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Thread: Dodecad V3

  1. #11
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    Quote Originally Posted by bmoney View Post
    v5 to v3 conversion

    Not bad @ a 1.41 fit - this is the lowest fit I've ever had in 2 way mix mode oracles IIRC though my Harappa oracle has a better fit when only South Asian pops are referenced

    My lowest oracle for Harappa was my unconverted v5 with a 1.51 fit

    Code:
    #	 	Primary Population (source)	Secondary Population (source)	Distance
    1	 	51.9%	kashmiri-pahari (harappa)	+	48.1%	lodi (reich)	@	1.51
    Our theorised origins are as avarna Nagas from the Indo-Nepalese border.

    Jatland surprisingly is the most comprehensive source I could find (though riddled with unverified and contradictory statements)

    https://www.jatland.com/home/Nagavansh

    Code:
    #	Population	Percent
    1	South_Asian	55.19
    2	West_Asian	14.92
    3	West_European	7.93
    4	Southeast_Asian	6.86
    5	East_European	6.02
    6	Mediterranean	4
    7	Southwest_Asian	2.54
    8	Northeast_Asian	1.37
    9	Neo_African	0.54
    10	East_African	0.51
    11	Palaeo_African	0.12
    
    Single Population Sharing:
    
    #	Population (source)	Distance
    1	Meghawal (Reich)	2.97
    2	TN_Brahmin (Xing)	4.01
    3	AP_Brahmin (Xing)	4.26
    4	Srivastava (Reich)	4.39
    5	Vaish (Reich)	5.51
    6	Cochin_Jews (Behar)	6.72
    7	Indian (Dodecad)	7.53
    8	Tharu (Reich)	9.45
    9	Velama (Reich)	9.68
    10	Lodi (Reich)	10.34
    11	INS (SGVP)	10.75
    12	Pakistani (Xing)	12.56
    13	Sindhi (HGDP)	12.6
    14	Hallaki (Reich)	13.28
    15	Kashmiri_Pandit (Reich)	13.97
    16	Naidu (Reich)	14.22
    17	GIH (HapMap)	14.39
    18	Burusho (HGDP)	15.7
    19	Vysya (Reich)	16.86
    20	Kamsali (Reich)	16.91
    
    Mixed Mode Population Sharing:
    
    #	 	Primary Population (source)	Secondary Population (source)	Distance
    1	 	86.6%	Lodi (Reich)	+	13.4%	Adygei (HGDP)	@	1.41
    2	 	57.6%	Lodi (Reich)	+	42.4%	Kashmiri_Pandit (Reich)	@	1.66
    3	 	55%	Lodi (Reich)	+	45%	Pakistani (Xing)	@	1.72
    4	 	86.8%	Lodi (Reich)	+	13.2%	Lezgins (Behar)	@	1.76
    5	 	65.1%	Lodi (Reich)	+	34.9%	Pathan (HGDP)	@	1.85
    6	 	62.5%	Pakistani (Xing)	+	37.5%	Chenchu (Reich)	@	1.96
    7	 	78.6%	AP_Brahmin (Xing)	+	21.4%	Kashmiri_Pandit (Reich)	@	2
    8	 	72.7%	Lodi (Reich)	+	27.3%	Balochi (HGDP)	@	2.03
    9	 	79.5%	Bhil (Reich)	+	20.5%	Lezgins (Behar)	@	2.04
    10	 	60.1%	Pakistani (Xing)	+	39.9%	Madiga (Reich)	@	2.05
    11	 	55.1%	Lodi (Reich)	+	44.9%	Sindhi (HGDP)	@	2.09
    12	 	73.6%	Lodi (Reich)	+	26.4%	Brahui (HGDP)	@	2.13
    13	 	84.7%	Lodi (Reich)	+	15.3%	Urkarah (Xing)	@	2.19
    14	 	94.8%	AP_Brahmin (Xing)	+	5.2%	Adygei (HGDP)	@	2.19
    15	 	65.1%	Vaish (Reich)	+	34.9%	Velama (Reich)	@	2.23
    16	 	87.8%	Lodi (Reich)	+	12.2%	Georgians (Behar)	@	2.24
    17	 	95.3%	AP_Brahmin (Xing)	+	4.7%	Georgians (Behar)	@	2.27
    18	 	57.5%	Kashmiri_Pandit (Reich)	+	42.5%	Madiga (Reich)	@	2.28
    19	 	76.8%	Srivastava (Reich)	+	23.2%	Sindhi (HGDP)	@	2.32
    20	 	61.7%	Pakistani (Xing)	+	38.3%	AP_Mala (Xing)	@	2.33
    What’s behind med component?

