View Full Version : EHG levels using Dstats
Here I use dstats to explore EHG levels in Kurds and others. I use 2 outgroups to get an idea of absolute total shared drift with EHG. I use EHG instead of Andronovo or Yamnaya, because it is less recently W Asian admixed, and a better indicater of inferred steppe ancestry. Of course, other dstat comparisons such as D (Iran N/ Anatolia N, X, EHG, Chimp) should be used to confirm whether the EHG is in excess of that found in some of these ancients believed to be basal to modern S and W Asians. The higher you are in the list, the better the likelihood that it is excess EHG inferred by steppe groups, above and beyond that found in for ex Anatolia N or Iran N
Ancients are highlighted purple, and Kurds orange. the table is sorted with highest EHG shared drift on top.
POP1
OUT1
TARGET
OUT2
D
Z
SNPs
AG3
Mbuti.DG
EHG
Chimp
0.4686
72.65
84866
Hungarian_KO1
Mbuti.DG
EHG
Chimp
0.4411
100.00
246317
WHG
Mbuti.DG
EHG
Chimp
0.4347
100.00
362186
MA1
Mbuti.DG
EHG
Chimp
0.4339
92.09
254765
Switzerland_HG
Mbuti.DG
EHG
Chimp
0.4283
97.50
361319
Andronovo
Mbuti.DG
EHG
Chimp
0.4275
100.00
359559
Villabruna
Mbuti.DG
EHG
Chimp
0.4262
90.38
288679
Kennewick
Mbuti.DG
EHG
Chimp
0.4071
78.78
201751
Clovis
Mbuti.DG
EHG
Chimp
0.4066
85.92
360330
Lithuanian
Mbuti.DG
EHG
Chimp
0.405
100.00
114018
Karitiana
Mbuti.DG
EHG
Chimp
0.3967
82.85
114018
.Dluffy
Mbuti.DG
EHG
Chimp
0.3959
100.00
360132
.Sapporo
Mbuti.DG
EHG
Chimp
0.3948
100.00
356417
.Melif
Mbuti.DG
EHG
Chimp
0.394
100.00
361347
.Rukha
Mbuti.DG
EHG
Chimp
0.3938
100.00
360400
Tajik
Mbuti.DG
EHG
Chimp
0.3935
100.00
354988
.Kurd_C3
Mbuti.DG
EHG
Chimp
0.3915
100.00
361232
.Mfa
Mbuti.DG
EHG
Chimp
0.3911
98.90
355938
.Sein
Mbuti.DG
EHG
Chimp
0.3908
100.00
358245
.Znertu
Mbuti.DG
EHG
Chimp
0.3898
98.15
359877
.McNinja
Mbuti.DG
EHG
Chimp
0.3889
95.08
358230
.Kaido
Mbuti.DG
EHG
Chimp
0.3888
100.00
360424
.Bol_Nat
Mbuti.DG
EHG
Chimp
0.3884
95.75
355466
.Kurd_F1
Mbuti.DG
EHG
Chimp
0.3882
100.00
361115
Kotias
Mbuti.DG
EHG
Chimp
0.3879
91.50
362100
.Parasar
Mbuti.DG
EHG
Chimp
0.3877
100.00
351465
Anatolia_N
Mbuti.DG
EHG
Chimp
0.3875
100.00
360901
GoyetQ116
Mbuti.DG
EHG
Chimp
0.3871
77.12
257495
.Kurd_Ezidi
Mbuti.DG
EHG
Chimp
0.3871
99.92
358014
.Kurd_F5
Mbuti.DG
EHG
Chimp
0.387
96.93
361240
.Khana
Mbuti.DG
