Gedmatch calculators on Vahaduo

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10-16-2020, 05:12 PM
Amon

10 Japanese kits

Code:

J1,0,4.73,0,10.38,0,0,0.7,0.01,0,84.18,0,0 J2,0,5.81,0,9.97,0,0,0,0,0,84.19,0,0 J3,0,4.38,0,9.56,0,0,0.78,0,0,85.24,0,0 J4,0,4.31,0,10.07,0,0,0.37,0,0,85.19,0,0.05 J5,0,5.34,0.12,15.43,0,0,0,0,0,78.91,0,0.21 J6,0,1.85,0.08,4.11,0,0,6.6,0,0,87.35,0,0 J7,0,4.14,0.60,14.93,0,0,0,0,0.57,79.76,0,0 J8,0,1.95,0,6.41,0,0,5.7,0,0,85.95,0,0 J9,0,4.66,0,9.08,0,0,1.35,0,0,84.9,0,0 J10,0,3.31,0.26,12.3,0,0,0,0,0,84.12,0,0

Avg: Japanese,0,4.05,0.1,10.23,0,0,1.55,0.00,0.06,83.99 ,0,0.02

Distance to: Japanese
3.47620483 Korean
13.71986152 Naga
19.76139165 Tu
20.65246232 Xibo
22.79960526 Mongola
24.33039252 Chinese

Target: Japanese
Distance: 1.5115% / 1.51154055
84.6 Korean
9.8 Naga
5.6 Daur
10-16-2020, 05:14 PM
Amon

Quote:

Originally Posted by Leto

Do Han, Japanese, Korean and Vietnamese if you can.

The Korean avg is pretty good IMO.
10-16-2020, 05:17 PM
Leto

Quote:

Originally Posted by Amon

The Korean avg is pretty good IMO.

Do actual Koreans get Korean as their first pop with a small distance? If they don't, it's no good.

Also, 1.55% South Asian for Japan sounds doubtful.
10-16-2020, 05:23 PM
Amon

Quote:

Originally Posted by Leto

Do actual Koreans get Korean as their first pop with a small distance? If they don't, it's no good.

Also, 1.55% South Asian for Japan sounds doubtful.

I can make a new avg for the Japs excluding the outliers - with likely Ainu ancestry - that's giving them SA. What say you?

I'll look into more Korean kits. Have you any?
10-16-2020, 05:26 PM
Leto

Quote:

Originally Posted by Amon

I can make a new avg for the Japs excluding the outliers - with likely Ainu ancestry - that's giving them SA. What say you?

I'll look into more Korean kits. Have you any?

Yeah, I think most Japs don't have that, so I suggest you remove that outlier.

I haven't collected East Asian kits on purpose. Some occasional ones here and there.
10-16-2020, 05:34 PM
Amon

Quote:

Originally Posted by Leto

Yeah, I think most Japs don't have that, so I suggest you remove that outlier.

New avg after excluding two outliers J6 and J8: Japanese,0,4.58,0.12,11.47,0,0,0.4,0.00,0.07,83.31 ,0,0.03

Distance to: Japanese
4.02313311 Korean
12.68279149 Naga
18.66732975 Tu
19.73086415 Xibo
21.73806799 Mongola
23.00038261 Chinese

Target: Japanese
Distance: 0.3378% / 0.33781019 | ADC: 0.25x
79.8 Korean
10.6 Naga
9.6 Xibo
10-17-2020, 01:35 PM
J. Ketch

This should be my last update of England and Wales K13/K15 for a while. Also made new averages for Connacht and Ulster, and a national average for England from all my references, which can replace the ones that are already there if you want (pretty sure it's more accurate).

K13

refs:

English - 231 (from North, Midlands and South)
English_North - 85
English_Midlands - 71
English_Southeast - 41
Irish_Munster - 38
Ulster_British - 27
Irish_Connacht - 21
Irish_Ulster - 21

Code:

English,49.92,23.49,13.55,5.65,3.78,0.70,1.08,0.14,0.26,0.55,0.41,0.22,0.19 English_Midlands,49.73,23.34,13.70,5.59,4.17,0.70,1.00,0.19,0.25,0.49,0.37,0.23,0.18 English_North,50.47,23.89,13.16,5.68,3.22,0.66,1.07,0.09,0.29,0.62,0.41,0.17,0.20 English_Southeast,49.15,23.27,13.63,5.91,4.21,0.76,1.11,0.17,0.27,0.56,0.44,0.26,0.19 Irish_Connacht,52.82,23.41,11.97,7.10,0.94,0.21,1.55,0.11,0.30,0.88,0.24,0.23,0.11 Irish_Munster,52.71,22.98,12.26,6.93,1.52,0.35,1.36,0.21,0.30,0.77,0.23,0.14,0.17 Irish_Ulster,53.28,22.32,12.60,7.05,0.84,0.56,1.49,0.10,0.32,0.88,0.32,0.05,0.14 Ulster_British,51.30,23.24,13.14,7.00,1.73,0.58,1.22,0.05,0.17,0.75,0.53,0.11,0.09

K15

refs:

English - 235
English_North - 86
English_Midlands - 71
Irish_Munster - 45
English_Southeast - 44
Welsh - 35
Ulster_British - 27
Irish_Connacht - 25
Irish_Ulster - 22

Code:

English,36.13,28.23,10.12,8.75,9.42,3.75,1.30,0.48,0.72,0.05,0.09,0.30,0.24,0.17,0.13 English_Midlands,36.14,28.03,10.01,8.68,9.50,3.82,1.49,0.56,0.66,0.06,0.07,0.26,0.20,0.18,0.13 English_North,36.62,28.38,10.27,8.93,9.02,3.63,0.95,0.40,0.70,0.03,0.11,0.34,0.26,0.15,0.13 English_Southeast,35.43,27.99,10.05,8.83,9.53,4.12,1.65,0.52,0.74,0.07,0.11,0.31,0.26,0.17,0.14 Irish_Connacht,37.84,30.38,9.02,9.26,7.24,3.80,0.08,0.08,1.04,0.04,0.12,0.68,0.14,0.12,0.07 Irish_Munster,37.12,30.05,9.80,8.52,7.53,4.57,0.20,0.17,0.91,0.08,0.16,0.48,0.14,0.07,0.12 Irish_Ulster,36.62,31.10,9.06,8.71,7.60,4.46,0.04,0.28,1.11,0.06,0.09,0.58,0.14,0.03,0.03 Ulster_British,37.69,28.74,9.10,9.21,8.30,4.48,0.38,0.30,0.80,0.03,0.05,0.44,0.29,0.03,0.06 Welsh,36.79,29.22,9.77,9.00,9.06,4.02,0.24,0.17,0.63,0.02,0.16,0.36,0.28,0.13,0.05 - ignore

Could you also remove the duplicate 'Southeast_English' average in K15 Lucas, seeing as it's already been removed from K13? Thanks.
10-17-2020, 02:22 PM
Dunai

Quote:

Originally Posted by Creoda

This should be my last update of England and Wales K13/K15 for a while. Also made new averages for Connacht and Ulster, and a national average for England from all my references, which can replace the ones that are already there if you want (pretty sure it's more accurate).

