Page 1 of 14 12311 ... LastLast
Results 1 to 10 of 140

Thread: Narasimhan values

  1. #1
    Bronze Class Member
    Posts
    2,218
    Sex
    Location
    Krasnoyarsk

    Siberian Tatars Tajikistan

    Narasimhan values

    Hey guys I created this so that we have a reference to look at right here from the paper, I've simplified it and just used the beginning values. It was difficult but I straightened nearly every row so that you only have to look at whats under the 3 components.

    Code:
    Group	qpAdm P-value	 Steppe	           IVCp	          Onge
    Kalash	  0.1309 	  0.288 	  0.683 	  0.028 
    Pathan	  -   	          0.282 	  0.659 	  0.060 
    Lohana	  0.0517 	  0.252 	  0.643 	  0.104 
    Khatri	  0.0004 	  0.271 	  0.598 	  0.131 
    Pandit	  0.0276 	  0.218 	  0.633 	  0.149 
    GujaratiA 0.3449 	  0.245 	  0.639 	  0.116 
    Dogra	  0.0028 	  0.224 	  0.608 	  0.168 
    Yadav_Rajasthan 0.0765 	  0.226 	  0.616 	  0.158 
    Brahmin_haryana 0.4269 	  0.246 	  0.572 	  0.182 
    Muslim_Kashmiri 0.0001 	  0.195 	  0.609 	  0.196 
    Brahmin_Nepal 0.0006 	  0.251 	  0.511 	  0.239 
    Bhumihar_Bihar	0.0514 	  0.266 	  0.527 	  0.207 
    Rajput	  0.0177 	  0.212 	  0.584 	  0.203 
    Brahmin_Tiwari	0.2150 	  0.259 	  0.515 	  0.226 
    Yadav_UP	0.2081 	  0.213 	  0.614 	  0.173 
    Sikh_Jatt	0.0128 	  0.248 	  0.541 	  0.211 
    Baniya	  0.0073 	  0.176 	  0.630 	  0.194 
    Kshatriya_Durg 0.1710 	  0.227 	  0.527 	  0.247 
    Brahmin_UP	0.2591 	  0.257 	  0.502 	  0.241 
    GujaratiB	0.0670 	  0.215 	  0.571 	  0.214 
    Bhumihar_UP	0.0174 	  0.234 	  0.532 	  0.234 
    Shiya	  0.0010 	  0.170 	  0.584 	  0.246 
    Backward_Caste	0.6548 	  0.208 	  0.523 	  0.269 
    Havik	  0.1454 	  0.172 	  0.589 	  0.239 
    GujaratiC	0.2437 	  0.159 	  0.594 	  0.247 
    Brahmin_Karnat 0.2536 	  0.188 	  0.574 	  0.238 
    Brahmin_Bhatt	0.1714 	  0.189 	  0.515 	  0.297 
    Chamar_Haryana	0.1852 	  0.196 	  0.509 	  0.296 
    Brahmin_Vaidik	0.0012 	  0.152 	  0.595 	  0.253 
    Br_Catholic_Goa 0.0186 	  0.183 	  0.531 	  0.285 
    Jain	   0.1246 	  0.141 	  0.558 	  0.300 
    Oswal_Jain	0.0068 	  0.151 	  0.578 	  0.272 
    Srivastava 0.1271 	  0.162 	  0.527 	  0.311 
    GujaratiD 0.1639 	  0.110 	  0.608 	  0.283 
    Meena	  0.1763 	  0.145 	  0.570 	  0.285 
    Patel	  0.0325 	  0.113 	  0.614 	  0.273 
    Agarwal	  0.1442 	  0.137 	  0.569 	  0.294 
    Kurmi_UP	0.1278 	  0.155 	  0.539 	  0.307 
    Nai	  0.0216 	  0.167 	  0.514 	  0.319 
