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Thread: Eurogenes Global 25 Full Moderns Spreadsheet Runs with Gradient Descent Algorithm

  1. #531
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    Quote Originally Posted by Reza View Post
    Are they related samples? They score quite similar to Bihari, possibly West Bengali samples non Brahmin samples.

    Even 2783 has a decent amount of Med to make up for the lower NE - so non neglible amounts.

    Do any other Punjabi samples you've come across score SE Asian? I've seen it pop up in some of the South Indian samples, but somehow doesn't strike me as AASI/Onge like.
    Apologies, do you mind unquoting me as I was still updating the data. Or updating your quote? I believe the PJL samples includes some relatives (parents and children)

    https://www.coriell.org/0/Sections/C...Id=763&coll=HG

    http://www.internationalgenome.org/d...population/PJL

    Population Description

    These cell lines and DNA samples were prepared from blood samples collected in Lahore, Pakistan. The samples are from a mix of parent- adult child trios and unrelated individuals who identified themselves and their parents as Punjabi.

    It is important to include a reference to "Lahore, Pakistan" when describing the source of these samples. Including the name of the city and the country where these samples were collected reinforces the point that the sample set, while not genetically "atypical", does not necessarily represent all Punjabi people, whose population history is complex. The population should not be described merely as "South Asian" or "Pakistani", since both of those designators encompasses many populations with different geographic ancestries.

    After the complete descriptor "Punjabi in Lahore, Pakistan" has been provided, it is acceptable to use the shorthand label "Punjabi" or the abbreviation "PJL" in the remainder of the article or presentation. However, the full descriptor for each population should be provided before the shorthand labels are used; this will help to avoid the risks associated with over-generalization of findings.
    As for SE Asian, it is uncommon if not quite rare for biradari populations to score it above noise levels (less than 1%). I've tended to (perhaps mistakenly) lump it together with Papuan scored on admixture calculators and thought it was AASI/Onge related ancestry leaking from other South Asian/South Indian components, which are based on tribal populations.

    Outside of minor Amerindian/Siberian/Beringian ancestry which I've typically associated with North Siberian/MA-1 related ancestry, actual East Asian ancestry seems to be rare in Punjab. The exception being populations on the fringe of Pahari/Pothohari areas who seem to occasionally score minor NE Asian ancestry. I will admit that it's possible for some of the Amerindian/Siberian/Beringian being scored by individuals to be more relatively "recent" East Asian related ancestry though.
    Last edited by Sapporo; 07-12-2019 at 01:51 PM.
    pegasus modeling:

    sample": "Punjabi_Jat:Sapporo_AGUser",
    "fit": 1.1506,
    "IRN_Shahr_I_Sokhta_BA3": 43.33,
    "TKM_Gonur1_BA": 31.67,
    "RUS_Sintashta_MLBA": 25,
    "closestDistances": [

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     laltota (07-12-2019),  Reza (07-12-2019)

  3. #532
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    Quote Originally Posted by Reza View Post
    Which PJL samples were selected for the average on G25? The entirety of the PJL samples span a wide range from near pashtun and other biraderi type, to like chamars.

    A very heterogenous group.
    Here's what the pop avg from G25 sheets models like -

    Compact Pop avgs model, w/ itself removed from the model

    GujaratiB Kol Iyer Velamas
    0.9175980 0.9303109 1.0062943 1.0725907
    GujaratiA Brahmin_West_Bengal Kerala_Nair:Kalashviv Kshatriya
    1.0792040 1.2760830 1.3606226 1.3717027

    "distance%=0.398" - Punjabi_Lahore

    Maratha,34.2
    Kshatriya,21.6
    Mala,13.6
    GujaratiA,9.4
    Kol,6
    Ganj_Dareh_N,5.6
    Bhumij,2.2
    Sintashta_MLBA_o3,1.8
    Balochi,1.4
    Pulliyar,1.4
    Brahmin_Nepali,1
    Belarusian,0.6
    Ethiopian_Gumuz,0.6
    Hawaiian,0.6