K13

refs:

English - 231 (from North, Midlands and South)
English_North - 85
English_Midlands - 71
English_Southeast - 41
Irish_Munster - 38
Ulster_British - 27
Irish_Connacht - 21
Irish_Ulster - 21

Code:

English,49.92,23.49,13.55,5.65,3.78,0.70,1.08,0.14,0.26,0.55,0.41,0.22,0.19 English_Midlands,49.73,23.34,13.70,5.59,4.17,0.70,1.00,0.19,0.25,0.49,0.37,0.23,0.18 English_North,50.47,23.89,13.16,5.68,3.22,0.66,1.07,0.09,0.29,0.62,0.41,0.17,0.20 English_Southeast,49.15,23.27,13.63,5.91,4.21,0.76,1.11,0.17,0.27,0.56,0.44,0.26,0.19 Irish_Connacht,52.82,23.41,11.97,7.10,0.94,0.21,1.55,0.11,0.30,0.88,0.24,0.23,0.11 Irish_Munster,52.71,22.98,12.26,6.93,1.52,0.35,1.36,0.21,0.30,0.77,0.23,0.14,0.17 Irish_Ulster,53.28,22.32,12.60,7.05,0.84,0.56,1.49,0.10,0.32,0.88,0.32,0.05,0.14 Ulster_British,51.30,23.24,13.14,7.00,1.73,0.58,1.22,0.05,0.17,0.75,0.53,0.11,0.09

K15

refs:

English - 235
English_North - 86
English_Midlands - 71
Irish_Munster - 45
English_Southeast - 44
Welsh - 35
Ulster_British - 27
Irish_Connacht - 25
Irish_Ulster - 22

Code:

English,36.13,28.23,10.12,8.75,9.42,3.75,1.30,0.48,0.72,0.05,0.09,0.30,0.24,0.17,0.13 English_Midlands,36.14,28.03,10.01,8.68,9.50,3.82,1.49,0.56,0.66,0.06,0.07,0.26,0.20,0.18,0.13 English_North,36.62,28.38,10.27,8.93,9.02,3.63,0.95,0.40,0.70,0.03,0.11,0.34,0.26,0.15,0.13 English_Southeast,35.43,27.99,10.05,8.83,9.53,4.12,1.65,0.52,0.74,0.07,0.11,0.31,0.26,0.17,0.14 Irish_Connacht,37.84,30.38,9.02,9.26,7.24,3.80,0.08,0.08,1.04,0.04,0.12,0.68,0.14,0.12,0.07 Irish_Munster,37.12,30.05,9.80,8.52,7.53,4.57,0.20,0.17,0.91,0.08,0.16,0.48,0.14,0.07,0.12 Irish_Ulster,36.62,31.10,9.06,8.71,7.60,4.46,0.04,0.28,1.11,0.06,0.09,0.58,0.14,0.03,0.03 Ulster_British,37.69,28.74,9.10,9.21,8.30,4.48,0.38,0.30,0.80,0.03,0.05,0.44,0.29,0.03,0.06 Welsh,36.79,29.22,9.77,9.00,9.06,4.02,0.24,0.17,0.63,0.02,0.16,0.36,0.28,0.13,0.05

Could you also remove the duplicate 'Southeast_English' average in K15 Lucas, seeing as it's already been removed from K13? Thanks.

You also seem to have quite many samples for making a new Irish average.
10-17-2020, 02:25 PM
J. Ketch

My British Isles averages marked in red:

K13
https://i.postimg.cc/GbZvHcWB/k13updatedoct18.png

K15
https://i.postimg.cc/vQF6G0nf/k15updatedoct18.png

I think they look pretty logical. I suspect the Southwest_English average on Gedmatch is Cornish.
10-17-2020, 02:35 PM
J. Ketch

Quote:

Originally Posted by Dunai

You also seem to have quite many samples for making a new Irish average.

I do, however they're too biased towards Munster, and Western Ireland generally. I have the least references from Leinster, which has the largest population.
10-18-2020, 06:31 AM
J. Ketch

Actually scratch the last one, this will be my last update for Wales K15, probably forever. Now based on 57 refs, with academic samples added, courtesy of Peterski.

Code:

Welsh,36.42,28.89,9.89,8.65,9.24,4.15,0.70,0.36,0.62,0.06,0.13,0.31,0.21,0.19,0.07
10-20-2020, 04:33 PM
Maguzanci

Quote:

Originally Posted by Leto

What we do need is new averages from major East Asian countries (Han Chinese, Japanese, Korean, Vietnamese) for Dodecad. The original references are terribly inaccurate in most cases. I'm going to ask Maguzanci since he claims he is an Asian himself. Others are welcome to contribute as well.

I can help you with the new averages for East Asian countries. Although I will have to look for more kits as most of my gedmatch data is rather of SE Asians.

If you don't mind, I will post some averages for SE Asians first.

Also how many number of samples do you want for each country?
10-20-2020, 05:08 PM
Leto

Quote:

Originally Posted by Maguzanci

I can help you with the new averages for East Asian countries. Although I will have to look for more kits as most of my gedmatch data is rather of SE Asians.

If you don't mind, I will post some averages for SE Asians first.

Also how many number of samples do you want for each country?

Thank you, that's great. Yeah, SE Asia is fine too, I just don't know much about them. Burmese, Cambodian, Lao, Malay, Filipino would be good.
I'd say at least 10 samples per group/country and preferably without possibly mixed individuals or blatant outliers.
10-21-2020, 05:35 AM
Maguzanci

Quote:

Originally Posted by Leto

Thank you, that's great. Yeah, SE Asia is fine too, I just don't know much about them. Burmese, Cambodian, Lao, Malay, Filipino would be good.
I'd say at least 10 samples per group/country and preferably without possibly mixed individuals or blatant outliers.

