Gedmatch calculators on Vahaduo

[Ajeje Brazorf]

Some "wise men" on Anthrogenica converted to G25 patients from Bari hospital from this study https://www.nature.com/articles/ejhg2014208
There is no single proof about their local ancestry only and G25 shows it clearly. So please don't post this bullshit coords and ask me to add them

This is wide spectrum of South and Central Italy simply, not Apulia only.

The average of all samples (except 4 that are probably Peruvian) is virtually identical to the old one, thus confirming that they are all Apulian. As far as I'm concerned they could be 100% local, it's normal in a group to have individuals plotting further south and others further north. Also, there are no Central Italians living in Apulia. On the contrary, thousands of Apulians are living in central Italy.

Italian_Apulia_old

Distance: 1.8664% / 0.01866449
45.4	TUR_Barcin_N
23.2	Yamnaya_RUS_Samara
17.2	Kura-Araxes_ARM_Kaps
11.0	Levant_PPNB
1.6	WHG
1.0	IRN_Ganj_Dareh_N
0.6	MAR_Taforalt

Italian_Apulia_new

Distance: 1.7427% / 0.01742725
46.0	TUR_Barcin_N
25.0	Yamnaya_RUS_Samara
14.4	Kura-Araxes_ARM_Kaps
10.8	Levant_PPNB
2.0	IRN_Ganj_Dareh_N
1.4	WHG
0.4	MAR_Taforalt

[Kaazi]

@Lucas,

Here are new Punjabi Hindu India, Punjabi Christian India and Punjabi Muslim India G25.
https://drive.google.com/file/d/1Xkq...ew?usp=sharing


Additional new Bengali Bangladesh (BEB) from 1000 Genomes are here.
https://drive.google.com/file/d/1TAM...ew?usp=sharing

You can run the code below here: https://rdrr.io/snippets/.

