Late Gupta era admixture in Western India

[26284729292]

Just went ahead and ran some users using strictly S/SC Asian populations:

"distance%=2.0733"

Bmoney_scaled

Kanjar,36.2
Brahmin_Tamil_Nadu,13.6
Brahui,12.6
Yadava,12.2
Iyer,11.2
Gujarati,6.6
Makrani,3.8
Sakilli,3.8

"distance%=2.3407"

Patel_scaled

Gujarati,70.2
Piramalai,16.8
Gupta,6.8
Balochi,6.2

"distance%=1.6118"

ssamlal scaled

Madiga,50.8
Brahmin_Tamil_Nadu,39
Tajik,8
Bhumij,2.2

"distance%=1.2365"

varun

Uttar_Pradesh,37.8
Brahmin_Uttar_Pradesh,33
Balochi,16
Gupta,5
Makrani,4.2
Kalash,4

"distance%=1.4574"

26 scaled

Brahmin_Tamil_Nadu,28.6
Punjabi_Lahore,27.8
Velamas,20
Kshatriya,16
Gujar,5.2
Sindhi,2
Brahmin_Manipuri,0.4

"distance%=1.3944"

Censored_scaled

Yadava,24.2
Chamar,22.2
Khatri,17
Brahmin_Tamil_Nadu,12.2
Sakilli,8
Tarkalani,6
Velamas,5.2
Piramalai,5
Bhumij,0.2

Obviously we can always take these with a grain of salt, but I lack more "foreign" components (i.e. Brahui, Tajik, etc.), but generally lack tribal in any fashion in these modern runs. It's always fun to see how these turn out. Bmoney has a really cool run here, completely different than mine, despite our similar gedmatches and all. He has a really strong Iran_N pull which seems to manifest differently to me, despite us scoring pretty similar BMAC in most calcs.

[Censored]

Obviously we can always take these with a grain of salt, but I lack more "foreign" components (i.e. Brahui, Tajik, etc.), but generally lack tribal in any fashion in these modern runs. It's always fun to see how these turn out. Bmoney has a really cool run here, completely different than mine, despite our similar gedmatches and all. He has a really strong Iran_N pull which seems to manifest differently to me, despite us scoring pretty similar BMAC in most calcs.

Yes-it just means you’re mostly pretty typical for your community I guess. You guys don’t have a strong IVC or tribal shift, and heavy NE Euro steppe rather than the “Caucasian” type that would push you towards SC Asians, and of course you’ve been inbreeding for centuries. Varun is an outlier.

[tipirneni]

Huns conquered many older groups such as Alans (Avar), Kangju (Kang-sogdians) etc.. & when they moved into India many of these were part of lakhs of people who settled during the late Gupta age.



When I check my lactose tolerant genes in MCM6 I found

rs4988243 -> CT

Found in Iberia/Tuscany/Middle east/China/Japan/South India-STU&ITU/Gujarat-GIH/Bangladesh-BEB at 10% (1000 genome)

Qatar -> 40+ % also Saudis share this allele

DA 125 Kangju U2e2a 1738YBP T-Y13279 was also derived on this allele

probably due to admixture during this late Gupta time with these West Asia autosome rich Steppe soldiers.



rs3213871 -> TC

Found among South Indians/Andhra Kammas

[tipirneni]

Kangju sample admixture information

Based on Dstats Kangju looks more WHG, EHG, IronGates, WEstSiberian_N,MA1 than GanjDareh,Barcin, LBK
MDLP K23b Oracle Rev 2014 Sep 16

Kit Z043955

Admix Results (sorted):

# Population Percent
1 European_Early_Farmers 36.11
2 South_Central_Asian 21.76
3 European_Hunters_Gatherers 16.2
4 Ancestral_Altaic 10.15
5 Arctic 6.98
6 African_Pygmy 2.65
7 South_Indian 2.46
8 Subsaharian 1.99
9 North_African 1.52
10 Melano_Polynesian 0.17

Single Population Sharing:

# Population (source) Distance
1 Orcadian ( ) 31.68
2 Scottish_Argyll_Bute_GBR ( ) 32.6
3 Icelandic ( ) 33.86
4 French_South ( ) 34.48
5 Spanish_Cantabria_IBS ( ) 35.07
6 Spanish_Pais_Vasco_IBS ( ) 35.1
7 Spanish_Aragon_IBS ( ) 35.13
8 Norwegian_West ( ) 35.52
9 British ( ) 35.83
10 Welsh ( ) 35.85
11 CEU ( ) 36.03
12 Spanish_Castilla_la_Mancha_IBS ( ) 36.15
13 Spanish_Galicia_IBS ( ) 36.19
14 Spanish_Canarias_IBS ( ) 36.23
15 Spanish_Castilla_y_Leon_IBS ( ) 36.23
16 Spanish_Extremadura_IBS ( ) 36.5
17 English_Cornwall_GBR ( ) 37.13
18 Spanish_Valencia_IBS ( ) 37.24
19 Spanish_Andalucia_IBS ( ) 37.38
20 Puerto_Rican ( ) 37.4

