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Thread: Chinese GEDmatch averages

  1. #1
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    Chinese GEDmatch averages

    Creating a separate thread for this from my East Eurasian megathread: https://anthrogenica.com/showthread....tch-megathread

    Anthroscape user @kushkush made some autosomal DNA (HarappaWorld) maps of Chinese provinces around the end of 2019, which were interesting but didn't seem completely accurate to me, since they weren't weighted by population distribution within specific provinces. I think these are just averages of random samples from specific provinces, they don't seem to be specific to Han Chinese.

    Posting their findings here for reference:

    HarappaWorld NE Asian


    HarappaWorld SE Asian


    HarappaWorld Siberian


    HarappaWorld table

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  3. #2
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    Reposting my own version of @kushkush's maps with MDLP K23b results here for comparison.

    This was based on the DNAConnect.org dataset as well as other samples found on GEDmatch and the WeGene forum. I'd like to think this is more representative of the Han in each province, since I tried to weigh my assigned samples for each province I had more than 10 samples for according to the population distibution for that province.

    The first 4 maps are of the 4 largest MDLP K23b East Eurasian ancestry components found among Chinese samples. The 5th one is of a North-South cline I created for East Asian MDLP K23b results. Basically "Tungus_Altaic" = northern, "Austronesian" = southern, while "South_East_Asian" and the 2 "Siberian" components are defined relative to T_A and AN. The original idea was that a Han Chinese person who scores the same amount of T_A and AN will score 0.5 on my North-South cline.











    Original methodology post (link is defunct)- https://www.tapatalk.com/groups/anth....html#p1848187

    TL;DR- I tried to weigh my available data according to the actual population distribution within each province (the DNAConnect.org data was geographically biased for most of the provinces- especially Jiangxi, Guangdong, Hunan, and Chongqing- which make up most of the DNAConnect.org dataset). Most of the samples in my original dataset and from DNAConnect.org were from the southern provinces, with very few from the northern provinces, so I had to use whatever I could get my hands on. The samples I used in my original dataset were just the ones of known regional/provincial ancestry.

    I weighed the Fujian, Jiangxi, Hunan, and Anhui DNAConnect.org subsets according to the population distribution of each province to make them more representative. The Guangdong and Shaanxi subsets only represented more remote parts of the provinces, and the Guizhou subset was mostly from minority-heavy areas. The only northern province represented in the DNAConnect.org dataset was Henan.

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    Quote Originally Posted by okarinaofsteiner View Post
    Creating a separate thread for this from my East Eurasian megathread: https://anthrogenica.com/showthread....tch-megathread

    Anthroscape user @kushkush made some autosomal DNA (HarappaWorld) maps of Chinese provinces around the end of 2019, which were interesting but didn't seem completely accurate to me, since they weren't weighted by population distribution within specific provinces. I think these are just averages of random samples from specific provinces, they don't seem to be specific to Han Chinese.

    Posting their findings here for reference:

    HarappaWorld NE Asian


    HarappaWorld SE Asian


    HarappaWorld Siberian


    HarappaWorld table
    That's pretty outdated table.

    You should create new average based on the large no. of Tibetan, Han samples available in genoplot. Tibet surely isn't 78% NE-Asian on average. It would be sth around early 70s.

     