    Br_CatholicMang 0.0135 	  0.128 	  0.580 	  0.292 
    Br_Catholic  0.0016 	  0.128 	  0.577 	  0.295 
    Coorghi	  0.0050 	  0.080 	  0.669 	  0.251 
    Chaurasia	0.1129 	  0.160 	  0.508 	  0.332 
    Punjabi	  0.2853 	  0.150 	  0.529 	  0.322 
    Jatav	  0.4202 	  0.159 	  0.502 	  0.339 
    Baniyas	  0.0186 	  0.154 	  0.522 	  0.325 
    Br_Catholic_Kumta 0.0255  0.128 	  0.576 	  0.296 
    Kurmi_MP 0.0056 	  0.144 	  0.532 	  0.324 
    Ansari	  0.2245 	  0.149 	  0.500 	  0.351 
    Kanjad	  0.4653 	  0.158 	  0.494 	  0.348 
    Jogi	  0.3087 	  0.141 	  0.518 	  0.341 
    Muslim_Bihar	0.1577 	  0.133 	  0.502 	  0.365 
    Panta_Kapu	0.0000 	  0.049 	  0.681 	  0.270 
    Malaikuarvar	0.2040 	  0.082 	  0.577 	  0.341 
    Narikuruvar	0.5866 	  0.095 	  0.550 	  0.355 
    Sindhi_MP	0.0323 	  0.133 	  0.512 	  0.356 
    Hakki_Pikki	0.0843 	  0.106 	  0.524 	  0.370 
    Baiswar	  0.3690 	  0.135 	  0.504 	  0.361 
    Lohar	  0.0138 	  0.114 	  0.571 	  0.316 
    Kalinga	  0.1194 	  0.120 	  0.534 	  0.346 
    Silawat	  0.3162 	  0.113 	  0.546 	  0.342 
    Dhobi	  0.6310 	  0.134 	  0.499 	  0.367 
    Maratha	  0.1065 	  0.106 	  0.568 	  0.325 
    Chamar_UP	0.0750 	  0.182 	  0.375 	  0.443 
    Gaud_Karnataka	0.0001 	  0.057 	  0.626 	  0.317 
    Lambadi	  0.1476 	  0.121 	  0.518 	  0.360 
    Dushadh	  0.1191 	  0.125 	  0.492 	  0.383 
    Lodhi	  0.2666 	  0.107 	  0.522 	  0.371 
    Schd_Caste_Haryana 0.3477 0.134 	   0.489 	  0.377 
    Pasi	  0.0307 	  0.149 	  0.443 	  0.408 
    Sah_Obc	  0.2743 	  0.140 	  0.451 	  0.409 
    Reddy_Telangana 0.0481 	  0.071 	  0.602 	  0.327 
    Pal	  0.4587 	  0.106 	  0.525 	  0.369 
    Sonkar	  0.1962 	  0.083 	  0.541 	  0.376 
    LingayathKarnataka 0.0203 0.098 	  0.563 	  0.340 
    Kuruba	  0.0362 	  0.108 	  0.533 	  0.359 
    Manjhi_MP 0.0470 	  0.113 	  0.467 	  0.420 
    Gaud_Telangana	0.0653 	  0.058 	  0.577 	  0.365 
    Ediga	  0.0079 	  0.071 	  0.562 	  0.367 
    Dhokkali	0.0473 	  0.064 	  0.571 	  0.366 
    Yadav_Pondicherry 0.0007 	0.048 	  0.607 	  0.344 
    Vysya	  0.0318 	  0.031 	  0.609 	  0.361 
    Naidu	  0.0150 	  0.053 	  0.591 	  0.356 
    Budagajangam	0.0143 	  0.092 	  0.511 	  0.397 
    Dudhekula	0.0213 	  0.057 	  0.562 	  0.381 
    Chamada	  0.0126 	  0.037 	  0.590 	  0.373 
    Nadar	  0.0001 	  0.039 	  0.595 	  0.366 
    Korava	  0.0077 	  0.069 	  0.560 	  0.371 
    Achary	  0.4012 	  0.046 	  0.602 	  0.351 
    Arunthatiar2 0.0470 	  0.059 	  0.567 	  0.374 
