    smaller set of individuals45,80 613 .



    . The most recent individual
    618 in our Danish dataset with Mesolithic WHG ancestry is “Dragsholm Man” (NEO962), dated to
    619 5,947-5,664 cal. BP (95%) and archaeologically assigned to the Neolithic Funnel Beaker farming
    culture based on his grave goods81,82 620 . Our data confirms a typical Neolithic diet matching the
    621 cultural affinity but contrasting his WHG ancestry. Thus, Dragsholm Man represents a local person
    622 of Mesolithic ancestry who lived in the short Mesolithic-Neolithic transition period
    and adopted a
    623 Neolithic culture and diet.

  2. #2
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    Thanks for sharing.
    Damn, this paper is huge and I don’t have any free time to read it.

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    Thanks.
    I'm particulary curious about this.

    Quote Originally Posted by etrusco View Post
    2) We identify hitherto genetically undescribed hunter-gatherers from the Middle Don region that contributed ancestry to the later Yamnaya steppe pastoralists; their fine-scale structure. .

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    “Genome imputation and co-analysis with previously published shotgun sequencing data resulted in >1600 complete ancient genome sequences offering fine-grained resolution into the Stone Age populations „

    The paper says it is based on imputation, so I will not take the results serious.
    Imputation needs a base in modern samples, because there are not thousands of ancient samples available.

    Even with modern people, imputation stays a kind of scam and cannot be used for specific SNPs that are disease related or associated with physical or metabolic traits. All traist will be created based on the associated population in the Imputer database and does not show individual differences and cannot simulate populations that where never sequenced before.
    My experience with Imputation is very bad.

    This is the downfall of ancient DNA research as I often was afraid of. Cheap cost, low quality DNA, big papers.

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    2 members found this post helpful.
    This paper is huge indeed, and more than 300 samples of ancient DNA bring us a lot of new information but at the same time there are some really ridiculous charts in this paper and some statements reek of eugenics. Genetic testing is a powerful tool, but many geneticists are still not up to it. I still have to read it carefully though.

    Last edited by Pax Augusta; 06-05-22 at 17:04.

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    3 members found this post helpful.
    Quote Originally Posted by Pax Augusta View Post
    This paper is huge indeed, and more than 300 samples of ancient DNA bring us a lot of new information but at the same time there are some really ridiculous charts in this paper and some statements reek of eugenics. Genetic testing is a powerful tool, but many geneticists are still not up to it. I still have to read it carefully though.

    These are indeed really strange charts.
    Also why is Uk divided in small areas and france and italy are taken as one? They have far more internal diversity than UK.
    And egypt and italy the same steppe admixture? ridiculous. The farmer in north africa is suspiciously high too.
    How can geneticist and academics produce such results? unbelievable

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    Quote Originally Posted by Stefano View Post
    These are indeed really strange charts.
    Also why is Uk divided in small areas and france and italy are taken as one? They have far more internal diversity than UK.
    And egypt and italy the same steppe admixture? ridiculous. The farmer in north africa is suspiciously high too.
    How can geneticist and academics produce such results? unbelievable

    Probably because for France and Italy they used some kind of average, I don't know, while not for UK. I guess the main focus of this paper is not so much regional divisions within a country, and that's understandable, UK might be the exception perhaps because the paper was funded or co-funded with UK money. I don't know. But these are the least of the problems, I think. Whereas this chart has very implausible results. Maybe it's me who has problems with my vision, there is more WHG in China than in south-eastern Europe and Italy, EHG has its peaks in Finland and Mongolia, CHG has its peak between Pakistan and India, Yamnaya is widespread in non-European countries where it has never been found, Farmer ancestry seems too high in North Africa... I really don't know if it's a problem of metodology, of errors, of sloppiness of whoever graphically made the chart, but the fact that geneticists may produce results with little credibility is nothing new.

    Last edited by Pax Augusta; 12-05-22 at 14:07.