    Sample NE-Asian Papuan SE-Asian S-Indian Siberian SW-Asian Beringian San NE-Euro American Caucasian Baloch Mediterranean E-African Pygmy W-African
    Tibetan Xinlong ► XL76 99.76 0.18 0.06 0 0 0 0 0 0 0 0 0 0 0 0 0
    Tibetan Xinlong ► XL60 80.43 0.62 0 7.41 11.2 0 0 0 0 0 0 0 0 0 0.03 0.31
    Tibetan Yajiang ► YJ140 80.03 1.05 1.32 4.3 11.06 0.01 0.4 0 0 1.13 0 0 0 0.49 0.21 0
    Tibetan Chamdo ► T274 79.97 1.72 0 5.14 10.64 0.54 1.02 0.1 0 0 0.86 0 0 0 0 0
    Tibetan Yajiang ► YJ111 79.8 1.38 2.57 3.71 11.14 0 0 0 0.23 0 0.04 0 0.71 0 0 0.4
    Tibetan Yajiang ► YJ37 79.3 2.56 0 3.94 11.81 0.49 0 0 0 1.5 0.25 0 0 0 0.14 0
    Tibetan Chamdo ► T247 78.99 1.09 0.34 7.87 11.29 0.41 0 0 0 0 0 0 0 0 0 0
    Tibetan Chamdo ► T74 78.42 1.14 0 6.58 13.3 0 0.55 0 0 0 0 0 0 0 0 0
    Tibetan Yajiang ► YJ01 78.31 0.42 1.59 4.02 12.59 0 0.45 0 0 0.86 0 0 0.51 0.32 0 0.94
    Tibetan Yajiang ► YJ25 78.14 0.31 0.93 5.04 11.54 0.55 1.99 0.14 0 0 0.17 0 0.02 0.11 0 1.06
    Tibetan Yajiang ► YJ104 78.01 2.1 3.02 3.88 12.01 0.03 0 0 0.43 0 0 0 0 0.51 0 0
    Tibetan Chamdo ► T283 77.91 1.07 1.22 5.43 12.28 0 0.81 0.28 0 0 0.93 0 0.06 0.01 0 0
    Tibetan Chamdo ► T273 77.66 1.29 1 4.87 11.04 0 1 0 0.59 1.23 0.95 0.37 0 0 0 0
    Tibetan Lhasa ► T76 77.64 1.19 0 5.71 11.21 0 1.99 0 0 0 0.84 1.06 0.35 0 0 0
    Tibetan Chamdo ► T194 77.64 2.21 2.49 4.5 12.58 0 0.05 0 0 0.17 0 0 0.36 0 0 0
    Tibetan Chamdo ► T173 77.58 1.28 0 6.64 12.49 0 1.84 0.17 0 0 0 0 0 0 0 0
    Tibetan Xinlong ► XL05 77.42 0.88 0 8.3 12.81 0.18 0 0 0.12 0.29 0 0 0 0 0 0
    Tibetan Chamdo ► T144 77.42 1.91 0 5.38 12.55 0 0.84 0 0 1 0.9 0 0 0 0 0
    Tibetan Chamdo ► T196 77.41 2.17 2.7 2.95 11.23 0.2 2.98 0 0.31 0.05 0 0 0 0 0 0
    Tibetan Nagqu ► T238 77.34 1.06 0.9 4.16 12.49 0.59 1.84 0.08 1.53 0 0 0 0 0 0 0
    Tibetan Nagqu ► T320 77.26 1.97 1.1 4.65 11.96 0.49 0.57 0 1.46 0 0.01 0.31 0 0.22 0 0
    Tibetan Chamdo ► T141 77.15 1.09 0.71 5.97 13.56 0 0.36 0 0.75 0.43 0 0 0 0 0 0
    Tibetan Yajiang ► YJ12 77.01 0.62 6.45 6.34 6.98 0 0 0.32 0.26 1.25 0 0.03 0.31 0.43 0 0
    Tibetan Lhasa ► T185 76.47 1 0.19 8.34 11.76 0 0 0 0 0.72 0.49 1.03 0 0 0 0
    Tibetan Chamdo ► T363 76.26 1.66 0.59 5.8 13.59 0 0.51 0 0 0.13 0 1.14 0 0 0.34 0
    Tibetan Lhasa ► T165 76.21 0.88 0 8.11 11.3 0 1.41 0 0 0 2.09 0 0 0 0 0
    Tibetan Shigatse ► T80 76.18 2.12 0.79 8.28 9.64 0.51 0.61 0 0 1.14 0 0.54 0 0.18 0 0
    Tibetan Nagqu ► T67 76.09 2.1 1.43 6.75 11.71 0 0.53 0.17 0.47 0 0.76 0 0 0 0 0
    Tibetan Nagqu ► T121 75.65 1.65 0 7.87 11.8 0.48 1.03 0 0 1.05 0 0.1 0 0.36 0 0
    Tibetan Lhasa ► T183 75.43 1.19 1.42 5.6 13.04 0 0 0 0 0 2.13 0.96 0.22 0 0 0
    Tibetan Nagqu ► T91 75.27 1.38 0 6.81 13.55 0.31 0.77 0.36 0 0.23 0.93 0 0 0.41 0 0
    Tibetan Shannan ► T127 75.24 2.7 0.25 7.83 11.06 0 0.53 0.06 0 0.94 0.36 0.66 0.35 0 0 0
    Tibetan Shigatse ► T22 75.08 2.03 1.29 7.02 11.71 0 0 0 0.02 1.18 1.31 0.37 0 0 0 0
    Tibetan Nagqu ► T312 74.92 1.19 0 9.31 14.06 0 0 0 0.3 0.06 0 0.17 0 0 0 0
    Tibetan Chamdo ► T282 74.92 1.98 0.52 6.2 12.8 0 0.08 0 0 1.24 1.78 0 0.36 0.09 0 0
    Tibetan Shigatse ► T215 74.7 1.83 0.92 7.44 12.3 0 0.98 0 0.17 0 0.6 1.05 0 0 0 0
    Tibetan Shannan ► T289 74.59 2.16 0 8.26 10.98 0 0.94 0 0.89 0.34 0 1.84 0 0 0 0
    Tibetan Xinlong ► XL61 74.4 1.07 10.96 4.28 8.25 0 0 0.12 0 0.84 0 0 0 0.05 0.02 0
    Tibetan Lhasa ► T170 74.34 2.38 1.1 7.79 10.81 1.52 1.06 0.09 0.44 0.28 0 0 0.2 0 0 0
    Tibetan Shannan ► T200 74.28 1.79 0 7.96 12.84 0.03 0 0 0 0.79 0 1.68 0 0.64 0 0
    Tibetan Shannan ► T188 74.05 2.01 0.17 8.51 10.46 0 0.72 0 0.75 1.43 1.82 0 0.06 0 0 0
    Tibetan Shigatse ► T78 73.82 2.86 0.59 6.04 13.79 0.3 0.04 0.14 0 0.65 0.54 1.21 0 0 0 0
    Tibetan Lhasa ► T181 73.78 1.36 1.56 6.93 12.24 0 0.71 0 1.8 1.18 0 0.44 0 0 0 0
    Tibetan Lhasa ► T125 73.78 1.88 0.52 7.14 11.24 0.49 2.92 0 0 0.38 0 1.66 0 0 0 0
    Tibetan Shannan ► T242 73.6 2.92 0.76 6.2 12 0.21 1.65 0 0 0.97 0.7 0.75 0 0 0.23 0
    Tibetan Xinlong ► XL120 73.53 1.59 0.43 6.16 13.35 0.23 2.01 0 0.13 0.93 0.86 0.13 0.1 0 0.3 0.25
    Tibetan Nagqu ► T187 73.52 1.65 2.26 6.56 13.14 0 0.71 0 0 0.61 0 1.56 0 0 0 0
    Tibetan Shigatse ► T297 73.5 2.56 0.84 6.5 13.54 0 0.34 0 0 0.62 0 2.1 0 0 0 0
    Tibetan Gangcha ► QH4 73.35 1.78 0 5.39 12.61 0 2.16 0 1.02 0.65 0 1.95 1.09 0 0 0