    Satnami	  0.1163 	  0.101 	  0.452 	  0.446 
    Dharikhar	0.4933 	  0.086 	  0.492 	  0.422 
    Kshatriya_Aquikula 0.1972 0.054 	  0.549 	  0.397 
    Vadde	  0.1977 	  0.090 	  0.490 	  0.421 
    Rathwa	  0.1440 	  0.114 	  0.445 	  0.441 
    Kallar	  0.1272 	  0.035 	  0.605 	  0.360 
    Bestha	  0.3383 	  0.044 	  0.580 	  0.376 
    Pattapu_Kapu	0.0018 	  0.042 	  0.570 	  0.388 
    Yerukali	0.0511 	  0.045 	  0.565 	  0.390 
    Muthuraja	0.0390 	  0.025 	  0.587 	  0.387 
    Hallaki	  0.0544 	  0.080 	  0.495 	  0.425 
    Paravar	  0.0015 	  0.058 	  0.519 	  0.423 
    Vishwabrahmin	0.1342 	  0.049 	  0.525 	  0.425 
    Bhil	  0.0871 	  0.080 	  0.483 	  0.437 
    Oddari	  0.5664 	  0.067 	  0.506 	  0.427 
    Schd_Caste_Kntka 0.0506   0.064 	  0.497 	  0.439 
    Bhilala	  0.1501 	  0.090 	  0.458 	  0.452 
    Gamit	  0.0643 	  0.083 	  0.452 	  0.465 
    Meddari	  0.4583 	  0.069 	  0.490 	  0.441 
    Mahadeo_Koli	0.0866 	  0.080 	  0.460 	  0.460 
    Chaudhary	0.1748 	  0.078 	  0.473 	  0.449 
    Garasia	  0.2277 	  0.110 	  0.426 	  0.465 
    Lingayath_TN	0.5949 	  0.038 	  0.550 	  0.413 
    Barela	  0.9949 	  0.073 	  0.454 	  0.473 
    Tadvi	  0.0517 	  0.091 	  0.439 	  0.470 
    Kunabi	  0.3655 	  0.081 	  0.452 	  0.467 
    Koli	  0.1285 	  0.101 	  0.396 	  0.503 
    Madiga	  0.2414 	  0.046 	  0.505 	  0.449 
    Sugali	  0.7586 	  0.036 	  0.489 	  0.475 
    Indumalayali	0.2960 	  0.023 	  0.547 	  0.431 
    Mala	  0.0956 	  0.055 	  0.460 	  0.484 
    Kotwalia	0.0770 	  0.081 	  0.424 	  0.495 
    Changpa	  0.2477 	  0.043 	  0.496 	  0.461 
    Chakkiliyan 0.0484 	  0.051 	  0.461 	  0.488 
    Kathodi	  0.0022 	  0.077 	  0.409 	  0.514 
    Gugavellalar	0.8473 	  0.045 	  0.460 	  0.495 
    Arunthatiar1	0.3558 	  0.030 	  0.478 	  0.492 
    Warli	  0.1635 	  0.057 	  0.400 	  0.542 
    Yanidi	  0.3875 	  0.032 	  0.498 	  0.470 
    Kolcha	  0.2429 	  0.082 	  0.379 	  0.539 
    Kumhar	  0.0259 	  0.060 	  0.461 	  0.479 
    Kurumans	0.1379 	  0.050 	  0.453 	  0.497 
    Kurchas	  0.0739 	  0.060 	  0.419 	  0.520 
    Adi_Dravider	0.0019 	  0.024 	  0.487 	  0.489 
    Irula	  0.2489 	  0.025 	  0.401 	  0.573 
    Malayan	  0.2649 	  0.037 	  0.371 	  0.592 
    Adiyan	  0.0130 	  0.040 	  0.297 	  0.663 
    Ulladan	  0.2386 	  0.024 	  0.357 	  0.619 
    Palliyar	0.0337 	  0.023 	  0.330 	  0.647 
    Pulliyar	0.5141 	  0.040 	  0.375 	  0.585
    Last edited by Censored; 08-08-2019 at 07:51 AM.