t=read.csv(header=F,row.names=1,text="Tajik_Ayni:TJ-0080,0.087644,0.014217,-0.036204,0.017765,-0.030467,0.01757,0.00517,0.000923,-0.014112,-0.008201,-0.007632,0.01109,-0.002676,-0.010735,0.009365,0.000398,-0.014342,-0.00114,-0.001131,-0.003126,-0.00025,-0.006554,-0.00419,-0.004579,0.003113
Tajik_Ayni:TJ-0086,0.087644,0.019295,-0.033187,0.015827,-0.042777,0.013666,0.011751,-0.002538,-0.02986,-0.016219,-0.012179,0.002698,-0.006541,-0.012248,0.012079,0.007823,0.000391,0.00076,-0.00176,-0.002501,-0.007986,-0.001855,-0.000616,0.004097,0.00491
Tajik_Ayni:TJ-0091,0.088782,0.019295,-0.032055,0.013243,-0.044316,0.014781,0.00517,0.000231,-0.027611,-0.016401,-0.007145,-0.008243,0.002676,-0.000963,0.014794,0.004773,-0.016559,-0.003674,-0.002765,-0.01063,-0.00025,0.001237,-0.001849,-0.006989,0.005628
Tajik_Ayni:TJ-0495,0.095611,-0.014217,-0.027907,0.018734,-0.044316,0.027889,0.011045,-0.007384,-0.024952,-0.022051,-0.010393,-0.000749,0.00446,-0.015827,0.012351,0.008618,0.00339,-0.008488,-0.001885,-0.012381,-0.009982,-0.00507,0.004314,0.001446,0.003592
Tajik_Ayni:TJ-0796,0.093335,0.004062,-0.037712,0.01615,-0.041238,0.016176,0.011045,0.000462,-0.026793,-0.017495,-0.019487,0.003147,-0.007136,-0.003716,0.013165,0.013126,-0.01004,0.001394,0.005531,-0.004627,-0.003868,-0.006554,0.011216,0.010965,0.005868
Tajik_Ayni:TJ-0798,0.086506,0.016248,-0.031301,0.020995,-0.044008,0.018407,0.011986,-0.000231,-0.020452,-0.029522,-0.001949,0.003897,0.010555,-0.015551,0.013843,0.011403,-0.001695,0.003167,0.005154,-0.008129,-0.005989,-0.00915,0.002958,-0.001205,0.003712
Tajik_Ayni:TJ-0807,0.091058,0.002031,-0.029038,0.022933,-0.030775,0.006972,0.00094,0.002538,-0.019839,-0.021322,-0.005196,0.002398,0.006838,-0.010046,0.013436,0.012596,-0.007171,0.00266,0.001257,-0.005002,-0.003619,0.002597,0.008751,0.008073,0.006706
Tajik_Ayni:TJ-0982,0.095611,0.007109,-0.010559,0.018088,-0.048317,0.011156,0.005875,0.002077,-0.031088,-0.018406,-0.012017,0.005095,-0.000892,-0.003303,0.009229,0.005569,-0.01682,-0.004941,-0.002514,-0.015632,-0.01435,-0.004081,0.003944,-0.000602,0.005628
Tajik_Ayni:TJ-0984,0.095611,0.003047,-0.018102,0.009044,-0.032314,0.014223,0.004935,-0.006461,-0.030883,-0.022962,-0.01153,0.001948,0.002379,-0.009771,0.011808,0.016971,-0.007171,-0.000633,0.002263,-0.012006,-0.004367,-0.002226,-0.001849,0.007471,0.004311
Tajik_Ayni:TJ-0986,0.099026,0.001016,-0.026776,0.02584,-0.042469,0.014502,0.00752,0.000692,-0.026179,-0.025149,-0.003573,0.003147,0.006095,-0.00234,0.000679,0.001989,-0.030249,-0.001647,0.004399,-0.014632,-0.009982,-0.009027,-0.001479,-0.011447,0.006347
Tajik_Ayni:TJ-1032,0.087644,0.015233,-0.029793,0.020026,-0.042777,0.010877,0.00752,-0.002077,-0.036814,-0.021139,-0.004872,-0.002248,-0.001338,-0.002202,0.007736,0.005171,-0.015255,-0.002914,0.002263,-0.004252,-0.010232,-0.004822,0.000739,-0.001084,0.010538
Tajik_Ayni:TJ-1041,0.08992,0.025388,-0.036581,0.020672,-0.045239,0.008088,-0.00141,0.003923,-0.026793,-0.022597,-0.009094,-0.003597,-0.006244,-0.008257,0.017101,0.008618,-0.002217,0.008488,-0.000126,-0.006378,-0.007986,0.000866,-0.002342,0.007591,0.000359
