Global25 automated nMonte for South/Central Asian members

[maroco]

That reminds me. On this past group run of interior Indians, I had the second lowest Brahmin (Kush had the lowest) yet the highest South Central Asian (Kalash + Burusho): https://anthrogenica.com/showthread....l=1#post494676

It sounds like whatever steppe contribution I have may be mediated via more recent Central Asian Muslim migrants rather the subcontinental Brahmin source?

Could you see which type of steppe group I have more affinity to?

misanthropy_scaled,0.0387,-0.058901,-0.144814,0.09044,-0.07386,0.048248,0,0.010384,0.043768,0.019135,-0.004547,-0.001948,-0.001784,0.003853,-0.003257,-0.00716,-0.00678,0.001647,0.001885,0.001626,0.002121,0.0059 35,-0.001232,0.007591,-0.005628

Target: misanthropy_scaled
Distance: 1.6214% / 0.01621359
24.2 S_AASI_Sim_Avg
17.2 Iran_Neolithic:IRN_Ganj_Dareh_N:I1945
14.8 NW_AASI_Sim_Avg
12.4 Iran_Neolithic:IRN_Wezmeh_N:WC1
7.6 Steppe_Pastoralist:RUS_Afanasievo:I5271
5.4 Eastern_Hunter-Gatherer:RUS_Karelia_HG:I0211
4.6 Early_Levantine_Farmer:Levant_Natufian:I1072
3.8 Iran_Neolithic:IRN_Ganj_Dareh_N:I1947
3.6 Early_European_Farmer:Anatolia_Barcin_N:I0727
2.2 Ancient_American:USA_Alaska_TrailCreek_9000BP:Trai lCreek
1.4 Early_European_Farmer:Anatolia_Barcin_N:I0709
1.4 Africa_Mesolithic:MWI_Hora_9000BP:I2966
1.2 Northeastern_Asia_Neolithic:RUS_Devils_Gate_Cave_N :NEO240
0.2 Steppe_Pastoralist:Yamnaya_RUS_Kalmykia:RISE547

Second model I found:
Target: misanthropy_scaled
Distance: 1.4795% / 0.01479474
23.8 S_AASI_Sim_Avg
12.8 NW_AASI_Sim_Avg
11.8 IranianFarmer:IRN_Ganj_Dareh_N:I1945
11.6 IranianFarmer:IRN_Wezmeh_N:WC1
7.2 IndoEuropeanSteppePastoralist:Yamnaya_RUS_Kalmykia :RISE547
7.0 IranianFarmer:IRN_Ganj_Dareh_N:I1947
6.0 Harappan:IRN_Shahr_I_Sokhta_BA2:I8728
4.8 WestSiberianHunterGatherer:RUS_Tyumen_HG:I1960
3.8 LevantineFarmer:Levant_Natufian:I1072
3.6 AnatolianFarmer:TUR_Barcin_N:I0709
2.4 AnatolianFarmer:TUR_Barcin_N:I0727
1.8 EastEuropeanHunterGatherer:RUS_Karelia_HG:I0211
1.4 NorthSiberian:RUS_Yana_MA:Yana_Young
1.2 Nilotic:KEN_Kakapel_900BP:KPL003
0.6 Sinic:CHN_Yellow_River_MN:WGM35
0.2 SouthAfricanForager:ZAF_2100BP:I9028

[kamil154]

@ Pegasus

I would appreciate some help modelling this guy, he's Kashmiri but seems to be heavily IVC shifted in my runs. How would you model him?

Bronze Age Model:
sample: Jurble
distance: 2.4694
Shahr_I_Sokhta_BA3: 52.5
Gonur1_BA: 30
Srubnaya_Alakul_MLBA: 12
Chokhopani_2700BP: 5.5

Neolithic Model:
sample: Jurble
distance: 2.5303
Ganj_Dareh_N: 39.5
Simulated_AASI_NW_by_DMXX: 20.5
Barcin_N: 15
Karelia_HG: 8
LAO_Hoabinhian: 7.5
Kolyma_Meso: 4.5
Tyumen_HG: 3
GEO_CHG: 1.5
Boshan_N: 0.5

Coordinates:
Jurble_scaled,0.063741,-0.026404,-0.115399,0.07752,-0.069859,0.053547,0.00094,0.001615,0.00409,0.00437 4,-0.013803,0.00045,0.000892,-0.001651,0.000407,0.002254,-0.004563,-0.001647,-0.001006,-0.006628,0.00262,0.001978,0.001849,0.000964,0.0016 76

[kpb]

@ Pegasus

I would appreciate some help modelling this guy, he's Kashmiri but seems to be heavily IVC shifted in my runs. How would you model him?

