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Thread: Towards a better modelling of NorthWest Europeans in Global 25

  1. #1
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    Towards a better modelling of NorthWest Europeans in Global 25

    I am experimenting with a new way to model the NorthWest Europeans in Global 25.
    The reason is that I am getting dissatisfied with the role of the labels of the samples. Modern samples are labelled by geography ('English_Cornwall'), ancient samples are often labelled by culture ('Bell_Beaker').
    In both cases the correlation of the labels with the DNA features is not 100%, to put it mildly. The number of different samples in the dataset is growing nearly by the month, and so is the labeling problem.
    The labeling problem is especially pronounced in regions without natural barriers, like for instance the North European plain. As a result of a long period of mixing, neighboring population groups are genetically nearly identical, while every subgroup wants to emphasize its own uniqueness.

    My dissatisfaction with the labels has reached the level that I prefer to model the samples without the labels altogether.
    When working with raw genetic markers there is the choice of using the labels with qpAdm, or not using the labels with ADMIXTURE.
    When working with PCA scores like in Global 25, one can use nMonte, which uses the labels. But there is no ADMIXTURE like tool which doesn't use the labels for clustering.
    So I had to invent my own ADMIXTURE look-alike clustering.
    Compared with raw data, the PCA scores have lost a lot of information. Therefore a high-K ADMIXTURE look-alike will be not be possible.
    But a K=3 mixture turned out satisfying results. At K=4 the results were less satisfying, apparently because separation of the components became too hard.

    As an example of the three-way mixture I present the results for the Dutch AG-members:
    A B C
    Radboud 0.993 0.006 0.001

    FinnMom 0.987 0.000 0.013
    FinnDad 0.985 0.004 0.011
    Huijbregts 0.785 0.204 0.011
    Miremont 0.663 0.320 0.016

    As I will explain in my next post, 'A' is a Northern component which represents the core NW-European component, 'B' is a Southern component with especially French (but not French_South and Basque), and 'C' represents Eastern German and Czech.
    All five of these samples have 'A' as the dominant component, but the two South Dutch samples also have 20%-30% component B, which the three Northern samples are missing. A nice result, that I have never seen before.

    Another example with other AG members:
    A B C
    Jessie 1.000 0.000 0.000
    Trelvern 0.718 0.282 0.000
    Helgenes 0.136 0.861 0.004
    DewslothDad 0.003 0.997 0.000

    In the three-way model Jessie has only the core NW-European component. Trelvern and Helgenes have a varying proportion of the French component.
    DewslothDad has such a high percentage of component B that I wonder whether his ancestry isn't Iberian or Italian. This model uses only NW-European reference samples, so using it out of this region isn't advisable.

    To demonstrate why I am disappointed by the labels, I present the classic ADMIXTURE bar graph of the Dutch and French samples in Global 25.
    ADMIXTURE.jpg
    Note the many samples with DNA proportions that are very different from typical samples under the same label.
    But both populations have a majority component, A for the Dutch and B for the French.

    Here is the ADMIXTURE plot for the Germans:
    ADMIXTURE_German.jpg
    The German samples don't even have a majority component.
    Particular striking is great number of samples with a very high proportion of the 'French' component B.
    The samples German2,53,48,14,7,72,70,61 are even more than 0.999 on component B; which is more then Hallstatt_Bylany_DA111 (0.990).
    I presume that these component B Germans were born in Southern Germany. Maybe Generalissimo can check this.

    In my next post I will more systematically explore the relation between labeled populations and the DNA components within NW-Europe.

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    Neat, that's pretty interesting. I wish someone could take the same approach to SW Europe eventually
    YDNA - E-Y31991>PF4428>BY36857. Domingos Rodrigues, b. circa 1680 Hidden Content , Viana do Castelo, Portugal
    mtDNA - H20. Maria Josefa de Almeida, b. circa 1750 Hidden Content , Porto, Portugal

    Global25 PCA West Eurasia dataset Hidden Content
    Hidden Content


    [1] "distance%=1.7726"

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    Quote Originally Posted by Huijbregts View Post
    I am experimenting with a new way to model the NorthWest Europeans in Global 25.