EHG
Chimp
0.3869
93.91
360237
.Kurd_F8
Mbuti.DG
EHG
Chimp
0.3862
97.79
358923
.Kurd_C2
Mbuti.DG
EHG
Chimp
0.3858
97.51
360930
.Kurd_F3
Mbuti.DG
EHG
Chimp
0.3858
94.55
361153
Spanish
Mbuti.DG
EHG
Chimp
0.3856
100.00
114018
.Bored
Mbuti.DG
EHG
Chimp
0.3856
97.70
361030
.Hanna
Mbuti.DG
EHG
Chimp
0.3851
97.43
359572
.Halgurd
Mbuti.DG
EHG
Chimp
0.3848
96.29
359852
.Kurd_F2
Mbuti.DG
EHG
Chimp
0.3848
97.47
360248
.NK19191
Mbuti.DG
EHG
Chimp
0.3839
91.60
359574
.Zara
Mbuti.DG
EHG
Chimp
0.3837
95.83
361135
.DMXX
Mbuti.DG
EHG
Chimp
0.3833
95.96
351472
.Kurd_C1
Mbuti.DG
EHG
Chimp
0.3831
98.72
360619
Kalash
Mbuti.DG
EHG
Chimp
0.383
95.94
114018
.Zephyrous
Mbuti.DG
EHG
Chimp
0.3828
94.78
359808
Iran_ChL
Mbuti.DG
EHG
Chimp
0.382
98.18
305840
.Varun
Mbuti.DG
EHG
Chimp
0.3819
94.72
359115
.Kurd_F4
Mbuti.DG
EHG
Chimp
0.3817
93.99
360199
.Kurd_F6
Mbuti.DG
EHG
Chimp
0.3811
94.95
351242
.Kurd_F7
Mbuti.DG
EHG
Chimp
0.3801
93.92
361100
.Iranian567
Mbuti.DG
EHG
Chimp
0.3795
99.67
360040
Satsurbila
Mbuti.DG
EHG
Chimp
0.3793
80.81
252458
.Humanist
Mbuti.DG
EHG
Chimp
0.3784
96.28
360396
Sardinian
Mbuti.DG
EHG
Chimp
0.3781
96.78
114018
Iran_Hotu
Mbuti.DG
EHG
Chimp
0.3743
44.66
47629
Iran_Lori
Mbuti.DG
EHG
Chimp
0.3721
95.58
113207
Iran_LN
Mbuti.DG
EHG
Chimp
0.3711
69.26
173262
Balochi
Mbuti.DG
EHG
Chimp
0.3703
96.18
114018
Iranian
Mbuti.DG
EHG
Chimp
0.3702
92.19
114018
Iran_Shirazi
Mbuti.DG
EHG
Chimp
0.3684
94.27
113207
Brahui
Mbuti.DG
EHG
Chimp
0.3682
95.81
114018
Makrani
Mbuti.DG
EHG
Chimp
0.3642
94.51
114018
Iran_N1
Mbuti.DG
EHG
Chimp
0.362
80.57
278301
Iranian_Bandari
Mbuti.DG
EHG
Chimp
0.3576
91.67
113207
Palliyar
Mbuti.DG
EHG
Chimp
0.357
67.28
63931
Kharia
Mbuti.DG
EHG
Chimp
0.3568
69.55
63931
Syrian
Mbuti.DG
EHG
Chimp
0.3548
89.29
114018
Natufian
Mbuti.DG
EHG
Chimp
0.3531
65.39
161111
Iran_N_WC1
Mbuti.DG
EHG
Chimp
0.3528
82.66
217852
Saudi
Mbuti.DG
EHG
Chimp
0.3519
87.28
114018
Han
Mbuti.DG
EHG
Chimp
0.3492
81.26
114018
Papuan
Mbuti.DG
EHG
Chimp
0.3149
66.46
114018
Australian
Mbuti.DG
EHG
Chimp
0.314
60.58
114018
Mota
Mbuti.DG
EHG
Chimp
0.1201
29.49
362180
Yoruba
Mbuti.DG
EHG
Chimp
0.0891
26.21
114018
As an added note, if your score is lower than for ex Anatolia N, you should not conclude that all your EHG is attributable to Anatolia Ns, because no member here is 100% Anatolia N.