    Tibetan Lhasa ► T21 73.23 1.41 2.1 7.44 12.73 0 1.39 0.12 0 1.26 0 0.32 0 0 0 0
    Tibetan Gangcha ► QH20 73.21 0.86 0.3 4.67 15.21 0 0 0 0 1.4 1.53 2.81 0 0 0 0
    Tibetan Shigatse ► T192 72.68 1.17 2.99 7.06 11.49 0 1.89 0 1.65 0.28 0 0.45 0 0.35 0 0
    Tibetan Lhasa ► T18 72.45 1.3 4.32 6.11 12.74 1.56 0 0 0 0.17 0.51 0.83 0 0 0 0
    Tibetan Yajiang ► YJ14 72.29 1.06 13.07 3.45 8.71 0.52 0 0 0.05 0.23 0.11 0 0 0 0 0.5
    Tibetan Shigatse ► T291 72 1.68 1.95 7.63 13.81 0 0 0 0 0.78 0 1.37 0 0.78 0 0
    Tibetan Shigatse ► T77 71.92 1.79 1.15 8.33 11.31 0 1.29 0 1.51 1.31 0 1.41 0 0 0 0
    Tibetan Xunhua ► XHTB7 71.82 1.15 5.6 3.54 10.73 2.21 0 0.04 1.8 0 0.07 2.45 0 0 0.21 0.38
    Tibetan Gangcha ► QH11 71.81 1.86 2.31 4.19 14.6 0.56 0.45 0.1 1 1.19 0 1.03 0.9 0 0 0
    Tibetan Gangcha ► QH1 71.71 1.73 0.54 5.97 12.27 0.91 0 0 1.43 2.3 0.26 2.89 0 0 0 0
    Tibetan Gangcha ► QH5 71.7 0.38 0.79 5.25 14.51 3.61 0.18 0.18 1.12 1.3 0.18 0.39 0.33 0 0.07 0
    Tibetan Shigatse ► T41 71.48 2.32 1.16 8.11 13.53 0 0 0.03 0.61 2.11 0 0 0.66 0 0 0
    Tibetan Shannan ► T190 71.28 1.89 3.03 6.88 13.03 0.78 0 0.11 0.73 1.26 1.01 0 0 0 0 0
    Tibetan Gangcha ► QH17 71.24 1.76 0.26 5.12 14.37 0.92 2.57 0 0.63 0 1.98 0.32 0.83 0 0 0
    Tibetan Gangcha ► QH12 71.16 0.48 3.15 4.71 15.44 0 1.05 0 2.02 1.37 0.62 0 0 0 0 0
    Tibetan Nagqu ► T278 71.04 1.74 3.51 7.65 13.33 0.64 0.47 0 0.06 1.55 0 0 0 0.01 0 0
    Tibetan Shannan ► T248 70.55 1.77 3.61 7.6 12.47 0.9 1.26 0.08 0 0.82 0 0.93 0 0 0 0
    Tibetan Gangcha ► QH8 70.5 2.56 0.18 5.38 14.3 0 1.72 0 2.62 0 0.3 1.93 0.51 0 0 0
    Tibetan Shigatse ► T189 70.2 2.24 3.58 9.19 13.41 0.01 0.13 0 0 0.3 0.51 0.31 0 0 0.11 0
    Tibetan Shannan ► T199 70.14 1.93 2.18 7.72 12.33 0 2.05 0 0.06 0.63 0.43 2.1 0 0 0.43 0
    Tibetan Gangcha ► QH10 70.1 1.56 0.88 4.63 14.67 0 0 0 1 1.12 1.51 3.32 0.96 0 0 0.26
    Tibetan Yajiang ► YJ39 69.98 0.98 14.61 3.36 8.97 0 0 0 0.88 1.09 0 0 0 0 0 0.13
    Tibetan Gangcha ► QH19 69.88 0.87 4.34 4.96 15.18 0 0.47 0 0.61 0 2.83 0.86 0 0 0 0
    Tibetan Gangcha ► QH18 69.87 1.5 0.51 4.74 15.4 0.48 1.08 0 1.08 0.91 3.29 1.13 0 0 0 0
    Tibetan Gangcha ► QH6 69.73 2.17 3.22 4.05 14.05 0.93 2.82 0 0.16 0 0.42 1.21 1.23 0 0 0
    Tibetan Xinlong ► XL47 69.48 0 14.48 4.13 8.47 0 0.98 0.15 0.94 0 0.33 0.75 0.05 0 0 0.25
    Tibetan Gangcha ► QH15 69.48 1.01 3.02 6.5 15.42 0 0 0 0.35 0.93 2.5 0 0.3 0 0.49 0
    Tibetan Shannan ► T126 69.35 1.54 2.81 6.11 15.25 0.63 0.98 0 0.22 0 2.47 0.51 0 0 0 0.13
    Tibetan Xinlong ► XL92 68.9 0 21.66 0.81 7.33 0.28 0.57 0 0 0 0.18 0 0 0 0 0.25
    Tibetan Xunhua ► XHTB20 68.85 1.64 4.19 6.35 10.68 0 0.98 0.52 3.17 0 0 0.82 0.29 0 0 2.53
    Tibetan Gangcha ► QH14 68.75 1.98 2.51 2.68 16.47 0 1.83 0 0.95 0 3.14 1.15 0.53 0 0 0
    Tibetan Gangcha ► QH7 68.45 0.7 2.8 3.38 16.46 0 1.81 0.16 1.08 0 0.96 2.5 1.68 0 0 0
    Tibetan Xinlong ► XL37 68.27 0.74 24.88 0.19 5.14 0 0.78 0 0 0 0 0 0 0 0 0
    Tibetan Gannan ► GN19 68.19 0 7.2 3.37 10.42 0.6 1.97 0.06 1.08 0.78 3.33 1.91 0.43 0 0.65 0
    Tibetan Xunhua ► XHTB5 68.07 1.16 5.41 6.84 12.39 0 1.16 0.23 0.93 1.12 1.83 0.79 0 0 0.06 0
    Tibetan Gannan ► GN01 68.02 0.44 9.07 2.18 9.95 0 0 0.43 0 1.37 1.49 4.72 2.34 0 0 0
    Tibetan Gangcha ► QH16 67.94 1.36 2.25 7.12 13.22 0 1.01 0 0.09 1.58 2.15 1.51 1.76 0 0 0
    Tibetan Gangcha ► QH9 67.76 0.96 5.32 3.86 15.55 0 0.35 0.14 1.74 0 0.91 3.41 0 0 0 0
    Tibetan Gangcha ► QH13 67.3 0.34 11.23 2.24 10.01 0.72 1.54 0 1.41 1.98 2.33 0.78 0 0.13 0 0
    Tibetan Gannan ► GN10 67.18 0 12.15 0.81 10.13 0 0.8 0 3.01 0 1.79 2.82 1.1 0.2 0 0
    Tibetan Xinlong ► XL22 66.94 0.7 27.69 0.05 2.51 0 1.52 0.11 0 0.34 0 0 0 0 0 0.14
    Tibetan Gannan ► GN17 65.98 0.37 12.02 3.02 8.78 0.5 1.21 0 3.64 0 2.57 1.05 0.87 0 0 0
    Tibetan Gannan ► GN03 65.58 0.95 12.13 1.76 8.85 1.19 0.3 0 1.27 0 2.08 5.04 0.13 0.41 0 0.31
    Tibetan Gangcha ► QH3 64.67 1.42 2.6 5.92 17.52 0.53 1.73 0 2.7 0.94 0.87 0.66 0.45 0 0 0
    Tibetan Xunhua ► XHTB17 63.94 1.38 14.61 0 10.79 0.43 0.04 0 1.17 0 1.4 2.79 1.74 0.5 0 1.22
    Tibetan Yajiang ► YJ86 63.29 0.66 12.51 2.25 7.16 1.19 1.45 0.08 1.17 0.61 6.87 2.76 0 0 0 0
    Tibetan Gangcha ► QH2 62.37 1.56 1.72 4.43 18.98 1.16 1.76 0.02 0.96 1.91 1.68 3.46 0 0 0 0
    Tibetan Xinlong ► XL20 59.36 0.17 36.45 0.17 2.49 0.88 0 0.1 0 0.14 0.12 0 0 0.12 0 0