Here are Indonesians from Western part of the archipelago (from Jakarta, other parts of Java and Sumatra- unfortunately cannot find Balinese or Madurese samples). I'm not sure about their ethnic groups so that is the reason I labelled them as Western Indonesians. I will also do the ones for Central Indonesia (Sulawesi) and Eastern Indonesia (Maluku, East Nusa Tenggara) if possible.

Western Indonesians (from Jakarta, other parts of Java and Sumatra), N=17

Code:

Western_Indonesian_1,3.15,0,0.36,57.78,0.23,0.79,10.5,0,0.33,26.86,0,0 Western_Indonesian_2,3.83,0.9,0,63.42,0.15,1.63,14.00,0.52,0.4,15.14,0,0 Western_Indonesian_3,2.3,0.66,0.18,68.59,0,1.35,16.14,0,0,10.77,0,0 Western_Indonesian_4,4.94,0,0,56.25,0,0.09,18.84,0.26,1.78,17.40,0,0.46 Western_Indonesian_5,5.41,0,0.6,60.35,0,1.43,16.69,0,0,15.14,0.38,0 Western_Indonesian_6,2.29,0,0,66.46,0.64,0,11.24,0.19,0.44,17.13,1.10,0.51 Western_Indonesian_7,2.92,1.56,0,65.10,0,2.03,16.16,0,0,11.85,0.30,0.08 Western_Indonesian_8,3.82,0.85,0,61.72,0.66,3.18,19.48,0.53,0.91,6.67,2.16,0 Western_Indonesian_9,7.3,0,0.87,59.82,0,0.86,18.33,0.05,0,12.31,0.44,0 Western_Indonesian_10,4.87,0,0,59.35,0,1.00,17.98,0.97,0.67,13.60,1.55,0 Western_Indonesian_11,5.82,0,0.32,57.67,0.36,1.76,16.2,0.47,0,15.84,1.49,0.07 Western_Indonesian_12,4.65,0.51,0,63.33,0,0.97,16.57,0,0.40,12.13,1.41,0 Western_Indonesian_13,4.62,0,0.09,61.79,0.09,0,15.32,0,0.30,14.69,3.08,0 Western_Indonesian_14,5.62,0.63,0,62.60,0,1.61,20.75,0.18,0,6.42,1.98,0.2 Western_Indonesian_15,1.12,1.48,0.63,67.62,0,0.07,16.44,0.13,0.57,10.96,0.92,0.07 Western_Indonesian_16,2.79,0.87,0,65.73,0,0,15.72,0.07,0.15,13.87,0.80,0 Western_Indonesian_17,1.36,0.92,0.49,71.82,0,0,13.63,0.21,0,11.58,0,0
10-21-2020, 08:08 AM
Leto

Quote:

Originally Posted by Maguzanci

Here are Indonesians from Western part of the archipelago (from Jakarta, other parts of Java and Sumatra- unfortunately cannot find Balinese or Madurese samples). I'm not sure about their ethnic groups so that is the reason I labelled them as Western Indonesians. I will also do the ones for Central Indonesia (Sulawesi) and Eastern Indonesia (Maluku, East Nusa Tenggara) if possible.

Western Indonesians (from Jakarta, other parts of Java and Sumatra), N=17

Cool. And the average?
10-21-2020, 08:32 AM
Amon

New averages for Dodecad K12b

Code:

Dungan,2.83,6.59,0.09,24.87,0.42,2.77,0.77,0.13,0.31,58.73,2.16,0.33 Han_north,0.5,5.79,0,25.76,0.09,0.06,0,0.05,0.03,67.38,0.27,0.07 Han_central,0.1,1.19,0.01,41.27,0.04,0.21,0,0,0,57.18,0,0 Japanese,0,4.58,0.12,11.47,0,0,0.4,0.00,0.07,83.31,0,0.03 Nogai_Astrakhan,10.43,22.12,0.04,3.15,7.04,19.16,2.67,0.03,1.41,23.91,10.02,0 Saudi,6.64,0.18,3.92,0.24,5.74,0.43,1.26,3.65,50.01,0.13,27.52,0.26 Egyptian,4.75,0.22,9.52,0.12,12.8,0.516,0.42,9.61,25.58,0.18,31.62,4.57 Coptic,1.95,0,10.72,0.20,11.82,0,0.03,9.88,31.32,0,33.88,0.18 Yemenite_Mahra,1.62,0.14,5.83,0.66,7.22,0,0.59,4.87,49.81,0.17,29.62,0 Yemenite_mixed,5.38,0.01,4.27,1.28,5.29,0.58,2.55,11.24,33.87,1.2,21.73,12.6
10-21-2020, 12:26 PM
Leto

Quote:

Originally Posted by Amon

New averages for Dodecad K12b

Is Yemenite_Jewish from Dodecad okay?
10-21-2020, 12:45 PM
Lucas

All updates from last few pages added.
10-21-2020, 12:48 PM
Lucas

Quote:

Originally Posted by Leto

Cool. And the average?

I made and added average too.
10-21-2020, 01:09 PM
Amon

Quote:

Originally Posted by Leto

Is Yemenite_Jewish from Dodecad okay?

This avg.'s Gedrosia and Mediterranean is kinda low and SW Asian is a lil higher. Gedrosia should have been around 1.5, Mediterranean around 6-6.5 and SW Asian around 50.
10-21-2020, 01:15 PM
Leto

Quote:

Originally Posted by Amon

This avg.'s Gedrosia and Mediterranean is kinda low and SW Asian is a lil higher. Gedrosia should have been around 1.5, Mediterranean around 6-6.5 and SW Asian around 50.

Yeah, dat's what I was thinkin' as well. :) But I got no data on them.
10-21-2020, 01:38 PM
Maguzanci

Quote:

Originally Posted by Leto

Cool. And the average?

Here is the average

Code:

Western Indonesian: 3.93,0.49,0.21,62.90,0.12,0.99,16.12,0.22,0.35,13.67,0.92,0.08

Distance to: Western_Indonesian
10.35946910 PANIYA (these samples are mislabelled, they seem to be SE Asian and not South Asian)
19.20949245 Lahu
20.78414299 Vietnamese
22.07255083 Malay_Singapore
23.99380337 Cambodians
26.36972886 Dai
31.17905547 Khasi
33.95818900 Burmanese
36.96647941 Garo
41.41437673 Miaozu
43.89232963 She

Here is my model of the Western_Indonesian average: Bonda (an Austroasiatic tribe from Eastern India) represented the South Asian ancestry in Indonesians as they are mostly AASI. Didn't use the mainstream South Asians because it could hide the West Eurasian admix of the Indoensian samples. Brahui+Iranian Mazandaran+Lithuanian is utilized to represent the West Eurasian admix in Indonesians (seems mostly Iran_N related due to Brahui with minor Steppe affinity represented by Lithuanian). Shows noise amounts of West African (Yoruba). Dai+Vietnamese is used to represent the predominant Mongoloid ancestry of Indos. Surprisingly shows minor Chinese-like admix (Han_Central) but could actually be an Taiwan_Aborigine/Austronesian signal instead as there are no Austronesian samples in this calculator. Sorry for the lenghty sentences; just my analysis.