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    Concerning the steppe part of this paper:

    Interestingly, two herein reported ~7,300-year-old imputed genomes from the Middle Don River region in the Pontic-Caspian steppe (Golubaya Krinitsa, NEO113 & NEO212) derive ~20-30% of their ancestry from a source cluster of hunter-gatherers from the Caucasus (Caucasus_13000BP_10000BP) (Fig. 3). Additional lower coverage (non imputed) genomes from the same site project in the same PCA space (Fig. 1D), shifted away from the European hunter-gatherer cline towards Iran and the Caucasus. Our results thus document genetic contact between populations from the Caucasus and the Steppe region as early as 7,300 years ago...

    From approximately 5,000 BP, an ancestry component appears on the eastern European plains in Early Bronze Age Steppe pastoralists associated with the Yamnaya culture and it rapidly spreads across Europe through the expansion of the Corded Ware complex (CWC) and related cultures. We demonstrate that this “steppe” ancestry (Steppe_5000BP_4300BP) can be modelled as a mixture of ~65% ancestry related to herein reported hunter-gatherer genomes from the Middle Don River region (MiddleDon_7500BP) and ~35% ancestry related to hunter-gatherers from Caucasus (Caucasus_13000BP_10000BP) (Extended Data Fig. 4). Thus, Middle Don hunter-gatherers, who already carry ancestry related to Caucasus hunter-gatherers (Fig. 2), serve as a hitherto unknown proximal source for the majority ancestry contribution into Yamnaya genomes. The individuals in question derive from the burial ground Golubaya Krinitsa (Supplementary Note 3). Material culture and burial practices at this site are similar to the Mariupol-type graves, which are widely found in neighbouring regions of Ukraine, for instance along the Dnepr River. They belong to the group of complex pottery-using hunter-gatherers mentioned above, but the genetic composition at Golubaya Krinitsa is different from the remaining Ukrainian sites (Fig 2A, Extended Data Fig. 4).
    In my opinion, the burial ground of Golubaya Krinitsa looks like the western-most spread of the EHG-CHG/Iran mating network in 5300BC (Neolithic), because neighbouring sites from Ukraine do have a different genetic composition, even though the material culture and burial practices are similar to the Mariupol-type graves.

    Y-DNA from the Middle Don is R1a and I2a, just like Sredny Stog:
    NEO113 Golubaya Krinitsa Russia CentralEasternEurope Russia_Neolithic_MiddleDon 50.073 39.878 thisStudy 7,423 7,172 7,298 direct XY R1a R1a 0.11 0.983 0 4.1.3.2.2 MiddleDon_7500BP Europe_15000BP_4000BP HG_EuropeE

    NEO212 Golubaya Krinitsa Russia CentralEasternEurope Russia_Neolithic_MiddleDon 50.072 39.894 thisStudy 7,475 7,311 7,393 direct XY I2a I2a1b1a2 3.18 0.996 0 4.1.3.1.1.1 MiddleDon_7500BP Europe_15000BP_4000BP HG_EuropeE

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    Quote Originally Posted by Anfänger View Post
    Concerning the steppe part of this paper:.
    In my opinion, the burial ground of Golubaya Krinitsa looks like the western-most spread of the EHG-CHG/Iran mating network in 5300BC (Neolithic), because neighbouring sites from Ukraine do have a different genetic composition, even though the material culture and burial practices are similar to the Mariupol-type graves.
    Y-DNA from the Middle Don is R1a and I2a, just like Sredny Stog:
    "And they also found one J1 here:
    NEO163 Vasilyevskiy kordon 17 Russia CentralEasternEurope Russia_Neolithic 52.9 40.03 thisStudy 5,721 5,486 5,604 direct XY J1 J1 0.23 0.990 0 4.1.1.2.1.2 DonRiver_5800BP_5300BP Europe_15000BP_4000BP HG_EuropeE"

    I think the R1a seems to be related with karelian R1a with mt DNA C and yDNA J. EHG were buried in the supine position, but SS in the supine and flexed leg position like yamna.

