Who has more Caucasoid admix: SE Asians or non-Horner East Africans (Kenyan, Tanzanian, Rwandan)?

[Maguzanci]

Or do both group have in equal amounts? Not including Horners cause it will be so obvious.

[Thambi]

probably SE asians if you exclude the viets, laotians. Its mostly cause south indian is partially caucasoid. some have extra through iran neolithic/baloch and SW asian mix as well.

[Maguzanci]

probably SE asians if you exclude the viets, laotians. Its mostly cause south indian is partially caucasoid. some have extra through iran neolithic/baloch and SW asian mix as well.

Yup excluding obvious ones like viets, laotians. Although i think laotians from southern parts close to cambodia might also have caucasoid admix if they were under the sphere of Khmer empire.

Also non-viet ethnic minorities like cham and khmer and even some hill tribes would have caucasoid admix.

[Thambi]

Yup excluding obvious ones like viets, laotians. Although i think laotians from southern parts close to cambodia might also have caucasoid admix if they were under the sphere of Khmer empire.

Also non-viet ethnic minorities like cham and khmer and even some hill tribes would have caucasoid admix.

I've seen some laotian kits. Most score around 1-2% south indian. but I've seen two that get 5-6%. They might be from southern laos as you mentioned.

[Thambi]

double post..

[Maguzanci]

double post..

I think it would be great if someone can synthesize simulated_aasi components for gedmatch calcs. In that case, it will be to see how much of the south indian se asians are scoring are actually caucasoid and aasi.

[Maguzanci]

double post..

Actually i have sort of create an aasi component on puntdna k13, what i did was creating a ghost pop scoring 100% south asian (i used paniya as base pop then remove all the caucasoid admix that the paniya have).

This ghost gives much better of aasi and caucasoid estimates for se asians imo

[Thambi]

I think it would be great if someone can synthesize simulated_aasi components for gedmatch calcs. In that case, it will be to see how much of the south indian se asians are scoring are actually caucasoid and aasi.

Here's how these three groups score with modern populations (Tibetan, Dai, AASI as south indian tribals, and Brahui). thais seem to have the highest caucasoid at 10%. Rest of the Se asian populations get worse fits if i use these populations. I could use htin mal for cambodians and the malay archipelago region but they have some AASI already so its hard to measure the actual AASI if i add htin mal or Lao haobinhan.


Target: Cambodian
Distance: 4.6563% / 0.04656277
88.0 Dai
8.4 AASI
3.6 Brahui

Target: Thai
Distance: 2.3700% / 0.02370034
81.4 Dai
9.4 AASI
9.2 Brahui

Target: Burmese
Distance: 2.2092% / 0.02209236
42.8 Dai
40.4 Tibetan_Lhasa
12.4 AASI
4.4 Brahui

[Thambi]

here's how they score with ancient bronze age populations

Target: Burmese
Distance: 1.1098% / 0.01109763
56.0 NPL_Chokhopani_2700BP
16.2 LAO_LN_BA
12.0 VNM_BA
6.2 MYS_LN
4.8 AASI
4.8 TKM_Gonur1_BA

Target: Thai
Distance: 1.5614% / 0.01561425
51.8 VNM_BA
12.4 MYS_LN
12.4 NPL_Chokhopani_2700BP
11.6 LAO_LN_BA
6.4 TKM_Gonur1_BA
5.4 AASI

Target: Cambodian
Distance: 1.6847% / 0.01684728
41.4 LAO_LN_BA
39.2 VNM_BA
11.6 NPL_Chokhopani_2700BP
3.8 MYS_LN
2.0 AASI
2.0 TKM_Gonur1_BA

Target: Kinh_Vietnam
Distance: 2.8557% / 0.02855685
88.8 VNM_BA
11.2 NPL_Chokhopani_2700BP

Target: Malay
Distance: 3.7518% / 0.03751777
44.8 VNM_BA
23.6 LAO_LN_BA
23.6 MYS_LN
4.6 TKM_Gonur1_BA
3.4 AASI

Target: Indonesian_Bali
Distance: 3.5052% / 0.03505195
41.2 MYS_LN
32.8 LAO_LN_BA
22.4 VNM_BA
2.2 TKM_Gonur1_BA
1.4 AASI

Target: Indonesian_Java
Distance: 2.7072% / 0.02707200
47.8 LAO_LN_BA
32.2 MYS_LN
19.2 VNM_BA
0.8 TKM_Gonur1_BA

[Maguzanci]

Bump

[Asif]

Here's how these three groups score with modern populations (Tibetan, Dai, AASI as south indian tribals, and Brahui). thais seem to have the highest caucasoid at 10%. Rest of the Se asian populations get worse fits if i use these populations. I could use htin mal for cambodians and the malay archipelago region but they have some AASI already so its hard to measure the actual AASI if i add htin mal or Lao haobinhan.

Target: Burmese
Distance: 2.2092% / 0.02209236
42.8 Dai
40.4 Tibetan_Lhasa
12.4 AASI
4.4 Brahui

Tibetan_Lhasa might have some South Asian ancestry.

[Thambi]

Tibetan_Lhasa might have some South Asian ancestry.

this might be a better model for burmese actually. Upper Yellow river is pure ancient tibetan/northern han without any outside (even AASI) influence. Lao neolithic is SE asian and roopkund is ivc (most similar to modern day south indian mid castes).

Target: Burmese
Distance: 2.2783% / 0.02278279
52.2 CHN_Upper_Yellow_River_LN
33.4 LAO_LN_BA
14.4 IND_Roopkund_A

this is how tibetans score. 91% upper yellow river with some indic/ancient se asian mix.