Mixed Mode Population Sharing:

# Primary Population (source) Secondary Population (source) Distance
1 64% Spanish_Pais_Vasco_IBS ( ) + 36% Kalash ( ) @ 14.37
2 59.4% Basque_Spanish ( ) + 40.6% Kalash ( ) @ 14.99
3 59.1% Basque_French ( ) + 40.9% Kalash ( ) @ 15.25
4 55.6% Basque_French ( ) + 44.4% Burusho ( ) @ 15.67
5 56.6% Basque_French ( ) + 43.4% Makrani ( ) @ 15.7
6 58% Basque_French ( ) + 42% Brahui ( ) @ 15.81
7 58.4% Basque_Spanish ( ) + 41.6% Brahui ( ) @ 15.88
8 57.5% Basque_French ( ) + 42.5% Balochi ( ) @ 15.94
9 63.3% Spanish_Pais_Vasco_IBS ( ) + 36.7% Brahui ( ) @ 16.04
10 57% Basque_Spanish ( ) + 43% Makrani ( ) @ 16.05
11 56% Basque_Spanish ( ) + 44% Burusho ( ) @ 16.13
12 57.9% Basque_Spanish ( ) + 42.1% Balochi ( ) @ 16.19
13 63% Spanish_Pais_Vasco_IBS ( ) + 37% Balochi ( ) @ 16.94
14 62.2% Spanish_Pais_Vasco_IBS ( ) + 37.8% Makrani ( ) @ 17.12
15 57.7% Basque_French ( ) + 42.3% Sindhi ( ) @ 17.21
16 61.4% Spanish_Pais_Vasco_IBS ( ) + 38.6% Burusho ( ) @ 17.47
17 58.1% Basque_Spanish ( ) + 41.9% Sindhi ( ) @ 17.5
18 55.8% Basque_French ( ) + 44.2% Tajik_Pomiri_Ishkashim ( ) @ 17.69
19 63.3% Spanish_Pais_Vasco_IBS ( ) + 36.7% Sindhi ( ) @ 18
20 57.5% Basque_French ( ) + 42.5% Pathan ( ) @ 18.07

Dodecad V3 Oracle results:
The GEDmatch version of Oracle may give slightly different results from Dienekes version. The GEDmatch version uses FST

weighting in its calculations.

Kit Z043955

Admix Results (sorted):

# Population Percent
1 West_European 44.43
2 West_Asian 16.27
3 Mediterranean 11.41
4 East_European 9.68
5 Northeast_Asian 7.07
6 South_Asian 6.98
7 Southwest_Asian 3.03
8 Northwest_African 1.13

Single Population Sharing:

# Population (source) Distance
1 N._European (Xing) 18.11
2 German (Dodecad) 19.05
3 Argyll (1000 Genomes) 19.24
4 Slovenian (Xing) 19.86
5 CEU (HapMap) 20.06
6 Orkney (1000 Genomes) 20.59
7 Orcadian (HGDP) 20.67
8 Mixed_Germanic (Dodecad) 21.06
9 Dutch (Dodecad) 23.03
10 Hungarians (Behar) 23.94
11 Swedish (Dodecad) 24.25
12 Kent (1000 Genomes) 24.7
13 French (HGDP) 25.49
14 British_Isles (Dodecad) 25.74
15 British (Dodecad) 25.89
16 Norwegian (Dodecad) 25.94
17 Cornwall (1000 Genomes) 26.16
18 FIN (1000Genomes) 26.21
19 French (Dodecad) 26.32
20 Irish (Dodecad) 27.15

Mixed Mode Population Sharing:

# Primary Population (source) Secondary Population (source) Distance
1 66% Swedish (Dodecad) + 34% Makrani (HGDP) @ 8.22
2 66.3% Swedish (Dodecad) + 33.7% Brahui (HGDP) @ 8.54
3 66.1% Swedish (Dodecad) + 33.9% Balochi (HGDP) @ 8.54
4 64.4% Norwegian (Dodecad) + 35.6% Makrani (HGDP) @ 8.6
5 55% Swedish (Dodecad) + 45% Stalskoe (Xing) @ 8.7
6 66.9% Swedish (Dodecad) + 33.1% Iranian (Dodecad) @ 8.86
7 64.4% Norwegian (Dodecad) + 35.6% Balochi (HGDP) @ 8.94
8 53.2% Norwegian (Dodecad) + 46.8% Stalskoe (Xing) @ 8.95
9 64.7% Norwegian (Dodecad) + 35.3% Brahui (HGDP) @ 8.97
10 67% Swedish (Dodecad) + 33% Iranians (Behar) @ 9.08
11 65% Swedish (Dodecad) + 35% Kurd (Dodecad) @ 9.24
12 65.3% Norwegian (Dodecad) + 34.7% Iranian (Dodecad) @ 9.28
13 65.3% Norwegian (Dodecad) + 34.7% Iranians (Behar) @ 9.59
14 76.2% N._European (Xing) + 23.8% Kalash (HGDP) @ 9.68
15 66% Swedish (Dodecad) + 34% Kurd (Xing) @ 9.71
16 63.3% Norwegian (Dodecad) + 36.7% Kurd (Dodecad) @ 9.73
17 68.4% Mixed_Germanic (Dodecad) + 31.6% Uzbeks (Behar) @ 10.05
18 68.9% Swedish (Dodecad) + 31.1% Kalash (HGDP) @ 10.15
19 74.9% Argyll (1000 Genomes) + 25.1% Kalash (HGDP) @ 10.17
20 64.3% Norwegian (Dodecad) + 35.7% Kurd (Xing) @ 10.19



Admix Results (sorted):

# Population Percent
1 West_European 44.43
2 West_Asian 16.27
3 Mediterranean 11.41
4 East_European 9.68
5 Northeast_Asian 7.07
6 South_Asian 6.98
7 Southwest_Asian 3.03
8 Northwest_African 1.13


Finished reading population data. 227 populations found.
12 components mode.

--------------------------------

Least-squares method.

Using 1 population approximation:
1 N._European_Xing @ 19.587975
2 German_Dodecad @ 20.626404
3 Argyll_1000 Genomes @ 20.870298
4 Slovenian_Xing @ 21.443300
5 CEU_HapMap @ 21.743351
6 Orkney_1000 Genomes @ 22.394287
7 Orcadian_HGDP @ 22.468826
8 Mixed_Germanic_Dodecad @ 22.877644
9 Dutch_Dodecad @ 25.155624
10 Hungarians_Behar @ 26.069534
11 Swedish_Dodecad @ 26.744907
12 Kent_1000 Genomes @ 27.057976
13 French_HGDP @ 27.757040
14 British_Isles_Dodecad @ 28.254417
15 British_Dodecad @ 28.426506
16 Norwegian_Dodecad @ 28.707932
17 Cornwall_1000 Genomes @ 28.718632
18 French_Dodecad @ 28.727285
19 FIN_1000Genomes @ 29.225655
20 Irish_Dodecad @ 29.886522

Using 2 populations approximation:
1 50% Swedish_Dodecad +50% Stalskoe_Xing @ 9.433094


Using 3 populations approximation:
1 50% British_Dodecad +25% Chuvashs_16_Behar +25% Kalash_HGDP @ 6.946078


Using 4 populations approximation:
++++++++++++++++++++++++++++++++++++++++++++++++++ ++++++++++++++++++++++++++++++++++++++++++++++++++ +++++
1 Chuvashs_16_Behar + Irish_Dodecad + Irish_Dodecad + Makrani_HGDP @ 6.192174
2 Brahui_HGDP + Chuvashs_16_Behar + Irish_Dodecad + Irish_Dodecad @ 6.259570
3 Chuvashs_16_Behar + Irish_Dodecad + Irish_Dodecad + Kalash_HGDP @ 6.329032
4 Chuvashs_16_Behar + Cornwall_1000 Genomes + Irish_Dodecad + Kalash_HGDP @ 6.438259
5 Balochi_HGDP + Chuvashs_16_Behar + Irish_Dodecad + Irish_Dodecad @ 6.475193
6 British_Dodecad + Chuvashs_16_Behar + Irish_Dodecad + Kalash_HGDP @ 6.579153
7 Chuvashs_16_Behar + Cornwall_1000 Genomes + Irish_Dodecad + Makrani_HGDP @ 6.605404
8 Brahui_HGDP + Chuvashs_16_Behar + Cornwall_1000 Genomes + Irish_Dodecad @ 6.621923
9 Chuvashs_16_Behar + Cornwall_1000 Genomes + Cornwall_1000 Genomes + Kalash_HGDP @ 6.661192
10 British_Isles_Dodecad + Chuvashs_16_Behar + Irish_Dodecad + Kalash_HGDP @ 6.685716
11 British_Dodecad + Chuvashs_16_Behar + Irish_Dodecad + Makrani_HGDP @ 6.798785
12 British_Dodecad + Chuvashs_16_Behar + Cornwall_1000 Genomes + Kalash_HGDP @ 6.801534
13 Brahui_HGDP + British_Dodecad + Chuvashs_16_Behar + Irish_Dodecad @ 6.819864
14 Balochi_HGDP + Chuvashs_16_Behar + Cornwall_1000 Genomes + Irish_Dodecad @ 6.822198
15 British_Isles_Dodecad + Chuvashs_16_Behar + Irish_Dodecad + Makrani_HGDP @ 6.846113
16 Brahui_HGDP + British_Isles_Dodecad + Chuvashs_16_Behar + Irish_Dodecad @ 6.871347
17 British_Isles_Dodecad + Chuvashs_16_Behar + Cornwall_1000 Genomes + Kalash_HGDP @ 6.887172
18 Chuvashs_16_Behar + Irish_Dodecad + Kalash_HGDP + Kent_1000 Genomes @ 6.889717
19 British_Dodecad + British_Dodecad + Chuvashs_16_Behar + Kalash_HGDP @ 6.946078
20 Balochi_HGDP + British_Dodecad + Chuvashs_16_Behar + Irish_Dodecad @ 7.026371