    Recent studies of ancient mitochondrial DNA (mtDNA) lineages have revealed the presence of East Eurasian mtDNA haplogroups in the Central European Neolithic. Here we report the finding of East Eurasian lineages in ancient mtDNA from two Neolithic cemeteries of the North Pontic Region (NPR) in Ukraine. In our study, comprehensive haplotyping information was obtained for 7 out of 18 specimens. Although the majority of identified mtDNA haplogroups belonged to the traditional West Eurasian lineages of H and U, three specimens were determined to belong to the lineages of mtDNA haplogroup C. This find extends the presence of East Eurasian lineages in Neolithic Europe from the Carpathian Mountains to the northern shores of the Black Sea and provides the first genetic account of Neolithic mtDNA lineages from the NPR.

    During the Neolithic, the North Pontic Region (NPR) was home to major prehistoric cultural conglomerates, among them—the Dnieper-Donets cultural complex (DD). The DD culture has been studied in approximately 200 sites in Ukraine and Byelorussia, including settlements and large collective cemeteries of the Mariupol-type (M-t).1 The main feature of M-t cemeteries is inhumation burial in the supine position. This burial rite differs from most local Mesolithic burial traditions and is characteristic of the ‘Euro-Siberian’ zone of extended burials, which are found from Lake Baikal and the forest and forest-steppe zones of the East European Plain to the northern part of Central Europe and Scandinavia.2, 3
    The distinct burial tradition of Neolithic populations of the NPR as well as their anthropological characteristics suggests an influx of external population sources. We set out to quantify the extent of gene flow into the NPR during the Neolithic. Recent advances in mitochondrial DNA (mtDNA) analysis have revealed global patterns of neutral marker variation that appear to correlate with the geographic origin of the source population.4, 5 To elucidate the maternal genetic lineages of the population that constructed the M-t cemeteries, we undertook a genetic analysis of ancient mtDNA extracted from specimens from two Neolithic NPR cemeteries2


    https://www.nature.com/articles/jhg2...0-6fe8245e2732

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    1 members found this post helpful.
    Thus we reach the magic 50% Caucasus/Iran in the steppe people, and the presence of Basal Eurasian. Some will no doubt not be happy with that conclusion.

    Likewise, they firmly place the LP gene uptick "before" the arrival of the steppe people, and the appearance of blonde hair to the Neolithic transition.

    They also claim to be able to pinpoint the source of the farmer ancestry in steppe invaders in admixture with GAC, which always seemed likely.

    That Willerslev is involved does give me pause.

    I have to read the whole thing carefully.


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    Very much looking forward to those (UP) Caucasian samples!

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    Quote Originally Posted by Jovialis View Post
    Very much looking forward to those (UP) Caucasian samples!
    Maybe someone can finally make an interesting new calculator using the UP Caucasus samples as a component with other contemporaneous samples.

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    2 members found this post helpful.
    Razib Khans opinion on this preprint:
    https://www.gnxp.com/WordPress/2022/...-of-the-danes/

    The comments are also interesting.

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    What I do not get...
    So to get to the supposed Yamnaya Steppe admix according to this model you would need these Don HGs, who within themselves had CHG, but on top of that you would need 30% more CHG. Meaning that the Don HGs admixed with a CHG heavy population to get to Yamnaya, all these predating steppe pastoralism? I mean its quite straight forward to get, simple really, but what confuses me is when you supplement these autosomal picture to the YDNA of modern Steppe derived populations as well as ancient samples.
    For one the female mediated admixture for the CHG makes 0 sense, as it would not increase the CHG post 5-4k BC in any population by 30%... even if these females were pure CHG, an autosomal profile which from what I gather did not exist at the time.

    So how do we reconcile the YDNA picture with what now 3(?) different groups of anthrogeneticists paint as far as autosomal early IE history goes?
    Can someone enlighten me what was the CHG YDNA?

    Edit: Wikipedia to the rescue:

    Jones et al. (2015) analyzed genomes from males from western Georgia, in the Caucasus, from the Late Upper Palaeolithic (13,300 years old) and the Mesolithic (9,700 years old). These two males carried Y-DNAhaplogroup: J* and J2a, later refined to J1-FT34521, and J2-Y12379*, and mitochondrial haplogroups of K3 and H13c, respectively.[9]

    So it seems J2a and J, I really am curious now if the 9700bp J2b is really a sample, or they just mislabeled the J2a as J2b on this paper.
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    Also for all the flack Davidski gets when his (quite often as the many deleted posts suggest) contrarian theories go south, credit where its due, if this paper can be relied on from what I gather IE homeland is indeed Europe, and not Iran. But I do think he mischaracterized Anthony's thesis, as from what I gather they are kind of saying the same thing, just interpreting it differently. An analysis I have no care to elaborate on.