Bronze Age Model:
sample: Jurble
distance: 2.4694
Shahr_I_Sokhta_BA3: 52.5
Gonur1_BA: 30
Srubnaya_Alakul_MLBA: 12
Chokhopani_2700BP: 5.5

Neolithic Model:
sample: Jurble
distance: 2.5303
Ganj_Dareh_N: 39.5
Simulated_AASI_NW_by_DMXX: 20.5
Barcin_N: 15
Karelia_HG: 8
LAO_Hoabinhian: 7.5
Kolyma_Meso: 4.5
Tyumen_HG: 3
GEO_CHG: 1.5
Boshan_N: 0.5

Coordinates:
Jurble_scaled,0.063741,-0.026404,-0.115399,0.07752,-0.069859,0.053547,0.00094,0.001615,0.00409,0.00437 4,-0.013803,0.00045,0.000892,-0.001651,0.000407,0.002254,-0.004563,-0.001647,-0.001006,-0.006628,0.00262,0.001978,0.001849,0.000964,0.0016 76

Finally-another Kashmiri! I have been waiting lol

[pegasus]

@ Pegasus

I would appreciate some help modelling this guy, he's Kashmiri but seems to be heavily IVC shifted in my runs. How would you model him?

Bronze Age Model:
sample: Jurble
distance: 2.4694
Shahr_I_Sokhta_BA3: 52.5
Gonur1_BA: 30
Srubnaya_Alakul_MLBA: 12
Chokhopani_2700BP: 5.5

Neolithic Model:
sample: Jurble
distance: 2.5303
Ganj_Dareh_N: 39.5
Simulated_AASI_NW_by_DMXX: 20.5
Barcin_N: 15
Karelia_HG: 8
LAO_Hoabinhian: 7.5
Kolyma_Meso: 4.5
Tyumen_HG: 3
GEO_CHG: 1.5
Boshan_N: 0.5

Coordinates:
Jurble_scaled,0.063741,-0.026404,-0.115399,0.07752,-0.069859,0.053547,0.00094,0.001615,0.00409,0.00437 4,-0.013803,0.00045,0.000892,-0.001651,0.000407,0.002254,-0.004563,-0.001647,-0.001006,-0.006628,0.00262,0.001978,0.001849,0.000964,0.0016 76

He is within range but he has lower Turan/BMAC related ancestry compared to most Kashmiris as well a bit higher AASI.



"sample": ":Kashmiri",
"distance": 1.824,
"Shahr_I_Sokhta_BA2": 61.5,
"TKM_IA": 32.5,
"Chokhopani_2700BP": 6

penalty =0

"sample": "Kashmiri",
"distance": 1.7689,
"Shahr_I_Sokhta_BA2": 60.5,
"TKM_IA": 34,
"Chokhopani_2700BP": 5.5


"sample": "Kashmiri",
"distance": 1.8325,
"Shahr_I_Sokhta_BA2": 58.5,
"Dzharkutan1_BA": 17.5,
"Srubnaya_MLBA": 17.5,
"Chokhopani_2700BP": 6.5

SIS2= 8728/1459

I have to also do models for others and have been a bit behind .

[kaazi]

Those 3 Bahun samples (Ba15, Ba25, Ba37) in the G25 collection are likely mislabeled Chhetris. Tried to get closer fits with simple random 4 way modeling with modern pops. All of these samples require excess AASI and IranN source over AG Bahun users to get better fits. So, I guess they're most likely not Bahuns with excess Mongoloid but rather Chhetris. As we know the gedmatch calculators also indicated Chhetris having a bit higher S-Indian ratio than Bahuns, so they're likely Bahuns + excess IVC + excess Tibetan.