So for example if you are 33% Natufian, 33% Iran N, and 33% Anatolia N, the avg D would be 0.3675. So if you scored above that level then all your EHG would not be attributable to those base pops (not to mention the problem of space and time)
I used a technique widely used by Lazaridis to determine whether certain admixture, in this case EHG, is attributable to a base pop. Basically one of the base pops is compared with the test sample (Kurd C) to see which one is more EHG shifted. If it turns out that one of those shares more drift with EHG, then the possibility can not be ruled out that they received their EHG via one of those.
Here is the result for Kurd C3:
result: Iran_N .Kurd_C3 EHG Chimp -0.0424 -5.272 6053 6590 85015
result: Iran_N .Kurd_C2 EHG Chimp -0.0390 -4.879 6051 6541 84944
result: Anatolia_N .Kurd_C3 EHG Chimp -0.0072 -1.430 7729 7840 106694
result: Anatolia_N .Kurd_C2 EHG Chimp -0.0049 -0.945 7719 7795 106602
result: Iran_Chl .Kurd_C3 EHG Chimp -0.0181 -2.647 6554 6795 91756
result: Iran_Chl .Kurd_C2 EHG Chimp -0.0154 -2.186 6547 6752 91665
Here we see that the Kurd samples share more drift with EHG than Iran N, Anatolia N, and Iran Chl.
The most likely candidate was Anatolia N, but even it apparently can't account for all the EHG in Kurds C2 and C3. Therefore, the most likely vector for EHG was a steppe group. The likely suspects are Andronovo and Scythian.
Since Kurds are not entirely any one of those 3, the stats should be averaged. for ex if they are 50/50 Iran N/ Anatolia N, then the average EHG shift would be halfway between the Iran N and Anatolia N stats .
The fits for Kurd C3 using qpAdm
Both, the one with Andronovo, and the one with Scythian IA, are very good fits
SAMPLE
IRAN N
ANDRONOVO
ANATOLIA N
CHISQ
TAIL PROBABILITY
KURD C3
35.0%
39.0%
25.0%
0.58
99.7%
STANDARD ERRORS
9.0%
9.0%
9.0%
SAMPLE
IRAN N
SCYTHIAN IA
ANATOLIA N
CHISQ
TAIL PROBABILITY
KURD C3
30.6%
33.7%
35.7%
0.88
99.0%
STANDARD ERRORS
9.6%
7.9%
9.0%
parameter file: parqpAdm
### THE INPUT PARAMETERS
##PARAMETER NAME: VALUE
indivname: LAZARDIS2.ind
snpname: LAZARDIS2.snp
genotypename: LAZARDIS2.geno
popleft: pleftx
popright: pright
allsnps: YES
maxrank: 4
## qpAdm version: 200
left pops:
.Kurd_C3
Iran_N
Andronovo
Anatolia_N
right pops:
Ust_Ishim
Kostenki14
MA1
Papuan
Eskimo
Karitiana
Mbuti
Natufian
Switzerland_HG
0 .Kurd_C3 1
1 Iran_N 1
2 Andronovo 3
3 Anatolia_N 24
4 Ust_Ishim 1
5 Kostenki14 1
6 MA1 1
7 Papuan 14
8 Eskimo 22
9 Karitiana 12
10 Mbuti 10
11 Natufian 1
12 Switzerland_HG 1
jackknife block size: 0.050
snps: 111304 indivs: 92
number of blocks for block jackknife: 703
dof (jackknife): 617.220
numsnps used: 27116
codimension 1
f4info:
f4rank: 2 dof: 6 chisq: 0.579 tail: 0.996747914 dofdiff: 8 chisqdiff: -0.579 taildiff: 1
B:
scale 1.000 1.000
Kostenki14 0.521 0.248
MA1 1.515 -0.758
Papuan -0.069 -0.299
Eskimo 0.604 -0.517
Karitiana 0.683 -0.748
Mbuti -0.520 0.135
Natufian 0.584 2.527
Switzerland_HG 1.997 0.210
A:
scale 468.963 532.519