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    3 members found this post helpful.
    These maps summarise well the evolution of each main regional ancestry in western Eurasia.

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    Quote Originally Posted by Maciamo View Post
    These maps summarise well the evolution of each main regional ancestry in western Eurasia.

    Extended Data Fig 6. Spatiotemporal kriging of four major ancestry clusters over the last 12,000 years of human history. LVN = ancestry maximized in Anatolian farmer populations. WHG = ancestry maximized in western European hunter

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    The thing is Caucasus in general is not the most hospitable place for HGs one would imagine. So possibly the geography rather acted like a refugia that kept particular pops alive to disperse in waves and affect all these other HG populations. At least that could explain it why it exuded so much influence on early European pops.

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    1 members found this post helpful.
    For anyone who has slogged through the whole paper and especially the parts on traits I'll just say that I find the methodology rather dodgy.

    Also, read through some of the comments on Khan's content. Irritability, more emotional lability etc. are not mental illnesses. They're personality traits.

    What I'm surprised by is that they didn't try to actually find the "real" mental illnesses, like clinical depression and bipolar disorder, which indeed track differently in Europe on a north south or south north cline.

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    Quote Originally Posted by Angela View Post
    For anyone who has slogged through the whole paper and especially the parts on traits I'll just say that I find the methodology rather dodgy.

    Also, read through some of the comments on Khan's content. Irritability, more emotional lability etc. are not mental illnesses. They're personality traits.

    What I'm surprised by is that they didn't try to actually find the "real" mental illnesses, like clinical depression and bipolar disorder, which indeed track differently in Europe on a north south or south north cline.
    Aren't those depressions often induced by the dark winters in the north and not by genetics?
    I know that DNA can make someone more prone to depression than others, but still there is still an external factor needed to induce it.

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    1 members found this post helpful.
    Quote Originally Posted by bicicleur 2 View Post
    Aren't those depressions often induced by the dark winters in the north and not by genetics?
    I know that DNA can make someone more prone to depression than others, but still there is still an external factor needed to induce it.
    Clinical depression is very highly genetically heritable, i.e. about 50% according to Stanford in their website.
    .Ed. Sorry, the site isn't letting me post the link to the paper. Just google Major Depressive Disorder-Stanford Medicine.


    They're talking about "MAJOR" depressive disorder, not temporary depression following certain life events, for example, or seasonal affective disorder.

    This is true for all mental health disorders, especially schizophrenia, which goes up above 80%, although more people fall prey to MDD than to the other types of mental illness.


    Alcoholism is another disorder with a strong genetic basis and it affects a LOT of people in certain countries, as does drug abuse. Both of them are often co-dependent disorders which are linked with Major Depressive Disorder.

    There are numerous papers on the subject if you're interested in the topic.

    I had to explore a lot of these issues for my work, and ultimately it left me feeling that although dangerous people have to be removed from society for the greater good, their actual "responsibility", while not a factor in criminal trials in most cases, is a very nuanced issue.

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    Quote Originally Posted by Angela View Post
    Clinical depression is very highly genetically heritable, i.e. about 50% according to Stanford in their website.
    .Ed. Sorry, the site isn't letting me post the link to the paper. Just google Major Depressive Disorder-Stanford Medicine.


    They're talking about "MAJOR" depressive disorder, not temporary depression following certain life events, for example, or seasonal affective disorder.

    This is true for all mental health disorders, especially schizophrenia, which goes up above 80%, although more people fall prey to MDD than to the other types of mental illness.


    Alcoholism is another disorder with a strong genetic basis and it affects a LOT of people in certain countries, as does drug abuse. Both of them are often co-dependent disorders which are linked with Major Depressive Disorder.

    There are numerous papers on the subject if you're interested in the topic.