Target: Likely Chhetri:Ba15
Distance: 1.6100% / 0.01610026
32.8 Brahmin_Uttar_Pradesh
30.0 Brahmin_Khas_Nepal
25.2 Tamang
12.0 Brahui

Target: Likely Chhetri:Ba25
Distance: 1.9529% / 0.01952893
44.8 Brahmin_Khas_Nepal
23.8 Yadava
22.8 Tibetan_Yajiang
8.6 Brahui

Target: Likely Chhetri:Ba37
Distance: 2.1646% / 0.02164591
33.4 Brahmin_Khas_Nepal
32.0 Kol
25.4 Tibetan_Yajiang
9.2 Brahui

An extra Yemenite and Amerindian source for Ba15 sample reduced the fit significantly.
Target: Likely Chhetri:Ba15
Distance: 1.4306% / 0.01430575
37.6 Brahmin_Uttar_Pradesh
25.6 Brahmin_Khas_Nepal
24.6 Tamang
8.2 Brahui
2.8 Yemenite_Mahra
1.2 Mixtec

For Ba25, when Tibetan_Yajiang is replaced by Dongxiang and Papuan, fit is reduced significantly.
Target: Likely Chhetri:Ba25
Distance: 1.7258% / 0.01725834
44.0 Brahmin_Khas_Nepal
25.0 Dongxiang
24.6 Yadava
5.0 Brahui
1.4 Papuan

For these samples, I've found Khatri, Sindhi, Brahui, Yadava, Kol, Gujar, Gujarati Brahmin, UP Brahmin, Tibetan Yajiang and Tamang gives better fits among many others.

[pegasus]

Looking at some ancients on G25 and testing them on qpAdm, the model is a resounding success and pretty much similar to my G25 model when you factor in standard errors.

sample: Bustan BA o2:Average
distance: 1.544
Shahr_I_Sokhta_BA3: 50
Alalakh_MLBA_o: 38
Zevakinskiy_MLBA: 12


left pops:
Uzbekistan_BA_Bustan_o2
SISI8728
Alalakh_MLBA_outlier
Kazakhstan_MLBA_Zevakinskiy

right pops:
Cameroon_SMA.DG
Russia_Steppe_Eneolithic
Australian.DG
PPNB
Iran_GanjDareh_N
DevilsCave_N.SG
Russia_UstBelaya_Angara
EHG
Anatolia_N
WSHG
Russia_MA1_HG.SG
CHG
Russia_MLBA_Sintashta
Sweden_HG_Motala

0 Uzbekistan_BA_Bustan_o2 1
1 SISI8728 1
2 Alalakh_MLBA_outlier 1
3 Kazakhstan_MLBA_Zevakinskiy 1
4 Cameroon_SMA.DG 1
5 Russia_Steppe_Eneolithic 3
6 Australian.DG 2
7 PPNB 11
8 Iran_GanjDareh_N 8
9 DevilsCave_N.SG 4
10 Russia_UstBelaya_Angara 11
11 EHG 3
12 Anatolia_N 30
13 WSHG 3
14 Russia_MA1_HG.SG 1
15 CHG 2
16 Russia_MLBA_Sintashta 19
17 Sweden_HG_Motala 6
jackknife block size: 0.050
snps: 1150471 indivs: 108
number of blocks for block jackknife: 714
## ncols: 1150471
coverage: Uzbekistan_BA_Bustan_o2 766753
coverage: SISI8728 645800
coverage: Alalakh_MLBA_outlier 733730
coverage: Kazakhstan_MLBA_Zevakinskiy 711062
coverage: Cameroon_SMA.DG 1140138
coverage: Russia_Steppe_Eneolithic 1008226
coverage: Australian.DG 1120959
coverage: PPNB 871625
coverage: Iran_GanjDareh_N 1057358
coverage: DevilsCave_N.SG 1149702
coverage: Russia_UstBelaya_Angara 1092594
coverage: EHG 1027057
coverage: Anatolia_N 1141220
coverage: WSHG 858019
coverage: Russia_MA1_HG.SG 805960
coverage: CHG 1149558
coverage: Russia_MLBA_Sintashta 1115156
coverage: Sweden_HG_Motala 1038843
Effective number of blocks: 618.643
numsnps used: 380056
codimension 1
f4info:
f4rank: 2 dof: 11 chisq: 4.460 tail: 0.954472637 dofdiff: 13 chisqdiff: -4.460 taildiff: 1
B:
scale 1.000 1.000
Russia_Steppe_Eneolithic 1.110 -0.547
Australian.DG -0.566 0.776
PPNB 0.857 -0.653
Iran_GanjDareh_N 0.193 -2.163
DevilsCave_N.SG -0.062 1.055
Russia_UstBelaya_Angara 0.317 1.317
EHG 1.606 0.836
Anatolia_N 1.017 -0.819
WSHG 1.289 0.774
Russia_MA1_HG.SG 0.966 0.323
CHG 0.489 -1.295
Russia_MLBA_Sintashta 1.299 -0.151
Sweden_HG_Motala 1.561 0.615
A:
scale 614.836 1472.057
SISI8728 -0.761 0.544
Alalakh_MLBA_outlier 0.598 -1.407
Kazakhstan_MLBA_Zevakinskiy 1.436 0.851