Tajik_Hisor:TJ-0484,0.088782,0.01828,-0.034318,0.020026,-0.040315,0.015339,0.00188,0.003231,-0.027202,-0.027335,-0.002111,0.004346,-0.00446,-0.009083,0.009636,0.008884,-0.002608,-0.003041,0.002263,-0.015632,-0.008859,0.001484,0.005053,-0.00253,0.001197
Tajik_Hisor:TJ-0486,0.088782,0.014217,-0.026021,0.017442,-0.046162,0.008925,0.001175,0,-0.024338,-0.022962,-0.011692,0.000599,0.001487,-0.010597,0.012893,0.020154,0.012386,0.00114,0.00088,0.003001,-0.00836,-0.000989,-0.005176,-0.00012,0.001916
Tajik_Hisor:TJ-0686,0.092197,-0.022342,-0.01697,0.020995,-0.042777,0.021196,0.009165,0.004615,-0.030474,-0.016583,-0.001137,0.004046,-0.005054,-0.00578,0.012758,0.009414,-0.002477,0.000127,0.001508,-0.011005,-0.006738,0.002226,0.004314,0.005302,-0.002155
Tajik_Hisor:TJ-0688,0.095611,0.01828,-0.020742,0.026486,-0.045239,0.019801,0.006345,-0.006923,-0.022907,-0.026606,-0.004384,-0.001049,0.001784,-0.010046,0.011265,0.018032,-0.001434,0.002027,0.00176,-0.011506,-0.008735,-0.001113,0.006286,0.004579,0.003592
Tajik_Kulob:TJ-0108,0.100164,0.051792,-0.021496,0.029716,-0.044008,0.031515,-0.000705,0.004154,-0.030474,-0.032074,-0.009256,0.001649,-0.000149,-0.011423,0.012758,0.020154,0.015255,-0.005321,-0.00176,-0.011756,-0.007112,-0.013107,0.003451,0.000964,-0.00479
Tajik_Kulob:TJ-0155,0.091058,0.003047,-0.030924,0.013889,-0.05201,0.01004,0.001175,0.003923,-0.03027,-0.026971,-0.010393,-0.005095,-0.006095,-0.004542,0.017372,0.00305,-0.000261,0.008108,0.004022,-0.009379,-0.000374,-0.00272,-0.005423,0,0.004071
Tajik_Kulob:TJ-0500,0.092197,0.014217,-0.049026,0.02907,-0.056318,0.014781,0.00611,-0.002538,-0.035587,-0.021139,-0.000325,-0.004946,-0.003865,-0.012386,0.008958,0.007955,-0.012256,0.000507,0.002765,-0.004377,-0.0141,0.005317,0.003697,0,0.004311
Tajik_Kulob:TJ-0517,0.100164,0.023357,-0.027153,0.028747,-0.041546,0.022032,-0.00141,-0.005769,-0.035383,-0.027518,-0.00682,-0.004646,-0.004311,-0.00867,0.018865,0.018695,-0.001043,0.000253,0.005531,-0.011255,-0.01697,0.000989,-0.000246,0.002771,-0.000239
Tajik_Kulob:TJ-0555,0.099026,0.030466,-0.037712,0.02261,-0.051702,0.01757,0.00611,0.000231,-0.037837,-0.026971,-0.011042,0.004946,0.002081,-0.004404,0.001086,-0.002519,-0.015255,0.00152,0.002514,-0.006753,-0.000125,-0.005812,-0.002095,0.005422,0.011137
Tajik_Kulob:TJ-0573,0.09675,0.017264,-0.038089,0.026163,-0.049548,0.0251,0.000705,-0.009461,-0.028633,-0.021868,-0.002923,-0.004496,0.001487,-0.008533,0.011265,0.013524,0.010952,0.001647,0.003645,-0.001626,-0.005615,0.001607,0.0053,0.010363,-0.008502
Tajik_Kulob:TJ-0574,0.093335,0.023357,-0.034318,0.026486,-0.04924,0.023985,0,0.001615,-0.026997,-0.018588,-0.007795,-0.004346,-0.008176,-0.006331,0.011401,0.02612,0.003129,-0.0019,-0.008296,-0.00988,0.000624,-0.000495,-0.003328,0.000241,0.007065")
t2=aggregate(t,list(sub(":.*","",rownames(t))),mean)
t3=data.frame(apply(t(t2[,-1]),1,sprintf,fmt="%.6f"),row.names=t2$Group.1)
write.table(t3,"",quote=F,col.names=F,sep=",")

