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An Italian Jewish average according to what Minardowicz sent me. Doesn't look convincing to be honest, they should be more Middle Eastern
@vbnetkhio, let me know if you have access to the Italian Jewish data from Eurogenes, they should score differently.Code:Italkim,5.65,0.38,4.20,0.26,26.26,12.58,0.35,0.62,13.68,0.29,35.59,0.13 Distance to: Italkim 1.45475084 Ashkenazi_Jews 1.96091893 Italy_Calabria 2.23823591 MD_Jewish 3.65472092 Italy_Sicily 3.99501264 Italy_Campania 5.45705049 Greek_Izmir 5.52735018 Greek_Crete 6.07383141 Italy_Apulia 6.51920575 Italy_Abruzzo 6.75185160 Greek_Kos Target: Italkim Distance: 0.6749% / 0.67487996 | ADC: 0.5x 61.0 Ashkenazi_Jews 39.0 Italy_Calabria Target: Italkim Distance: 0.5801% / 0.58012062 | ADC: 0.25x 56.8 Ashkenazi_Jews 37.2 Italy_Calabria 4.4 Greek_Izmir 1.6 Sephardic_Jews
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New Russian averages for K12b based on academic samples from Yunusbaev. Added.
http://vahaduo.genetics.ovh/dodecad-k12b-vahaduo.htm
I removed old Tver, Smolensk, and Kostroma. Tver has much more Siberian than academic av. and even more than Kostroma. Kostroma has too little Siberian. Smolensk also has slightly too big Siberian comparing to academic average.Code:RU_Kostroma,4.1233333333,6.725,0.2983333333,0.5233333333,18.6116666667,58.64,0.355,0.0433333333,1.2933333333,0.73,8.64,0.0166666667 RU_Tver,4.15,2.92,0.045,0.445,19.9625,59.795,0.7975,0,0.8975,0.6275,10.3575,0 RU_Smolensk,3.7957142857,1.3257142857,0.1385714286,0.1728571429,21.8271428571,58.85,0.6385714286,0.0485714286,1.38,0.3014285714,11.5257142857,0 RU_Oryol,4.9257142857,2.31,0.2128571429,0.1485714286,20.9685714286,57.11,0.6085714286,0,0.9242857143,0.1585714286,12.6285714286,0 RU_Voronoezh,3.4125,1.93,0.3225,0.2675,20.72,57.73,0.4925,0.09,1.6275,0,13.4075,0 RU_Kursk,4.4475,1.8,0.18,0.22,20.29,57.495,1.065,0.0075,1.1475,0.6425,12.7075,0
==========
I added also Pskov and Ryazan academic refs from different study.
Code:RU_Pskov 2.8371428571,2.0057142857,0.2414285714,0.3528571429,20.4271428571,62.6414285714,0.7871428571,0.0414285714,1.3828571429,0.0142857143,8.9757142857,0.2914285714 RU_Ryazan 4.84,4.0723076923,0.3376923077,0.2323076923,20.1553846154,56.1953846154,0.8115384615,0.1961538462,0.7992307692,0.3838461538,11.9053846154,0.0661538462
Last edited by Lucas; 10-22-2020 at 01:02 PM.
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Actually all of that was academic too. Since it's you who has done the update I'm not really ready to object. Will check them.
There probably will be an Ural Russian average too, I'm currently collecting data and comparing various kits.
Lukasz, can you please round them to two decimal places? 2.12 instead of 2.116. It looks much better.
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10 Italian Jewish academic samples:
https://easyupload.io/hgchrg
Last edited by vbnetkhio; 10-22-2020 at 10:08 PM.
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I see. Thanks.
Malays from Peninsular Malaysia (western region). There are some Malays from Eastern part of Malaysia (Borneo) as well but their gedmatch kits are rare.