Target: Tibetan_Lhasa
Distance: 1.9095% / 0.01909503
90.6 CHN_Upper_Yellow_River_LN
7.6 IND_Roopkund_A
1.8 LAO_LN_BA

Target: Sherpa
Distance: 2.4530% / 0.02452956
88.8 CHN_Upper_Yellow_River_LN
9.0 IND_Roopkund_A
2.2 LAO_LN_BA

[Maguzanci]

Here's how these three groups score with modern populations (Tibetan, Dai, AASI as south indian tribals, and Brahui). thais seem to have the highest caucasoid at 10%. Rest of the Se asian populations get worse fits if i use these populations. I could use htin mal for cambodians and the malay archipelago region but they have some AASI already so its hard to measure the actual AASI if i add htin mal or Lao haobinhan.


Target: Cambodian
Distance: 4.6563% / 0.04656277
88.0 Dai
8.4 AASI
3.6 Brahui

Target: Thai
Distance: 2.3700% / 0.02370034
81.4 Dai
9.4 AASI
9.2 Brahui

Target: Burmese
Distance: 2.2092% / 0.02209236
42.8 Dai
40.4 Tibetan_Lhasa
12.4 AASI
4.4 Brahui

Here are my attempts at modelling. I will also use Latvian to represent Steppe ancestry as Indian admixture also brings minor Steppe Euro admix besides Iran_N

I will do it individually for Cambodians (there are only 3 individuals), as I feel there are some differences between them. (The results are from modern individuals G25)

1. Cambodian_1: Have the most Caucasoid out of the three, but also shows minor Chinese admix

Target: Cambodian:HGDP00713
Distance: 2.1937% / 0.02193720
47.2 LAO_LN_BA
16.4 MYS_LN
14.2 Dai
10.0 Han_Guangdong
4.6 Balochi
3.6 Simulated_AASI
2.4 Igorot
1.6 Latvian

2. Cambodian_2: Seems to be mostly Chinese: have very little Caucasoid admix compared to the first and third individuals. Also the Caucasoid here is Latvian instead of Balochi which is interesting.

Target: Cambodian:HGDP00712
Distance: 1.5830% / 0.01583023
79.0 Han_Guangdong
10.8 LAO_LN_BA
5.4 Dai
3.2 Simulated_AASI
1.0 Latvian
0.6 Papuan

3. Cambodian_3:

Target: Cambodian:HGDP00711: No chinese admix but also lower West Eurasian than the first individual
Distance: 1.8221% / 0.01822107
42.2 LAO_LN_BA
25.0 MYS_LN
17.0 Dai
6.6 Murut
4.6 Simulated_AASI
2.6 Balochi
1.6 Latvian
0.4 Papuan

Now I will do for Burmese, Thai, Malay, Balinese, Javanese, Luzon and Vizayan (the last two are Pinos)

Target: Burmese: Interesting shows only Balochi but no Latvian. Maybe the individual results will be different
Distance: 1.4301% / 0.01430123
63.8 Naxi
16.2 MYS_LN
8.6 Simulated_AASI
7.0 Balochi
4.4 LAO_LN_BA

Target: Thai: seems to be the most Caucasoid out of all the SE Asians, its strange than Burmese have lower than them despite being further west geographically. Also is heavily Chinese admixed at 37% but individual results might be different

Target: Thai
Distance: 0.8649% / 0.00864890
37.0 Han_Guangdong
21.4 MYS_LN
15.4 Dai
8.4 Balochi
7.2 LAO_LN_BA
7.0 Simulated_AASI
2.6 Murut
0.8 Latvian
0.2 Papuan

Target: Malay
Distance: 1.0631% / 0.01063120
31.4 Igorot
31.0 MYS_LN
16.8 LAO_LN_BA
9.0 Han_Guangdong
5.8 Balochi
3.8 Simulated_AASI
1.6 Papuan
0.4 Murut
0.2 Latvian


Target: Indonesian_Java
Distance: 1.1824% / 0.01182366
38.2 LAO_LN_BA
30.6 MYS_LN
27.0 Murut
1.6 Balochi
1.2 Han_Guangdong
0.8 Simulated_AASI
0.6 Papuan

Target: Indonesian_Bali: has more Caucasoid admix than Javanese which is interesting as they are located further east but maybe because Bali is much smaller than Java, its easier for Indian admix to distribute more evenly among the population.

Distance: 0.9003% / 0.00900343
35.0 Murut
29.6 LAO_LN_BA
28.8 MYS_LN
3.2 Balochi
1.8 Papuan
1.6 Simulated_AASI

Target: Luzon (Pino): only 0.4% Cauacsoid (Spanish), no South Asian admix but have AASI?
Distance: 1.2081% / 0.01208128
77.6 Igorot
11.6 Dai
6.0 MYS_LN
3.8 Simulated_AASI
0.6 Papuan
0.4 Spanish_Murcia

Target: Vizayan (Pino): also hardly any Caucasoid admix, no South Asian admix but have AASI? Maybe AASI is Negrito-related rather than Indian?
Distance: 1.6604% / 0.01660389
61.4 Igorot
16.8 Murut
5.8 Simulated_AASI
4.8 MYS_LN
3.8 Han_Guangdong
3.8 Papuan
2.4 Latvian
0.8 IRN_Shahr_I_Sokhta_BA1
0.4 Spanish_Murcia

I will post non-Horner East Africans to compare who has more Caucasoid here.

[Maguzanci]

this might be a better model for burmese actually. Upper Yellow river is pure ancient tibetan/northern han without any outside (even AASI) influence. Lao neolithic is SE asian and roopkund is ivc (most similar to modern day south indian mid castes).