Target: Western_Indonesian
Distance: 0.7471% / 0.74706758
63.4 Dai
23.0 Bonda
5.8 Han_central
4.6 Brahui
2.0 Iran_Mazandaran
1.2 Lithuanian

I will later do some samples from Sulawesi and Eastern Indonesia (Maluku, Sumba). It will be difficult for the Eastern Indonesian samples though as this calculator lacks the Oceanian/Papuan component (Eastern Indos have very high Oceanian), therefore the results might be misleading. But if you want them also, I can do it.
10-21-2020, 01:39 PM
Maguzanci

Quote:

Originally Posted by Lucas

All updates from last few pages added.

Can you edited the Western_Indonesian average please? I decided to add one more sample.

Code:

Western_Indonesian,3.93,0.49,0.21,62.90,0.12,0.99,16.12,0.22,0.35,13.67,0.92,0.08
10-21-2020, 01:54 PM
Leto

Quote:

Originally Posted by Maguzanci

Here is the average

Code:

Western Indonesian: 3.93,0.49,0.21,62.90,0.12,0.99,16.12,0.22,0.35,13.67,0.92,0.08

Closest pops
Distance to: Western_Indonesian
10.35946910 PANIYA (looks like it is mislabelled. Its actually a SE Asian population, not South Asian).
19.20949245 Lahu
20.78414299 KHV30
22.07255083 MAS30
23.99380337 Cambodians
25.38024035 CDX30
26.36972886 Dai
31.17905547 Khasi
33.95818900 Burmanese
36.96647941 Garo
41.41437673 Miaozu

Here is my model of the Western_Indonesian average: Bonda (an Austroasiatic tribe from Eastern India) represented the South Asian ancestry in Indonesians as they are mostly AASI. Didn't use the mainstream South Asians because it could hide the West Eurasian admix of the Indoensian samples. Brahui+Iranian Mazandaran+Lithuanian is utilized to represent the West Eurasian admix in Indonesians (seems mostly Iran_N related due to Brahui with minor Steppe affinity represented by Lithuanian). Shows noise amounts of West African (Yoruba). Dai+Vietnamese is used to represent the predominant Mongoloid ancestry of Indos. Surprisingly shows minor Chinese admix (Han_Central). Sorry for the lenghty sentences; just my analysis.

Target: Western_Indonesian
Distance: 0.7689% / 0.76887318
50.4 Dai
22.4 Bonda
17.0 Vietnamese
4.6 Brahui
2.2 Han_central
2.0 Iran_Mazandaran
1.2 Lithuanian
0.2 Yoruba

I will later do some samples from Sulawesi and Eastern Indonesia (Maluku, Sumba). It will be difficult for the Eastern Indonesian samples though as this calculator lacks the Oceanian/Papuan component (Eastern Indos have very high Oceanian), therefore the results might be misleading. But if you want them also, I can do it.

Well, we probably do not need such a detailed division for Southeast Asia on Dodecad. I'd say 3-4 major groups would be enough. Also make sure to overhaul the existing pops, some of them may be seriously inaccurate. As you pointed out, Dodecad lacks certain components necessary for that region (just like it lacks Amerindian).
10-21-2020, 01:59 PM
Maguzanci

Quote:

Originally Posted by Leto

Well, we probably do not need such a detailed division for Southeast Asia on Dodecad. I'd say 3-4 major groups would be enough. Also make sure to overhaul the existing pops, some of them may be seriously inaccurate. As you pointed out, Dodecad lacks certain components necessary for that region (just like it lacks Amerindian).

Alright. What does overhaul mean?

Yup it lack necessary components. Even though it lacks Amerindian, I feel it can gives sort of glimpse of how much East Eurasian and West Eurasian ancestries Native Americans do have.

For example, Anzick, a Paleo-Indian individual from North America. The North Euro+Gedrosia suggest ANE-related West Eurasian ancestry. While the Siberian+East Asian+SE Asian suggests East Eurasian ancestry or even some Onge/Australoid-like affinity (in SE Asian component). Not sure about South Asian but is probably ANE or (Onge-related afifnity) as well.

Admix Results (sorted):

# Population Percent
1 Siberian 33.5
2 East_Asian 29.08
3 North_European 17.79
4 Gedrosia 7.82
5 Southeast_Asian 6.33
6 South_Asian 4.42
7 Sub_Saharan 1.07

But anyway still take it will a grain of salt.
10-21-2020, 02:36 PM
Lucas

@MAzgunaci
Done

=-==========

I found on my computer some Polish averages for K23b based on Gedmatch kits (made by Michał from Anthrogenica).
Why not to add them to http://vahaduo.genetics.ovh/mdlp_k23...ed-vahaduo.htm

Masovia - 18 kits
Kashubian - 8 kits
South Poland - 24 kits

Now my results are better:

Target: Luk
Distance: 1.5813% / 1.58130667 | ADC: 0.25x
70.0 PL_Masovian
21.2 Russian_Meshtchyora
6.6 PL_Kashubian
2.2 BedouinB

Target: Luk
Distance: 1.1083% / 1.10831508
65.2 PL_Masovian
13.6 Latvian
13.6 PL_Kashubian
3.0 BedouinB
2.2 Dargin_Urkarah
1.4 Udmurd
0.8 Frisian
0.2 Pygmy

without Polish averages it was disaster

Target: Luk
Distance: 1.9697% / 1.96966173 | ADC: 0.25x
46.2 Kashub (original, rather strange reference)
25.0 Ukrainian_Center
14.8 Russian-West
11.2 Russian-Upper-Volga
2.0 BedouinB
0.8 Russian_Meshtchyora

Target: Luk
Distance: 1.5198% / 1.51977394
39.0 Latvian
28.8 Ukrainian_Center
15.6 Frisian
7.2 Pole
4.2 Greek_Phokaia
2.0 BedouinB
1.8 Udmurd
1.2 Georgian_Svan
0.2 Pygmy
10-21-2020, 02:50 PM
Leto

Quote:

Originally Posted by Maguzanci

Alright. What does overhaul mean?