Wow, Great work. Is this okay or need to modify??

Scaled individuals

t=read.csv(header=F,row.names=1,text="Punjabi_Christian_India:7854,0.042115,-0.075149,-0.168196,0.115635,-0.072937,0.059961,-0.00094,0.010615,0.044586,0.026424,-0.003248,0.005695,0,0.006881,-0.004886,-0.002652,-0.007562,0.000507,0.00176,-0.005503,0.008485,0.000989,0.004683,-0.009399,0.000239
Punjabi_Christian_India:33021,0.036423,-0.108662,-0.180264,0.128878,-0.068013,0.06526,0.00047,0.009,0.060948,0.040821,-0.008282,-0.000749,0.00223,-0.004679,-0.01045,-0.015911,0.000522,-0.003421,-0.00352,0.002876,0.003868,0.005193,0.000986,0.003615,-0.008263
Punjabi_Hindu_India:8677,0.071709,-0.015233,-0.11917,0.091732,-0.072013,0.054384,0.001175,0.009692,0.012271,0.000547,-0.001949,0.003747,0.00223,-0.010184,0.011943,0.005171,-0.003129,-0.0019,0.007416,-0.007754,0.001123,-0.006059,0.00419,0.004217,-0.005269
Punjabi_Hindu_India:11201,0.050082,-0.059916,-0.162539,0.109821,-0.08063,0.052153,-0.000235,0.00923,0.033951,0.018224,-0.008607,-0.00015,-0.002379,-0.002064,-0.003393,0.009812,0.005346,0.00114,-0.000251,0.003627,0.003494,0.000618,-0.002835,-0.000964,0.001317
Punjabi_Hindu_India:19752,0.071709,-0.002031,-0.113513,0.084949,-0.072629,0.053547,-0.001645,0.006692,0.005727,-0.001822,-0.009256,0.004496,0.000595,-0.00289,0.003936,0.001061,-0.001434,0.006081,0.001885,-0.009254,-0.005366,-0.008903,0.008504,0.002048,-0.005748
Punjabi_Hindu_India:20668,0.079676,0.008124,-0.104462,0.07106,-0.060934,0.043507,0.00141,0.006461,-0.009817,-0.005467,0.001299,0.000749,-0.001041,0.001239,0.011808,0.006364,-0.001956,-0.0019,-0.001508,-0.010755,-0.008111,-0.010016,-0.00037,-0.000964,0.00012
Punjabi_Hindu_India:21049,0.068294,-0.001016,-0.110119,0.086564,-0.074168,0.049085,0,0.003923,-0.00225,-0.004374,-0.00682,-0.00045,0.005352,-0.006331,0.005565,0.019623,0.008475,0.005068,-0.00176,-0.01088,-0.003993,-0.013849,-0.001849,0.00012,-0.009101
Punjabi_Hindu_India:21055,0.067156,-0.004062,-0.111251,0.079458,-0.074475,0.049643,0.005875,0.007846,-0.001023,-0.004738,-0.00406,-0.002098,0.003865,0.000138,0.0019,0.004773,-0.007562,0.000127,0.000503,-0.015007,0.002246,-0.004451,0.00912,0.002289,0.004191
Punjabi_Hindu_India:21080,0.078538,-0.00914,-0.109365,0.086241,-0.067089,0.050758,0.00188,0.002538,-0.000614,0.002734,-0.006658,0.001349,-0.003568,-0.008945,0.012079,0.003182,-0.002868,0.002787,0.001885,-0.013757,-0.00287,0.002349,0.006039,-0.003012,0.000479
Punjabi_Hindu_India:22595,0.076261,-0.008124,-0.109742,0.082688,-0.066474,0.03765,0.003055,0.007154,0.002863,-0.007289,-0.004872,0.002548,-0.000149,-0.01156,0.009229,0.008751,0.006389,-0.00266,-0.006662,-0.012006,-0.002496,0.000124,0.004683,-0.003253,-0.008143