  2. #12
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    V5 to V3 conversion

    Admix Results (sorted):

    # Population Percent
    1 South_Asian 55.85
    2 West_Asian 13.03
    3 Mediterranean 6.31
    4 Southeast_Asian 6.31
    5 West_European 6.27
    6 Southwest_Asian 2.91
    7 East_European 2.84
    8 East_African 2.77
    9 Northeast_Asian 2.2
    10 Palaeo_African 0.77
    11 Northwest_African 0.74

    Single Population Sharing:

    # Population (source) Distance
    1 TN_Brahmin (Xing) 4.56
    2 AP_Brahmin (Xing) 4.7
    3 Meghawal (Reich) 5.4
    4 Cochin_Jews (Behar) 5.81
    5 Srivastava (Reich) 6.95
    6 Indian (Dodecad) 7.96
    7 Vaish (Reich) 8.07
    8 Velama (Reich) 8.29
    9 Tharu (Reich) 8.44
    10 INS (SGVP) 9.96
    11 Lodi (Reich) 9.98
    12 Hallaki (Reich) 11.83
    13 Naidu (Reich) 12.19
    14 GIH (HapMap) 13.8
    15 Sindhi (HGDP) 14.12
    16 Pakistani (Xing) 14.45
    17 Kamsali (Reich) 15.25
    18 Vysya (Reich) 15.38
    19 Bhil (Reich) 15.94
    20 Kashmiri_Pandit (Reich) 16.21

    Mixed Mode Population Sharing:

    # Primary Population (source) Secondary Population (source) Distance
    1 89.8% Velama (Reich) + 10.2% N._European (Xing) @ 2.82
    2 90.1% Velama (Reich) + 9.9% Orkney (1000 Genomes) @ 2.87
    3 89.9% Velama (Reich) + 10.1% CEU (HapMap) @ 2.87
    4 90.1% Velama (Reich) + 9.9% Orcadian (HGDP) @ 2.88
    5 90% Velama (Reich) + 10% Argyll (1000 Genomes) @ 2.9
    6 90.4% Velama (Reich) + 9.6% Dutch (Dodecad) @ 2.91
    7 90.3% Velama (Reich) + 9.7% Mixed_Germanic (Dodecad) @ 2.92
    8 90.6% Velama (Reich) + 9.4% Kent (1000 Genomes) @ 2.94
    9 90.7% Velama (Reich) + 9.3% British_Isles (Dodecad) @ 2.96
    10 90.7% Velama (Reich) + 9.3% British (Dodecad) @ 2.96
    11 89.9% Velama (Reich) + 10.1% German (Dodecad) @ 2.98
    12 90.7% Velama (Reich) + 9.3% Cornwall (1000 Genomes) @ 2.98
    13 90.1% Velama (Reich) + 9.9% French (HGDP) @ 2.99
    14 90.9% Velama (Reich) + 9.1% Irish (Dodecad) @ 3.04
    15 90.9% Velama (Reich) + 9.1% Norwegian (Dodecad) @ 3.05
    16 83% Naidu (Reich) + 17% Urkarah (Xing) @ 3.08
    17 52% Sindhi (HGDP) + 48% Kamsali (Reich) @ 3.08
    18 90.8% Velama (Reich) + 9.2% Swedish (Dodecad) @ 3.09
    19 52.2% Mala (Reich) + 47.8% Pathan (HGDP) @ 3.12
    20 57.9% Kamsali (Reich) + 42.1% Pathan (HGDP) @ 3.14

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  4. #13
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    V5 to V3, thanks again misanthropy:

    Admix Results (sorted):

    # Population Percent
    1 South_Asian 55.29
    2 West_Asian 12.61
    3 West_European 8.43
    4 Southeast_Asian 7.11
    5 East_European 6.64
    6 Mediterranean 5.03
    7 Northeast_Asian 2.88
    8 Southwest_Asian 1.17
    9 Neo_African 0.75
    10 Palaeo_African 0.1

    Single Population Sharing:

    # Population (source) Distance
    1 Meghawal (Reich) 3.79
    2 AP_Brahmin (Xing) 3.9
    3 Srivastava (Reich) 3.99
    4 TN_Brahmin (Xing) 4.29
    5 Vaish (Reich) 5.16
    6 Indian (Dodecad) 7.5
    7 Cochin_Jews (Behar) 7.7
    8 Tharu (Reich) 7.97
    9 Lodi (Reich) 9.49
    10 INS (SGVP) 10.3
    11 Velama (Reich) 10.44
    12 Hallaki (Reich) 12.49
    13 Naidu (Reich) 13.72
    14 Pakistani (Xing) 13.74
    15 Sindhi (HGDP) 13.98
    16 GIH (HapMap) 14.04
    17 Kashmiri_Pandit (Reich) 14.97
    18 Satnami (Reich) 15.92
    19 Burusho (HGDP) 16.23
    20 Kamsali (Reich) 16.26

    Mixed Mode Population Sharing:

    # Primary Population (source) Secondary Population (source) Distance
    1 50.5% Chenchu (Reich) + 49.5% Pathan (HGDP) @ 1.73
    2 76.2% Indian (Dodecad) + 23.8% Nepalese (Xing) @ 1.89
    3 58.9% Pakistani (Xing) + 41.1% Chenchu (Reich) @ 1.89
    4 63.5% Lodi (Reich) + 36.5% Burusho (HGDP) @ 2.01
    5 61.6% Lodi (Reich) + 38.4% Kashmiri_Pandit (Reich) @ 2.12
    6 88.2% Indian (Dodecad) + 11.8% Uygur (HGDP) @ 2.18
    7 94% AP_Brahmin (Xing) + 6% Uzbeks (Behar) @ 2.23
    8 68.3% Lodi (Reich) + 31.7% Pathan (HGDP) @ 2.23
    9 95.4% Srivastava (Reich) + 4.6% North_Italian (HGDP) @ 2.26
    10 87.4% Indian (Dodecad) + 12.6% Hazara (HGDP) @ 2.26
    11 94.4% AP_Brahmin (Xing) + 5.6% Uygur (HGDP) @ 2.29
    12 95.6% Srivastava (Reich) + 4.4% Spaniards (Behar) @ 2.31
    13 95.7% Srivastava (Reich) + 4.3% Sardinian (HGDP) @ 2.32
    14 95.3% Srivastava (Reich) + 4.7% Tuscan (HGDP) @ 2.33
    15 71.8% Meghawal (Reich) + 28.2% Tharu (Reich) @ 2.33
    16 95.6% Srivastava (Reich) + 4.4% IBS (1000Genomes) @ 2.34
    17 95.7% Srivastava (Reich) + 4.3% Spanish (Dodecad) @ 2.35
    18 95.9% Srivastava (Reich) + 4.1% French_Basque (HGDP) @ 2.35
    19 94.1% AP_Brahmin (Xing) + 5.9% Hazara (HGDP) @ 2.35
    20 95.4% Srivastava (Reich) + 4.6% N_Italian (Dodecad) @ 2.36

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  6. #14
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    Dad V5 - V3:
    Admix Results (sorted):

    # Population Percent
    1 South_Asian 50.36
    2 West_European 15.39
    3 West_Asian 12.25
    4 Southeast_Asian 6.65
    5 East_European 6.45
    6 Mediterranean 5.09
    7 Northeast_Asian 2.91
    8 Northwest_African 0.45
    9 Palaeo_African 0.31
    10 Neo_African 0.14

    Single Population Sharing:

    # Population (source) Distance
    1 Vaish (Reich) 4.63
    2 Meghawal (Reich) 8.28
    3 AP_Brahmin (Xing) 10.08
    4 Srivastava (Reich) 10.47
    5 TN_Brahmin (Xing) 10.79
    6 Cochin_Jews (Behar) 11.58
    7 Pakistani (Xing) 12.29
    8 Indian (Dodecad) 12.66
    9 Sindhi (HGDP) 13.25
    10 Tharu (Reich) 13.37
    11 Burusho (HGDP) 13.38
    12 Kashmiri_Pandit (Reich) 13.72
    13 Lodi (Reich) 15.43
    14 INS (SGVP) 16.76
    15 Pathan (HGDP) 16.94
    16 Velama (Reich) 17.69
    17 Hallaki (Reich) 19.01
    18 GIH (HapMap) 19.55
    19 Bnei_Menashe_Jews (Behar) 19.74
    20 Naidu (Reich) 19.97

    Mixed Mode Population Sharing:

    # Primary Population (source) Secondary Population (source) Distance
    1 86.8% Srivastava (Reich) + 13.2% Cornwall (1000 Genomes) @ 2.41
    2 87% Srivastava (Reich) + 13% Irish (Dodecad) @ 2.42
    3 86.8% Srivastava (Reich) + 13.2% British (Dodecad) @ 2.47
    4 86.8% Srivastava (Reich) + 13.2% British_Isles (Dodecad) @ 2.5
    5 86.6% Srivastava (Reich) + 13.4% Kent (1000 Genomes) @ 2.53
    6 86.4% Srivastava (Reich) + 13.6% Dutch (Dodecad) @ 2.68
    7 86.3% Srivastava (Reich) + 13.7% Mixed_Germanic (Dodecad) @ 2.8
    8 87.1% Srivastava (Reich) + 12.9% Norwegian (Dodecad) @ 3.1
    9 86.1% Srivastava (Reich) + 13.9% Orkney (1000 Genomes) @ 3.27
    10 95.4% Vaish (Reich) + 4.6% Irish (Dodecad) @ 3.29
    11 86.2% Srivastava (Reich) + 13.8% Orcadian (HGDP) @ 3.29
    12 95.4% Vaish (Reich) + 4.6% Cornwall (1000 Genomes) @ 3.31
    13 95.4% Vaish (Reich) + 4.6% British (Dodecad) @ 3.32
    14 86% Srivastava (Reich) + 14% Argyll (1000 Genomes) @ 3.32
    15 95.4% Vaish (Reich) + 4.6% British_Isles (Dodecad) @ 3.32
    16 95.3% Vaish (Reich) + 4.7% Kent (1000 Genomes) @ 3.33
    17 95.5% Vaish (Reich) + 4.5% Norwegian (Dodecad) @ 3.34
    18 95.3% Vaish (Reich) + 4.7% Dutch (Dodecad) @ 3.37
    19 95.3% Vaish (Reich) + 4.7% Mixed_Germanic (Dodecad) @ 3.39
    20 95.5% Vaish (Reich)

    Anyone know what Vaish is?

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  8. #15
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    These oracles are hilarious. We need to compile a list of kits for each ethnicity and run nmonte on Gedmatch results.
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  10. #16
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    Quote Originally Posted by poi View Post
    These oracles are hilarious. We need to compile a list of kits for each ethnicity and run nmonte on Gedmatch results.
    Shows how much of a game changer the G25 is. G25 is proprietary, right?

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  12. #17
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    Quote Originally Posted by agent_lime View Post
    Shows how much of a game changer the G25 is. G25 is proprietary, right?
    No question about G25's value and combined with nMonte logic, it is definitely letting everybody DIY admixture very fast and close enough assuming good modeling. G25 basically does now what I'm sure Gedmatch/calculators did 10 years(?) ago. I think Gedmatch calcs are obsolete if G25 has the same data. But they are extremely valuable still because G25 does not cover all groups that Gedmatch kits do.