To revise, to revisit. I mean check if they are correct or realistic. We've had to delete many of them because they were completely impossible to be like real people. The calculator effect.
10-21-2020, 04:07 PM
Maguzanci

Quote:

Originally Posted by Leto

To revise, to revisit. I mean check if they are correct or realistic. We've had to delete many of them because they were completely impossible to be like real people. The calculator effect.

Ok I can check and scrutinize them. But I cannot delete them though. I will have to tell you and Lukasz?

Forget to ask when making averages, its not good to use family members or relatives as the samples right?
10-21-2020, 04:11 PM
Leto

Quote:

Originally Posted by Maguzanci

Ok I can check and scrutinize them. But I cannot delete them though. I will have to tell you and Lukasz?

Forget to ask when making averages, its not good to use family members or relatives as the samples right?

Yeah, just leave a message on this thread or submit the averages to him directly.

I would avoid immediate family members like parents/children/siblings. More distant ones are okay.
10-21-2020, 04:52 PM
Ajeje Brazorf

Could someone make a spreadsheet of ancient samples, but with HarappaWorld? I did what I could, but I don't have access to many kits... :/

Spoiler!

Code:

Bell_Beaker_FRA_lowSteppe:I1392,0,0,12.87,21.03,0,0,0.21,0,0,0,58.43,7.31,0,0,0,0.16 Bell_Beaker_POL:I4253,0,11.73,13.21,44.41,0,0.96,0,0.64,1.21,0,27.46,0,0.37,0,0,0 Corded_Ware_Proto-Unetice_POL:RISE431,0,9.32,3.94,52.68,0,0,0,0,3.34,0,25.71,0,1.51,1.91,0,1.59 CZE_Hallstatt_Bylany:DA111,0.43,6.25,6.97,44.74,0,0,0,0,0.29,0,38.88,0.96,0,0,0,1.48 CZE_Hallstatt_Bylany:DA112,0,4.87,10.51,47.06,0.45,0.54,0.65,0,1.74,0,33.15,0.05,0.14,0,0,0.84 DEU_Roman:FN_2,0,3.14,12.39,37.76,0.38,0,0,0,0,0,42.00,3.45,0,0,0,0.87 EGY_Late_Period:JK2911,0.19,0.48,38.56,0,0,0,0,0,0,0.14,19.41,38.59,0,2.62,0,0 England_LBA:I5383,0.87,14.35,4.57,48.21,0,0,0,0.27,1.03,1.80,28.68,0.09,0,0.13,0,0 England_MBA:I2458,0,12.11,6.45,46.59,0,0.83,0,0.49,0,0.19,32.75,0,0,0,0,0.59 England_MBA:I3082,2.75,10.41,4.42,48.12,0.24,0,0,0,0.55,0,32.72,0,0,0,0,0.80 ETH_4500BP:mota,1.60,1.29,0,0.72,0,0.46,0.37,0.99,1.23,0,0,1.90,17.17,52.34,10.79,11.16 GRC_Mycenaean:I9006,0,2.33,44.13,10.51,0,0,0,0.34,0,1.26,28.05,12.75,0.64,0,0,0 GRC_Mycenaean:I9010,0.38,0.60,38.24,10.83,0.60,0,0,0.21,0,0.60,33.68,14.35,0.51,0,0,0 GRC_Mycenaean:I9033,3.47,4.13,33.27,14.12,0,0,1.16,0,0,0,33.85,7.62,0.33,0,0.36,1.70 GRC_Mycenaean:I9041,0,5.21,38.39,12.20,0,0,0.06,0.32,0,0,32.61,11.21,0,0,0,0 HUN_Avar_Szolad:Av1,0.61,7.74,10.75,53.59,0,0.93,0,0,0.65,0,22.77,2.70,0.23,0,0,0 HUN_Avar_Szolad:Av2,0,7.85,8.56,59.15,0,1.22,0,0,0.56,0.33,20.45,1.33,0.13,0,0,0.40 HUN_MA:DA199,0,8.05,13.68,37.67,0,1.62,1.10,1.50,0,0.72,29.46,5.54,0.06,0,0,0.60 HUN_MA_Szolad:SZ2,0,9.74,4.41,58.43,0,0,0,0.26,0,0.32,25.60,0,0,0,0,1.23 HUN_MA_Szolad:SZ3,0.27,8.44,11.73,45.06,0.37,0,0,0,0.29,0,31.04,2.08,0.41,0,0,0.31 HUN_MA_Szolad:SZ4,0,9.49,5.51,55.62,0,0.68,0,0.64,0,0.27,26.67,0.52,0,0,0,0.61 HUN_MA_Szolad:SZ5,0.64,7.42,13.93,43.68,0,0.23,0,0.91,0.87,0.59,28.60,2.87,0,0.09,0,0.17 HUN_MA_Szolad:SZ7,0,8.37,6.27,54.55,0,0,0,0.13,1.29,0,26.18,2.82,0.19,0,0,0.19 HUN_MA_Szolad:SZ8,0,7.92,11.68,45.92,0,0,0,0.60,0,0,33.18,0,0.69,0,0,0 HUN_MA_Szolad:SZ9,1.44,11.34,1.58,58.08,0,0,0,0,0,0,27.04,0,0,0,0,0.52 HUN_MA_Szolad:SZ11,0,9.95,9.95,52.45,0.08,0,0.51,0,0,0,26.27,0,0,0,0,0.77 HUN_MA_Szolad:SZ12,0.19,8.17,10.38,51.77,0,0,0,0.98,0.45,0,27.22,0.44,0,0.22,0,0.18 HUN_MA_Szolad:SZ13,0,9.62,7.64,54.06,0,0,0,0,1.39,0,27.01,0,0,0,0,0.23 HUN_MA_Szolad:SZ14,0.26,10.51,9.37,48.41,0.78,0,0,0.11,0.42,0.26,26.80,2.54,0,0,0,0.54 HUN_MA_Szolad:SZ15,0.41,10.09,6.27,52.51,0.91,1.09,0,0.41,0.17,0,26.86,0.87,0,0,0,0.41 HUN_MA_Szolad:SZ16,0,10.92,2.26,57.40,0,1.39,0,0,0,0,27.73,0,0,0,0,0.30 HUN_MA_Szolad:SZ18,0,8.05,15.53,39.06,0,0.31,0,0,0.25,0.97,29.72,5.66,0.39,0,0,0.06 HUN_MA_Szolad:SZ22,0.35,10.01,6.72,51.93,0,0,0,1.92,0.52,0,28.12,0,0.13,0,0,0.25 HUN_MA_Szolad:SZ23,0,9.65,10.69,46.21,0.42,0.63,0,0.12,0.21,0,29.94,1.54,0.09,0.48,0,0 HUN_MA_Szolad:SZ24,0.76,7.76,9.85,50.38,0.26,0.24,0,0,0.73,0.29,27.58,1.83,0,0,0,0.32 HUN_MA_Szolad:SZ27,0,9.21,20.30,38.03,0,0,0,0,0.25,0.97,25.27,5.62,0,0,0,0.35 HUN_MA_Szolad:SZ28,1.87,3.48,23.08,30.37,0,0,0.26,0.28,0.22,0,35.09,5.18,0,0,0.18,0 HUN_MA_Szolad:SZ30,0.58,9.45,11.31,50.80,0,0,1.14,0,0,0,25.72,0,0,0,0,1.00 HUN_MA_Szolad:SZ32,0,6.83,26.45,26.09,0,0,0,0,0.10,0.17,30.82,8.15,0.22,1.17,0,0 