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  7. #4
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    Quote Originally Posted by kaazi View Post
    That's pretty outdated table.

    You should create new average based on the large no. of Tibetan, Han samples available in genoplot. Tibet surely isn't 78% NE-Asian on average. It would be sth around early 70s.

     

    Sample NE-Asian Papuan SE-Asian S-Indian Siberian SW-Asian Beringian San NE-Euro American Caucasian Baloch Mediterranean E-African Pygmy W-African
    Tibetan Xinlong ► XL76 99.76 0.18 0.06 0 0 0 0 0 0 0 0 0 0 0 0 0
    Tibetan Xinlong ► XL60 80.43 0.62 0 7.41 11.2 0 0 0 0 0 0 0 0 0 0.03 0.31
    Tibetan Yajiang ► YJ140 80.03 1.05 1.32 4.3 11.06 0.01 0.4 0 0 1.13 0 0 0 0.49 0.21 0
    Tibetan Chamdo ► T274 79.97 1.72 0 5.14 10.64 0.54 1.02 0.1 0 0 0.86 0 0 0 0 0
    Tibetan Yajiang ► YJ111 79.8 1.38 2.57 3.71 11.14 0 0 0 0.23 0 0.04 0 0.71 0 0 0.4
    Tibetan Yajiang ► YJ37 79.3 2.56 0 3.94 11.81 0.49 0 0 0 1.5 0.25 0 0 0 0.14 0
    Tibetan Chamdo ► T247 78.99 1.09 0.34 7.87 11.29 0.41 0 0 0 0 0 0 0 0 0 0
    Tibetan Chamdo ► T74 78.42 1.14 0 6.58 13.3 0 0.55 0 0 0 0 0 0 0 0 0
    Tibetan Yajiang ► YJ01 78.31 0.42 1.59 4.02 12.59 0 0.45 0 0 0.86 0 0 0.51 0.32 0 0.94
    Tibetan Yajiang ► YJ25 78.14 0.31 0.93 5.04 11.54 0.55 1.99 0.14 0 0 0.17 0 0.02 0.11 0 1.06
    Tibetan Yajiang ► YJ104 78.01 2.1 3.02 3.88 12.01 0.03 0 0 0.43 0 0 0 0 0.51 0 0
    Tibetan Chamdo ► T283 77.91 1.07 1.22 5.43 12.28 0 0.81 0.28 0 0 0.93 0 0.06 0.01 0 0
    Tibetan Chamdo ► T273 77.66 1.29 1 4.87 11.04 0 1 0 0.59 1.23 0.95 0.37 0 0 0 0
    Tibetan Lhasa ► T76 77.64 1.19 0 5.71 11.21 0 1.99 0 0 0 0.84 1.06 0.35 0 0 0
    Tibetan Chamdo ► T194 77.64 2.21 2.49 4.5 12.58 0 0.05 0 0 0.17 0 0 0.36 0 0 0
    Tibetan Chamdo ► T173 77.58 1.28 0 6.64 12.49 0 1.84 0.17 0 0 0 0 0 0 0 0
    Tibetan Xinlong ► XL05 77.42 0.88 0 8.3 12.81 0.18 0 0 0.12 0.29 0 0 0 0 0 0
    Tibetan Chamdo ► T144 77.42 1.91 0 5.38 12.55 0 0.84 0 0 1 0.9 0 0 0 0 0
    Tibetan Chamdo ► T196 77.41 2.17 2.7 2.95 11.23 0.2 2.98 0 0.31 0.05 0 0 0 0 0 0
    Tibetan Nagqu ► T238 77.34 1.06 0.9 4.16 12.49 0.59 1.84 0.08 1.53 0 0 0 0 0 0 0
    Tibetan Nagqu ► T320 77.26 1.97 1.1 4.65 11.96 0.49 0.57 0 1.46 0 0.01 0.31 0 0.22 0 0
    Tibetan Chamdo ► T141 77.15 1.09 0.71 5.97 13.56 0 0.36 0 0.75 0.43 0 0 0 0 0 0
    Tibetan Yajiang ► YJ12 77.01 0.62 6.45 6.34 6.98 0 0 0.32 0.26 1.25 0 0.03 0.31 0.43 0 0
    Tibetan Lhasa ► T185 76.47 1 0.19 8.34 11.76 0 0 0 0 0.72 0.49 1.03 0 0 0 0
    Tibetan Chamdo ► T363 76.26 1.66 0.59 5.8 13.59 0 0.51 0 0 0.13 0 1.14 0 0 0.34 0
    Tibetan Lhasa ► T165 76.21 0.88 0 8.11 11.3 0 1.41 0 0 0 2.09 0 0 0 0 0
    Tibetan Shigatse ► T80 76.18 2.12 0.79 8.28 9.64 0.51 0.61 0 0 1.14 0 0.54 0 0.18 0 0
    Tibetan Nagqu ► T67 76.09 2.1 1.43 6.75 11.71 0 0.53 0.17 0.47 0 0.76 0 0 0 0 0
    Tibetan Nagqu ► T121 75.65 1.65 0 7.87 11.8 0.48 1.03 0 0 1.05 0 0.1 0 0.36 0 0
    Tibetan Lhasa ► T183 75.43 1.19 1.42 5.6 13.04 0 0 0 0 0 2.13 0.96 0.22 0 0 0