Target: Burmese
Distance: 2.2783% / 0.02278279
52.2 CHN_Upper_Yellow_River_LN
33.4 LAO_LN_BA
14.4 IND_Roopkund_A

this is how tibetans score. 91% upper yellow river with some indic/ancient se asian mix.

Target: Tibetan_Lhasa
Distance: 1.9095% / 0.01909503
90.6 CHN_Upper_Yellow_River_LN
7.6 IND_Roopkund_A
1.8 LAO_LN_BA

Target: Sherpa
Distance: 2.4530% / 0.02452956
88.8 CHN_Upper_Yellow_River_LN
9.0 IND_Roopkund_A
2.2 LAO_LN_BA

Sorry dude. My bad, the Vizayan (from Visayas in the Philippines) result actually score close to 4% Caucasoid unlike the Luzon sample who is almost zero. This result also seem to have actual minor Indian ancestry as seen by the Latvian and Baloch. What's interesting is the Latvian is higher than Baloch..maybe Northern Indian/Pakistani-like ancestry instead of South Indian? Also 5.6% AASI so it seems the Indian admixture is legit.

Target: Vizayan
Distance: 1.6456% / 0.01645559
63.0 Igorot
14.8 Murut
5.6 Simulated_AASI
5.4 MYS_LN
3.8 Papuan
3.6 Han_Guangdong
2.2 Latvian
1.6 Balochi


Replacing Balochi with Shahr_Sokhta_I (its another Iran_Neo component I believe): Now this result is around 3.6% Caucasoid with the Spanish showing up at 0.4%. Its funny how this Philippine result is scoring higher Iran_N and Latvian/Steppe/NE Euro than Spanish admixture lol.

Target: Vizayan
Distance: 1.6604% / 0.01660389
61.4 Igorot
16.8 Murut
5.8 Simulated_AASI
4.8 MYS_LN
3.8 Han_Guangdong
3.8 Papuan
2.4 Latvian
0.8 IRN_Shahr_I_Sokhta_BA1
0.4 Spanish_Murcia

I am actually intrigued to find a Pinoy result that score actual South Asian admixture and this result doesn't seem recent. It's fascinating because you hardly here about Indians in precolonial Philippines although there are historically Indicized kingdoms there before Spanish colonialism.

It's as if this result is a relic from the precolonial times when the Indicized kingdoms were still ruling the archipelago lol. Maybe, there are more Pino results like these in isolated areas of the country where the Spanish didn't have much genetic impact or the areas that used to be under Hindu -Buddhist influence.

[Maguzanci]

this might be a better model for burmese actually. Upper Yellow river is pure ancient tibetan/northern han without any outside (even AASI) influence. Lao neolithic is SE asian and roopkund is ivc (most similar to modern day south indian mid castes).

Target: Burmese
Distance: 2.2783% / 0.02278279
52.2 CHN_Upper_Yellow_River_LN
33.4 LAO_LN_BA
14.4 IND_Roopkund_A

this is how tibetans score. 91% upper yellow river with some indic/ancient se asian mix.

Target: Tibetan_Lhasa
Distance: 1.9095% / 0.01909503
90.6 CHN_Upper_Yellow_River_LN
7.6 IND_Roopkund_A
1.8 LAO_LN_BA

Target: Sherpa
Distance: 2.4530% / 0.02452956
88.8 CHN_Upper_Yellow_River_LN
9.0 IND_Roopkund_A
2.2 LAO_LN_BA

Sorry dude. My bad, the Vizayan (from Visayas in the Philippines) result actually score close to 4% Caucasoid unlike the Luzon sample who is almost zero. This result also seem to have actual minor Indian ancestry as seen by the Latvian and Baloch. What's interesting is the Latvian is higher than Baloch..maybe Northern Indian/Pakistani-like ancestry instead of South Indian?


Target: Vizayan
Distance: 1.6456% / 0.01645559
63.0 Igorot
14.8 Murut
5.6 Simulated_AASI
5.4 MYS_LN
3.8 Papuan
3.6 Han_Guangdong
2.2 Latvian
1.6 Balochi

[Token]

Mmmmmm....
Non-Horner East Africans win by quite a wide margin.

[Maguzanci]

Here's how these three groups score with modern populations (Tibetan, Dai, AASI as south indian tribals, and Brahui). thais seem to have the highest caucasoid at 10%. Rest of the Se asian populations get worse fits if i use these populations. I could use htin mal for cambodians and the malay archipelago region but they have some AASI already so its hard to measure the actual AASI if i add htin mal or Lao haobinhan.


Target: Cambodian
Distance: 4.6563% / 0.04656277
88.0 Dai
8.4 AASI
3.6 Brahui

Target: Thai
Distance: 2.3700% / 0.02370034
81.4 Dai
9.4 AASI
9.2 Brahui

Target: Burmese
Distance: 2.2092% / 0.02209236
42.8 Dai
40.4 Tibetan_Lhasa
12.4 AASI
4.4 Brahui

Here are Indonesian_Java (Javanese or Sundanese I believe,, not sure the ethnicity) and Indonesian_Bali (Balinese) individuals (the last one were average). I am choosing the most West Eurasian shifted-ones so we can see how Caucasoid the samples can be...