HUN_MA_Szolad:SZ36,0,7.33,26.07,25.13,0.37,0.24,0,0.36,0,0,30.45,9.21,0,0,0,0.83 HUN_MA_Szolad:SZ37,0.74,6.76,28.66,26.03,0,1.33,0,0,0.31,0,26.76,8.83,0,0.57,0,0 HUN_MA_Szolad:SZ38,0,8.06,11.36,48.26,0.21,0,0.06,0.53,0.92,0,27.86,1.29,0.65,0.80,0,0 HUN_MA_Szolad:SZ40,0,7.50,32.00,18.09,0.12,0,0,0,0.28,0,29.48,11.39,0,0.12,0,0.97 HUN_MA_Szolad:SZ42,0,8.59,12.32,49.70,0,0,0,0,0,0.66,26.21,1.98,0,0,0,0.54 HUN_MA_Szolad:SZ43,0.42,6.63,25.26,25.85,0,0,0,0.10,0,1.10,33.35,6.70,0,0,0,0.60 HUN_MA_Szolad:SZ45,0,7.90,15.23,38.91,0.63,0.23,0.39,0.30,0,0,30.05,5.08,0,0.26,0.32,0.70 HUN_MA_Szolad_o1:SZ19,0,2.56,33.99,20.03,0,0.37,0,0,0.10,0,32.17,10.40,0.07,0,0.12,0.19 HUN_MA_Szolad_o2:SZ25,0.75,10.04,9.28,48.30,0.39,2.86,1.80,0,0,0,23.50,1.30,0.77,0.63,0,0.37 Iberia_Northeast_Empuries2:I8215,0,2.75,36.90,13.64,0,0.20,0.33,0.11,0,0,32.95,12.35,0,0,0,0.77 IRN_Hajji_Firuz_BA:I4243,0.68,25.63,29.72,32.53,0,0.17,0,0.98,1.15,0,6.83,1.53,0,0,0.40,0.38 ITA_Collegno_MA:CL23,0.96,7.91,19.97,29.33,0,0,0.67,0.12,0.48,0.14,32.20,7.18,0,0.45,0.55,0 ITA_Collegno_MA:CL36,0,6.83,24.49,26.86,0.17,0,0,0,1.02,0.91,30.64,8.26,0.54,0,0.26,0 ITA_Collegno_MA:CL47,0.66,5.77,14.91,40.90,0,0.43,0,0,0,0,31.38,5.48,0.40,0,0,0 ITA_Collegno_MA:CL53,1.06,8.57,16.27,39.76,0.69,0,0.15,0.91,0,0,30.61,1.76,0.14,0,0,0.06 ITA_Collegno_MA:CL57,0,8.33,17.72,36.18,0,0,0,0.19,0.61,0.59,30.62,4.68,0.58,0,0.39,0.10 ITA_Collegno_MA:CL63,0,9.07,15.96,42.65,0,0,0,0.67,0,0.25,28.50,2.13,0.13,0.44,0.16,0 ITA_Collegno_MA:CL83,0,9.86,5.72,50.62,0,0,0.37,0,0,0.38,30.50,2.33,0.21,0,0,0 ITA_Collegno_MA:CL84,0,7.64,5.26,54.25,0,0.52,0,0.11,0,0.19,30.46,1.25,0.33,0,0,0 ITA_Collegno_MA:CL87,0,8.29,8.74,49.65,0,0.06,0.31,0,0,0,29.67,2.64,0.11,0,0,0.53 ITA_Collegno_MA:CL92,0,10.16,5.10,53.38,0,0.94,0,0,0,1.06,28.55,0.16,0,0,0,0.64 ITA_Collegno_MA:CL93,0,10.74,6.08,54.19,0,0.44,0,0.20,0,0,26.72,0.74,0,0.53,0,0.35 ITA_Collegno_MA:CL94,0.60,8.36,16.12,34.15,0,0,0,0,0.59,0.41,34.75,4.32,0.24,0,0.31,0.11 ITA_Collegno_MA:CL102,0,6.58,17.80,40.80,0.97,0,0,0,0,0,30.23,3.61,0,0,0,0 ITA_Collegno_MA:CL121,0,10.21,29.64,18.04,0,0,0.44,0.13,0,0,29.78,10.54,0,0,0,1.22 ITA_Collegno_MA:CL145,0.51,11.18,5.65,54.63,0,0,0,0.16,0,0.23,26.75,0,0,0.87,0,0 ITA_Collegno_MA:CL146,0.69,8.93,4.92,55.36,0,0,0,0,0.34,0.56,28.09,0,0,1.05,0,0.06 ITA_Collegno_MA:CL151,0.16,12.50,2.05,55.59,0,0,0,1.03,0,0,27.84,0,0.17,0,0,0.67 ITA_Collegno_MA_o1:CL25,1.86,9.51,39.49,11.09,0,0,0,0.61,0,0,23.81,12.81,0,0.42,0,0.40 ITA_Collegno_MA_o1:CL30,0,10.10,39.18,10.19,0.45,0,0,0.61,0,0,23.48,15.98,0,0,0,0 ITA_Collegno_MA_o1:CL38,0,10.66,37.82,11.18,0.10,0,0.16,0,0.41,0.75,23.56,14.37,0.23,0.07,0.30,0.38 ITA_Collegno_MA_o2:CL31,0.60,5.66,24.55,14.52,1.77,0.48,17.65,0.94,0.73,0,23.45,8.11,0.38,1.13,0,0 ITA_Proto-Villanovan:RMPR1,0,6.34,23.32,29.35,0,1.56,0,0,0.52,0,31.39,6.71,0,0,0.14,0.67 KAZ_Turk:DA228,0,8.80,10.51,27.86,1.22,13.04,25.37,0,1.07,0,11.37,0,0,0,0,0.76 Levant_LBN_MA_o4:SI-53,0.76,8.96,33.27,22.42,0,0,0,0.45,0,1.24,23.55,8.13,0.28,0.06,0,0.87 Levant_PPNB:I0867,0,0,26.20,0,0,0,0,0,0.68,0,30.36,36.38,0,3.96,0,2.41 Levant_Sidon_MBA:ERS1790729,0,8.29,47.83,0.59,0,0,0,0,0,0,15.20,27.54,0,0,0.28,0.28 Levant_Sidon_MBA:ERS1790730,0,7.50,48.89,0.55,0,0,0,0,0,0,15.37,27.06,0,0,0,0.59 Levant_Sidon_MBA:ERS1790731,0,5.92,43.95,0.20,0.36,0,0,0,0,0,14.72,33.93,0,0.19,0,0.73 Levant_Sidon_MBA:ERS1790732,0,6.03,44.18,0,0,0,0,0,0,0,18.11,31.23,0,0,0,0.45 Levant_Sidon_MBA:ERS1790733,0,5.53,50.94,0,0.11,0,0,0,0,0,13.17,29.52,0.14,0,0,0.60 NLD_BA:I4070,0,13.32,6.28,50.17,0,0,0,0,2.15,0,27.24,0,0.34,0,0,0.51 NLD_BA:I4071,0,12.29,1.39,55.58,0,0.74,0,0.87,0.49,0,27.50,0,0.83,0,0.32,0 POL_Globular_Amphora:I2441,0,0,6.83,27.66,0,0,0,0,0,0,57.73,7.62,0,0,0,0.14 ROU_N:I2532,0,0,34.50,1.96,0.05,0,0,0,0,0,49.84,13.55,0,0,0.09,0 RUS_Steppe_Maykop:SA6004,0.67,34.72,0,46.36,0,1.90,0,0,10.52,5.70,0,0,0,0,0,0.13 Scotland_N:I2637,0,0,9.53,22.22,0,0,0,0,0,0,61.57,6.47,0,0,0,0.19 Scotland_N:I2651,0,0,5.78,23.17,0,0,0,0,0,0,64.16,6.58,0,0,0.17,0.15 SVK_Poprad_MA:DA119,0,9.00,5.84,51.98,0,0,0.24,0,0.28,0,31.73,0.39,0,0,0,0.53 TKM_IA:DA382,2.50,34.32,23.68,34.08,0,0,0,0,1.54,0.23,2.72,0,0,0,0,0.93 TUR_Kaman-Kalehoyuk_MLBA:MA2200,0,7.88,54.01,0,0,0,0,0,0,0,22.07,15.48,0,0,0,0.56 TUR_Kaman-Kalehoyuk_MLBA:MA2206,0,3.80,49.54,4.40,0,0,0,0,0,0,27.33,14.32,0,0,0,0.62 TUR_Kaman-Kalehoyuk_MLBA_low_res:MA2208,0,9.58,47.44,6.71,0,0,0,0,0,0,24.85,10.58,0,0,0,0.84 TUR_Ovaoren_EBA:MA2212,0,7.34,44.78,3.51,0,0,0,0,0,0,26.48,17.56,0,0,0,0.33 UKR_N_o:I3719,0,0,33.16,4.51,0.44,0,0,0,0,0,47.07,14.67,0,0,0,0.14
10-21-2020, 06:31 PM
Leto