    Tibetan Nagqu ► T91 75.27 1.38 0 6.81 13.55 0.31 0.77 0.36 0 0.23 0.93 0 0 0.41 0 0
    Tibetan Shannan ► T127 75.24 2.7 0.25 7.83 11.06 0 0.53 0.06 0 0.94 0.36 0.66 0.35 0 0 0
    Tibetan Shigatse ► T22 75.08 2.03 1.29 7.02 11.71 0 0 0 0.02 1.18 1.31 0.37 0 0 0 0
    Tibetan Nagqu ► T312 74.92 1.19 0 9.31 14.06 0 0 0 0.3 0.06 0 0.17 0 0 0 0
    Tibetan Chamdo ► T282 74.92 1.98 0.52 6.2 12.8 0 0.08 0 0 1.24 1.78 0 0.36 0.09 0 0
    Tibetan Shigatse ► T215 74.7 1.83 0.92 7.44 12.3 0 0.98 0 0.17 0 0.6 1.05 0 0 0 0
    Tibetan Shannan ► T289 74.59 2.16 0 8.26 10.98 0 0.94 0 0.89 0.34 0 1.84 0 0 0 0
    Tibetan Xinlong ► XL61 74.4 1.07 10.96 4.28 8.25 0 0 0.12 0 0.84 0 0 0 0.05 0.02 0
    Tibetan Lhasa ► T170 74.34 2.38 1.1 7.79 10.81 1.52 1.06 0.09 0.44 0.28 0 0 0.2 0 0 0
    Tibetan Shannan ► T200 74.28 1.79 0 7.96 12.84 0.03 0 0 0 0.79 0 1.68 0 0.64 0 0
    Tibetan Shannan ► T188 74.05 2.01 0.17 8.51 10.46 0 0.72 0 0.75 1.43 1.82 0 0.06 0 0 0
    Tibetan Shigatse ► T78 73.82 2.86 0.59 6.04 13.79 0.3 0.04 0.14 0 0.65 0.54 1.21 0 0 0 0
    Tibetan Lhasa ► T181 73.78 1.36 1.56 6.93 12.24 0 0.71 0 1.8 1.18 0 0.44 0 0 0 0
    Tibetan Lhasa ► T125 73.78 1.88 0.52 7.14 11.24 0.49 2.92 0 0 0.38 0 1.66 0 0 0 0
    Tibetan Shannan ► T242 73.6 2.92 0.76 6.2 12 0.21 1.65 0 0 0.97 0.7 0.75 0 0 0.23 0
    Tibetan Xinlong ► XL120 73.53 1.59 0.43 6.16 13.35 0.23 2.01 0 0.13 0.93 0.86 0.13 0.1 0 0.3 0.25
    Tibetan Nagqu ► T187 73.52 1.65 2.26 6.56 13.14 0 0.71 0 0 0.61 0 1.56 0 0 0 0
    Tibetan Shigatse ► T297 73.5 2.56 0.84 6.5 13.54 0 0.34 0 0 0.62 0 2.1 0 0 0 0
    Tibetan Gangcha ► QH4 73.35 1.78 0 5.39 12.61 0 2.16 0 1.02 0.65 0 1.95 1.09 0 0 0
    Tibetan Lhasa ► T21 73.23 1.41 2.1 7.44 12.73 0 1.39 0.12 0 1.26 0 0.32 0 0 0 0
    Tibetan Gangcha ► QH20 73.21 0.86 0.3 4.67 15.21 0 0 0 0 1.4 1.53 2.81 0 0 0 0
    Tibetan Shigatse ► T192 72.68 1.17 2.99 7.06 11.49 0 1.89 0 1.65 0.28 0 0.45 0 0.35 0 0
    Tibetan Lhasa ► T18 72.45 1.3 4.32 6.11 12.74 1.56 0 0 0 0.17 0.51 0.83 0 0 0 0
    Tibetan Yajiang ► YJ14 72.29 1.06 13.07 3.45 8.71 0.52 0 0 0.05 0.23 0.11 0 0 0 0 0.5
    Tibetan Shigatse ► T291 72 1.68 1.95 7.63 13.81 0 0 0 0 0.78 0 1.37 0 0.78 0 0
    Tibetan Shigatse ► T77 71.92 1.79 1.15 8.33 11.31 0 1.29 0 1.51 1.31 0 1.41 0 0 0 0
    Tibetan Xunhua ► XHTB7 71.82 1.15 5.6 3.54 10.73 2.21 0 0.04 1.8 0 0.07 2.45 0 0 0.21 0.38
    Tibetan Gangcha ► QH11 71.81 1.86 2.31 4.19 14.6 0.56 0.45 0.1 1 1.19 0 1.03 0.9 0 0 0
    Tibetan Gangcha ► QH1 71.71 1.73 0.54 5.97 12.27 0.91 0 0 1.43 2.3 0.26 2.89 0 0 0 0
    Tibetan Gangcha ► QH5 71.7 0.38 0.79 5.25 14.51 3.61 0.18 0.18 1.12 1.3 0.18 0.39 0.33 0 0.07 0
    Tibetan Shigatse ► T41 71.48 2.32 1.16 8.11 13.53 0 0 0.03 0.61 2.11 0 0 0.66 0 0 0
    Tibetan Shannan ► T190 71.28 1.89 3.03 6.88 13.03 0.78 0 0.11 0.73 1.26 1.01 0 0 0 0 0
    Tibetan Gangcha ► QH17 71.24 1.76 0.26 5.12 14.37 0.92 2.57 0 0.63 0 1.98 0.32 0.83 0 0 0
    Tibetan Gangcha ► QH12 71.16 0.48 3.15 4.71 15.44 0 1.05 0 2.02 1.37 0.62 0 0 0 0 0