    But a K=3 mixture turned out satisfying results. At K=4 the results were less satisfying, apparently because separation of the components became too hard.

    As an example of the three-way mixture I present the results for the Dutch AG-members:
    A B C
    Radboud 0.993 0.006 0.001

    FinnMom 0.987 0.000 0.013
    FinnDad 0.985 0.004 0.011
    Huijbregts 0.785 0.204 0.011
    Miremont 0.663 0.320 0.016

    As I will explain in my next post, 'A' is a Northern component which represents the core NW-European component, 'B' is a Southern component with especially French (but not French_South and Basque), and 'C' represents Eastern German and Czech.
    All five of these samples have 'A' as the dominant component, but the two South Dutch samples also have 20%-30% component B, which the three Northern samples are missing. A nice result, that I have never seen before.

    Another example with other AG members:
    A B C
    Jessie 1.000 0.000 0.000
    Trelvern 0.718 0.282 0.000
    Helgenes 0.136 0.861 0.004
    DewslothDad 0.003 0.997 0.000

    In the three-way model Jessie has only the core NW-European component. Trelvern and Helgenes have a varying proportion of the French component.
    DewslothDad has such a high percentage of component B that I wonder whether his ancestry isn't Iberian or Italian. This model uses only NW-European reference samples, so using it out of this region isn't advisable.

    To demonstrate why I am disappointed by the labels, I present the classic ADMIXTURE bar graph of the Dutch and French samples in Global 25.
    ADMIXTURE.jpg
    Note the many samples with DNA proportions that are very different from typical samples under the same label.
    But both populations have a majority component, A for the Dutch and B for the French.

    Here is the ADMIXTURE plot for the Germans:
    ADMIXTURE_German.jpg
    The German samples don't even have a majority component.
    Particular striking is great number of samples with a very high proportion of the 'French' component B.
    The samples German2,53,48,14,7,72,70,61 are even more than 0.999 on component B; which is more then Hallstatt_Bylany_DA111 (0.990).
    I presume that these component B Germans were born in Southern Germany. Maybe Generalissimo can check this.

    In my next post I will more systematically explore the relation between labeled populations and the DNA components within NW-Europe.

    I am usually close to French16 ( mostly A)
    A sample found in Brittany?

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    It is very interesting. The labels are based, I assume, on the location of the individuals whose sample are analyzed. They are accurate labels, just not if you try to attached them statistically. I ran different KMeans on the datasets before and there is obvious continuity overlapping current boundaries (labels). Germany can be split in at least four components.
    K8: French East/German South/Austrian 26%, French North East/Belgian/German West 25%, French North 25%, Irish/Scottish/Welsh 10%, French South/French Basque 9%, German East/Czech/Austrian 5%
    K36: French East/German South/Swiss German 33%, French North East/Belgian/German West 31%, French North Central 21%, Irish/Scottish/Welsh 9%, French South/ French Basque 6%
    K16: German 50%, French North West 19%, French North East 11%, Irish/English 11%, French South 9%

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    Quote Originally Posted by Huijbregts View Post
    I am experimenting with a new way to model the NorthWest Europeans in Global 25.
    The reason is that I am getting dissatisfied with the role of the labels of the samples. Modern samples are labelled by geography ('English_Cornwall'), ancient samples are often labelled by culture ('Bell_Beaker').
    In both cases the correlation of the labels with the DNA features is not 100%, to put it mildly. The number of different samples in the dataset is growing nearly by the month, and so is the labeling problem.
    The labeling problem is especially pronounced in regions without natural barriers, like for instance the North European plain. As a result of a long period of mixing, neighboring population groups are genetically nearly identical, while every subgroup wants to emphasize its own uniqueness.