N=17
AverageCode:Malay_Malaysia_1,4.37,0.78,0.3,58.6,0.72,1.13,16.96,0,0.91,15.36,0.87,0 Malay_Malaysia_2,5.93,0.47,1.39,59.47,0,1.32,15.67,0,0,13.94,1.76,0 Malay_Malaysia_3,5.23,0,0,54.01,0.53,0.67,22.5,0.2,0.13,16,0.72,0 Malay_Malaysia_4,5.92,2.47,0.08,57.95,1.11,1.78,19.16,0,0.19,10.38,0.95,0 Malay_Malaysia_5,3.52,0,0,63.66,0.63,0,16.47,0,1.3,12.75,1.26,0.36 Malay_Malaysia_6,5.3,1.09,0,56.71,0.77,1.12,22.34,0.26,0,10.1,2.14,0.17 Malay_Malaysia_7,4.44,1.21,0,60.77,0,0,17.56,0.61,1.75,11.05,2.6,0 Malay_Malaysia_8,2.01,1.07,0,63.24,0,1.68,17.98,1.23,0.64,10.96,1.18,0 Malay_Malaysia_9,4.09,1.83,1.68,62.12,0,0.21,16.18,0,0,12.04,1.38,0.48 Malay_Malaysia_10,2.62,1.89,0,65.3,0.33,1.38,15.22,0,0.36,12.57,0,0.32 Malay_Malaysia_11,3.31,2.49,0.6,57.82,0.7,0.61,17.77,0,0.83,14.02,1.75,0.11 Malay_Malaysia_12,3,1.67,0,62.81,0.92,1.84,15.33,0.43,0,13.33,0.64,0 Malay_Malaysia_13,4.21,0.76,1.53,56.75,0,0.88,16.22,0,0,17.22,2.42,0 Malay_Malaysia_14,2.5,0.3,0.39,58.97,0,0.74,14.46,0.24,0.51,20.89,1.01,0 Malay_Malaysia_15,3.89,0,0,61.66,0,1.6,13.78,0,0,15.64,3.44,0 Malay_Malaysia_16,6.56,2.06,0,55.45,0,0,20.79,0.19,0,14.52,0,0.4 Malay_Malaysia_17,5.25,1.99,0,58.72,1.1,1.93,17.08,0.07,0.13,9.51,2.79,1.44
Distance to Malay_MalaysiaCode:Malay_Malaysia,4.24,1.18,0.35,59.65,0.40,0.99,17.38,0.19,0.40,13.55,1.47,0.19
9.84586715 PANIYA (once again a SE Asian pop mislabelled as South Asian tribal group, not sure the exact SE Asian ethnic group though)
22.19475388 Lahu
23.57280425 Vietnamese
24.95915063 Malay_Singapore (strange how its so far from Malay_Singapore. I checked and it looks like Malay_Singapore is a faulty and terrible pop reference)
26.74066379 Cambodians
27.82123649 Khasi
29.76916358 Dai
31.51851995 Burmanese
34.60891648 Garo
41.24578888 Miaozu
43.58273856 She
My model of Malay_Malaysia average. Around 9.4% West Eurasian. The rest is mainly Mongoloid and Australoid/Negrito or AASI represented by the Bonda, an Austrosiatic tribe in Eastern part of India who is mainly AASI+Mongoloid with very little Caucasoid admix (around 10% and less). Even noice amounts of Negroid (Luhya)
Target: Malay_Malaysia
Distance: 1.3259% / 1.32586244
41.4 Dai
24.2 Bonda
21.8 Vietnamese
4.6 Brahui
3.4 Iran_Mazandaran
2.8 Han_central
1.4 Lithuanian
0.4 Luhya
Replacing the Bonda with my simulated AASI I created:
Around 74% Mongoloid 16% AASI and 9.4% West Eurasian and noise levels of Negroid (represented by Luhya).Code:Simulated_AASI,0,0,0,0,0,0,100,0,0,0,0,0
Target: Malay_Malaysia
Distance: 1.3537% / 1.35372769
56.0 Dai
17.6 Vietnamese
16.2 AASI
4.6 Brahui
3.4 Iran_Mazandaran
1.4 Lithuanian
0.4 Han_central
0.4 Luhya
Take it with a grain of salt though especially my simulated AASI lol.
I will do Flips, Thais, Burmese, Cambodians for a few more SE Asian references and that should be enough lol.
Last edited by Maguzanci; 10-23-2020 at 06:48 PM.
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