  2. The Following 10 Users Say Thank You to Censored For This Useful Post:

     26284729292 (08-08-2019),  agent_lime (08-08-2019),  bmoney (08-13-2019),  client (08-10-2019),  kush (08-08-2019),  laltota (08-08-2019),  misanthropy (08-08-2019),  parasar (08-08-2019),  scobar (08-09-2019),  Yggdrasi^ (08-08-2019)

  3. #2
    Gold Class Member
    Posts
    1,756
    Sex
    Location
    NorCal
    Ethnicity
    Jatt Sikh
    Nationality
    American & Canadian
    mtDNA (M)
    HV2a3 / R0
    Y-DNA (P)
    L1a2a1 / L1a1

    United States of America California Republic Canada India Punjab Sikh Empire Nishan Sahib
    Unscaled, 1000 cycles, 200 batches, pen=0.001:

    {
    "sample": "Punjabi_Jat:Average",
    "fit": 0.9052,
    "IVCp": 66.5,
    "RUS_Sintashta_MLBA": 31,
    "Onge": 2.5,

    {
    "sample": "Khatri:Average",
    "fit": 1.1372,
    "IVCp": 70.5,
    "RUS_Sintashta_MLBA": 28,
    "Onge": 1.5,

    I imagine their IVCp is less AASI shifted than the variant in nMonte.
    pegasus modeling:

    sample": "Punjabi_Jat:Sapporo_AGUser",
    "fit": 1.1506,
    "IRN_Shahr_I_Sokhta_BA3": 43.33,
    "TKM_Gonur1_BA": 31.67,
    "RUS_Sintashta_MLBA": 25,
    "closestDistances": [

  4. The Following 2 Users Say Thank You to Sapporo For This Useful Post:

     laltota (08-08-2019),  MonkeyDLuffy (08-08-2019)

  5. #3
    Bronze Class Member
    Posts
    2,218
    Sex
    Location
    Krasnoyarsk

    Siberian Tatars Tajikistan
    Quote Originally Posted by Sapporo View Post
    Unscaled, 1000 cycles, 200 batches, pen=0.001:

    {
    "sample": "Punjabi_Jat:Average",
    "fit": 0.9052,
    "IVCp": 66.5,
    "RUS_Sintashta_MLBA": 31,
    "Onge": 2.5,

    {
    "sample": "Khatri:Average",
    "fit": 1.1372,
    "IVCp": 70.5,
    "RUS_Sintashta_MLBA": 28,
    "Onge": 1.5,

    I imagine their IVCp is less AASI shifted than the variant in nMonte.
    Use simulated AASI instead because onge just doesn’t work on nmonte, though somehow it does in formal stats

  6. The Following 3 Users Say Thank You to Censored For This Useful Post:

     laltota (08-08-2019),  MonkeyDLuffy (08-08-2019),  Sapporo (08-08-2019)

  7. #4
    Registered Users
    Posts
    3,454
    Sex

    Onge doesn't really work as a source of indigenous admixture for South Asians in the Global25 because it's not.

    It does work in theory in formal stat-based analyses because these analyses apparently don't need proximate sources of gene flow to produce accurate models, but it's hard to say whether that's true in every case if you can't actually check it with more realistic reference samples.

    So we'll see if that's true for South Asians when an ASI genome is released.

  8. The Following 8 Users Say Thank You to Generalissimo For This Useful Post:

     bmoney (08-13-2019),  laltota (08-08-2019),  MonkeyDLuffy (08-08-2019),  Orchid (08-08-2019),  parasar (08-08-2019),  poi (08-08-2019),  Sapporo (08-08-2019),  Yggdrasi^ (08-08-2019)

  9. #5
    Gold Class Member
    Posts
    1,756
    Sex
    Location
    NorCal
    Ethnicity
    Jatt Sikh
    Nationality
    American & Canadian
    mtDNA (M)
    HV2a3 / R0
    Y-DNA (P)
    L1a2a1 / L1a1

    United States of America California Republic Canada India Punjab Sikh Empire Nishan Sahib
    Quote Originally Posted by Censored View Post
    Use simulated AASI instead because onge just doesn’t work on nmonte, though somehow it does in formal stats
    Using Simulated South and NW:

    {
    "sample": "Khatri:Average",
    "fit": 1.1204,
    "IVCp": 70.5,
    "RUS_Sintashta_MLBA": 29,
    "Simulated_AASI_South_by_DMXX_Averaged": 0.5,
    "Simulated_AASI_NW_by_DMXX_Averaged": 0,

    {
    "sample": "Punjabi_Jat:Average",
    "fit": 0.8746,
    "IVCp": 65.5,
    "RUS_Sintashta_MLBA": 32.5,
    "Simulated_AASI_South_by_DMXX_Averaged": 2,
    "Simulated_AASI_NW_by_DMXX_Averaged": 0,

    Using the previous Simulated AASI:

    {
    "sample": "Khatri:Average",
    "fit": 1.1525,
    "IVCp": 71,
    "RUS_Sintashta_MLBA": 29,
    "Simulated_AASI": 0,