[tipirneni]

MATCH BETWEEN SURESH TIPIRNENI & PAK GUJAR SAMPLES on Genesis GED
Out of 3 kits FTDNA, MyHeritage & Ancestry DNA there is only a minor difference between them

GED	DEsc	Total	Max	No of
ZA8180497	(*PK_GUJAR::G-60)	408.1 cM (11.395 Pct)	10.2 cM	194
UJ1114322	(*PK_GUJAR::G-72)	408.8 cM (11.414 Pct)	8.8 cM	194
RF6904387	(*PK_GUJAR::G-81)	379.5 cM (10.596 Pct)	6.6 cM	191
WY3185562	(*PK_GUJAR::G-82)	380.2 cM (10.615 Pct)	5.3 cM	188
WA8871320	(*PK_GUJAR::G-83)	362.5 cM (10.121 Pct)	6.8 cM	173
YH3386715	(*PK_GUJAR::G-84)	402.6 cM (11.240 Pct)	8.1 cM	197
AP5209679	(*PK_GUJAR::G-89)	376.0 cM (10.497 Pct)	6.8 cM	189
HN7606120	(*PK_GUJAR::G-95)	383.8 cM (10.716 Pct)	5.4 cM	191
NZ2721526	(*PK_GUJAR::G-97)	321.6 cM (8.978 Pct)	8.0 cM	155
DQ3353422	(*PK_GUJAR::G-113)	412.9 cM (11.529 Pct)	7.0 cM	197
XM4818987	(*PK_GUJAR::G-117)	387.0 cM (10.804 Pct)	7.5 cM	186
TL8063285	(*PK_GUJAR::G-140)	380.8 cM (10.632 Pct)	5.6 cM	186
ZM1080505	(*PK_GUJAR::G-157)	377.8 cM (10.549 Pct)	5.2 cM	183
RF3163214	(*PK_GUJAR::G-168)	402.7 cM (11.244 Pct)	6.3 cM	190
YK3610676	(*PK_GUJAR::G-182)	428.6 cM (11.967 Pct)	6.4 cM	207
QX9647569	(*PK_GUJAR::G-185)	375.1 cM (10.473 Pct)	7.2 cM	179
ZF7468281	(*PK_GUJAR::GM-35)	427.6 cM (11.939 Pct)	7.0 cM	210

Out of the unrelated groups on Genesis, these Gujar samples are high match after which there are Kshatriya samples, Uyghur samples, Burusho, Iranian, Bengali samples. The Gypsy samples & Iraqi samples are also unrelated ones that produce high matches.

Gujar Uploaded by Khana
https://anthrogenica.com/showthread.php?14155-Global25-automated-nMonte-for-South-Central-Asian-members&p=397486&viewfull=1#post397486

[tipirneni]

Excavations near Peda Vegi of Andhra Ikshvaku timefrme 3rd/4th CE (Capital of Eastern Chalukyas)


Talk about Maharaja Ka'kichi or kichi maharaj found from 3rd CE from Peda Vegi excavation site of Dhanam Dibba. The Andhra Iksvaku had many Saka princess from Ujjain ( capital of Saka). These talk about Saka & hunnic people moving into Krishna Valley. These people started mixing with the existing Generals & Kings like Chalukyas & Rashtrakutas and later formed the Agnivansha dynasty of Indian kings
http://www.indianrajputs.com/history/