full rank
f4info:
f4rank: 3 dof: 0 chisq: 0.000 tail: 1 dofdiff: 11 chisqdiff: 4.460 taildiff: 0.954472637
B:
scale 771.094 713.050 397.966
Russia_Steppe_Eneolithic -1.174 1.220 0.989
Australian.DG 0.698 -0.928 -0.315
PPNB -1.018 1.092 0.694
Iran_GanjDareh_N -0.769 1.696 -0.249
DevilsCave_N.SG 0.353 -0.728 0.238
Russia_UstBelaya_Angara 0.102 -0.621 0.665
EHG -1.092 0.737 1.815
Anatolia_N -1.246 1.271 0.786
WSHG -0.949 0.411 1.449
Russia_MA1_HG.SG -1.184 0.189 0.903
CHG -0.735 1.357 0.245
Russia_MLBA_Sintashta -1.240 1.069 1.238
Sweden_HG_Motala -1.499 0.565 1.568
A:
scale 1.732 1.732 1.732
SISI8728 1.732 0.000 0.000
Alalakh_MLBA_outlier 0.000 1.732 0.000
Kazakhstan_MLBA_Zevakinskiy 0.000 0.000 1.732


best coefficients: 0.538 0.304 0.159
Jackknife mean: 0.537324048 0.303438160 0.159237792
std. errors: 0.047 0.058 0.046

error covariance (* 1,000,000)
2232 -1762 -470
-1762 3421 -1659
-470 -1659 2129


summ: Uzbekistan_BA_Bustan_o2 3 0.954473 0.537 0.303 0.159


Narasimhan's models for him are very poor

Indus_Periphery_Pool Western_Steppe_MLBA ..p= 0.01 0.847 0.153
Indus_Periphery_Pool Central_Steppe_MLBA .. p=0.021 0.836 0.164




With Loebanr_0

sample: Loebanr IA o:Average
distance: 2.492
Oy_Dzhaylau_MLBA: 38
Alalakh_MLBA_o: 34
Shahr_I_Sokhta_BA2: 28


On qpAdm another great working model but not as good as the one for Bustan_o2.



Best coefficients: 0.344 0.313 0.343
Jackknife mean: 0.342802153 0.314344114 0.342853733
std. errors: 0.067 0.086 0.035

error covariance (* 1,000,000)
4448 -5308 860
-5308 7428 -2119
860 -2119 1259


summ: Pakistan_IA_Loebanr_o_published 3 0.745909 0.343 0.314 0.343 4448




Modern populations which work well with Alalakh_o:

sample: Gujar Pakistan:G-83
distance: 1.9193
Shahr_I_Sokhta_BA2: 57.5
Alalakh_MLBA_o: 27.5
Oy_Dzhaylau_MLBA: 12.5
Chokhopani_2700BP: 2.5




sample: Kashmiri Pandit:Average
distance: 1.9064
Shahr_I_Sokhta_BA3: 30.5
Alalakh_MLBA_o: 24
Shahr_I_Sokhta_BA2: 22
Oy_Dzhaylau_MLBA: 19
Chokhopani_2700BP: 4.5

sample: Punjabi Sikh India PanSikh27270
distance: 1.5372
Shahr_I_Sokhta_BA2: 50
Oy_Dzhaylau_MLBA: 27
Alalakh_MLBA_o: 23

[maroco]

Looking at some ancients on G25 and testing them on qpAdm, the model is a resounding success and pretty much similar to my G25 model when you factor in standard errors.

sample: Bustan BA o2:Average
distance: 1.544
Shahr_I_Sokhta_BA3: 50
Alalakh_MLBA_o: 38
Zevakinskiy_MLBA: 12


left pops:
Uzbekistan_BA_Bustan_o2
SISI8728
Alalakh_MLBA_outlier
Kazakhstan_MLBA_Zevakinskiy

right pops:
Cameroon_SMA.DG
Russia_Steppe_Eneolithic
Australian.DG
PPNB
Iran_GanjDareh_N
DevilsCave_N.SG
Russia_UstBelaya_Angara
EHG
Anatolia_N
WSHG
Russia_MA1_HG.SG
CHG
Russia_MLBA_Sintashta
Sweden_HG_Motala