Indonesian_Java_1
Target: Indonesian_Java:GRC10041183: score around 3% Caucasoid here (Balochi) but barely any AASI which is strange.
Distance: 2.1103% / 0.02110298
56.8 LAO_LN_BA
19.4 Murut
13.6 MYS_LN
6.6 Igorot
3.0 Balochi
0.6 Simulated_AASI

Indonesian_Java_2
Target: Indonesian_Java:GRC10041184: score around 3.4% Caucasoid here. Higher AASI than the first individual
Distance: 2.2968% / 0.02296815
43.4 LAO_LN_BA
22.4 Murut
18.4 MYS_LN
9.4 Dai
3.4 Balochi
1.8 Simulated_AASI
1.2 Papuan

Indonesian_Java_3: around 2.6% Caucasoid. More Latvian than Balochi so maybe upper caste admixture like Brahmin or Kshatriya?
Target: Indonesian_Java:GRC10041225
Distance: 2.3072% / 0.02307165
39.6 MYS_LN
28.4 LAO_LN_BA
27.0 Murut
2.0 Simulated_AASI
1.8 Latvian
0.8 Balochi
0.4 Papuan

Indonesian_Java_4: 2.4% Caucasoid and almost zero AASI.
Target: Indonesian_Java:GRC10041186
Distance: 1.7527% / 0.01752743
52.6 MYS_LN
34.8 Murut
9.2 LAO_LN_BA
2.4 Balochi
0.8 Papuan
0.2 Simulated_AASI

Indonesian_Java_5: only 1% Caucasoid (Latvian but no Balochi) and a bit higher AASI.
Target: Indonesian_Java:GRC10041224
Distance: 1.5253% / 0.01525256
59.4 MYS_LN
17.6 Murut
15.2 LAO_LN_BA
4.8 Igorot
1.2 Simulated_AASI
1.0 Latvian
0.4 Han_Guangdong
0.4 Papuan

Indonesian_Java_6: only 1.2% Caucasoid and 1.4% AASI
Target: Indonesian_Java:GRC10041231
Distance: 2.0402% / 0.02040157
40.2 MYS_LN
34.4 LAO_LN_BA
13.4 Murut
8.2 Igorot
1.4 Simulated_AASI
1.2 Papuan
1.0 Balochi
0.2 Latvian

Here are Balinese individual samples:

Indonesian_Bali_1: Scores almost 5% Caucasoid but only 1.6% AASI.
Target: Indonesian_Bali:GRC10041174
Distance: 2.4741% / 0.02474101
40.4 Murut
31.8 MYS_LN
19.6 LAO_LN_BA
4.8 Balochi
1.8 Papuan
1.6 Simulated_AASI

Indonesian_Bali_2: Also score almost 5% Caucasoid but no AASI which is strange. Maybe the MYS_LN and Lao_LN_BA absorbed the AASI?
Target: Indonesian_Bali:GRC10041162
Distance: 2.3307% / 0.02330694
44.4 MYS_LN
30.8 Murut
19.6 LAO_LN_BA
3.4 Balochi
1.4 Latvian
0.4 Papuan

Indonesian_Bali_3: around 4% Caucasoid, AASI around 1.6%.
Target: Indonesian_Bali:GRC10041153
Distance: 2.0081% / 0.02008101
40.0 MYS_LN
25.2 Murut
23.2 LAO_LN_BA
4.2 Dai
2.8 Balochi
1.8 Papuan
1.6 Simulated_AASI
1.2 Latvian

Indonesian_Bali_4: 3.6% Caucasoid, 2.4% AASI
Target: Indonesian_Bali:GRC10041159
Distance: 2.0092% / 0.02009176
35.4 LAO_LN_BA
33.6 MYS_LN
19.2 Igorot
4.6 Murut
3.6 Balochi
2.4 Simulated_AASI
1.2 Papuan

Indonesian_Bali_5: almost 4% Caucasoid and 3.2% AASI.
Target: Indonesian_Bali:GRC10041168
Distance: 2.3372% / 0.02337241
44.0 LAO_LN_BA
34.6 Murut
12.8 MYS_LN
3.6 Balochi
3.2 Simulated_AASI
1.6 Papuan
0.2 Latvian

Indonesian_Bali_6: around 3.6% Caucasoid. Strangely score Spanish but not Latvia. Also almost zero AASI.
Target: Indonesian_Bali:GRC10041161
Distance: 1.7913% / 0.01791277
37.8 LAO_LN_BA
29.0 Murut
25.4 MYS_LN
3.0 Balochi
2.4 Igorot
1.4 Papuan
0.6 Spanish_Murcia
0.4 Simulated_AASI

Overall it seems the Balinese have more West Eurasian than the their counterparts from Java (Javanese/Sundanese) which makes sense as Bali is much smaller than Java, making it easier for Indian settlers to spread around and intermarried with the locals. Their South Asian ancestry seems to be mostly South Indian/Sri Lankan-related rather than North Indian/Pakistani though based on how they score mainly Baloch.

[Maguzanci]

Here are Thai, Malay and Burmese individual samples. For, the Thais, I am choosing the most Western-shifted ones from the G25 spreadsheet, while there are only 5 Malays and 3 Burmese individuals, so I will run all of them.

Thai_1: Almost 13% Caucasoid which is the highest out of all the 5 individuals I specifically selected. Also pretty high AASI at 10%. Maybe has recent South Asian ancestry or comes from an area with historical large scale Indian migration. Also very significant Chinese admixture at 44%, the highest out of all 5 individuals.

Target: Thai: DCH002
Distance: 1.8920% / 0.01891984
44.6 Han_Guangdong
32.4 MYS_LN
10.8 Balochi
10.2 Simulated_AASI
2.0 Latvian


Thai_2: 11.2% Caucasoid and 7.4% AASI. Significant Chinese ancestry at 23%..