    I had to explore a lot of these issues for my work, and ultimately it left me feeling that although dangerous people have to be removed from society for the greater good, their actual "responsibility", while not a factor in criminal trials in most cases, is a very nuanced issue.
    I myself am prone to depression.
    But cycling and tennis keep both my body and mind in good shape.
    Winter can be a bit harder sometimes.

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    Quote Originally Posted by bicicleur 2 View Post
    I myself am prone to depression.
    But cycling and tennis keep both my body and mind in good shape.
    Winter can be a bit harder sometimes.
    That's a very healthy way to keep depression in check, as exercise releases natural endorphins. Anti-depressive drugs are of questionable use, imo, as some people have the opposite reaction to them, with them increasing the depression. So much so that the medications often come with the warning to cease taking if suicidal thoughts appear.

    My weakness is anxiety, inherited from my mother (my father didn't have an anxious bone in his body) although it's not generalized anxiety disorder; it's more a case of having a heightened anxiety reaction to very stressful situations (serious illness, even sometimes not so serious, or approaching death of a loved one, or myself for that matter, etc. being the most common one).

    It can extend to things like being pretty anxious when my children learned to drive and would get home very late. I would worry they'd drink too much or do too much weed or fall asleep at the wheel. Even though I missed them terribly, I was so glad when they moved out. :) What I didn't see didn't trigger me.

    Deep breathing and meditation are very helpful, and cognitive processes, like refusing to let your thoughts spiral into all the possible reasonable consequences of the situation. "Take everything one day at a time", is a cliché, but a helpful one.

    Interestingly, I'm drawn to other people who have some anxiety or can be a bit depressed at times. I find phlegmatic types really boring after a while, and I'm ashamed to say that when I'm with people who are like that, always calm and cheery, I sometimes wonder if they're pretending, or are really stupid, or have no experience of the darker aspects of reality, or are on the autism spectrum or something. It's like positivity that is so extreme it's toxic. Mean, and undoubtedly wrong, I know, but I'm being honest.