0 Uzbekistan_BA_Bustan_o2 1
1 SISI8728 1
2 Alalakh_MLBA_outlier 1
3 Kazakhstan_MLBA_Zevakinskiy 1
4 Cameroon_SMA.DG 1
5 Russia_Steppe_Eneolithic 3
6 Australian.DG 2
7 PPNB 11
8 Iran_GanjDareh_N 8
9 DevilsCave_N.SG 4
10 Russia_UstBelaya_Angara 11
11 EHG 3
12 Anatolia_N 30
13 WSHG 3
14 Russia_MA1_HG.SG 1
15 CHG 2
16 Russia_MLBA_Sintashta 19
17 Sweden_HG_Motala 6
jackknife block size: 0.050
snps: 1150471 indivs: 108
number of blocks for block jackknife: 714
## ncols: 1150471
coverage: Uzbekistan_BA_Bustan_o2 766753
coverage: SISI8728 645800
coverage: Alalakh_MLBA_outlier 733730
coverage: Kazakhstan_MLBA_Zevakinskiy 711062
coverage: Cameroon_SMA.DG 1140138
coverage: Russia_Steppe_Eneolithic 1008226
coverage: Australian.DG 1120959
coverage: PPNB 871625
coverage: Iran_GanjDareh_N 1057358
coverage: DevilsCave_N.SG 1149702
coverage: Russia_UstBelaya_Angara 1092594
coverage: EHG 1027057
coverage: Anatolia_N 1141220
coverage: WSHG 858019
coverage: Russia_MA1_HG.SG 805960
coverage: CHG 1149558
coverage: Russia_MLBA_Sintashta 1115156
coverage: Sweden_HG_Motala 1038843
Effective number of blocks: 618.643
numsnps used: 380056
codimension 1
f4info:
f4rank: 2 dof: 11 chisq: 4.460 tail: 0.954472637 dofdiff: 13 chisqdiff: -4.460 taildiff: 1
B:
scale 1.000 1.000
Russia_Steppe_Eneolithic 1.110 -0.547
Australian.DG -0.566 0.776
PPNB 0.857 -0.653
Iran_GanjDareh_N 0.193 -2.163
DevilsCave_N.SG -0.062 1.055
Russia_UstBelaya_Angara 0.317 1.317
EHG 1.606 0.836
Anatolia_N 1.017 -0.819
WSHG 1.289 0.774
Russia_MA1_HG.SG 0.966 0.323
CHG 0.489 -1.295
Russia_MLBA_Sintashta 1.299 -0.151
Sweden_HG_Motala 1.561 0.615
A:
scale 614.836 1472.057
SISI8728 -0.761 0.544
Alalakh_MLBA_outlier 0.598 -1.407
Kazakhstan_MLBA_Zevakinskiy 1.436 0.851


full rank
f4info:
f4rank: 3 dof: 0 chisq: 0.000 tail: 1 dofdiff: 11 chisqdiff: 4.460 taildiff: 0.954472637
B:
scale 771.094 713.050 397.966
Russia_Steppe_Eneolithic -1.174 1.220 0.989
Australian.DG 0.698 -0.928 -0.315
PPNB -1.018 1.092 0.694
Iran_GanjDareh_N -0.769 1.696 -0.249
DevilsCave_N.SG 0.353 -0.728 0.238
Russia_UstBelaya_Angara 0.102 -0.621 0.665
EHG -1.092 0.737 1.815
Anatolia_N -1.246 1.271 0.786
WSHG -0.949 0.411 1.449
Russia_MA1_HG.SG -1.184 0.189 0.903
CHG -0.735 1.357 0.245
Russia_MLBA_Sintashta -1.240 1.069 1.238
Sweden_HG_Motala -1.499 0.565 1.568
A:
scale 1.732 1.732 1.732
SISI8728 1.732 0.000 0.000
Alalakh_MLBA_outlier 0.000 1.732 0.000
Kazakhstan_MLBA_Zevakinskiy 0.000 0.000 1.732


best coefficients: 0.538 0.304 0.159
Jackknife mean: 0.537324048 0.303438160 0.159237792
std. errors: 0.047 0.058 0.046

error covariance (* 1,000,000)
2232 -1762 -470
-1762 3421 -1659
-470 -1659 2129


summ: Uzbekistan_BA_Bustan_o2 3 0.954473 0.537 0.303 0.159


Narasimhan's models for him are very poor

Indus_Periphery_Pool Western_Steppe_MLBA ..p= 0.01 0.847 0.153
Indus_Periphery_Pool Central_Steppe_MLBA .. p=0.021 0.836 0.164




