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Thread: Share a scaled G25 dataset

  1. #1
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    Red face Share a scaled G25 dataset

    I've created this dataset, and you're welcome to try it out with your G25 coordinates.

    explanation:
    ENF: Early Neolithic Farmer
    WHG: Western Hunters&Gatherer
    ANE: Ancestral North Eurasian
    SEA: South Eurasian
    EEA: East Eurasian
    OCE: Oceanian
    SSA: Sub Saharan African

    Code:
    ENF,0.0853674,0.1486734,-0.0573978,-0.0925072,-0.0096018,-0.0350286,-0.002209,-0.0081688,0.016321,0.008237,0.0091586,-0.0099212,0.0204854,0.0118632,-0.0055372,0.0021214,-0.011239,0.0010134,0.0021116,-0.003652,-0.0005242,0.003116,0.000567,-0.0029402,0.0046462
    ANE,0.0529278,-0.328016,0.1120048,0.0774392,-0.1061738,-0.0184068,-0.1983488,-0.2347978,-0.0037325,-0.020137,0.0082817,-0.0034098,0.0063925,6.87e-05,-0.0151328,-0.004508,-0.0005215,0.0032622,0.0115015,0.0073472,0.0038682,-0.0107578,0.0027115,0.0062055,0.0092805
    WHG,0.1246365,0.116278,0.184789,0.189279,0.1546445,0.0464355,0.0131605,0.0372675,0.0890705,0.017768,-0.0153455,-0.015811,0.0159065,-0.0030275,0.053338,0.0582065,0.00502,0.016343,-0.0093015,0.055589,0.0944585,0.0111905,-0.049607,-0.160866,0.0170045
    EEA,0.0166182,-0.4441924,0.0136518,-0.0588506,0.0390226,0.0112114,0.0082252,0.0014306,-0.0015546,0.0055034,-0.092269,-0.01106,0.0142414,-0.0075692,-0.0129476,-0.0003978,0.0051112,-0.0020522,-0.0057318,-0.0019008,0.0136762,0.015704,0.0105996,-0.002458,0.0122624
    OCE,-0.0459844,-0.2410866,-0.2637584,0.3092414,0.2069308,-0.509589,-0.001974,0.0070152,-0.041273,-0.0119546,-0.018902,0.000869,0.0013974,-0.00311,0.0001628,-0.0003186,-0.0021906,-0.0018498,-0.001081,-0.0015256,0.0023458,0.0018052,0.0012324,0.0018314,0.0016048
    SSA,-0.6300625,0.0625011,0.022113,0.0167079,0.0005035,0.0124741,-0.044417,0.0477673,-0.0488813,0.0327694,0.0046205,0.0007904,0.0230561,0.0009509,0.0125232,-0.0096067,0.0070763,0.0004491,0.006022,-0.00299,0.0015542,0.0023156,-0.0017592,-0.0004711,-0.0004246
    SEA,0.033009,-0.081242,-0.184789,0.125325,-0.09848,0.06777,0.002585,0.007615,0.03661,0.02606,-0.002273,0.010041,-0.005798,0.006744,0.004479,0.001326,0.008605,0.001267,0.00352,0.001376,0.003743,-0.004946,-0.00037,-0.00253,-0.007305

  2. The Following User Says Thank You to Sansui Yang For This Useful Post:

     hantrolugharsts (07-17-2021)

  3. #2
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    Here are some results.
    K=7.png

  4. #3
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    Focused in Chinese people?

  5. #4
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    Quote Originally Posted by mokordo View Post
    Focused in Chinese people?
    Ummm... I'm not sure because I don't have enough data. That's why I'm Posting it on this platform. lol

  6. #5
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    Quote Originally Posted by Sansui Yang View Post
    Ummm... I'm not sure because I don't have enough data. That's why I'm Posting it on this platform. lol
    You have all the data you need in the datasheet, you can try your model with individual samples to check if distances are as good for different ethnicities as they are for Chinese or East Asians.

    I my case and my parents case, your model gave us too high distances, above 9 and 10, so I thought that maybe it was a model focused in Asian people.

  7. #6
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    Quote Originally Posted by mokordo View Post
    You have all the data you need in the datasheet, you can try your model with individual samples to check if distances are as good for different ethnicities as they are for Chinese or East Asians.

    I my case and my parents case, your model gave us too high distances, above 9 and 10, so I thought that maybe it was a model focused in Asian people.
    Thanks! By the way, Can I see these results?

  8. #7
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    You can try to add some populations from G25 official datasheets, and then screening people by comparison.
    你可以试着从G25的官方数据表中添加一些人口,然后通过比较来筛选人群。

    These are some of the benchmarking population fitting results I obtained by calculating the official G25 official average datasheets on Vahaduo using your benchmark population. I hope it will be useful:
    这些是我在Vahaduo上用你的基准测试人群计算G25官方平均值数据表而得到的部分标杆人群拟合结果, 希望有用:

    Q.png

  9. #8
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    Quote Originally Posted by MistH028 View Post
    You can try to add some populations from G25 official datasheets, and then screening people by comparison.
    你可以试着从G25的官方数据表中添加一些人口,然后通过比较来筛选人群。

    These are some of the benchmarking population fitting results I obtained by calculating the official G25 official average datasheets on Vahaduo using your benchmark population. I hope it will be useful:
    这些是我在Vahaduo上用你的基准测试人群计算G25官方平均值数据表而得到的部分标杆人群拟合结果, 希望有用:

    Q.png
    ok, got it.
    好的,我了解了

  10. #9
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    Target: hantro_scaled
    Distance: 8.4467% / 0.08446692
    69.6 ENF
    29.2 WHG
    1.2 ANE


    Distance to: hantro_scaled
    0.08474829 70.40% ENF + 29.60% WHG
    0.16006781 93.40% ENF + 6.60% ANE
    0.16290646 95.40% ENF + 4.60% EEA
    0.16369615 94.00% ENF + 6.00% SEA
    0.16475196 98.40% ENF + 1.60% OCE
    0.16502634 98.80% ENF + 1.20% SSA
    0.24430037 54.80% WHG + 45.20% SEA
    0.30256626 75.40% WHG + 24.60% EEA
    0.30543590 24.40% ANE + 75.60% WHG
    0.30704056 80.60% WHG + 19.40% SSA
    0.32551636 85.60% WHG + 14.40% OCE
    0.37909306 11.60% SSA + 88.40% SEA
    0.38552263 9.60% ANE + 90.40% SEA
    0.38820606 4.00% EEA + 96.00% SEA
    0.51263112 63.40% ANE + 36.60% SSA
    0.52923785 63.80% EEA + 36.20% SSA
    0.56094534 72.60% ANE + 27.40% OCE
    0.56916451 52.40% ANE + 47.60% EEA
    0.58090859 75.20% EEA + 24.80% OCE
    0.61509427 45.00% OCE + 55.00% SSA

  11. The Following User Says Thank You to hantrolugharsts For This Useful Post:

     Sansui Yang (07-30-2021)

  12. #10
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    Target: Kulin_scaled
    Distance: 3.3543% / 0.03354328
    72.2 SEA
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    7.6 ENF
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    0.6 ANE
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  13. The Following User Says Thank You to Kulin For This Useful Post:

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