    Full ancients

    "distance%=0.6861" - Punjabi_Lahore

    Saidu_Sharif_IA_o,39.2
    Shahr_I_Sokhta_BA3,28.2
    Aligrama_IA,11
    Saidu_Sharif_IA,8.6
    Great_Andamanese_100BP,3.2
    Sweden_Viking_Age_Sigtuna,2.2
    Vietnam_LN,2.2
    ALPc_MN,1.4
    Balkans_N,1
    Iberia_Southwest_CA,1
    Latvia_BA,1
    Baden_LCA,0.2
    Balaton_Lasinja_CA,0.2
    Barcin_N,0.2
    Butkara_IA,0.2
    Norway_Mesolithic,0.2

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  5. #533
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    Quote Originally Posted by Yggdrasi^ View Post
    Cheers and thanks!

    Could you please do a second run without the summing to group level like you did for 26284729292?
    Yes, sure:

    distance%: 0.71

    Kohistani_K-296 14.72
    Pallan_PL1-17 13.94
    Kadar_KA-10 12.52
    Pallan_PL1-47 8.12
    Kamboj_KJ_32 7.18
    Yusufzai_Y-401 6.92
    Maratha_MT-02 6.66
    Chamar_A260 6.43
    Gujarati_NA20853 6.39
    Chenchu_CHEND85 6.11
    Punjabi_Lahore_HG02688 3.91
    Kadar_KA-34 3.28
    Brahmin_Uttar_Pradesh_DEL007 1.57
    Paniya_PNYD36 1.18
    Iyer_IR-1-20 0.48
    Kanjar_evo_33 0.26
    Maratha_MT-08 0.2
    Sindhi_HGDP00195 0.12

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  7. #534
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    thank you, randwulf.

    This explains a lot. Cheers!

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     randwulf (07-13-2019)

  9. #535
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    Quote Originally Posted by Reza View Post

    Edit.... We have G25 coordinates rather than relying on an outdated admixture calculator. Anyone able to model the individual samples specifically?
    This is at 1000 cycles, 120 (default) batches and 0.001 (default) penalty:

    "sample": "Punjabi_Lahore:Average",
    "fit": 3.0646,
    "Simulated_AASI": 47.5,
    "IRN_Ganj_Dareh_N": 33.33,
    "RUS_Sintashta_MLBA": 15.83,
    "RUS_West_Siberia_N": 2.5,
    "Anatolia_Barcin_N": 0.83,
    "NPL_Chokhopani_2700BP": 0,



    "sample": "Brahmin_Uttar_Pradesh:Average",
    "fit": 3.898,
    "IRN_Ganj_Dareh_N": 35,
    "Simulated_AASI": 34.17,
    "RUS_Sintashta_MLBA": 25.83,
    "RUS_West_Siberia_N": 5,
    "Anatolia_Barcin_N": 0,
    "NPL_Chokhopani_2700BP": 0,

    "sample": "Chamar:Average",
    "fit": 2.9599,
    "Simulated_AASI": 55.83,
    "IRN_Ganj_Dareh_N": 29.17,
    "RUS_Sintashta_MLBA": 10.83,
    "RUS_West_Siberia_N": 4.17,
    "Anatolia_Barcin_N": 0,
    "NPL_Chokhopani_2700BP": 0,

    "sample": "Khatri:Average",
    "fit": 3.1624,
    "IRN_Ganj_Dareh_N": 46.67,
    "RUS_Sintashta_MLBA": 24.17,
    "Simulated_AASI": 19.17,
    "RUS_West_Siberia_N": 6.67,
    "Anatolia_Barcin_N": 2.5,
    "NPL_Chokhopani_2700BP": 0.83,

    "sample": "Potohar_Brahmin:Average",
    "fit": 2.8862,
    "IRN_Ganj_Dareh_N": 42.5,
    "RUS_Sintashta_MLBA": 25,
    "Simulated_AASI": 25,
    "RUS_West_Siberia_N": 5.83,
    "NPL_Chokhopani_2700BP": 1.67,
    "Anatolia_Barcin_N": 0,

    "sample": "Punjabi_Jat:Average",
    "fit": 3.2,
    "IRN_Ganj_Dareh_N": 42.5,
    "RUS_Sintashta_MLBA": 32.5,
    "Simulated_AASI": 19.17,
    "RUS_West_Siberia_N": 4.17,
    "NPL_Chokhopani_2700BP": 1.67,
    "Anatolia_Barcin_N": 0,