    Tibetan Nagqu ► T278 71.04 1.74 3.51 7.65 13.33 0.64 0.47 0 0.06 1.55 0 0 0 0.01 0 0
    Tibetan Shannan ► T248 70.55 1.77 3.61 7.6 12.47 0.9 1.26 0.08 0 0.82 0 0.93 0 0 0 0
    Tibetan Gangcha ► QH8 70.5 2.56 0.18 5.38 14.3 0 1.72 0 2.62 0 0.3 1.93 0.51 0 0 0
    Tibetan Shigatse ► T189 70.2 2.24 3.58 9.19 13.41 0.01 0.13 0 0 0.3 0.51 0.31 0 0 0.11 0
    Tibetan Shannan ► T199 70.14 1.93 2.18 7.72 12.33 0 2.05 0 0.06 0.63 0.43 2.1 0 0 0.43 0
    Tibetan Gangcha ► QH10 70.1 1.56 0.88 4.63 14.67 0 0 0 1 1.12 1.51 3.32 0.96 0 0 0.26
    Tibetan Yajiang ► YJ39 69.98 0.98 14.61 3.36 8.97 0 0 0 0.88 1.09 0 0 0 0 0 0.13
    Tibetan Gangcha ► QH19 69.88 0.87 4.34 4.96 15.18 0 0.47 0 0.61 0 2.83 0.86 0 0 0 0
    Tibetan Gangcha ► QH18 69.87 1.5 0.51 4.74 15.4 0.48 1.08 0 1.08 0.91 3.29 1.13 0 0 0 0
    Tibetan Gangcha ► QH6 69.73 2.17 3.22 4.05 14.05 0.93 2.82 0 0.16 0 0.42 1.21 1.23 0 0 0
    Tibetan Xinlong ► XL47 69.48 0 14.48 4.13 8.47 0 0.98 0.15 0.94 0 0.33 0.75 0.05 0 0 0.25
    Tibetan Gangcha ► QH15 69.48 1.01 3.02 6.5 15.42 0 0 0 0.35 0.93 2.5 0 0.3 0 0.49 0
    Tibetan Shannan ► T126 69.35 1.54 2.81 6.11 15.25 0.63 0.98 0 0.22 0 2.47 0.51 0 0 0 0.13
    Tibetan Xinlong ► XL92 68.9 0 21.66 0.81 7.33 0.28 0.57 0 0 0 0.18 0 0 0 0 0.25
    Tibetan Xunhua ► XHTB20 68.85 1.64 4.19 6.35 10.68 0 0.98 0.52 3.17 0 0 0.82 0.29 0 0 2.53
    Tibetan Gangcha ► QH14 68.75 1.98 2.51 2.68 16.47 0 1.83 0 0.95 0 3.14 1.15 0.53 0 0 0
    Tibetan Gangcha ► QH7 68.45 0.7 2.8 3.38 16.46 0 1.81 0.16 1.08 0 0.96 2.5 1.68 0 0 0
    Tibetan Xinlong ► XL37 68.27 0.74 24.88 0.19 5.14 0 0.78 0 0 0 0 0 0 0 0 0
    Tibetan Gannan ► GN19 68.19 0 7.2 3.37 10.42 0.6 1.97 0.06 1.08 0.78 3.33 1.91 0.43 0 0.65 0
    Tibetan Xunhua ► XHTB5 68.07 1.16 5.41 6.84 12.39 0 1.16 0.23 0.93 1.12 1.83 0.79 0 0 0.06 0
    Tibetan Gannan ► GN01 68.02 0.44 9.07 2.18 9.95 0 0 0.43 0 1.37 1.49 4.72 2.34 0 0 0
    Tibetan Gangcha ► QH16 67.94 1.36 2.25 7.12 13.22 0 1.01 0 0.09 1.58 2.15 1.51 1.76 0 0 0
    Tibetan Gangcha ► QH9 67.76 0.96 5.32 3.86 15.55 0 0.35 0.14 1.74 0 0.91 3.41 0 0 0 0
    Tibetan Gangcha ► QH13 67.3 0.34 11.23 2.24 10.01 0.72 1.54 0 1.41 1.98 2.33 0.78 0 0.13 0 0
    Tibetan Gannan ► GN10 67.18 0 12.15 0.81 10.13 0 0.8 0 3.01 0 1.79 2.82 1.1 0.2 0 0
    Tibetan Xinlong ► XL22 66.94 0.7 27.69 0.05 2.51 0 1.52 0.11 0 0.34 0 0 0 0 0 0.14
    Tibetan Gannan ► GN17 65.98 0.37 12.02 3.02 8.78 0.5 1.21 0 3.64 0 2.57 1.05 0.87 0 0 0
    Tibetan Gannan ► GN03 65.58 0.95 12.13 1.76 8.85 1.19 0.3 0 1.27 0 2.08 5.04 0.13 0.41 0 0.31
    Tibetan Gangcha ► QH3 64.67 1.42 2.6 5.92 17.52 0.53 1.73 0 2.7 0.94 0.87 0.66 0.45 0 0 0
    Tibetan Xunhua ► XHTB17 63.94 1.38 14.61 0 10.79 0.43 0.04 0 1.17 0 1.4 2.79 1.74 0.5 0 1.22
    Tibetan Yajiang ► YJ86 63.29 0.66 12.51 2.25 7.16 1.19 1.45 0.08 1.17 0.61 6.87 2.76 0 0 0 0
    Tibetan Gangcha ► QH2 62.37 1.56 1.72 4.43 18.98 1.16 1.76 0.02 0.96 1.91 1.68 3.46 0 0 0 0
    Tibetan Xinlong ► XL20 59.36 0.17 36.45 0.17 2.49 0.88 0 0.1 0 0.14 0.12 0 0 0.12 0 0