Punjabi_Hindu_India:22626,0.067156,-0.015233,-0.108988,0.080104,-0.065858,0.046296,0.001645,0.006461,0.007772,-0.01221,-0.007957,-0.002248,0.002825,-0.009634,0.009908,0.008221,-0.008475,-0.001014,0.000251,-0.005378,0.001872,0.002844,0.007395,0.001446,-0.001197
Punjabi_Hindu_India:22664,0.072847,-0.004062,-0.104462,0.072675,-0.069244,0.052431,-0.001175,0.004384,-0.008999,-0.004374,-0.012179,-0.001349,0.005352,-0.014863,0.010586,0.006497,-0.012647,0.001647,-0.000126,-0.008504,-0.007237,-0.006306,0.002465,-0.002771,-0.000718
Punjabi_Hindu_India:22730,0.067156,-0.006093,-0.113513,0.07429,-0.073552,0.052153,-0.00047,0.006923,0.009613,-0.001276,-0.007632,0.00015,-0.000743,-0.004266,0.009772,0.001591,-0.01682,0.003421,-0.002891,-0.010005,0.007237,0.002597,0.004807,-0.002892,0.008981
Punjabi_Hindu_India:22761,0.0774,-0.011171,-0.11653,0.089471,-0.065243,0.052989,-0.002585,0.006923,-0.004909,-0.004374,-0.008931,0.001948,0.001041,-0.009634,0.009772,0.005701,0.000261,-0.007348,-0.007039,-0.012881,-0.003369,0.003215,0.005176,0.000723,-0.007903
Punjabi_Hindu_India:23665,0.072847,-0.012186,-0.112005,0.072675,-0.069859,0.046575,0.004465,0.007846,0.002863,-0.000547,-0.008931,0.000899,-0.004608,-0.008808,0.004072,0.020419,0.008996,0.00266,-0.00088,-0.009129,-0.001497,-0.012736,0.000123,0.003735,0.002515
Punjabi_Hindu_India:23815,0.063741,-0.032497,-0.121056,0.091732,-0.066166,0.0502,-0.00047,-0.000692,0.015135,0.01057,-0.00406,0.001798,-0.001189,0.004679,0.00285,-0.010607,-0.012778,0.001267,-0.005279,-0.007504,-0.003119,0.004204,0.008504,0.002771,0.000838
Punjabi_Hindu_India:23816,0.076261,-0.017264,-0.116907,0.086887,-0.067705,0.047133,0.009165,0.006461,0.005522,0.003462,-0.015265,-0.000899,0.002973,-0.000826,0.008279,0.012198,-0.007823,-0.004434,0.005782,-0.005503,-0.000873,-0.00507,-0.004067,-0.001928,-0.001676
Punjabi_Hindu_India:23896,0.066017,-0.014217,-0.105217,0.08721,-0.07109,0.040439,0.00376,0.005769,-0.001023,-0.008201,0.000487,-0.003897,0.000743,-0.013212,0.007736,0.015646,0.012778,-0.010642,-0.003268,-0.005753,0.00025,-0.004328,-0.001602,0.001325,0.000958
Punjabi_Hindu_India:24211,0.069432,-0.011171,-0.103708,0.081719,-0.070167,0.046017,0.003995,0.007846,0.001227,-0.005649,-0.010393,-0.000899,-0.001338,-0.003028,0.001221,-0.003713,-0.00339,0.003041,0.008296,-0.007253,-0.000873,0.00136,0.000123,0.000843,-0.000359
Punjabi_Hindu_India:25529,0.069432,-0.014217,-0.098428,0.082365,-0.069859,0.051874,0.003525,-0.001615,0.005318,-0.005832,0.001624,0,-0.000743,-0.012799,0.012215,0.016309,0.006258,-0.003421,-0.003394,5e-04,-0.010482,-0.005317,0.007765,0.001928,-0.001317
Punjabi_Hindu_India:26216,0.068294,-0.012186,-0.117285,0.086564,-0.067397,0.047969,0.003995,0.010384,0.000205,-0.006378,-0.009256,0.001948,-0.000743,-0.009496,0.010993,0.018828,0.007953,-0.001014,0.00176,-0.000125,0.000374,-0.007296,0.003204,0.006386,-0.007784