Target: Thai: DCH006
Distance: 2.1816% / 0.02181621
31.6 MYS_LN
23.0 Han_Guangdong
13.8 LAO_LN_BA
12.8 Dai
9.0 Balochi
7.4 Simulated_AASI
2.2 Latvian
0.2 Papuan

Thai_3: almost 12% Caucasoid. What's interesting is this individual also score minor Spanish-which suggests possible distant European ancestry. There were some historical European settlers in Ayutthaya-mainly the Portuguese as far as I know. Also around 15.4% Chinese which is the lowest out of the 5 selected samples. Also pretty high AASI at 10.4%

Target: Thai: DCH008
Distance: 1.6285% / 0.01628475
32.0 Dai
22.4 LAO_LN_BA
15.4 Han_Guangdong
10.4 Simulated_AASI
9.8 Balochi
6.4 Murut
1.4 Igorot
1.4 Spanish_Murcia
0.6 Latvian
0.2 Papuan

Thai_4: 12% Caucasoid and almost 9% AASI. Around 20% Chinese admixture.

Target: Thai: DCH010
Distance: 1.8316% / 0.01831559
34.6 Dai
24.2 MYS_LN
20.2 Han_Guangdong
9.8 Balochi
8.8 Simulated_AASI
2.2 Latvian
0.2 Papuan

Thai_5: 11% Caucasoid and 7.4% AASI. No Latvian in this case. Also approx 38% Chinese which is the second most out of all 5 individual samples.

Target: Thai: DCH011
Distance: 1.9950% / 0.01994993
37.6 Han_Guangdong
35.0 MYS_LN
11.4 Balochi
8.2 Dai
7.4 Simulated_AASI
0.4 Papuan

Now compared to the Malays

Malay_1: 5% Caucasoid and 4.6% AASI.

Target: Malay:SGVP00089
Distance: 1.9255% / 0.01925507
36.6 Igorot
26.8 LAO_LN_BA
25.0 MYS_LN
5.0 Balochi
4.6 Simulated_AASI
2.0 Papuan

Malay_2: 7.2% Caucasoid and 1.4% AASI. Seems heavily Iran_N/Balochi but very low AASI. Maybe Pashtun/Iranic-Pakistani ancestry instead of Indian admix?

Target: Malay:SGVP00091
Distance: 1.7660% / 0.01765978
34.6 LAO_LN_BA
32.4 Murut
21.6 MYS_LN
7.0 Balochi
2.6 Papuan
1.4 Simulated_AASI
0.2 Han_Guangdong
0.2 Latvian

Malay_3: 11.4% Caucasoid and 7.4% AASI. Also minor Chinese affinity-approx 4%. The most West Eurasian shifted out of all the Malay samples.

Target: Malay:SGVP00120
Distance: 1.7619% / 0.01761906
41.8 MYS_LN
24.6 Murut
10.8 Balochi
9.0 Dai
7.4 Simulated_AASI
3.8 Han_Guangdong
2.0 Igorot
0.6 Latvian


Malay_4: 2.6% Caucasoid and 1.6% AASI- the lowest West Eurasian of all the 5 Malays.

Target: Malay:SGVP00026
Distance: 2.4512% / 0.02451185
33.8 MYS_LN
27.8 Igorot
21.0 Murut
10.8 LAO_LN_BA
2.4 Papuan
2.2 Balochi
1.6 Simulated_AASI
0.4 Latvian


Malay_5: 4.2% Caucasoid and 3.4% AASI. This individual also seems to be half Chinese.

Target: Malay:SGVP00063
Distance: 1.7606% / 0.01760600
49.6 Han_Guangdong
18.6 Igorot
10.4 LAO_LN_BA
6.4 MYS_LN
6.2 Murut
3.4 Simulated_AASI
2.2 Balochi
2.0 Latvian
1.2 Papuan

Now here are the 3 Burmese individuals: Naxi has to be added to the proxy for their Sino-Tibetan ancestry

Burmese_1: 9% Caucasoid and 9.6% AASI.

Target: Burmese:bumaBR50
Distance: 1.7981% / 0.01798124
58.8 Naxi
12.6 MYS_LN
10.0 LAO_LN_BA
9.6 Simulated_AASI
9.0 Balochi

Burmese_2: 8.6% Caucasoid and 8.4% AASI.

Target: Burmese:bumaBR54
Distance: 1.9042% / 0.01904155
61.4 Naxi
13.2 MYS_LN
8.4 Simulated_AASI
8.0 Balochi
8.0 LAO_LN_BA
0.6 Latvian
0.4 Papuan

Burmese_3: only 3.4% Caucasoid-has the lowest West Eurasian out of the 3 samples. But 7.8% AASI. This individual seems to be off purer Sino-Tibetan (Naxi) ancestry than the previous two.

Target: Burmese:bumaBR55
Distance: 1.9196% / 0.01919599
71.2 Naxi
17.6 MYS_LN
7.8 Simulated_AASI
3.4 Balochi

Overall, it seems the Thais have the highest West Eurasian out of all the SE Asian groups in G25 followed by the Burmese and Malays. What's noteworthy is also that the Thais/Siamese have very high Chinese ancestry as can be seen by how all the 5 selected samples have Han_Guangdong component. Now there are other Thai samples that might have none to very minor Chinese but I didn't selected them as I am intentionally picking the most Western-shifted samples.

[Thambi]

bro its so hard to differentiate between dai (original thai groups) and southern chinese (han guangdong) mix in thais. they're fairly similar but are of different origins/mixes in thailand. I tried using both of them but it gives random mixup of percentages for both components and seems so arbitrary.

[Maguzanci]

bro its so hard to differentiate between dai (original thai groups) and southern chinese (han guangdong) mix in thais. they're fairly similar but are of different origins/mixes in thailand. I tried using both of them but it gives random mixup of percentages for both components and seems so arbitrary.

Maybe Han_Fujian, Han_Chongqing or Han_Hubei might be better proxy for the Southern Chinese admixture in Thais as the Han_Guangdong might also contain some Tai-Kradai/Austronesian admixture that causes confusion with the Dai component.