    Yeah, it would be cool if we can have an update.

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    Quote Originally Posted by Shuzam87 View Post
    Yeah, it would be cool if we can have an update.
    That was all Anthroscape user @kushkush- I have no idea if he's still into this stuff or have time for that. I don't even know how he found all those Tibetan GEDmatch samples back in the day.

    I've considered making a HarappaWorld version of my MDLP K23b maps, but idk if the kits I used to calibrate results for different provinces are still publicly available. I'm guessing most have been deleted or removed from public access by now.

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    Quote Originally Posted by Shuzam87 View Post
    Yeah, it would be cool if we can have an update.
    Just made 100 Tibetans' average from genoplot.
    https://anthrogenica.com/showthread....l=1#post791994


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    Chinese GEDmatch averages

    A former Anthroscape member sent me a chart of 23mofang averages for various suburban and rural districts in China, which are believed to be more autosomally "representative" of specific regions and linguistic subgroups (as opposed to the city centers, which are more cosmopolitan and therefore more "mixed".) https://imgur.com/a/pxuhh3D



    Map (latitude + longitude) with ChinaMAP study clusters added


    I attempted to model the MDLP K23b results for these averages earlier this year by substituting the listed ancestry components with various combinations of MDLP K23b reference populations. For example, I would model "She" with MDLP K23b's She, "Tibetan" with the average of the 2 MDLP K23b "Tibetan" reference populations, etc.

    Full model for all non-Han 23mofang components:

     
    Daic (Tai-Kradai) = 5/16 "Chinese_Dai" + 1/16 part "Tai_Lue" + 2/16 parts "Jiamao" + 2/16 parts "Zhuang" + 5/16 parts "Dai" + 1/16 part "Ami_Taiwan"
    Tungusic = average of: "Xibo" + "Oroqen" + "Hezhen" + "Daur"
    Hmong-Mien = average of: "Yao" + "Miao" + "Hmong_Miao" + "Hmong"
    Japanese = "Japanese" (the one that scores highest on T_A)
    Korean = average of: "Korean_" + "Korean_KR"
    Lahu = "Lahu"
    Buryat = "Buryat"
    Yakut = "Yakut" (doesn't include the other "Yakut" reference pop)
    Uyghur = average of: "Uygur" + "Uygur-Han"
    Tibeto-Burman = average of: "Tibetian_Madou" + "Tibetian_TTR"



    This was difficult to do for the N_Han and S_Han components, because using HGDP's "Han_North" and "Han_" did not result in the Taiwanese, Hakka, and Cantonese area averages resembling the "Chinese_Taiwan", "Hakka", and "Cantonese" reference populations, so I did some tweaking.