Punjabi_Hindu_India:26448,0.073985,-0.016248,-0.114645,0.084949,-0.064012,0.05773,0.00188,0.006923,0.003886,-0.001276,-0.002598,-0.00045,-0.004608,-0.00867,0.010043,0.003845,-0.003912,-0.000127,0.001885,-0.007379,0.000998,-0.005441,0.001479,0.000602,-0.001197
Punjabi_Hindu_India:26564,0.066017,-0.008124,-0.105971,0.084949,-0.064935,0.050479,-0.00423,0.001154,-0.006954,-0.004374,-0.017213,-0.00015,-0.002973,-0.001101,0.008415,-0.006629,-0.007041,0.000507,0.000628,-0.009004,-0.005989,-0.010387,0.002465,-0.003615,0.001796
Punjabi_Hindu_India:26899,0.076261,-0.01828,-0.101445,0.091409,-0.069859,0.053268,-0.00282,0.006231,0.011044,-0.000547,-0.006333,0.004946,-0.000297,-0.003716,0.019544,0.001724,-0.003129,-0.005701,-0.000503,0.00075,0.004243,0.008408,0.000986,-0.004579,0.006466
Punjabi_Hindu_India:26918,0.072847,-0.014217,-0.107857,0.087856,-0.070475,0.047133,0.004465,0.009923,0.003477,-0.005103,-0.006496,-0.000749,0.002527,-0.006744,0.003664,0.005038,-0.002347,0.007348,0.002891,-0.010005,-0.000624,-0.006554,0.005176,0.000843,-0.004311
Punjabi_Hindu_India:26966,0.0774,-0.011171,-0.105217,0.077843,-0.063704,0.054105,0.00047,0.002077,0.002863,-0.003827,-0.008119,-0.001948,0.00223,-0.012524,0.007736,0.01485,0.009648,0.000507,-0.010433,-0.003627,0.00262,-0.004081,0.00419,0.003735,-0.001197
Punjabi_Hindu_India:26992,0.07057,-0.012186,-0.09692,0.079458,-0.073552,0.042949,-0.00141,0.001615,0.006954,-0.00656,0.000812,0.003597,-0.002527,-0.013762,0.005157,0.009546,0.007041,0.002407,-0.00176,-0.004877,-0.007112,-0.001607,0.003697,0.011929,0.000479
Punjabi_Hindu_India:27269,0.076261,-0.00914,-0.106725,0.081073,-0.072937,0.046017,0.002115,0.002538,0.00225,-0.003098,-0.005684,-0.001649,-0.002676,-0.006468,0.00475,0.006497,-0.00326,0.000127,0.000754,-0.006753,-0.005989,0.005441,0.002835,-0.001205,0.002754
Punjabi_Hindu_India:27578,0.078538,0.012186,-0.101445,0.075582,-0.070475,0.036535,0.003995,-0.002538,-0.006545,-0.01385,-0.007632,-0.004196,0.000446,-0.009083,0.008686,0.017634,-0.01017,-0.001394,-0.002765,-0.008504,-0.001747,-0.005317,0.005793,-0.003735,0.008981
Punjabi_Muslim_India:22413,0.063741,-0.016248,-0.12181,0.063308,-0.061242,0.04016,-0.003525,0.003692,0.022089,0.020046,-0.004547,0.004046,-0.003568,0.001651,-0.001493,0.00305,0.004433,0.002914,-0.002891,0.004127,-0.000374,-0.000371,0.001972,-0.000964,0.004311
Punjabi_Muslim_India:23026,0.066017,-0.011171,-0.113513,0.074936,-0.065858,0.049643,0,0.005538,-0.005113,0.001276,-0.007957,0.005095,-0.00446,-0.005643,0.011265,0.00769,0.002999,0.003041,0.005154,-0.011506,0.000125,-0.007914,0.005793,-0.001566,-0.00479")
t2=aggregate(t,list(sub(":.*","",rownames(t))),mean)
t3=data.frame(apply(t(t2[,-1]),1,sprintf,fmt="%.6f"),row.names=t2$Group.1)
write.table(t3,"",quote=F,col.names=F,sep=",")