Iran_N -1.336 -0.332
Andronovo 1.085 -0.690
Anatolia_N 0.192 1.554
full rank 1
f4info:
f4rank: 3 dof: 0 chisq: 0.000 tail: 1 dofdiff: 6 chisqdiff: 0.579 taildiff: 0.996747914
B:
scale 1.000 1.000 1.000
Kostenki14 0.531 0.239 -0.984
MA1 1.481 -0.760 2.033
Papuan -0.076 -0.308 0.004
Eskimo 0.611 -0.525 -0.440
Karitiana 0.681 -0.755 0.152
Mbuti -0.546 0.129 1.018
Natufian 0.580 2.523 0.696
Switzerland_HG 2.012 0.208 -1.078
A:
scale 474.041 532.104 4110.139
Iran_N -1.297 -0.348 1.094
Andronovo 1.126 -0.704 1.112
Anatolia_N 0.221 1.544 0.754
best coefficients: 0.356 0.394 0.251
ssres:
-0.000207523 0.000638463 0.000002967 -0.000047078 0.000110802 0.000205195 0.000151667 -0.000113742
-0.796363668 2.450088338 0.011386335 -0.180661386 0.425199036 0.787431138 0.582019473 -0.436480440
Jackknife mean: 0.351802612 0.394267850 0.253929539
std. errors: 0.092 0.088 0.095
error covariance (* 1000000)
8529 -3674 -4855
-3674 7824 -4151
-4855 -4151 9006
fixed pat wt dof chisq tail prob
000 0 6 0.579 0.996748 0.356 0.394 0.251
001 1 7 6.362 0.498206 0.498 0.502 0.000
010 1 7 16.859 0.0183274 0.646 -0.000 0.354
100 1 7 16.331 0.0222597 0.000 0.566 0.434
011 2 8 24.611 0.00180859 1.000 0.000 0.000
101 2 8 36.418 1.47191e-05 0.000 1.000 -0.000
110 2 8 74.008 7.79003e-13 0.000 0.000 1.000
best pat: 000 0.996748 - -
best pat: 001 0.498206 chi(nested): 5.783 p-value for nested model: 0.0161801
best pat: 011 0.00180859 chi(nested): 18.250 p-value for nested model: 1.93767e-05
## end of run
parameter file: parqpAdm
### THE INPUT PARAMETERS
##PARAMETER NAME: VALUE
indivname: LAZARDIS2.ind
snpname: LAZARDIS2.snp
genotypename: LAZARDIS2.geno
popleft: pleftx
popright: pright
allsnps: YES
maxrank: 4
## qpAdm version: 200
left pops:
.Kurd_C3
Iran_N
Scythian_IA
Anatolia_N
right pops:
Ust_Ishim
Kostenki14
MA1
Papuan
Eskimo
Karitiana
Mbuti
Natufian
Switzerland_HG
0 .Kurd_C3 1
1 Iran_N 1
2 Scythian_IA 1
3 Anatolia_N 24
4 Ust_Ishim 1
5 Kostenki14 1
6 MA1 1
7 Papuan 14
8 Eskimo 22
9 Karitiana 12
10 Mbuti 10
11 Natufian 1
12 Switzerland_HG 1
jackknife block size: 0.050
snps: 111304 indivs: 90
number of blocks for block jackknife: 703
dof (jackknife): 615.705
numsnps used: 25653
codimension 1
f4info:
f4rank: 2 dof: 6 chisq: 0.875 tail: 0.98989528 dofdiff: 8 chisqdiff: -0.875 taildiff: 1
B:
scale 1.000 1.000
Kostenki14 -0.014 0.536
MA1 1.044 1.208
Papuan 0.224 -0.182
Eskimo 0.852 0.654
Karitiana 0.957 0.563
Mbuti -0.613 -0.828
Natufian -2.201 1.281
Switzerland_HG 0.017 1.775
A:
scale 475.067 518.907
Iran_N 0.070 -1.463
Scythian_IA 1.220 0.692
Anatolia_N -1.227 0.617
full rank 1
f4info:
f4rank: 3 dof: 0 chisq: 0.000 tail: 1 dofdiff: 6 chisqdiff: 0.875 taildiff: 0.98989528
B:
scale 1.000 1.000 1.000
Kostenki14 -0.037 0.547 -0.727
MA1 1.069 1.186 0.823
Papuan 0.233 -0.205 0.463
Eskimo 0.882 0.640 0.890
Karitiana 0.992 0.538 1.121
Mbuti -0.600 -0.839 0.332
Natufian -2.163 1.269 1.279
Switzerland_HG -0.032 1.800 -1.668
A:
scale 474.781 522.818 2852.773
Iran_N 0.069 -1.437 0.964