An Italian Jewish average according to what Minardowicz sent me. Doesn't look convincing to be honest, they should be more Middle Eastern

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

@vbnetkhio, let me know if you have access to the Italian Jewish data from Eurogenes, they should score differently.
10-22-2020, 12:35 PM
Lucas

New Russian averages for K12b based on academic samples from Yunusbaev. Added.
http://vahaduo.genetics.ovh/dodecad-k12b-vahaduo.htm

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 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.

==========

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
10-22-2020, 01:37 PM
Leto

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.
10-22-2020, 02:18 PM
Ajeje Brazorf

Quote:

Originally Posted by Leto

An Italian Jewish average according to what Minardowicz sent me. Doesn't look convincing to be honest, they should be more Middle Eastern

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

@vbnetkhio, let me know if you have access to the Italian Jewish data from Eurogenes, they should score differently.

Are they academic kits? Could you pm me them?
10-22-2020, 02:36 PM
Tacitus

Quote:

Originally Posted by Leto

An Italian Jewish average according to what Minardowicz sent me. Doesn't look convincing to be honest, they should be more Middle Eastern

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

@vbnetkhio, let me know if you have access to the Italian Jewish data from Eurogenes, they should score differently.

Honestly that looks like someone who's half-southern Italian half-Ashkie. Italian Jews should be closer to Sephardics and North African Jews imo:

Spoiler!

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
7.48768990 Greek_Foca
7.66072451 Sephardic_Jews
9.29767175 Greek_Athens
9.82694764 Greek_Central
10.60868041 Morocco_Jews
11.14348240 Turk_Cyprus
11.68066779 Greek_Peloponnese
12.00412171 Italy_Marche
12.16546029 Italy_Lazio
14.28350447 Greek_Cypriot
14.34376868 Greek_Thrace
14.48258264 Greek_Thessaloniki
14.95814160 Greek_Thessaly
15.49190434 Italy_Romagna
16.10051241 Turk_West_BlackSea

Compare to the Italian_Jewish sample in Eurogenes K13:

Spoiler!

Distance to: Italian_Jewish
3.89400822 Sephardic_Jewish
4.34360449 Algerian_Jewish
4.54528327 Moroccan_Jew
6.54155180 Tunisian_Jewish
7.53173951 Greek_Symi_Island
7.57202087 Libyan_Jewish
7.77530707 IT_Calabria
7.82108688 IT_Calabria_ultra
8.26970374 IT_Calabria_citra
8.78153745 Malta
8.96445202 Ashkenazi
10.37279133 Greek_Dodecanese
10.45388445 Greek_North_Aegean
10.61307684 IT_Campania
10.98278198 Sicily
11.25255971 IT_Salento
11.30891684 IT_Sannio
11.57349126 Greek_Crete
12.14496192 Greek_Chios
12.16735797 IT_Apulia
12.31660668 IT_Molise
12.81290755 IT_Abruzzo
12.82399704 IT_Lucania
12.83816186 IT_Basilicata
12.96026234 Turk_Cypriot
10-22-2020, 06:39 PM
Dr_Maul

Afghan_Hazara is missing the 'f' (its spelled Aghan Hazara) on the Eurogenes K13 updated spreadsheet
10-22-2020, 07:02 PM
Leto

Quote:

Originally Posted by Dr_Maul

Afghan_Hazara is missing the 'f' (its spelled Aghan Hazara) on the Eurogenes K13 updated spreadsheet

Glad someone has noticed that! I did a long time ago but thought it was something not too important to write a message.
10-22-2020, 08:49 PM
Leto

Quote:

Originally Posted by Lucas

New Russian averages for K12b based on academic samples from Yunusbaev. Added.
http://vahaduo.genetics.ovh/dodecad-k12b-vahaduo.htm

My family's results with these new averages

Spoiler!