Average scaled

Punjabi_Christian_India,0.039269,-0.091906,-0.174230,0.122256,-0.070475,0.062610,-0.000235,0.009808,0.052767,0.033622,-0.005765,0.002473,0.001115,0.001101,-0.007668,-0.009282,-0.003520,-0.001457,-0.000880,-0.001313,0.006176,0.003091,0.002834,-0.002892,-0.004012
Punjabi_Hindu_India,0.071329,-0.011810,-0.111055,0.083753,-0.069426,0.048630,0.001549,0.005265,0.003363,-0.002801,-0.006544,0.000261,0.000022,-0.006835,0.007721,0.007494,-0.001318,-0.000127,-0.000540,-0.007286,-0.001751,-0.003206,0.003259,0.000593,-0.000594
Punjabi_Muslim_India,0.064879,-0.013709,-0.117662,0.069122,-0.063550,0.044901,-0.001762,0.004615,0.008488,0.010661,-0.006252,0.004570,-0.004014,-0.001996,0.004886,0.005370,0.003716,0.002978,0.001131,-0.003690,-0.000124,-0.004142,0.003883,-0.001265,-0.000240

Average unscaled

Punjabi_Christian_India,0.003450,-0.009050,-0.046200,0.037850,-0.022900,0.022450,-0.000100,0.004250,0.025800,0.018450,-0.003550,0.001650,0.000750,0.000800,-0.005650,-0.007000,-0.002700,-0.001150,-0.000700,-0.001050,0.004950,0.002500,0.002300,-0.002400,-0.003350
Punjabi_Hindu_India,0.006267,-0.001163,-0.029448,0.025930,-0.022559,0.017437,0.000659,0.002281,0.001644,-0.001537,-0.004030,0.000174,0.000015,-0.004967,0.005689,0.005652,-0.001011,-0.000100,-0.000430,-0.005826,-0.001404,-0.002593,0.002644,0.000493,-0.000496
Punjabi_Muslim_India,0.005700,-0.001350,-0.031200,0.021400,-0.020650,0.016100,-0.000750,0.002000,0.004150,0.005850,-0.003850,0.003050,-0.002700,-0.001450,0.003600,0.004050,0.002850,0.002350,0.000900,-0.002950,-0.000100,-0.003350,0.003150,-0.001050,-0.000200

[Komintasavalta]

Wow, Great work. Is this okay or need to modify??

Looks right. Another way is to run this shell command:

sed 's/:[^,]*//' g25individuals.csv|awk -F, '{n[$1]++;for(i=2;i<=NF;i++){a[$1,i]+=$i}}END{for(i in n){o=i;for(j=2;j<=NF;j++)o=o FS sprintf("%.6f",a[i,j]/n[i]);print o}}'

[Leto]

The Punjabi Hindu average is almost a quarter Andronovo (Aktogai)

Target: Punjabi_Hindu_India
Distance: 1.9099% / 0.01909899
71.4 IRN_Shahr_I_Sokhta_BA2
23.6 KAZ_Aktogai_MLBA
3.2 MNG_Ulaanzuukh_Slab_Grave
1.2 TUR_Kaman-Kalehoyuk_MLBA
0.6 Levant_Megiddo_MLBA

I wonder if that's also the case for Bobby Jindal, an Indian-American and a former governor of Louisiana (2008-2016), born in the U.S. to Punjabi Hindu parents.

[Kaazi]

Looks right. Another way is to run this shell command:

sed 's/:[^,]*//' g25individuals.csv|awk -F, '{n[$1]++;for(i=2;i<=NF;i++){a[$1,i]+=$i}}END{for(i in n){o=i;for(j=2;j<=NF;j++)o=o FS sprintf("%.6f",a[i,j]/n[i]);print o}}'

Can you share the similar R code for a single group of samples to run on that easy site?? Sometime, we only need to average one group.

https://rdrr.io/snippets/.

[Komintasavalta]

Can you share the similar R code for a single group of samples to run on that easy site?? Sometime, we only need to average one group.

https://rdrr.io/snippets/.

That's easier:

t=read.csv(header=F,row.names=1,text="Moksha:MOE-014,0.133173,0.075149,0.068636,0.063954,0.021235,0.017012,0.00799,0.011999,-0.001227,-0.02442,-0.000325,-0.006744,0.019475,0.012937,-0.015472,-0.001193,0.008345,-0.008868,-0.00352,-0.012631,-0.009483,-0.002473,-0.002958,-0.003494,0.004431
Erzya:MOE-001,0.125205,0.080227,0.077687,0.060724,0.028313,0.021753,0.011751,0.014307,-0.005318,-0.028611,0.00406,-0.007793,0.023042,0.01734,-0.015336,0.001591,0.004042,0.00114,0.000628,-0.005753,-0.004243,-0.011871,-0.001849,-0.01205,0.003952")
paste(sprintf("%.6f",colMeans(t)),collapse=",")

You can also download R here and then paste the code to the R console application: https://cran.r-project.org.