Scythian_IA 1.220 0.725 0.993
Anatolia_N -1.228 0.639 1.041
best coefficients: 0.309 0.338 0.353
ssres:
-0.000226993 0.000348900 0.000151879 0.000344565 0.000419975 0.000073966 0.000516201 -0.000493020
-0.639979275 0.983679478 0.428202756 0.971457411 1.184067038 0.208538039 1.455364422 -1.390009225
Jackknife mean: 0.305751126 0.337278654 0.356970220
std. errors: 0.096 0.079 0.090
error covariance (* 1000000)
9239 -3699 -5540
-3699 6212 -2514
-5540 -2514 8054
fixed pat wt dof chisq tail prob
000 0 6 0.875 0.989895 0.309 0.338 0.353
001 1 7 12.417 0.0876595 0.553 0.447 0.000
010 1 7 16.797 0.018752 0.605 -0.000 0.395
100 1 7 11.370 0.123284 0.000 0.479 0.521
011 2 8 26.511 0.000858371 1.000 0.000 0.000
101 2 8 35.989 1.76408e-05 0.000 1.000 0.000
110 2 8 66.842 2.08432e-11 0.000 0.000 1.000
best pat: 000 0.989895 - -
best pat: 100 0.123284 chi(nested): 10.494 p-value for nested model: 0.00119746
best pat: 011 0.000858371 chi(nested): 15.141 p-value for nested model: 9.97626e-05
## end of run
MonkeyDLuffy
08-14-2016, 09:15 PM
Kurd, why do I share the most drift with EHG? Because of Scythian connection as well?
kenji.aryan
08-14-2016, 09:59 PM
@Kurd Can you add me here too ?
Kurd, why do I share the most drift with EHG? Because of Scythian connection as well?
Yes, that is likely. I don't have the Scythian IA sample in the run, but I believe it would be 2nd in the list. Although in your case multiple admixture events, with Scythian and other steppe groups is also possible. They would each have to be tested against Scythian to find out more
@Kurd Can you add me here too ?
Will do when I have my software issue resolved
@Kurd Can you add me here too ?
Here is yours and Reza's. You would be 33rd in the table, and Reza 57th.
POP1
OUT1
TARGET
OUT2
D
Z
SNPs
.Reza1
Mbuti.DG
EHG
Chimp
0.3711
93.77
358714
.Kenji
Mbuti.DG
EHG
Chimp
0.386
100.00
360053
MA1 ~ 24,000 years old
AG3 ~ 16,500 years old
EHG ~ 8,000 year old
Considering the geneflow in the ANE line as follows: MA1 --> AG3 --> EHG, one would generally expect that a modern individual with EHG ancestry would share more genetic drift with AG3 than MA1, because of less genetic drift between 16 kya and now vs 24kya and now, and because EHG appears to be principally AG3 derived.
So then what does it mean, when S or W Asian individuals, who we have verified through other tests, have considerable EHG ancestry via the steppe, end up sharing more genetic drift or comparable genetic drift with MA1 vs AG3? After all, that should not be the case because MA1 and W and S Asians only share some old ancestry basal to both, right? whereas AG3 would have been inferred more recently via the steppe.
I wrote this to stimulate some thinking on what the implications are. More recent geneflow from unknown MA1 decendents who formed a parallel clade to AG3/EHG - <---> W / S Asians ?
Or is the explanation simply that most of Iran N represents a clade parallel to MA1
Maybe you can come up with some more scenarios.