    Unlike those calculators, however, G25 is proprietary and the data is entirely dependent on Davidski/Eurogenes. AFAIK he is doing this all by himself and probably on the side, so grateful for his effort. That being said, it would be great if he expands the ancients to include those Himalayan/Nepali 1000BCE samples... they could easily explain East Asian seen in SwatIA and in modern people in the Himalayan belt (if admixture was ancient enough). Also, it would be great to have more moderns from all parts of the world. I'm sure Davidski has ways to the data given he has all gathered data from all over the place, so it is just an additional effort.

    Basically, G25 is a gamechanger for amateurs/hobbyists but it needs more data. More East Asian (and I'm sure African) would be great. Also, it is missing many other modern South Asian groups -- even groups like Kashmiris, Punjabi Brahmins, and those Himalayan East Asians! (sorry, biased here).
    Last edited by poi; 02-24-2019 at 06:07 AM.
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  14. #18
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    Quote Originally Posted by poi View Post
    No question about G25's value and combined with nMonte logic, it is definitely letting everybody DIY admixture. G25 basically does now what I'm sure Gedmatch/calculators did 10 years(?) ago.

    Unlike those calculators, however, G25 is proprietary and the data is entirely dependent on Davidski/Eurogenes. AFAIK he is doing this all by himself and probably on the side, so grateful for his effort. That being said, it would be great if he expands the ancients to include those Himalayan/Nepali 1000BCE samples... they could easily explain East Asian seen in SwatIA and in modern people in the Himalayan belt (if admixture was ancient enough). Also, it would be great to have more moderns from all parts of the world. I'm sure Davidski has ways to the data given he has all gathered data from all over the place, so it is just an additional effort.

    Basically, G25 is a gamechanger for amateurs/hobbyists but it needs more data. More East Asian (and I'm sure African) would be great. Also, it is missing many other modern South Asian groups -- even groups like Kashmiris, Punjabi Brahmins, and those Himalayan East Asians! (sorry, biased here).
    We are really missing mainstream Indian groups. The expats are heavy on Punjabi, and Southern Indians. Central, NE, Western ones are few and far in between. States with missing data is a long list. Arunachal, Assam, Bihar, Chattisgard, Goa, Jharkhand, Maharashtra, Manipur, Meghalya, Manipur, Nagaland, Odisha, Sikkim, Tripura, Uttar Pradesh, West Bengal, Rajasthan could all use more data.

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  16. #19
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    Admix Results (sorted):

    # Population Percent
    1 South_Asian 39.8
    2 West_Asian 25.55
    3 West_European 10.86
    4 Mediterranean 6.43
    5 East_European 5.99
    6 Northeast_Asian 3.83
    7 Southwest_Asian 3.39
    8 Southeast_Asian 3.38
    9 Neo_African 0.4
    10 East_African 0.37


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

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

    Least-squares method.

    Using 1 population approximation:
    1 Pathan_HGDP @ 3.049776
    2 Kashmiri_Pandit_Reich @ 6.080205
    3 Burusho_HGDP @ 6.177816
    4 Pakistani_Xing @ 6.833589
    5 Sindhi_HGDP @ 7.789811
    6 Balochi_HGDP @ 12.302539
    7 Bnei_Menashe_Jews_Behar @ 12.878052
    8 Brahui_HGDP @ 13.761832
    9 Makrani_HGDP @ 17.302725
    10 Cochin_Jews_Behar @ 18.076128
    11 Meghawal_Reich @ 18.702444
    12 Vaish_Reich @ 18.875360
    13 Kalash_HGDP @ 19.394264
    14 TN_Brahmin_Xing @ 22.206997
    15 Srivastava_Reich @ 23.211794
    16 AP_Brahmin_Xing @ 23.306330
    17 Nepalese_Xing @ 23.511559
    18 Indian_Dodecad @ 25.799536
    19 Velama_Reich @ 27.420168
    20 Tharu_Reich @ 28.324633

    Using 2 populations approximation:
    1 50% Burusho_HGDP +50% Pathan_HGDP @ 2.804291


    Using 3 populations approximation:
    1 50% Burusho_HGDP +25% Makrani_HGDP +25% Pakistani_Xing @ 1.951413