Or in a shell:

awk -F, '{for(i=2;i<=NF;i++)a[i]+=$i}END{for(i in a)printf"%.6f"(i==NF?"\n":FS),a[i]/NR}' g25individuals.csv

[Kaazi]

The Punjabi Hindu average is almost a quarter Andronovo (Aktogai)

Target: Punjabi_Hindu_India
Distance: 1.9099% / 0.01909899
71.4 IRN_Shahr_I_Sokhta_BA2
23.6 KAZ_Aktogai_MLBA
3.2 MNG_Ulaanzuukh_Slab_Grave
1.2 TUR_Kaman-Kalehoyuk_MLBA
0.6 Levant_Megiddo_MLBA

I wonder if that's also the case for Bobby Jindal, an Indian-American and a former governor of Louisiana (2008-2016), born in the U.S. to Punjabi Hindu parents.

Guys on AG told that Bobby Jindal belongs to Baniya caste which looks like this one Hindu sample 11201.

IRN_Shahr_I_Sokhta_BA3,0.033009,-0.081242,-0.180641,0.120157,-0.097557,0.065539,0.0047,0.009461,0.035996,0.026789,-0.002273,0.003747,-0.005649,0.007982,0.003529,0.003182,0.008736,0.001647,0.000126,-0.002001,0.001373,-0.008161,0.000246,-0.001928,-0.008023

Target: Punjabi_Hindu_India:11201
Distance: 2.5776% / 0.02577635
85.6 IRN_Shahr_I_Sokhta_BA3
8.8 RUS_Srubnaya_Alakul_MLBA
4.2 UZB_Dzharkutan1_BA
1.4 NPL_Chokhopani_2700BP


Target: Punjabi_Hindu_India
Distance: 1.3561% / 0.01356054
53.4 IRN_Shahr_I_Sokhta_BA3
23.0 RUS_Srubnaya_Alakul_MLBA
21.4 UZB_Dzharkutan1_BA
2.2 NPL_Chokhopani_2700BP

The Hindu average is the most representative of two Punjabi Hindu castes: Brahmins and Tarkhans. So, they are more Steppe than other Punjabis (non Jatt) and can score upto 29% Steppe like this sample.

Target: Punjabi_Hindu_India:26899
Distance: 2.6991% / 0.02699138
56.4 IRN_Shahr_I_Sokhta_BA3
28.8 RUS_Srubnaya_Alakul_MLBA
12.2 UZB_Dzharkutan1_BA
2.6 NPL_Chokhopani_2700BP

[Leto]

Can anyone here obtain G25 coordinates for some of the sample from Yunusbayev?
https://evolbio.ut.ee/caucasus/
I'm interested in the Tajiks (n=15), would like to compare them to those from Jeong.

[Komintasavalta]

Can anyone here obtain G25 coordinates for some of the sample from Yunusbayev?
https://evolbio.ut.ee/caucasus/
I'm interested in the Tajiks (n=15), would like to compare them to those from Jeong.

I did a SmartPCA run where I compared the samples from that paper against samples from the Human Origins dataset.

When the same sample was included in both sets, I omitted the sample from HO. The Nogai samples in that paper are the same as the samples labeled Nogai_Karachay_Cherkessia in HO, so I omitted all the samples from HO.

The Tajik samples from that paper look a bit more western than the Tajik samples in HO. Out of the samples labeled Tajik in HO, 23 are from Jeong 2019 and 8 are from Lazaridis 2014.

The Turkmen samples from that paper are a lot more western than the Turkmen samples in HO. No Turkmen sample was included in both sets.

[Leto]

I did a SmartPCA run where I compared the samples from that paper against samples from the Human Origins dataset.

When the same sample was included in both sets, I omitted the sample from HO. The Nogai samples in that paper are the same as the samples labeled Nogai_Karachay_Cherkessia in HO, so I omitted all the samples from HO.

The Tajik samples from that paper look a bit more western than the Tajik samples in HO. Out of the samples labeled Tajik in HO, 23 are from Jeong 2019 and 8 are from Lazaridis 2014.

The Turkmen samples from that paper are a lot more western than the Turkmen samples in HO. No Turkmen sample was included in both sets.

[img]https://i.ibb.co/7NqncFz/1.png

Interesting. Unfortunately, there seems to be no info on the exact origin of those samples (unlike the Jeong study).