     
    N_Han = 46.17% S_EA, 33.55% T_A, 18.43% AN, based on "Han_North" (HGDP)
    S_Han v1 = 4/10 Han_ (HGDP) + 2/10 Chinese_Dai + 1/10 Tujia + 1/10 She + 1/10 Korean (avg) + 1/10 Vietnamese
    S_Han v2 = 4/14 Han_ (HGDP) + 2/14 Korean (avg) + 2/14 Vietnamese + 2/14 Chinese_Dai + 1/14 Tujia + 1/14 She + 1/14 Ami + 1/14 Naxi


    The N_Han reference component was modeled as something slightly more "NE Asian" shifted than most actual Northern Han, since this was highest among the Shandong, Hebei, and southern Shanxi averages. The S_Han reference component was modeled as something in-between "Chinese_Taiwan"/"Hakka" and "Cantonese", since this component was highest among the Teochew, Leizhou, and Hakka-speaking area averages in Guangdong [the Yue-speaking areas scored lower on this component].

    I decided to make another model for S_Han after seeing how my averages plotted on the Austronesian vs Tungus_Altaic graph against the Chinese samples from my original GEDmatch dataset. S_Han v2 matched the distribution better than S_Han v1.

    Old version (S_Han v1)
     


    New version (S_Han v2)
     

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    Thanks for this awesome chart. We can see that Yue-speaking peoples indeed scored very low on Northern Han but rather high on Zhuang-Dai, indicating that they're mostly sinicized natives.
    Last edited by MNOPSC1b; 10-04-2021 at 02:13 AM.
    南北有别,反汉反儒,复兴百越,弘益人间

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    Quote Originally Posted by okarinaofsteiner View Post
    A former Anthroscape member sent me a chart of 23mofang averages for various suburban and rural districts in China, which are believed to be more autosomally "representative" of specific regions and linguistic subgroups (as opposed to the city centers, which are more cosmopolitan and therefore more "mixed".) https://imgur.com/a/pxuhh3D

     


    Map (latitude + longitude) with ChinaMAP study clusters added


    I attempted to model the MDLP K23b results for these averages earlier this year by substituting the listed ancestry components with various combinations of MDLP K23b reference populations. For example, I would model "She" with MDLP K23b's She, "Tibetan" with the average of the 2 MDLP K23b "Tibetan" reference populations, etc.
    N_Han = 46.17% S_EA, 33.55% T_A, 18.43% AN, based on "Han_North" (HGDP)
    S_Han v1 = 4/10 Han_ (HGDP) + 2/10 Chinese_Dai + 1/10 Tujia + 1/10 She + 1/10 Korean (avg) + 1/10 Vietnamese
    S_Han v2 = 4/14 Han_ (HGDP) + 2/14 Korean (avg) + 2/14 Vietnamese + 2/14 Chinese_Dai + 1/14 Tujia + 1/14 She + 1/14 Ami + 1/14 Naxi

    The N_Han reference component was modeled as something slightly more "NE Asian" shifted than most actual Northern Han, since this was highest among the Shandong, Hebei, and southern Shanxi averages. The S_Han reference component was modeled as something in-between "Chinese_Taiwan"/"Hakka" and "Cantonese", since this component was highest among the Teochew, Leizhou, and Hakka-speaking area averages in Guangdong [the Yue-speaking areas scored lower on this component].

    I decided to make another model for S_Han after seeing how my averages plotted on the Austronesian vs Tungus_Altaic graph against the Chinese samples from my original GEDmatch dataset. S_Han v2 matched the distribution better than S_Han v1.




    Austronesian vs Tungus_Altaic with original GEDmatch dataset (S_Han v1)


    Austronesian vs Tungus_Altaic with original GEDmatch dataset (S_Han v2)


    In the new version, the Foshan and Jiangmen averages score quite closely to the "Cantonese" reference population on Tungus_Altaic and Austronesian. The Quanzhou average is fairly close to "Chinese_Taiwan", which I believe corresponds to native Minnan/Hokkien speakers from Taipei and should also be a good proxy for Minnan/Hokkien speakers in Fujian itself. The relative positions of the 23mofang location averages are the same in both the old and new versions.


    N-S cline with original GEDmatch dataset + DNAConnect.org adoptees (S_Han v1)


    N-S cline with original GEDmatch dataset + DNAConnect.org adoptees (S_Han v2)


    The newer version also seems to fit the distribution of actual Chinese samples on the N-S cline + East Eurasian percentages graph better. Which makes sense because I used a greater number of source populations for S_Han v2 that aren't 100% East Eurasian. I thought this would better simulate how the "S_Han" component seems to include some of the Daic-like ancestry (but not necessarily "Dai") that the Lingnan/Min+Hakka-speaking Han groups which score highest on 23mofang's "Southern Han" seem to have.








    Versions of the above with ChinaMAP clusters labeled. Gray = Southeast [Fujian], green = Hubei.

    On the N-S cline graph, the Jianghuai Mandarin, Wu, and Mindong samples fall on the line between the Northern Han and Southern Han reference points. There also seems to be an inland-coastal cline in that the inland samples seem to be less pure East Eurasian; we can clearly see the transition from Min speakers (Fujian) to Gan speakers (Jiangxi), Xiang speakers (Hunan), and SW Mandarin speakers (Sichuan/Chongqing)- all of which seem similarly "southern" to some extent but as shown in the original 23mofang chart [in Chinese] have different affinities to different SEA-like ancestries.






    Map showing where the 0.45 (blue), 0.50 (purple), and 0.55 (red) values of my N-S cline fall on my new model (S_Han v2). The 0.45 value corresponds almost exactly to the Qinling-Huaihe line. While the 0.55 value roughly corresponds to which Southern Han (rice area) groups are more SEA-influenced and which ones are more "Central"

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    When inland samples score less "East Eurasian" what component increases instead?

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