    {
    "sample": "Punjabi_Jat:Average",
    "fit": 0.8995,
    "IVCp": 65.5,
    "RUS_Sintashta_MLBA": 32.5,
    "Simulated_AASI": 2,



    It doesn't appear they're needed/desired much on top of IVCp.
    pegasus modeling:

    sample": "Punjabi_Jat:Sapporo_AGUser",
    "fit": 1.1506,
    "IRN_Shahr_I_Sokhta_BA3": 43.33,
    "TKM_Gonur1_BA": 31.67,
    "RUS_Sintashta_MLBA": 25,
    "closestDistances": [

  10. The Following 4 Users Say Thank You to Sapporo For This Useful Post:

     agent_lime (08-08-2019),  Censored (08-08-2019),  laltota (08-08-2019),  MonkeyDLuffy (08-08-2019)

  11. #6
    Registered Users
    Posts
    1,124
    Sex
    Location
    US/ India
    Ethnicity
    Punjabi Khatri/J&K/Multan
    Nationality
    India
    mtDNA (M)
    U2b
    Y-DNA (P)
    J2b2a

    India United States of America India Punjab Jammu and Kashmir
    I get terrible fits with SSo. No NWener needs more AASI. Scaled and averaged.

    [1] "distance%=6.6647"

    Khatri

    PAK_Swat_Saidu_Sharif_IA_o,60.8
    RUS_Sintashta_MLBA,39.2

    [1] "distance%=7.219"

    Pashtun

    PAK_Swat_Saidu_Sharif_IA_o,53.2
    RUS_Sintashta_MLBA,46.8

    [1] "distance%=5.8913"

    Punjabi_Jat

    PAK_Swat_Saidu_Sharif_IA_o,58.4
    RUS_Sintashta_MLBA,41.6

    Trying with Piramalai

    [1] "distance%=5.4082"

    Punjabi_Jat

    Piramalai,58.8
    RUS_Sintashta_MLBA,41.2

    [1] "distance%=6.6464"

    Pashtun

    Piramalai,53.6
    RUS_Sintashta_MLBA,46.4

    [1] "distance%=6.1546"

    Khatri

    Piramalai,61.2
    RUS_Sintashta_MLBA,38.8

  12. #7
    Registered Users
    Posts
    2,774
    Location
    Gonur Tepe

    Afghanistan Jammu and Kashmir United States of America Canada
    Quote Originally Posted by Sapporo View Post
    Using Simulated South and NW:

    {
    "sample": "Khatri:Average",
    "fit": 1.1204,
    "IVCp": 70.5,
    "RUS_Sintashta_MLBA": 29,
    "Simulated_AASI_South_by_DMXX_Averaged": 0.5,
    "Simulated_AASI_NW_by_DMXX_Averaged": 0,

    {
    "sample": "Punjabi_Jat:Average",
    "fit": 0.8746,
    "IVCp": 65.5,
    "RUS_Sintashta_MLBA": 32.5,
    "Simulated_AASI_South_by_DMXX_Averaged": 2,
    "Simulated_AASI_NW_by_DMXX_Averaged": 0,

    Using the previous Simulated AASI:

    {
    "sample": "Khatri:Average",
    "fit": 1.1525,
    "IVCp": 71,
    "RUS_Sintashta_MLBA": 29,
    "Simulated_AASI": 0,

    {
    "sample": "Punjabi_Jat:Average",
    "fit": 0.8995,
    "IVCp": 65.5,
    "RUS_Sintashta_MLBA": 32.5,
    "Simulated_AASI": 2,



    It doesn't appear they're needed/desired much on top of IVCp.
    Once those Rakhgarhi samples drop this model will be useless and for all the reasons DMXX mentioned. I would say the impact these Indo Iranians made was massive and they are arriving in a composite form clearly.


    "sample": "Pashtun:Average",
    "fit": 0.437,
    "TKM_Gonur1_BA": 44.17,
    "IRN_Shahr_I_Sokhta_BA3": 25,
    "RUS_Sintashta_MLBA": 25,
    "Naxi": 5.83,

    "sample": "Pashtun:Average",
    "fit": 0.768,
    "TKM_IA": 36.67,
    "IRN_Shahr_I_Sokhta_BA3": 29.17,
    "TKM_Gonur1_BA": 28.33,
    "Naxi": 5.83,
    "closestDista.nces":
    ....