With Loebanr_0

sample: Loebanr IA o:Average
distance: 2.492
Oy_Dzhaylau_MLBA: 38
Alalakh_MLBA_o: 34
Shahr_I_Sokhta_BA2: 28


On qpAdm another great working model but not as good as the one for Bustan_o2.



Best coefficients: 0.344 0.313 0.343
Jackknife mean: 0.342802153 0.314344114 0.342853733
std. errors: 0.067 0.086 0.035

error covariance (* 1,000,000)
4448 -5308 860
-5308 7428 -2119
860 -2119 1259


summ: Pakistan_IA_Loebanr_o_published 3 0.745909 0.343 0.314 0.343 4448




Modern populations which work well with Alalakh_o:

sample: Gujar Pakistan:G-83
distance: 1.9193
Shahr_I_Sokhta_BA2: 57.5
Alalakh_MLBA_o: 27.5
Oy_Dzhaylau_MLBA: 12.5
Chokhopani_2700BP: 2.5




sample: Kashmiri Pandit:Average
distance: 1.9064
Shahr_I_Sokhta_BA3: 30.5
Alalakh_MLBA_o: 24
Shahr_I_Sokhta_BA2: 22
Oy_Dzhaylau_MLBA: 19
Chokhopani_2700BP: 4.5

sample: Punjabi Sikh India PanSikh27270
distance: 1.5372
Shahr_I_Sokhta_BA2: 50
Oy_Dzhaylau_MLBA: 27
Alalakh_MLBA_o: 23

Can we see your results? Thank you

[pegasus]

Looking at some ancients on G25 and testing them on qpAdm, the model is a resounding success and pretty much similar to my G25 model when you factor in standard errors.

sample: Bustan BA o2:Average
distance: 1.544
Shahr_I_Sokhta_BA3: 50
Alalakh_MLBA_o: 38
Zevakinskiy_MLBA: 12


left pops:
Uzbekistan_BA_Bustan_o2
SISI8728
Alalakh_MLBA_outlier
Kazakhstan_MLBA_Zevakinskiy

right pops:
Cameroon_SMA.DG
Russia_Steppe_Eneolithic
Australian.DG
PPNB
Iran_GanjDareh_N
DevilsCave_N.SG
Russia_UstBelaya_Angara
EHG
Anatolia_N
WSHG
Russia_MA1_HG.SG
CHG
Russia_MLBA_Sintashta
Sweden_HG_Motala

0 Uzbekistan_BA_Bustan_o2 1
1 SISI8728 1
2 Alalakh_MLBA_outlier 1
3 Kazakhstan_MLBA_Zevakinskiy 1
4 Cameroon_SMA.DG 1
5 Russia_Steppe_Eneolithic 3
6 Australian.DG 2
7 PPNB 11
8 Iran_GanjDareh_N 8
9 DevilsCave_N.SG 4
10 Russia_UstBelaya_Angara 11
11 EHG 3
12 Anatolia_N 30
13 WSHG 3
14 Russia_MA1_HG.SG 1
15 CHG 2
16 Russia_MLBA_Sintashta 19
17 Sweden_HG_Motala 6
jackknife block size: 0.050
snps: 1150471 indivs: 108
number of blocks for block jackknife: 714
## ncols: 1150471
coverage: Uzbekistan_BA_Bustan_o2 766753
coverage: SISI8728 645800
coverage: Alalakh_MLBA_outlier 733730
coverage: Kazakhstan_MLBA_Zevakinskiy 711062
coverage: Cameroon_SMA.DG 1140138
coverage: Russia_Steppe_Eneolithic 1008226
coverage: Australian.DG 1120959
coverage: PPNB 871625
coverage: Iran_GanjDareh_N 1057358
coverage: DevilsCave_N.SG 1149702
coverage: Russia_UstBelaya_Angara 1092594
coverage: EHG 1027057
coverage: Anatolia_N 1141220
coverage: WSHG 858019
coverage: Russia_MA1_HG.SG 805960
coverage: CHG 1149558
coverage: Russia_MLBA_Sintashta 1115156
coverage: Sweden_HG_Motala 1038843
Effective number of blocks: 618.643
numsnps used: 380056
codimension 1
f4info:
f4rank: 2 dof: 11 chisq: 4.460 tail: 0.954472637 dofdiff: 13 chisqdiff: -4.460 taildiff: 1
B:
scale 1.000 1.000
Russia_Steppe_Eneolithic 1.110 -0.547
Australian.DG -0.566 0.776
PPNB 0.857 -0.653
Iran_GanjDareh_N 0.193 -2.163
DevilsCave_N.SG -0.062 1.055
Russia_UstBelaya_Angara 0.317 1.317
EHG 1.606 0.836
Anatolia_N 1.017 -0.819
WSHG 1.289 0.774
Russia_MA1_HG.SG 0.966 0.323
CHG 0.489 -1.295
Russia_MLBA_Sintashta 1.299 -0.151
Sweden_HG_Motala 1.561 0.615
A:
scale 614.836 1472.057
SISI8728 -0.761 0.544
Alalakh_MLBA_outlier 0.598 -1.407
Kazakhstan_MLBA_Zevakinskiy 1.436 0.851