Anyhow, its interesting that the Thais/Siamese are heavily Chinese admixed (15 to almost 50%) but they also have the highest West Eurasian out of all the SE Asians as well. Luzon (Pino) have the least but it is only one individual result. Also its fascinating how the Vizayan result from the Philippines have Indian admixture (Baloch+Latvian+AASI) but hardly any Spanish.

I will soon post East African samples to compare in terms of Caucasoidness.

[Maguzanci]

bro its so hard to differentiate between dai (original thai groups) and southern chinese (han guangdong) mix in thais. they're fairly similar but are of different origins/mixes in thailand. I tried using both of them but it gives random mixup of percentages for both components and seems so arbitrary.

Here are the new models using Fujian, Zhejiang and Chongqing samples instead to gauge the Chinese ancestry. Also I change the Balochi to IRN_Shahr_Sokhta_1 because the latter is purer proxy for Iran_Neolithic West Eurasian ancestry in SE Asians (have lesser AASI affinity than Balochi).

Interesting how almost all samples prefer Zhejiang rather than Fujian. One example also score minor Chongqing.

Here are the new models using Fujian, Zhejiang and Chongqing samples instead to gauge the Chinese ancestry. Also I change the Balochi to IRN_Shahr_Sokhta_1 because the latter is purer proxy for Iran_Neolithic West Eurasian ancestry in SE Asians (have lesser AASI affinity than Balochi).

Interesting how almost all samples prefer Zhejiang rather than Fujian. One example also score minor Chongqing.

Target: Thai: DCH002
Distance: 2.0307% / 0.02030669
36.6 MYS_LN
22.6 Han_Zhejiang
9.6 Dai
8.0 IRN_Shahr_I_Sokhta_BA1
7.8 Han_Fujian
6.4 AASI_related_Sim
4.0 Latvian
2.6 Igorot
2.4 Simulated_AASI

Target: Thai: DCH006
Distance: 2.1178% / 0.02117792
34.0 MYS_LN
17.4 Dai
16.4 Han_Zhejiang
14.8 LAO_LN_BA
7.0 IRN_Shahr_I_Sokhta_BA1
6.8 AASI_related_Sim
3.4 Latvian
0.2 Papuan

Target: Thai: DCH008
Distance: 1.5169% / 0.01516868
35.8 Dai
23.8 LAO_LN_BA
10.0 Han_Zhejiang
9.0 AASI_related_Sim
7.8 IRN_Shahr_I_Sokhta_BA1
7.0 Murut
2.8 Spanish_Murcia
2.2 Igorot
0.8 Latvian
0.6 Simulated_AASI
0.2 Papuan

Target: Thai: DCH010
Distance: 1.8186% / 0.01818596
42.4 Dai
25.2 MYS_LN
10.4 Han_Zhejiang
9.0 AASI_related_Sim
7.4 IRN_Shahr_I_Sokhta_BA1
3.2 Latvian
1.8 Han_Chongqing
0.6 Spanish_Murcia

Target: Thai: DCH011
Distance: 1.8138% / 0.01813754
42.2 MYS_LN
29.0 Han_Fujian
10.2 IRN_Shahr_I_Sokhta_BA1
6.4 AASI_related_Sim
6.4 Dai
5.2 Han_Zhejiang
0.4 Spanish_Murcia
0.2 Papuan

Target: Thai: DCH012
Distance: 2.2958% / 0.02295835
32.2 Han_Zhejiang
29.0 MYS_LN
13.4 LAO_LN_BA
10.4 Murut
8.0 IRN_Shahr_I_Sokhta_BA1
5.2 AASI_related_Sim
1.4 Latvian
0.4 Papuan

[Sora]

Oh, Gosh! I clicked the wrong choice! :picard1: Indeed SE Asians are more Caucasoid than non-Horner East Afircans. 'Cause non-Horners are little-to-non Caucasoid

[Thambi]

Here are the new models using Fujian, Zhejiang and Chongqing samples instead to gauge the Chinese ancestry. Also I change the Balochi to IRN_Shahr_Sokhta_1 because the latter is purer proxy for Iran_Neolithic West Eurasian ancestry in SE Asians (have lesser AASI affinity than Balochi).

Interesting how almost all samples prefer Zhejiang rather than Fujian. One example also score minor Chongqing.

Here are the new models using Fujian, Zhejiang and Chongqing samples instead to gauge the Chinese ancestry. Also I change the Balochi to IRN_Shahr_Sokhta_1 because the latter is purer proxy for Iran_Neolithic West Eurasian ancestry in SE Asians (have lesser AASI affinity than Balochi).

Interesting how almost all samples prefer Zhejiang rather than Fujian. One example also score minor Chongqing.

Target: Thai: DCH002
Distance: 2.0307% / 0.02030669
36.6 MYS_LN
22.6 Han_Zhejiang
9.6 Dai
8.0 IRN_Shahr_I_Sokhta_BA1
7.8 Han_Fujian
6.4 AASI_related_Sim
4.0 Latvian
2.6 Igorot
2.4 Simulated_AASI

Target: Thai: DCH006
Distance: 2.1178% / 0.02117792
34.0 MYS_LN
17.4 Dai
16.4 Han_Zhejiang
14.8 LAO_LN_BA
7.0 IRN_Shahr_I_Sokhta_BA1
6.8 AASI_related_Sim
3.4 Latvian
0.2 Papuan

Target: Thai: DCH008
Distance: 1.5169% / 0.01516868
35.8 Dai
23.8 LAO_LN_BA
10.0 Han_Zhejiang
9.0 AASI_related_Sim
7.8 IRN_Shahr_I_Sokhta_BA1
7.0 Murut
2.8 Spanish_Murcia
2.2 Igorot
0.8 Latvian
0.6 Simulated_AASI
0.2 Papuan