    "sample": "Khatri:Average",
    ."fit": 0.6708,
    ".IRN_Shahr_I_Sokhta_BA3": 43.33,
    ".TKM_IA": 32.5,
    ".TKM_Gonur1_BA": 21.67,
    ".Naxi": 2.5,

    "sample": "Khatri:Average",
    "fit": 0.6058,
    "IRN_Shahr_I_Sokhta_BA3": 39.17,
    "TKM_Gonur1_BA": 37.5,
    "RUS_Sintashta_MLB.A": 19.17,
    "NPL_Chokhopani_2.700BP": 4.17,
    "closestDistances": [

  13. The Following 5 Users Say Thank You to pegasus For This Useful Post:

     agent_lime (08-08-2019),  bmoney (08-13-2019),  Censored (08-08-2019),  MonkeyDLuffy (08-08-2019),  Sapporo (08-08-2019)

  14. #8
    Registered Users
    Posts
    2,774
    Location
    Gonur Tepe

    Afghanistan Jammu and Kashmir United States of America Canada
    Quote Originally Posted by agent_lime View Post
    I get terrible fits with SSo. No NWener needs more AASI. Scaled and averaged.

    [1] "distance%=6.6647"

    Khatri

    PAK_Swat_Saidu_Sharif_IA_o,60.8
    RUS_Sintashta_MLBA,39.2

    [1] "distance%=7.219"

    Pashtun

    PAK_Swat_Saidu_Sharif_IA_o,53.2
    RUS_Sintashta_MLBA,46.8

    [1] "distance%=5.8913"

    Punjabi_Jat

    PAK_Swat_Saidu_Sharif_IA_o,58.4
    RUS_Sintashta_MLBA,41.6

    Trying with Piramalai

    [1] "distance%=5.4082"

    Punjabi_Jat

    Piramalai,58.8
    RUS_Sintashta_MLBA,41.2

    [1] "distance%=6.6464"

    Pashtun

    Piramalai,53.6
    RUS_Sintashta_MLBA,46.4

    [1] "distance%=6.1546"

    Khatri

    Piramalai,61.2
    RUS_Sintashta_MLBA,38.8
    SSo does not have enough Iran_N for those populations , you know this.

  15. The Following User Says Thank You to pegasus For This Useful Post:

     bmoney (08-13-2019)

  16. #9
    Registered Users
    Posts
    196
    Nationality
    Portugal, France & Canada

    The data is very strange. I'd highly doubt that Lohanas are the least "Onge" shifted amongst all other Indic groups. Plus, how are Lohanas scoring more steppe than Jatts and twice as low "Onge" than Jatts?

  17. #10
    Registered Users
    Posts
    1,124
    Sex
    Location
    US/ India
    Ethnicity
    Punjabi Khatri/J&K/Multan
    Nationality
    India
    mtDNA (M)
    U2b
    Y-DNA (P)
    J2b2a

    India United States of America India Punjab Jammu and Kashmir
    Quote Originally Posted by pegasus View Post
    SSo does not have enough Iran_N for those populations , you know this.
    I agree. I am just saying the model is absurd. Did you add more Iran_N in the simulation IVCp?

Page 1 of 14 12311 ... LastLast

Similar Threads

  1. Cladogram from STR Values
    By jbarry6899 in forum Other
    Replies: 16
    Last Post: 12-08-2016, 06:23 PM
  2. Page Help for Y-DNA - Standard Y-STR Values
    By Mac von Frankfurt in forum FTDNA
    Replies: 2
    Last Post: 11-30-2015, 02:55 AM
  3. Y-DNA NGS testing and STR values
    By Salkin in forum General
    Replies: 0
    Last Post: 01-17-2015, 07:54 PM
  4. R1b-L21 STR Allele Values and Frequencies
    By MikeWhalen in forum R1b General
    Replies: 0
    Last Post: 11-22-2014, 05:45 PM
  5. Different Values for DYS487
    By Caesarea in forum FTDNA
    Replies: 0
    Last Post: 04-18-2014, 10:49 PM

Posting Permissions

  • You may not post new threads
  • You may not post replies
  • You may not post attachments
  • You may not edit your posts
  •