Table sorted with the most shared drift with AG3 on top
POP1
POP2
TARGET
OUT
D
Z
SNPs
MA1
AG3
EHG
Chimp
-0.0767
-6.718
61997
MA1
AG3
Clovis
Chimp
-0.046
-3.728
63868
MA1
AG3
Kennewick
Chimp
-0.0435
-2.664
33046
MA1
AG3
Andronovo
Chimp
-0.0273
-2.628
63791
MA1
AG3
Iran_Hotu
Chimp
-0.0245
-0.897
11461
MA1
AG3
Karitiana
Chimp
-0.0206
-1.425
25191
MA1
AG3
.Kurd_C2
Chimp
-0.0184
-1.678
63646
MA1
AG3
.Parasar
Chimp
-0.0169
-1.536
61731
MA1
AG3
.Zara
Chimp
-0.0165
-1.422
63693
MA1
AG3
.Bored
Chimp
-0.0165
-1.492
63685
MA1
AG3
.Kurd_F4
Chimp
-0.0155
-1.436
63517
MA1
AG3
.Iranian567
Chimp
-0.013
-1.26
63470
MA1
AG3
Iranian
Chimp
-0.0126
-0.984
25191
MA1
AG3
Tajik
Chimp
-0.0122
-1.369
62590
MA1
AG3
Iranian_Bandari
Chimp
-0.0111
-0.884
25094
MA1
AG3
.Mfa
Chimp
-0.011
-1.003
62723
MA1
AG3
Satsurbila
Chimp
-0.0108
-0.767
43699
MA1
AG3
Iran_N1
Chimp
-0.0107
-0.839
56638
MA1
AG3
.Melif
Chimp
-0.0102
-0.95
63743
MA1
AG3
.Khana
Chimp
-0.01
-0.91
63531
MA1
AG3
Balochi
Chimp
-0.0099
-0.793
25191
MA1
AG3
Brahui
Chimp
-0.0099
-0.779
25191
MA1
AG3
Kalash
Chimp
-0.0098
-0.769
25191
MA1
AG3
.Zephyrous
Chimp
-0.0098
-0.88
63407
MA1
AG3
Makrani
Chimp
-0.0094
-0.743
25191
MA1
AG3
Lithuanian
Chimp
-0.0091
-0.706
25191
MA1
AG3
Iran_LN
Chimp
-0.0087
-0.562
37795
MA1
AG3
.Kaido
Chimp
-0.0084
-0.76
63557
MA1
AG3
.Kurd_SE
Chimp
-0.0082
-0.766
63658
MA1
AG3
.Kurd_F6
Chimp
-0.0066
-0.601
61700
MA1
AG3
Yoruba
Chimp
-0.0064
-0.462
25191
MA1
AG3
Spanish
Chimp
-0.0063
-0.501
25191
MA1
AG3
.NK19191
Chimp
-0.0062
-0.57
63390
MA1
AG3
.Kurd_F2
Chimp
-0.006
-0.553
63597
MA1
AG3
Iran_N_WC1
Chimp
-0.0059
-0.462
40151
MA1
AG3
Iran_Lori
Chimp
-0.0052
-0.413
25094
MA1
AG3
Kotias
Chimp
-0.0051
-0.414
63911
MA1
AG3
Iran_Shirazi
Chimp
-0.005
-0.4
25094
MA1
AG3
.Znertu
Chimp
-0.005
-0.465
63417
MA1
AG3
.Sein
Chimp
-0.0049
-0.44
63108
MA1
AG3
Papuan
Chimp
-0.0047
-0.326
25191
MA1
AG3
.Kurd_F1
Chimp
-0.0044
-0.41
63744
MA1
AG3
.Kurd_F3
Chimp
-0.0043
-0.384
63744
MA1
AG3
.Dluffy
Chimp
-0.0037
-0.341
63540
MA1
AG3
Mota
Chimp
-0.0034
-0.262
63920
MA1
AG3
Han
Chimp
-0.0029
-0.213
25191
MA1
AG3
.Kurd_F7
Chimp
-0.0028
-0.249
63733
MA1
AG3
.Sapporo
Chimp
-0.0028
-0.27
62806
MA1
AG3
Iran_ChL
Chimp
-0.0027
-0.242
59028
MA1
AG3
WHG
Chimp
-0.0024
-0.238
63933
MA1
AG3
.Kenji
Chimp
-0.0024
-0.23
63504
MA1
AG3
Syrian
Chimp
-0.0021
-0.16
25191
MA1
AG3
Saudi
Chimp
-0.0019
-0.147
25191
MA1
AG3
.Kurd_C3
Chimp
-0.0018
-0.167
63721
MA1
AG3
Anatolia_N
Chimp
-0.0013
-0.139
63914
MA1
AG3
.Reza1
Chimp
-0.0009
-0.08
63348
MA1
AG3
.Varun
Chimp
0.0009
0.09
63308
MA1
AG3
.Kurd_C1
Chimp
0.0011
0.1
63590
MA1
AG3
Villabruna
Chimp
0.0012
0.098
59657
MA1
AG3
Australian
Chimp
0.0013
0.082
25191
MA1
AG3
Sardinian
Chimp
0.0016
0.125
25191