Target: Thai: DCH010
Distance: 1.8186% / 0.01818596
42.4 Dai
25.2 MYS_LN
10.4 Han_Zhejiang
9.0 AASI_related_Sim
7.4 IRN_Shahr_I_Sokhta_BA1
3.2 Latvian
1.8 Han_Chongqing
0.6 Spanish_Murcia

Target: Thai: DCH011
Distance: 1.8138% / 0.01813754
42.2 MYS_LN
29.0 Han_Fujian
10.2 IRN_Shahr_I_Sokhta_BA1
6.4 AASI_related_Sim
6.4 Dai
5.2 Han_Zhejiang
0.4 Spanish_Murcia
0.2 Papuan

Target: Thai: DCH012
Distance: 2.2958% / 0.02295835
32.2 Han_Zhejiang
29.0 MYS_LN
13.4 LAO_LN_BA
10.4 Murut
8.0 IRN_Shahr_I_Sokhta_BA1
5.2 AASI_related_Sim
1.4 Latvian
0.4 Papuan

nice modeling. zhejiang is very interesting indeed. its hard to differentiate similar populations sometimes but zhejiang is a bit northern than fujian genetically so thats weird. Did thai chinese come from all over southern china or did they come from fujian/guangdong like most se asian chinese in philippines, indonesia, malaysia, etc?

I modeled based on ancient neolithic populations. comes out pretty well in distance, except for the bataks. but they're very isolated so its harder to model them even better i guess lol

upper yellow river = tibetan/northwest han
yellow river = regular hans from central china
roopkund = IVC
Lao = Austro asiatic
Tongan = austronesian/taiwanese aboriginal
Vanuatu = melanesian/papuan


https://i.imgur.com/LAWs5X7.png

[Maguzanci]

nice modeling. zhejiang is very interesting indeed. its hard to differentiate similar populations sometimes but zhejiang is a bit northern than fujian genetically so thats weird. Did thai chinese come from all over southern china or did they come from fujian/guangdong like most se asian chinese in philippines, indonesia, malaysia, etc?

I modeled based on ancient neolithic populations. comes out pretty well in distance, except for the bataks. but they're very isolated so its harder to model them even better i guess lol

upper yellow river = tibetan/northwest han
yellow river = regular hans from central china
roopkund = IVC
Lao = Austro asiatic
Tongan = austronesian/taiwanese aboriginal
Vanuatu = melanesian/papuan


https://i.imgur.com/LAWs5X7.png

The Chinese Thai mainly come from Fujian and Guangdong (not the Cantonese but from Chaoshan region which is literally genetically identical to Fujian/Taiwanese Han Chinese) like most other ethnic SE Asian Chinese minorities.

Nice! The Bataks seem to be Negritos rather than mainstream SE Asian groups imo. If you added the Aeta/Agta Negrito as the main component, the fit for them improves substantially. Also can you add the Vizayan from Philippines? They seem to have minor Indian admixure unlike the Luzon sample. Also can you add the Bajo samples from Indonesian/Philippines, Tripuri and Jamatia samples from NE India?

Here are the Bajo, which is an Austronesian seafaring ethnic group from Southern Philippines and around Borneo and Sulawesi in Indonesia: https://en.wikipedia.org/wiki/Sama-Bajau

I am not sure if these results come from the Philippines or Sulawesi. It seems they also have minor West Eurasian ancestry based on these three individual samples.

Bajo_1: 2.6% Caucasoid (IRN_Shahr_Sokhta_BA1 which is a Neolithic sample from Iran) and around 2% AASI. This suggests that this sample has South Asian admixture (South Asians/Indians are a mixture of Iran Neolithic type West Eurasians and AASI/South Eurasians and some additional Steppe/NE Euro-like West Eurasian ancestry).

Target: Bajo:GS000017006
Distance: 1.2593% / 0.01259338
66.0 Murut
11.4 MYS_LN
8.2 Han_Zhejiang
8.0 Papuan
2.6 IRN_Shahr_I_Sokhta_BA1
2.0 Simulated_AASI
1.8 LAO_LN_BA

Bajo_2: 1.4% Caucasoid (Spanish). Interesting, it seems like this sample might have minor European admixture.

Target: Bajo:GS000017005
Distance: 1.0669% / 0.01066880
41.4 Murut
22.2 Igorot
15.4 Papuan
10.6 MYS_LN
6.8 LAO_LN_BA
2.2 AASI_related_Sim
1.4 Spanish_Murcia

Bajo_3: 2.4% Caucasoid (Latvian +some Spanish). No IRN_Shahr_Sokhta_BA1/Iran Neolithic though but have around 3.4% AASI. Im not sure if this sample has South Asian admixture or not because it must also score Iran Neolithic. While this sample seems to have NE Euro/Steppe-like ancestry (Latvian) and some Spanish?

Target: Bajo:GS000017004
Distance: 1.2846% / 0.01284627
37.6 Murut
26.6 Papuan
17.8 Igorot
5.4 LAO_LN_BA
3.6 Han_Zhejiang
3.4 Simulated_AASI
1.8 Latvian
1.8 MYS_LN
1.4 Dai
0.6 Spanish_Murcia


I will also added some groups from NE India which are the Tripuri and Jamatia (the two population samples seem to be mainly from Tripura state close to Bangladesh and Burma). Unfortunately, G25 spreadsheet doesn't seem to have data for any other NE Indian group except Brahmin_Manipur but they are heavily South Asian admixed like almost 50%.