full rank
f4info:
f4rank: 3 dof: 0 chisq: 0.000 tail: 1 dofdiff: 11 chisqdiff: 4.460 taildiff: 0.954472637
B:
scale 771.094 713.050 397.966
Russia_Steppe_Eneolithic -1.174 1.220 0.989
Australian.DG 0.698 -0.928 -0.315
PPNB -1.018 1.092 0.694
Iran_GanjDareh_N -0.769 1.696 -0.249
DevilsCave_N.SG 0.353 -0.728 0.238
Russia_UstBelaya_Angara 0.102 -0.621 0.665
EHG -1.092 0.737 1.815
Anatolia_N -1.246 1.271 0.786
WSHG -0.949 0.411 1.449
Russia_MA1_HG.SG -1.184 0.189 0.903
CHG -0.735 1.357 0.245
Russia_MLBA_Sintashta -1.240 1.069 1.238
Sweden_HG_Motala -1.499 0.565 1.568
A:
scale 1.732 1.732 1.732
SISI8728 1.732 0.000 0.000
Alalakh_MLBA_outlier 0.000 1.732 0.000
Kazakhstan_MLBA_Zevakinskiy 0.000 0.000 1.732


best coefficients: 0.538 0.304 0.159
Jackknife mean: 0.537324048 0.303438160 0.159237792
std. errors: 0.047 0.058 0.046

error covariance (* 1,000,000)
2232 -1762 -470
-1762 3421 -1659
-470 -1659 2129


summ: Uzbekistan_BA_Bustan_o2 3 0.954473 0.537 0.303 0.159


Narasimhan's models for him are very poor

Indus_Periphery_Pool Western_Steppe_MLBA ..p= 0.01 0.847 0.153
Indus_Periphery_Pool Central_Steppe_MLBA .. p=0.021 0.836 0.164




With Loebanr_0

sample: Loebanr IA o:Average
distance: 2.492
Oy_Dzhaylau_MLBA: 38
Alalakh_MLBA_o: 34
Shahr_I_Sokhta_BA2: 28


On qpAdm another great working model but not as good as the one for Bustan_o2.



Best coefficients: 0.344 0.313 0.343
Jackknife mean: 0.342802153 0.314344114 0.342853733
std. errors: 0.067 0.086 0.035

error covariance (* 1,000,000)
4448 -5308 860
-5308 7428 -2119
860 -2119 1259


summ: Pakistan_IA_Loebanr_o_published 3 0.745909 0.343 0.314 0.343




Modern populations which work well with Alalakh_o:

sample: Gujar Pakistan:G-83
distance: 1.9193
Shahr_I_Sokhta_BA2: 57.5
Alalakh_MLBA_o: 27.5
Oy_Dzhaylau_MLBA: 12.5
Chokhopani_2700BP: 2.5




sample: Kashmiri Pandit:Average
distance: 1.9064
Shahr_I_Sokhta_BA3: 30.5
Alalakh_MLBA_o: 24
Shahr_I_Sokhta_BA2: 22
Oy_Dzhaylau_MLBA: 19
Chokhopani_2700BP: 4.5

sample: Punjabi Sikh India PanSikh27270
distance: 1.5372
Shahr_I_Sokhta_BA2: 50
Oy_Dzhaylau_MLBA: 27
Alalakh_MLBA_o: 23

This is all in line with Lubotsky's paper.