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    Country Prevalence Cases 2022 Population
    Ukraine 6.30% 2,800,587 43,192,122
    Estonia 5.90% 75,667 1,321,910
    Australia 5.90% 1,318,599 26,068,792
    United States 5.90% 17,491,047 334,805,269
    Brazil 5.80% 11,548,577 215,353,593
    Portugal 5.70% 578,234 10,140,570
    Greece 5.70% 593,136 10,316,637
    Lithuania 5.60% 169,685 2,661,708
    Finland 5.60% 293,921 5,554,960
    Belarus 5.60% 510,764 9,432,800
    Cuba 5.50% 605,879 11,305,652
    Russia 5.50% 7,815,714 145,805,947
    Barbados 5.40% 14,586 288,023
    Moldova 5.40% 207,247 4,013,171
    New Zealand 5.40% 221,338 4,898,203
    Bahamas 5.20% 19,138 400,516
    Trinidad And Tobago 5.20% 67,614 1,406,585
    Bulgaria 5.20% 360,724 6,844,597
    Paraguay 5.20% 332,628 7,305,843
    Czech Republic 5.20% 525,488 10,736,784
    Spain 5.20% 2,408,700 46,719,142
    Germany 5.20% 2,116,728 83,883,596
    Antigua And Barbuda 5.10% 4,424 99,509
    Malta 5.10% 20,049 444,033
    Djibouti 5.10% 43,909 1,016,097
    Cyprus 5.10% 42,662 1,223,387
    Slovenia 5.10% 99,864 2,078,034
    Qatar 5.10% 105,684 2,979,915
    Bosnia And Herzegovina 5.10% 185,557 3,249,317
    Croatia 5.10% 205,541 4,059,286
    Slovakia 5.10% 268,516 5,460,193
    Austria 5.10% 415,916 9,066,710
    Hungary 5.10% 493,783 9,606,259
    United Arab Emirates 5.10% 444,016 10,081,785
    Poland 5.10% 1,878,988 37,739,785
    Italy 5.10% 3,049,986 60,262,770
    Luxembourg 5.00% 26,350 642,371
    Armenia 5.00% 142,712 2,971,966
    Uruguay 5.00% 158,005 3,496,016
    Georgia 5.00% 189,241 3,968,738
    Kuwait 5.00% 181,756 4,380,326
    Denmark 5.00% 267,213 5,834,950
    Serbia 5.00% 419,302 8,653,016
    Switzerland 5.00% 388,870 8,773,637
    Romania 5.00% 931,842 19,031,335
    Chile 5.00% 844,253 19,250,195
    Saint Vincent And the Grenadines 4.90% 5,144 111,551
    Saint Lucia 4.90% 8,892 185,113
    Cape Verde 4.90% 24,240 567,678
    Latvia 4.90% 102,702 1,848,837
    Sweden 4.90% 446,734 10,218,971
    Tunisia 4.90% 518,432 12,046,656
    Iran 4.90% 3,637,308 86,022,837
    Suriname 4.80% 24,914 596,831
    Montenegro 4.80% 28,627 627,950
    Bahrain 4.80% 62,549 1,783,983
    Lesotho 4.80% 98,988 2,175,699
    Albania 4.80% 131,048 2,866,374
    Jamaica 4.80% 134,054 2,985,094
    Ireland 4.80% 212,555 5,020,199
    Belgium 4.80% 502,075 11,668,278
    Peru 4.80% 1,443,513 33,684,208
    France 4.80% 2,949,572 65,584,518
    Grenada 4.70% 4,848 113,475
    Botswana 4.70% 102,065 2,441,162
    Costa Rica 4.70% 216,608 5,182,354
    Oman 4.70% 199,961 5,323,993
    Norway 4.70% 227,446 5,511,370
    Lebanon 4.70% 255,280 6,684,849
    Dominican Republic 4.70% 464,164 11,056,370
    Netherlands 4.70% 752,777 17,211,447
    Canada 4.70% 1,566,903 38,388,419
    Argentina 4.70% 1,914,354 46,010,234
    Colombia 4.70% 2,177,280 51,512,762
    Ethiopia 4.70% 4,480,113 120,812,698
    Singapore 4.60% 162,203 5,943,546
    Israel 4.60% 342,181 8,922,892
    Azerbaijan 4.60% 428,873 10,300,205
    Uganda 4.60% 1,747,769 48,432,863
    South Africa 4.60% 2,402,230 60,756,135
    Guyana 4.50% 33,700 794,045
    Libya 4.50% 265,833 7,040,745
    Saudi Arabia 4.50% 1,339,976 35,844,909
    Morocco 4.50% 1,484,441 37,772,756
    Algeria 4.50% 1,683,914 45,350,148
    United Kingdom 4.50% 2,692,081 68,497,907
    India 4.50% 56,675,969 1,406,631,776
    Belize 4.40% 14,956 412,190
    Comoros 4.40% 33,769 907,419
    Mauritius 4.40% 52,570 1,274,727
    Namibia 4.40% 104,001 2,633,874
    Panama 4.40% 162,293 4,446,964
    El Salvador 4.40% 255,032 6,550,389
    South Sudan 4.40% 529,011 11,618,511
    Bolivia 4.40% 453,716 11,992,656
    Kazakhstan 4.40% 732,699 19,205,043