    Individuals:

    "sample": "Punjabi_Lahore:HG02600",
    "fit": 3.6013,
    "Simulated_AASI": 41.67,
    "IRN_Ganj_Dareh_N": 35,
    "RUS_Sintashta_MLBA": 20,
    "RUS_West_Siberia_N": 3.33,
    "Anatolia_Barcin_N": 0,
    "NPL_Chokhopani_2700BP": 0,

    "sample": "Punjabi_Lahore:HG02687",
    "fit": 3.2097,
    "Simulated_AASI": 48.33,
    "IRN_Ganj_Dareh_N": 35,
    "RUS_Sintashta_MLBA": 14.17,
    "RUS_West_Siberia_N": 2.5,
    "Anatolia_Barcin_N": 0,
    "NPL_Chokhopani_2700BP": 0,

    "sample": "Punjabi_Lahore:HG02688",
    "fit": 3.2106,
    "Simulated_AASI": 46.67,
    "IRN_Ganj_Dareh_N": 32.5,
    "RUS_Sintashta_MLBA": 19.17,
    "Anatolia_Barcin_N": 0.83,
    "RUS_West_Siberia_N": 0.83,
    "NPL_Chokhopani_2700BP": 0,

    "sample": "Punjabi_Lahore:HG02724",
    "fit": 3.2436,
    "Simulated_AASI": 48.33,
    "IRN_Ganj_Dareh_N": 34.17,
    "RUS_Sintashta_MLBA": 14.17,
    "RUS_West_Siberia_N": 3.33,
    "Anatolia_Barcin_N": 0,
    "NPL_Chokhopani_2700BP": 0,

    "sample": "Punjabi_Lahore:HG02783",
    "fit": 4.1282,
    "Simulated_AASI": 47.5,
    "IRN_Ganj_Dareh_N": 32.5,
    "RUS_Sintashta_MLBA": 16.67,
    "RUS_West_Siberia_N": 3.33,
    "Anatolia_Barcin_N": 0,
    "NPL_Chokhopani_2700BP": 0,

    "sample": "Punjabi_Lahore:HG02790",
    "fit": 3.7297,
    "Simulated_AASI": 49.17,
    "IRN_Ganj_Dareh_N": 32.5,
    "RUS_Sintashta_MLBA": 14.17,
    "RUS_West_Siberia_N": 3.33,
    "Anatolia_Barcin_N": 0.83,
    "NPL_Chokhopani_2700BP": 0,

    "sample": "Punjabi_Lahore_CustomOutlier:HG02491",
    "fit": 3.2152,
    "IRN_Ganj_Dareh_N": 40.83,
    "Simulated_AASI": 33.33,
    "RUS_Sintashta_MLBA": 23.33,
    "RUS_West_Siberia_N": 2.5,
    "Anatolia_Barcin_N": 0,
    "NPL_Chokhopani_2700BP": 0,

    "sample": "Punjabi_Lahore_CustomOutlier:HG02601",
    "fit": 2.9444,
    "Simulated_AASI": 39.17,
    "IRN_Ganj_Dareh_N": 35.83,
    "RUS_Sintashta_MLBA": 21.67,
    "RUS_West_Siberia_N": 2.5,
    "Anatolia_Barcin_N": 0.83,
    "NPL_Chokhopani_2700BP": 0,


    Using the TJK_Sarazm_En model:

    "sample": "Punjabi_Lahore:HG02600",
    "fit": 3.5251,
    "TJK_Sarazm_En": 45.5,
    "Simulated_AASI": 39.5,
    "RUS_Sintashta_MLBA": 13.5,
    "NPL_Chokhopani_2700BP": 1,
    "Anatolia_Barcin_N": 0.5,
    "RUS_West_Siberia_N": 0,

    "sample": "Punjabi_Lahore:HG02687",
    "fit": 3.3058,
    "Simulated_AASI": 48,
    "TJK_Sarazm_En": 43,
    "RUS_Sintashta_MLBA": 8,
    "Anatolia_Barcin_N": 1,
    "NPL_Chokhopani_2700BP": 0,
    "RUS_West_Siberia_N": 0,