It seems therefore worthwhile to seriously consider another scenario.13 It seems attractive to assume that the southward movement of Indo-Aryans was simultaneous with the decline of the BMAC and was even triggered by it, since the profound changes in the economy of the BMAC would have forced the Indo-Aryan pastoralists to look for new markets. In the situation of an economic and political crisis, it is only to be expected that in their movement, the Indo-Aryans were joined by a sizable group of the BMAC people, who would bring their culture and the agricultural lifestyle with them.

This scenario may account for the prolonged contacts of the Indo-Aryans and the BMAC people in the Swat valley and the Punjab and, consequently, for a large number of loanwords when the Indo-Aryans started to get settled and to learn agriculture. At the same time, it perfectly explains the fact that “intrusive BMAC material is subsequently found further to the south in Iran, Afghanistan and Pakistan.”14 As we know from major people movements of the past, they often were multiethnic, and a joint movement of Indo-Aryans and the BMAC people would not be surprising at all.

It would be nice to hear from the geneticists whether this scenario is in line with the genetic evidence. In view of the many samples from the necropolis in Gonur, we will undoubtedly hear more about this issue in the future. Up till now, the linguistic scenarios have time and again found support in the analyses of ancient DNA. Will this also here be the case?

[karnalIroh]

here are my brothers coordinates can someone help me get a goodfit

butt_scaled,0.068294,-0.022342,-0.112005,0.081396,-0.083708,0.042391,0.00376,0.011076,0.009204,-0.00893,-0.007632,-0.000749,-0.001933,-0.007844,0.007465,0.006497,-0.006128,0.00152,0.002765,-0.02051,0.000624,-0.014096,-0.001356,0.004699,-0.002036

butt,0.006,-0.0022,-0.0297,0.0252,-0.0272,0.0152,0.0016,0.0048,0.0045,-0.0049,-0.0047,-0.0005,-0.0013,-0.0057,0.0055,0.0049,-0.0047,0.0012,0.0022,-0.0164,0.0005,-0.0114,-0.0011,0.0039,-0.0017

Target: Butt_scaled
Distance: 3.0284% / 0.03028450
52.4 IRN_Shahr_I_Sokhta_BA2
19.2 TJK_Sarazm_En
16.8 RUS_Sintashta_MLBA
4.0 IRN_Ganj_Dareh_N
3.0 Levant_PPNB
3.0 Nganassan
1.4 Han
0.2 Anatolia_Tepecik_Ciftlik_N

mine for comparision;
istance: 2.5497% / 0.02549668
56.4 IRN_Shahr_I_Sokhta_BA2
18.0 RUS_Sintashta_MLBA
13.0 IRN_Ganj_Dareh_N
4.0 KAZ_Botai
3.0 TJK_Sarazm_En
2.6 Han
2.6 Levant_PPNB
0.4 Anatolia_Barcin_N

[pegasus]

Hahaha I agree re my grandad, the recurrent Med has me stumped. I recently returned from Pakistan and made a point of asking every other relative for their version of the family history. They all repeatedly said we're Rajputs with an 18th or 19th century Iranian ancestor, but not so sure the admixture supports that ...

In relation to Mom/Khala, I had a chat with my Naani and she said the only known heritage is Potohari/AJK (Awan/Rajput)

I looked at both your mom and Khala, they both need a more ANF enriched BMAC source , I had to use Sappali Tepe2 which often works well with Afghan Pashtuns, which infers a bit more Iran Chl ancestry. So there was definitely some further admixture beyond the traditional combinations used for populations here. I have looked at Potohari and Mirpuri results galore thanks to Amber and they are not like your mom's family.

sample: Ahmedmom
distance: 1.4976
Shahr_I_Sokhta_BA2: 57
Oy_Dzhaylau_MLBA: 21.5
Sappali_Tepe2_BA: 18
Chokhopani_2700BP: 3.5

sample: AhmedKhaala
distance: 1.8908
Shahr_I_Sokhta_BA2: 55.5
Oy_Dzhaylau_MLBA: 18.5
Sappali_Tepe2_BA: 22.5
Chokhopani_2700BP: 3.5