MA1
AG3
Switzerland_HG
Chimp
0.0017
0.142
63874
MA1
AG3
.McNinja
Chimp
0.0018
0.16
63125
MA1
AG3
.Bol_Nat
Chimp
0.0024
0.21
62657
MA1
AG3
.Kurd_F8
Chimp
0.0027
0.245
63450
MA1
AG3
.Hanna
Chimp
0.0028
0.258
63369
MA1
AG3
.Halgurd
Chimp
0.0035
0.32
63550
MA1
AG3
Hungarian_KO1
Chimp
0.0039
0.268
44367
MA1
AG3
Palliyar
Chimp
0.005
0.252
13250
MA1
AG3
Kharia
Chimp
0.0072
0.366
13250
MA1
AG3
.Kurd_Ezidi
Chimp
0.0074
0.687
63094
MA1
AG3
Natufian
Chimp
0.0088
0.538
35434
MA1
AG3
.Humanist
Chimp
0.0107
0.98
63535
MA1
AG3
.Kurd_F5
Chimp
0.013
1.206
63767
MA1
AG3
GoyetQ116
Chimp
0.0142
1.041
54457
Varun R
08-15-2016, 03:10 PM
^^^ I think that there may be some unknown element also involved in producing these D-stats. Looking at Baloch, Brahui and Makrani, I can't seem to reconcile their relative positions from one comparison to the next. If they have so much Iran_N input and comparatively little EHG input, then why is the D-stat for MA1, EHG, X, Outgroup negative? Looking at the positions of various project members, it would appear to me that a population X on the MA1 cline is influencing central and eastern subcontinental populations in some way...
^^^ I think that there may be some unknown element also involved in producing these D-stats. Looking at Baloch, Brahui and Makrani, I can't seem to reconcile their relative positions from one comparison to the next. If they have so much Iran_N input and comparatively little EHG input, then why is the D-stat for MA1, EHG, X, Outgroup negative? Looking at the positions of various project members, it would appear to me that a population X on the MA1 cline is influencing central and eastern subcontinental populations in some way...
I think you mean D (MA1, AG3 ...) since I didn't post D(MA1, EHG) for further clues, as well as D (AG3, EHG)
Well, for Balochis it can be argued that although -ve, it is insignificant and consistent with them having lower steppe input. But what about some Punjabis W or S Asians with high EHG absolute shift being close to 0 or +ve. I guess we may get a clue when I post other tables
Dr_McNinja
08-15-2016, 10:52 PM
That's unexpected. I usually score very low in ANE components in calculators using only MA1 or modern populations as a reference and, along with Sapporo, got significantly higher (more if you consider the swing from one to the other) with calculators using AG3 (IIRC, you used AG3 in the new K6 calculators?). On the PCA plots, we look like we're even branching off directly towards AG3.
It could be the Iranian Neolithic samples are what increased my affinity and their ANE is from a source that isn't MA1 or AG3, but a parallel branch.
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