    "sample": "Punjabi_Lahore:HG02688",
    "fit": 2.9823,
    "Simulated_AASI": 44,
    "TJK_Sarazm_En": 41,
    "RUS_Sintashta_MLBA": 12,
    "Anatolia_Barcin_N": 2,
    "NPL_Chokhopani_2700BP": 1,
    "RUS_West_Siberia_N": 0,

    "sample": "Punjabi_Lahore:HG02724",
    "fit": 3.5864,
    "Simulated_AASI": 48.5,
    "TJK_Sarazm_En": 42,
    "RUS_Sintashta_MLBA": 9,
    "Anatolia_Barcin_N": 0.5,
    "NPL_Chokhopani_2700BP": 0,
    "RUS_West_Siberia_N": 0,

    "sample": "Punjabi_Lahore:HG02783",
    "fit": 3.0899,
    "Simulated_AASI": 47.5,
    "TJK_Sarazm_En": 43.5,
    "RUS_Sintashta_MLBA": 7.5,
    "Anatolia_Barcin_N": 1.5,
    "NPL_Chokhopani_2700BP": 0,
    "RUS_West_Siberia_N": 0,

    "sample": "Punjabi_Lahore:HG02790",
    "fit": 3.2005,
    "Simulated_AASI": 49,
    "TJK_Sarazm_En": 40,
    "RUS_Sintashta_MLBA": 9.5,
    "Anatolia_Barcin_N": 1.5,
    "NPL_Chokhopani_2700BP": 0,
    "RUS_West_Siberia_N": 0,

    "sample": "Punjabi_Lahore_CustomOutlier:HG02491",
    "fit": 2.9134,
    "TJK_Sarazm_En": 52.5,
    "Simulated_AASI": 31.5,
    "RUS_Sintashta_MLBA": 14.5,
    "Anatolia_Barcin_N": 1,
    "NPL_Chokhopani_2700BP": 0.5,
    "RUS_West_Siberia_N": 0,

    "sample": "Punjabi_Lahore_CustomOutlier:HG02601",
    "fit": 3.4479,
    "TJK_Sarazm_En": 45,
    "Simulated_AASI": 38.5,
    "RUS_Sintashta_MLBA": 14,
    "Anatolia_Barcin_N": 2.5,
    "NPL_Chokhopani_2700BP": 0,
    "RUS_West_Siberia_N": 0,
    Last edited by Sapporo; 07-13-2019 at 04:48 PM.
    pegasus modeling:

    sample": "Punjabi_Jat:Sapporo_AGUser",
    "fit": 1.1506,
    "IRN_Shahr_I_Sokhta_BA3": 43.33,
    "TKM_Gonur1_BA": 31.67,
    "RUS_Sintashta_MLBA": 25,
    "closestDistances": [

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     scobar (07-13-2019)

  11. #536
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    Results for @Espoir:

    With the all of the moderns run at once, I get this for your coordinates:

    distance%: 0.83

    Somali 16.18
    Luo 11.63
    Ethiopian_Anuak 11.56
    Ethiopian_Ari_cultivator 10.44
    Ethiopian_Tigray 9.6
    Datog 9.19
    Masai 7.63
    Berber_Algeria 7.09
    Ethiopian_Oromo 6.63
    Luhya_Kenya 3.42
    Gambian 2.31
    Libyan 1.7
    Bantu_Kenya 1.31
    Papuan 1.15
    Sudanese 0.14

    Ideally, it would be nice to find a relatively simple model with a distance from 0.5 to 1.0 or close to that. So, I removed some of the references that may be duplicating each other and got this for a starting point:

    distance%: 1.05

    Somali 27.69
    Masai 25.65
    Luo 17.31
    Datog 13.9
    Gambian 9.3
    Berber_Algeria 6.14

    It may be that there aren't enough Tutsi-like references to model you, but new references get added all of the time. You may have some suggestions on how to change this a little to make it better, but it may give you some thoughts, too. Just let me know and I can try some other models.