    Madagascar 4.40% 1,041,000 29,178,077
    Kenya 4.40% 1,952,981 56,215,221
    Thailand 4.40% 2,885,221 70,078,203
    Turkey 4.40% 3,260,677 85,561,976
    Gabon 4.30% 7,303 2,331,533
    Eritrea 4.30% 219,549 3,662,244
    Haiti 4.30% 437,639 11,680,283
    Bhutan 4.20% 30,947 787,941
    Eswatini 4.20% 53,223 1,184,817
    Equatorial Guinea 4.20% 34,909 1,496,662
    Mongolia 4.20% 117,436 3,378,078
    Central African Republic 4.20% 202,081 5,016,678
    Turkmenistan 4.20% 214,010 6,201,943
    Nicaragua 4.20% 238,161 6,779,100
    Burundi 4.20% 448,822 12,624,840
    Venezuela 4.20% 1,270,099 29,266,991
    Ghana 4.20% 110,048 32,395,450
    Uzbekistan 4.20% 1,186,450 34,382,084
    Japan 4.20% 5,058,124 125,584,838
    Mexico 4.20% 4,936,614 131,562,772
    Pakistan 4.20% 7,436,224 229,488,994
    China 4.20% 54,815,739 1,448,471,400
    Iceland 4.10% 12,533 345,393
    Mauritania 4.10% 160,624 4,901,981
    Kyrgyzstan 4.10% 229,637 6,728,271
    Malawi 4.10% 679,385 20,180,839
    Sri Lanka 4.10% 802,321 21,575,842
    Mozambique 4.10% 1,122,987 33,089,461
    South Korea 4.10% 1,904,645 51,329,899
    Tanzania 4.10% 2,138,939 63,298,550
    Bangladesh 4.10% 6,391,760 167,885,689
    Seychelles 4.00% 3,722 99,426
    Guinea Bissau 4.00% 71,467 2,063,367
    Honduras 4.00% 308,862 10,221,247
    Jordan 4.00% 287,844 10,300,869
    Zimbabwe 4.00% 603,529 15,331,428
    Somalia 4.00% 420,387 16,841,795
    Zambia 4.00% 636,819 19,470,234
    Vietnam 4.00% 3,564,934 98,953,541
    Sao Tome And Principe 3.90% 7,270 227,679
    Gambia 3.90% 74,821 2,558,482
    Sierra Leone 3.90% 243,895 8,306,436
    Togo 3.90% 277,532 8,680,837
    Benin 3.90% 411,695 12,784,726
    Guinea 3.90% 474,541 13,865,691
    Senegal 3.90% 560,991 17,653,671
    Syria 3.90% 688,074 19,364,809
    Cameroon 3.90% 886,273 27,911,548
    Nigeria 3.90% 7,079,815 216,746,934
    Tajikistan 3.80% 304,018 9,957,464
    Rwanda 3.80% 425,516 13,600,464
    Ivory Coast 3.80% 843,736 27,742,298
    Malaysia 3.80% 1,127,643 33,181,072
    DR Congo 3.80% 2,871,309 95,240,792
    Maldives 3.70% 12,739 540,985
    Guatemala 3.70% 580,994 18,584,039
    North Korea 3.70% 874,632 25,990,679
    Iraq 3.70% 1,263,249 42,164,965
    Myanmar 3.70% 1,917,983 55,227,143
    Indonesia 3.70% 9,162,886 279,134,505
    Mali 3.60% 605,969 21,473,764
    Burkina Faso 3.60% 640,502 22,102,838
    Yemen 3.60% 915,231 31,154,867
    Angola 3.60% 892,128 35,027,343
    Sudan 3.60% 1,376,305 45,992,020
    Fiji 3.50% 30,568 909,466
    Liberia 3.50% 155,406 5,305,117
    Chad 3.50% 478,228 17,413,580
    Egypt 3.50% 2,995,824 106,156,692
    Cambodia 3.40% 508,823 17,168,639
    Niger 3.40% 653,348 26,083,660
    Afghanistan 3.30% 1,038,610 40,754,388
    Philippines 3.30% 3,298,652 112,508,994
    Tonga 3.20% 3,205 107,749
    Samoa 3.20% 5,803 202,239
    Laos 3.20% 209,326 7,481,023
    Nepal 3.20% 890,361 30,225,582
    Micronesia 3.10% 3,182 117,489
    Kiribati 3.10% 3,452 123,419
    Vanuatu 3.10% 7,917 321,832
    Timor Leste 3.00% 33,932 1,369,429
    Papua New Guinea 3.00% 223,094 9,292,169
    Solomon Islands 2.90% 16,535 721,159
    https://worldpopulationreview.com/co...tes-by-country
    "No-winter nations" seem to do much better.

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    I think people are overreaching with this stuff. Not everything is related to HG/EEF ratios. This paper made clear that even steppe-heavy people adopted farming thousands of years ago.

    The kind of rapid population expansion of steppe people wouldn't have been possible without thousands of years of agriculture. Pastoralist nomads are low in population.

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