    Using 4 populations approximation:
    ++++++++++++++++++++++++++++++++++++++++++++++++++ ++++++++++++
    1 Balochi_HGDP + Burusho_HGDP + Pathan_HGDP + Pakistani_Xing @ 1.504437
    2 Brahui_HGDP + Burusho_HGDP + Burusho_HGDP + Pakistani_Xing @ 1.589312
    3 Balochi_HGDP + Burusho_HGDP + Burusho_HGDP + Pakistani_Xing @ 1.661774
    4 Brahui_HGDP + Burusho_HGDP + Pathan_HGDP + Pakistani_Xing @ 1.718341
    5 Balochi_HGDP + Burusho_HGDP + Burusho_HGDP + Pathan_HGDP @ 1.753863
    6 Balochi_HGDP + Burusho_HGDP + Pathan_HGDP + Sindhi_HGDP @ 1.795339
    7 Brahui_HGDP + Burusho_HGDP + Burusho_HGDP + Sindhi_HGDP @ 1.803121
    8 Balochi_HGDP + Burusho_HGDP + Burusho_HGDP + Sindhi_HGDP @ 1.823079
    9 Pathan_HGDP + Urkarah_Xing + Meghawal_Reich + Velama_Reich @ 1.908859
    10 Brahui_HGDP + Burusho_HGDP + Burusho_HGDP + Pathan_HGDP @ 1.923505
    11 Burusho_HGDP + Burusho_HGDP + Makrani_HGDP + Pakistani_Xing @ 1.951413
    12 Balochi_HGDP + Burusho_HGDP + Pathan_HGDP + Kashmiri_Pandit_Reich @ 2.008041
    13 Brahui_HGDP + Burusho_HGDP + Pathan_HGDP + Sindhi_HGDP @ 2.026918
    14 Kalash_HGDP + AP_Madiga_Xing + Pakistani_Xing + Stalskoe_Xing @ 2.118249
    15 Burusho_HGDP + Pathan_HGDP + Pathan_HGDP + Pathan_HGDP @ 2.144356
    16 Balochi_HGDP + Burusho_HGDP + North_Kannadi_Behar + Urkarah_Xing @ 2.149216
    17 Brahui_HGDP + Burusho_HGDP + Pathan_HGDP + Kashmiri_Pandit_Reich @ 2.173323
    18 Kalash_HGDP + Sindhi_HGDP + AP_Madiga_Xing + Stalskoe_Xing @ 2.193473
    19 Balochi_HGDP + Urkarah_Xing + Hallaki_Reich + Meghawal_Reich @ 2.203296
    20 Balochi_HGDP + INS_SGVP + Urkarah_Xing + Meghawal_Reich @ 2.209928

    Done.
    Last edited by khanabadoshi; 02-24-2019 at 06:31 AM.
    “Chahar chez est tohfay Multan, Gard-o- Garma, Gada-o- Goristan”.

    Four things are the gift of Multan: Dusty winds, hot seasons, beggars and graveyards.




  17. The Following 5 Users Say Thank You to khanabadoshi For This Useful Post:

     26284729292 (02-24-2019),  agent_lime (02-24-2019),  Jatt1 (02-24-2019),  laltota (02-24-2019),  poi (02-24-2019)

  18. #20
    Registered Users
    Posts
    966
    Ethnicity
    Brahmin (mixed)
    Nationality
    Indian
    Y-DNA (P)
    R-1A (Z-93)
    mtDNA (M)
    M-30

    Quote Originally Posted by agent_lime View Post
    We are really missing mainstream Indian groups. The expats are heavy on Punjabi, and Southern Indians. Central, NE, Western ones are few and far in between. States with missing data is a long list. Arunachal, Assam, Bihar, Chattisgard, Goa, Jharkhand, Maharashtra, Manipur, Meghalya, Manipur, Nagaland, Odisha, Sikkim, Tripura, Uttar Pradesh, West Bengal, Rajasthan could all use more data.
    Not to mention a significant forward caste bias, even in the south/central states we do have. This is even true for the bengal/bihar/UP samples we have.

  19. The Following User Says Thank You to 26284729292 For This Useful Post:

     agent_lime (02-24-2019)

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