    My dissatisfaction with the labels has reached the level that I prefer to model the samples without the labels altogether.
    When working with raw genetic markers there is the choice of using the labels with qpAdm, or not using the labels with ADMIXTURE.
    When working with PCA scores like in Global 25, one can use nMonte, which uses the labels. But there is no ADMIXTURE like tool which doesn't use the labels for clustering.
    So I had to invent my own ADMIXTURE look-alike clustering.
    Compared with raw data, the PCA scores have lost a lot of information. Therefore a high-K ADMIXTURE look-alike will be not be possible.
    But a K=3 mixture turned out satisfying results. At K=4 the results were less satisfying, apparently because separation of the components became too hard.

    As an example of the three-way mixture I present the results for the Dutch AG-members:
    A B C
    Radboud 0.993 0.006 0.001

    FinnMom 0.987 0.000 0.013
    FinnDad 0.985 0.004 0.011
    Huijbregts 0.785 0.204 0.011
    Miremont 0.663 0.320 0.016

    As I will explain in my next post, 'A' is a Northern component which represents the core NW-European component, 'B' is a Southern component with especially French (but not French_South and Basque), and 'C' represents Eastern German and Czech.
    All five of these samples have 'A' as the dominant component, but the two South Dutch samples also have 20%-30% component B, which the three Northern samples are missing. A nice result, that I have never seen before.

    Another example with other AG members:
    A B C
    Jessie 1.000 0.000 0.000
    Trelvern 0.718 0.282 0.000
    Helgenes 0.136 0.861 0.004
    DewslothDad 0.003 0.997 0.000

    In the three-way model Jessie has only the core NW-European component. Trelvern and Helgenes have a varying proportion of the French component.
    DewslothDad has such a high percentage of component B that I wonder whether his ancestry isn't Iberian or Italian. This model uses only NW-European reference samples, so using it out of this region isn't advisable.

    To demonstrate why I am disappointed by the labels, I present the classic ADMIXTURE bar graph of the Dutch and French samples in Global 25.
    ADMIXTURE.jpg
    Note the many samples with DNA proportions that are very different from typical samples under the same label.
    But both populations have a majority component, A for the Dutch and B for the French.

    Here is the ADMIXTURE plot for the Germans:
    ADMIXTURE_German.jpg
    The German samples don't even have a majority component.
    Particular striking is great number of samples with a very high proportion of the 'French' component B.
    The samples German2,53,48,14,7,72,70,61 are even more than 0.999 on component B; which is more then Hallstatt_Bylany_DA111 (0.990).
    I presume that these component B Germans were born in Southern Germany. Maybe Generalissimo can check this.

    In my next post I will more systematically explore the relation between labeled populations and the DNA components within NW-Europe.
    He's not, that I know of. He's (in decreasing amounts) German>British>Belgian>Ashkenazi.
    From your comment on the B Germans, it seems to be fairly typical of the region.

    However, in chromosome-by-chromosome analysis, some of the Iberian (or Iberian-like) affinity is quite strong, almost like the mixed Visigoths fled back north after residing in Spain:

    https://anthrogenica.com/showthread....l=1#post551941

    quick chromosome summary for those who don't want to click:
    1. Cantabria (Cantabria 70.8 Asturias 12.4)
    2. Walloons (Walloons 32, SV_Norrland 10.8, Norway_north 7)
    3. South-West_Romania (GR_Thrace 40.2, Croats_BIH 15.6, South-West_Romania 9.2, Sathmarer_Schwaben 8.8)
    4. NL_Limburg (NL_Limburg 50, SV_Norrland 11, NL_Drenthe 6)
    5. Rheinland-Pfalz (Tirol 38.4, SW_England 22.2, NL_Noord-Brabant 22)
    6. Nordrhein-Westfalen (Nordrhein-Westfalen 56.4, NL_Friesland 14.4, Oberland(western_Ost_Preussen) 10.8)
    7. Austria (Tirol 32, IT_Bolzano 26.8, Ukraine_Chernivtsi 21.8)
    8. Nordrhein-Westfalen (Nordrhein-Westfalen 42.6, Neumark(East_Brandenburg) 23.8, Northern_Ireland 6.8)
    9. Tirol (Tirol 65, IT_Trentino 18.2)
    10. Iceland (Ireland 46.3, Norway_north 13, Iceland 9.4, Finnish_Swede 7.2)
    11. GR_Thrace (Saarland 41.2, Jordan 21, Lebanon_Muslim 12, GR_Thrace 9.2)
    12. IT_Bolzano (IT_Bolzano 79.8, Tirol 6.2)
    13. Extremadura (País_Vasco 32, IT_Sardinia 30.4, Ukraine_Chernivtsi 23.4)
    14. NL_Gelderland (NL_Gelderland 57.2, NL_Friesland 35.4)
    15. Swiss_Italian (Swiss_Italian 32.4, Sathmarer_Schwaben 31.4, GR_Ipeiros 16.8, IT_Tuscany 15.6)
    16. Tirol (Tirol 43.4, IT_Bolzano 22.4, IT_Veneto 15.4)
    17. Tirol (Tirol 61.8, IT_Tuscany 31.2)
    18. NL_Overijssel (Ireland 51, Mari 19.4, Lebanon_Christian 16.4)
    19. South-West_Romania (Ukraine_Kharkiv 52, IT_Bolzano 16.2, IQ_Kirkuk 13.8, Lebanon_Christian 8.8)
    20. FR_West (Northern_Ireland 60.8, FR_Basque 18.6, Lebanon_Christian 18.6)
    21. NL_Limburg (NL_Drenthe 62.8, Lebanon_Muslim 21)
    22. IT_Bolzano (Saarland 41.8, IT_Bolzano 41.6)
    R1b>M269>L23>L51>L11>P312>DF19>DF88>FGC11833 >S4281>S4268>Z17112 (S17075-)

    Y-cousin: 6DRIF-23 (DF19>>Z17112+, S17075+)

    Ancestors: Francis Cooke (M223/I2a2a) b1583; Hester Mahieu (Cooke) (J1c2 mtDNA) b.1584; Richard Warren (E-M35) b1578; Elizabeth Walker (Warren) (H1j mtDNA) b1583;
    John Mead (I2a1/P37.2) b1634; Rev. Joseph Hull (I1, L1301+ L1302-) b1595; Benjamin Harrington (M223/I2a2a-Y5729) b1618; Joshua Griffith (L21>DF13) b1593;
    John Wing (U106) b1584; Hermann Wilhelm (DF19) b1635

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  11. #6
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    Quote Originally Posted by Dewsloth View Post
    He's not, that I know of. He's (in decreasing amounts) German>British>Belgian>Ashkenazi.
    From your comment on the B Germans, it seems to be fairly typical of the region.

    However, in chromosome-by-chromosome analysis, some of the Iberian (or Iberian-like) affinity is quite strong, almost like the mixed Visigoths fled back north after residing in Spain:

    https://anthrogenica.com/showthread....l=1#post551941

    quick chromosome summary for those who don't want to click:
    1. Cantabria (Cantabria 70.8 Asturias 12.4)
    2. Walloons (Walloons 32, SV_Norrland 10.8, Norway_north 7)
    3. South-West_Romania (GR_Thrace 40.2, Croats_BIH 15.6, South-West_Romania 9.2, Sathmarer_Schwaben 8.8)
    4. NL_Limburg (NL_Limburg 50, SV_Norrland 11, NL_Drenthe 6)
    5. Rheinland-Pfalz (Tirol 38.4, SW_England 22.2, NL_Noord-Brabant 22)
    6. Nordrhein-Westfalen (Nordrhein-Westfalen 56.4, NL_Friesland 14.4, Oberland(western_Ost_Preussen) 10.8)
    7. Austria (Tirol 32, IT_Bolzano 26.8, Ukraine_Chernivtsi 21.8)
    8. Nordrhein-Westfalen (Nordrhein-Westfalen 42.6, Neumark(East_Brandenburg) 23.8, Northern_Ireland 6.8)
    9. Tirol (Tirol 65, IT_Trentino 18.2)
    10. Iceland (Ireland 46.3, Norway_north 13, Iceland 9.4, Finnish_Swede 7.2)
    11. GR_Thrace (Saarland 41.2, Jordan 21, Lebanon_Muslim 12, GR_Thrace 9.2)
    12. IT_Bolzano (IT_Bolzano 79.8, Tirol 6.2)
    13. Extremadura (País_Vasco 32, IT_Sardinia 30.4, Ukraine_Chernivtsi 23.4)
    14. NL_Gelderland (NL_Gelderland 57.2, NL_Friesland 35.4)
    15. Swiss_Italian (Swiss_Italian 32.4, Sathmarer_Schwaben 31.4, GR_Ipeiros 16.8, IT_Tuscany 15.6)
    16. Tirol (Tirol 43.4, IT_Bolzano 22.4, IT_Veneto 15.4)
    17. Tirol (Tirol 61.8, IT_Tuscany 31.2)
    18. NL_Overijssel (Ireland 51, Mari 19.4, Lebanon_Christian 16.4)
    19. South-West_Romania (Ukraine_Kharkiv 52, IT_Bolzano 16.2, IQ_Kirkuk 13.8, Lebanon_Christian 8.8)
    20. FR_West (Northern_Ireland 60.8, FR_Basque 18.6, Lebanon_Christian 18.6)
    21. NL_Limburg (NL_Drenthe 62.8, Lebanon_Muslim 21)
    22. IT_Bolzano (Saarland 41.8, IT_Bolzano 41.6)
    Thank you for your clarification. If he has much German ancestry, you are right that he seems typical for B Germans. I don't quite understand that group, I wonder whether they are related to Hallstatt_Bylany.

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  13. #7
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    Quote Originally Posted by Theconqueror View Post
    It is very interesting. The labels are based, I assume, on the location of the individuals whose sample are analyzed. They are accurate labels, just not if you try to attached them statistically. I ran different KMeans on the datasets before and there is obvious continuity overlapping current boundaries (labels). Germany can be split in at least four components.
    With the term 'label' I refer to the name that the samples get in the Global 25 PCA.
    For modern samples that name is derived from the self-reported genealogy. I suppose the label is usually correct, at least in a genealogical sense.
    My assertion is that these labels are not very helpful in a genetical sense; the Germans are a frightening example.
    This is worrying me, because software like nMonte use the labels to genetically classify the samples and assume that the labels are genetically 1:1 with the DNA.
    I assume that this also applies to qpAdm.
    You wrote "there is obvious continuity overlapping current boundaries(labels)". If you are saying that the present labels are too fine-grained, I quite agree.
    My self-imposed limitation was to develop a common DNA-only classification for the NW-Europeans.
    This limited the number of overlappings of German. Moreover my common model appeared not to be reliable with K=4 or higher. Therefore I ended up with only three components.

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    Quote Originally Posted by Trelvern View Post
    I am usually close to French16 ( mostly A)
    A sample found in Brittany?
    In my three-way estimation French16 is 0.931 'A'.
    I would not be surprised by elevated 'A' in Brittany. But I cannot tell you where French16 is born, only Generalissimo knows.

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    Quote Originally Posted by Huijbregts View Post
    In my three-way estimation French16 is 0.931 'A'.
    I would not be surprised by elevated 'A' in Brittany. But I cannot tell you where French16 is born, only Generalissimo knows.
    Are you going to do this analysis for more people?

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    I've got a lot of programming experience but not in R and not working on population genetics so you can take this with a grain of salt but my advice would be to check the available resources online
    you could try to incorporate some other tools. There are tons of R libraries on for R on https://rdrr.io/ or you could try https://bio.tools for useful software.

    You can probably find a package with a much better than naive or at least a hw-accelerated brute force comparison type clustering based on shared snps in some software package. I'm assuming that cluster significance comes from either more SNPs matched or a smaller number if a group is shared among a larger number of samples to the exclusion of the rest but again i don't have experience working with these algorithms.

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