Distance to: Leto_Mom
3.44504636 RU_Kostroma
5.57324860 Veps
5.64688855 RU_Ryazan
5.65183156 Rus_North_Dvina
5.98461360 Southwest_Rus
6.27365573 RU_Tver
7.22250865 RU_Kursk
7.28236015 RU_Oryol
7.31693925 Mordovian
7.80090379 Rus_Pinega
7.80476057 RU_Voronoezh
8.14842980 RU_Smolensk
8.69391166 Belorussian
8.74857318 RU_Pskov
8.93990492 Ukrainian
9.26281815 PL_Mazovia
10.58137042 Estonian
10.84753889 Karelian
11.18502570 PL_Warmia-Masuria
11.33674115 Lithuanian

Target: Leto(Bel-Rus)
Distance: 1.6794% / 1.67938632 | ADC: 0.5x
66.8 RU_Ryazan
32.0 RU_Kostroma
1.2 Kuwait2

Target: Leto(Bel-Rus)
Distance: 1.0095% / 1.00949813 | ADC: 0.25x
42.6 RU_Ryazan
34.6 RU_Kostroma
18.4 Southwest_Rus
2.4 Kuwait2
1.8 Mordovian
0.2 Balochi

Me scoring like a Central Russian kinda makes sense.

Distance to: Leto_Dad
2.04644490 RU_Smolensk
2.18503003 RU_Tver
2.32107734 Southwest_Rus
2.73603728 Belorussian
2.93648280 RU_Kursk
3.00334066 RU_Oryol
3.24154284 PL_Mazovia
3.60147019 RU_Voronoezh
4.34237221 RU_Ryazan
4.55830684 RU_Pskov
5.40706020 Ukrainian
6.39078590 RU_Kostroma
6.79973529 PL_Warmia-Masuria
7.38438217 PL_GreaterPoland
7.38789551 Estonian
7.92953340 Lithuanian
8.16679864 Rus_North_Dvina
8.26945585 Sorb_Lusatia
10.25682212 Mordovian
10.50004762 Latvian

Target: Leto_Dad
Distance: 1.4746% / 1.47457040 | ADC: 0.5x
48.8 RU_Smolensk
45.4 RU_Tver
5.8 RU_Oryol

Target: Leto_Dad
Distance: 1.1587% / 1.15872324 | ADC: 0.25x
38.2 RU_Smolensk
32.0 RU_Oryol
18.6 RU_Tver
10.4 Latvian
0.4 Icelandic
0.4 Jatt

Distance to: Leto_Mom
3.44504636 RU_Kostroma
5.57324860 Veps
5.64688855 RU_Ryazan
5.65183156 Rus_North_Dvina
5.98461360 Southwest_Rus
6.27365573 RU_Tver
7.22250865 RU_Kursk
7.28236015 RU_Oryol
7.31693925 Mordovian
7.80090379 Rus_Pinega
7.80476057 RU_Voronoezh
8.14842980 RU_Smolensk
8.69391166 Belorussian
8.74857318 RU_Pskov
8.93990492 Ukrainian
9.26281815 PL_Mazovia
10.58137042 Estonian
10.84753889 Karelian
11.18502570 PL_Warmia-Masuria
11.33674115 Lithuanian

Target: Leto_Mom
Distance: 1.8275% / 1.82746626 | ADC: 0.5x
86.4 RU_Kostroma
6.0 Veps
5.4 Mordovian
2.2 Saudi

Target: Leto_Mom
Distance: 1.1940% / 1.19398217 | ADC: 0.25x
70.4 RU_Kostroma
15.0 Veps
10.8 Mordovian
2.4 Kuwait2
0.8 RU_Kursk
0.6 Saudi

Kostroma looks super Eastern, pretty much like Vyatka where my mom is from.

Distance to: VSh
3.59143425 Southwest_Rus
3.90556953 RU_Ryazan
4.38980139 RU_Tver
4.51990180 RU_Oryol
4.73164660 RU_Smolensk
4.84835138 RU_Kostroma
5.05564041 Belorussian
5.11075643 RU_Kursk
5.41737021 Ukrainian
5.53263048 PL_Mazovia
5.54055954 PL_Warmia-Masuria
5.77998162 RU_Voronoezh
6.82245557 PL_GreaterPoland
6.92088071 RU_Pskov
7.14158946 Sorb_Lusatia
7.57050857 Rus_North_Dvina
8.33822523 Estonian
8.49433341 Veps
9.81493760 Slovak
10.02905280 Rus_Pinega

Target: VSh
Distance: 1.3250% / 1.32503203 | ADC: 0.5x
37.2 PL_Warmia-Masuria
34.8 RU_Kostroma
27.2 RU_Ryazan
0.8 Veps

Target: VSh
Distance: 0.7402% / 0.74019649 | ADC: 0.25x
44.2 PL_Warmia-Masuria
21.4 RU_Ryazan
18.8 RU_Kostroma
15.2 Veps
0.4 Tajiks
10-22-2020, 08:53 PM
vbnetkhio

Quote:

Originally Posted by Leto

An Italian Jewish average according to what Minardowicz sent me. Doesn't look convincing to be honest, they should be more Middle Eastern

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

@vbnetkhio, let me know if you have access to the Italian Jewish data from Eurogenes, they should score differently.

10 Italian Jewish academic samples:
https://easyupload.io/hgchrg
10-23-2020, 04:56 PM
Maguzanci

Quote:

Originally Posted by Leto

Yeah, just leave a message on this thread or submit the averages to him directly.

I would avoid immediate family members like parents/children/siblings. More distant ones are okay.

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

Code:

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

Average

Code:

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

Distance to Malay_Malaysia
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:

Code:

Simulated_AASI,0,0,0,0,0,0,100,0,0,0,0,0

Around 74% Mongoloid 16% AASI and 9.4% West Eurasian and noise levels of Negroid (represented by Luhya).
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.

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