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  13. #537
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    Quote Originally Posted by randwulf View Post
    Results for @Espoir:

    With the all of the moderns run at once, I get this for your coordinates:

    distance%: 0.83

    Somali 16.18
    Luo 11.63
    Ethiopian_Anuak 11.56
    Ethiopian_Ari_cultivator 10.44
    Ethiopian_Tigray 9.6
    Datog 9.19
    Masai 7.63
    Berber_Algeria 7.09
    Ethiopian_Oromo 6.63
    Luhya_Kenya 3.42
    Gambian 2.31
    Libyan 1.7
    Bantu_Kenya 1.31
    Papuan 1.15
    Sudanese 0.14

    Ideally, it would be nice to find a relatively simple model with a distance from 0.5 to 1.0 or close to that. So, I removed some of the references that may be duplicating each other and got this for a starting point:

    distance%: 1.05

    Somali 27.69
    Masai 25.65
    Luo 17.31
    Datog 13.9
    Gambian 9.3
    Berber_Algeria 6.14

    It may be that there aren't enough Tutsi-like references to model you, but new references get added all of the time. You may have some suggestions on how to change this a little to make it better, but it may give you some thoughts, too. Just let me know and I can try some other models.
    I never got to that fit, cool! Try TZN PN, Gambian, Dinka/Sudanese and Hadza
    I don’t think using Masai and Datog is a good idea. They are pretty much like us but somehow different

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     randwulf (07-16-2019)

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    Quote Originally Posted by randwulf View Post
    I agree with Ruderico on not putting too much weight on the phenotype. You do seem to have a possible affinity with some North African and maybe there could be Sephardic, too. So, just for kicks, I added one of the North African references and Sephardic to your prior custom model. They both get picked for the model:

    distance%: 0.88

    Spanish_Castilla_La_Mancha 53.11
    Basque_Spanish 14.96
    Spanish_Extremadura 10.89
    Moroccan_South 10.14
    Spanish_Baleares 4.82
    Sephardic_Jew 4.09
    Spanish_Cantabria 1.99

    It is an interesting result. The fit improves quite a bit. I wouldn't say this kind of thing proves anything definite, but it does seem possible.
    I noticed he said his family is from Portugal but got a Spanish as a better fit is it possible for Spanish to show up as Portuguese instead i know i show up with galicia spain as mine but makes me wonder if i could be Portuguese? maybe by way of Brazil

  16. #539
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    Code:
    ,PC1,PC2,PC3,PC4,PC5,PC6,PC7,PC8,PC9,PC10,PC11,PC12,PC13,PC14,PC15,PC16,PC17,PC18,PC19,PC20,PC21,PC22,PC23,PC24,PC25 
    Aspar_scaled,0.121791,0.144205,0.026398,-0.012597,0.024928,-0.005578,0.00094,-0.001846,0.001636,0.006925,-0.002923,-0.002997,-0.000595,0.011423,-0.014794,0.010607,0.013299,0.000127,0.010182,-0.006753,-0.006239,0.006677,0.007641,0.002169,-0.000958
    Code:
    ,PC1,PC2,PC3,PC4,PC5,PC6,PC7,PC8,PC9,PC10,PC11,PC12,PC13,PC14,PC15,PC16,PC17,PC18,PC19,PC20,PC21,PC22,PC23,PC24,PC25 
    Aspar,0.0107,0.0142,0.007,-0.0039,0.0081,-0.002,0.0004,-0.0008,0.0008,0.0038,-0.0018,-0.002,-0.0004,0.0083,-0.0109,0.008,0.0102,0.0001,0.0081,-0.0054,-0.005,0.0054,0.0062,0.0018,-0.0008
    Thanks
    [1] "distance%=1.5058"

    Aspar

    HUN_Avar_Szolad,29.4
    Scythian_MDA,27.6
    ITA_Collegno_Med_o1,22.4
    BGR_IA,20.6

  17. #540
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    Quote Originally Posted by Samuel7312 View Post
    I noticed he said his family is from Portugal but got a Spanish as a better fit is it possible for Spanish to show up as Portuguese instead i know i show up with galicia spain as mine but makes me wonder if i could be Portuguese? maybe by way of Brazil
    I think they are very similar, so it is possible for the tools when doing the fitting to use what it needs, which could mean more Spanish/less Portuguese.

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