These are the averages for the two groups though. I will post Tripuri and Jamatia individuals later.

The Tripuri is NE Indian Mongoloid ethnic group who speak Sino-Tibetan language. They should be genetically fairly close to the Burmese. Jamatia seems to be a subgroup of the Tripuri: https://en.wikipedia.org/wiki/Tripuri_people

This average Tripuri seems around 7.6% Caucasoid and 4.8% AASI but individual samples should varied.

Target: Tripuri
Distance: 1.6646% / 0.01664575
64.4 Naxi
15.4 MYS_LN
7.8 Simulated_AASI
7.4 IRN_Shahr_I_Sokhta_BA1
4.8 AASI_related_Sim
0.2 Latvian

The average Jamatia seems 8.4% Caucasoid and 5.4% AASI, once again, the individual samples should varied.

Target: Jamatia
Distance: 1.5383% / 0.01538266
63.6 Naxi
11.0 MYS_LN
7.6 IRN_Shahr_I_Sokhta_BA1
7.4 Simulated_AASI
5.4 AASI_related_Sim
4.2 LAO_LN_BA
0.8 Latvian


I am perplexed that they are only slightly more Western-shifted than the average Burmese. I would expected them to be more Caucasoid as they are geographically further west of Burma and directly borders the eastern part of Bangladesh.

It's also fascinating how the average Thai sample have more West Eurasian ancestry than these two NE Indian populations despite being geographically a lot more distant from the Indian subcontinent than the former two who are literally situated in the far eastern regions of South Asia.
......

Here are the most Western-shifted Tripuri and Jamatia samples. Its interesting that they still have less Caucasoid admix than the most Western-shifed Thai samples. Is their Indian ancestry mainly Bengali/Bihari-like or more South Indian/Sri Lankan-affiliated?

Tripuri #1: 10.4% Caucasoid and 11.6% AASI (AASI_related_Sim by Traject and Simulated AASI by Matt).

Target: Tripuri:TRI-05
Distance: 2.1724% / 0.02172427
62.4 Naxi
15.6 MYS_LN
11.0 AASI_related_Sim
10.4 IRN_Shahr_I_Sokhta_BA1
0.6 Simulated_AASI

Tripuri #2: 10% Caucasoid and 15% AASI.

Target: Tripuri:TRI-19
Distance: 1.5608% / 0.01560831
66.8 Naxi
10.8 Simulated_AASI
9.4 IRN_Shahr_I_Sokhta_BA1
8.2 MYS_LN
4.2 AASI_related_Sim
0.6 Latvian


Tripuri #3: 9% Caucasoid and 13.6% AASI.

Target: Tripuri:TRI-27
Distance: 1.9870% / 0.01987021
58.2 Naxi
19.0 MYS_LN
8.8 IRN_Shahr_I_Sokhta_BA1
8.6 AASI_related_Sim
5.0 Simulated_AASI
0.4 Latvian


Jamatia #1: 8.6% Caucasoid and 10.8% AASI (AASI_sim by Traject and Simulated AASI by Matt)

Target: Jamatia:JAM_05
Distance: 2.5669% / 0.02566947
65.0 Naxi
15.0 MYS_LN
8.2 AASI_related_Sim
7.6 IRN_Shahr_I_Sokhta_BA1
2.6 Simulated_AASI
1.0 Latvian
0.6 Papuan

Jamatia #2: 8.6% Caucasoid and 11.4% AASI.

Target: Jamatia:JAM_11
Distance: 2.0986% / 0.02098553
65.0 Naxi
15.2 LAO_LN_BA
9.2 AASI_related_Sim
8.4 IRN_Shahr_I_Sokhta_BA1
2.0 Simulated_AASI
0.2 Latvian


Jamatia #3: 8.4% Caucasoid and 11.2% AASI.

Target: Jamatia:JAM_17
Distance: 2.3888% / 0.02388810
67.0 Naxi
8.4 IRN_Shahr_I_Sokhta_BA1
6.8 LAO_LN_BA
6.6 MYS_LN
6.6 Simulated_AASI
4.6 AASI_related_Sim

Now compared to the three most Western-shifted Thai samples. Its insane how the three Thai individuals have more Western Eurasian than the three most shifted Tripuri and Jamatia individuals lmao.

Thai #1: 12% Caucasoid and 8.8% AASI.

Target: Thai: DCH002
Distance: 2.0307% / 0.02030669
36.6 MYS_LN
22.6 Han_Zhejiang
9.6 Dai
8.0 IRN_Shahr_I_Sokhta_BA1
7.8 Han_Fujian
6.4 AASI_related_Sim
4.0 Latvian
2.6 Igorot
2.4 Simulated_AASI

Thai #2: 11.4% Caucasoid (intriguing how this sample has higher Spanish than Latvian- possibly some distant European ancestry from during Ayutthaya or early Rattanakosin period) and 9.6% AASI.

Target: Thai: DCH008
Distance: 1.5169% / 0.01516868
35.8 Dai
23.8 LAO_LN_BA
10.0 Han_Zhejiang
9.0 AASI_related_Sim
7.8 IRN_Shahr_I_Sokhta_BA1
7.0 Murut
2.8 Spanish_Murcia
2.2 Igorot
0.8 Latvian
0.6 Simulated_AASI
0.2 Papuan

Thai #3: 11.2% Caucasoid and 9% AASI.

Target: Thai: DCH010
Distance: 1.8186% / 0.01818596
42.4 Dai
25.2 MYS_LN
10.4 Han_Zhejiang
9.0 AASI_related_Sim
7.4 IRN_Shahr_I_Sokhta_BA1
3.2 Latvian
1.8 Han_Chongqing
0.6 Spanish_Murcia