Bhai can I share some of these thakuri results on my FB page. I don't want to without your consent. It is always a good online etiquette to request.
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Bhai can I share some of these thakuri results on my FB page. I don't want to without your consent. It is always a good online etiquette to request.
Sent from my Mi 10 using Tapatalk
I wonder Tharu results a lot. Cus Harappaworld say they're only 7% Mongoloid but their phenotypes look very Tibetan :confused:
True indeed. The Maithil Bihari Brahmins are same as Eastern Uttar Pradesh Brahmins.Quote:
Originally Posted by Leto
Spoiler!
No problem at all. I posted these kits for all of you enthusiast guys. The Shahi kit is mixed, so better not to post the Shahi.Quote:
Originally Posted by Godardt
Tharus are half Tibetan half South Asian. Some are mixed with Gangetic folks, so the East Asian is reduced in them. A typical Tharu shows East Asian features a lot.Quote:
Originally Posted by Sora
Spoiler!
Tharu average is 60 percent Chokhopani/Tibetan and rest 40 percent South Asian.
Target: Tharu
Distance: 2.7037% / 0.02703749
60.8 NPL_Chokhopani_2700BP
39.2 IRN_Shahr_I_Sokhta_BA3
Academic Tharu average for Dodecad
Quite strongly Mongoloid, basically half. I didn't know this tribe/ethnic group.Code:Tharu,15.85,4.64,0.19,10.28,0.34,2.39,31.17,0.00,0.13,34.60,0.41,0.00
Chhetri (Karki)
Kit FW5124484
# Population Percent
1 S-Indian 29.18
2 NE-Asian 26.87
3 Baloch 22.32
4 Siberian 6.38
5 NE-Euro 5.06
6 SE-Asian 4.42
7 Caucasian 2.62
8 Mediterranean 1.52
9 Beringian 1.03
10 Papuan 0.6
Single Population Sharing:
# Population (source) Distance
1 nepalese-c (xing) 7.54
2 hazara (hgdp) 25.88
3 nepalese-b (xing) 26
4 uyghur (hgdp) 26.55
5 bengali (harappa) 26.91
6 burusho (hgdp) 27.55
7 nepalese-a (xing) 27.57
8 up-muslim (harappa) 28.22
9 khasi (chaubey) 28.93
10 bengali-brahmin (harappa) 28.96
11 bihari-muslim (harappa) 29.29
12 kashmiri (harappa) 29.3
13 uzbek (behar) 29.82
14 gujarati-muslim (harappa) 29.87
15 punjabi (harappa) 30.46
16 punjabi-jatt-muslim (harappa) 30.94
17 kashmiri-pandit (reich) 31.01
18 kashmiri-pahari (harappa) 31.12
19 cochin-jew (behar) 31.22
20 singapore-indian-c (sgvp) 31.28
Mixed Mode Population Sharing:
# Primary Population (source) Secondary Population (source) Distance
1 63.5% up-kshatriya (metspalu) + 36.5% japanese (hgdp) @ 1.98
2 65.7% bihari-muslim (harappa) + 34.3% japanese (hgdp) @ 2.12
3 63.9% karnataka-brahmin (harappa) + 36.1% japanese (hgdp) @ 2.44
4 63.9% vaish (reich) + 36.1% japanese (hgdp) @ 2.46
5 63.1% meghawal (reich) + 36.9% japanese (hgdp) @ 2.63
6 66% bengali-brahmin (harappa) + 34% japanese (hgdp) @ 2.68
7 63.7% maharashtrian (harappa) + 36.3% japanese (hgdp) @ 2.77
8 61.9% up-kshatriya (metspalu) + 38.1% tu (hgdp) @ 2.88
9 64.2% bihari-muslim (harappa) + 35.8% tu (hgdp) @ 2.93
10 62.7% iyengar-brahmin (harappa) + 37.3% japanese (hgdp) @ 2.98
11 61.6% karnataka-brahmin (harappa) + 38.4% xibo (hgdp) @ 3.09
12 62.3% karnataka-brahmin (harappa) + 37.7% tu (hgdp) @ 3.11
13 60.5% ap-brahmin (xing) + 39.5% xibo (hgdp) @ 3.14
14 61% gujarati (harappa) + 39% xibo (hgdp) @ 3.17
15 60.3% iyer-brahmin (harappa) + 39.7% xibo (hgdp) @ 3.2
16 63.3% gujarati (harappa) + 36.7% japanese (hgdp) @ 3.24
17 60.4% iyengar-brahmin (harappa) + 39.6% xibo (hgdp) @ 3.24
18 62.8% ap-brahmin (xing) + 37.2% japanese (hgdp) @ 3.25
19 64.5% bengali-brahmin (harappa) + 35.5% tu (hgdp) @ 3.26
20 61.5% meghawal (reich) + 38.5% tu (hgdp) @ 3.27
Thats why you can’t rely on any calculator for total Mongoloid.Quote:
Originally Posted by Sora
You’ve already seen how 23andme is able to make Turks 0 mongoloid.
We can even make Kazakh 0 mongoloid if we want. All we have to do is make 1 component Chinese Han and the 2nd component Central Asian. We can make Kazakh score near 0 E. Asian and near 100% C. Asian
If we want to make Kurds 0 Mongoloid all we have to do is make one component Caucasian and the other W.Asian since we know Kurds have alot of ancestry from there and will score nearly 100% Caucasian and W. Asian. This way their E. Asian will be forced to zero. If we want to make Kurds very mongoloid we don’t use any Caucasian or W. Asian. Instead we use African and W. European. This way they’ll score high E. Asian
If we want to make Turks 0 mongoloid we should use a Balkan and W Asian and Greek components. If we want to make Turks very mongoloid we leave out Balkan, W. Asian and Greek components.
So you may ask how do we know how mongoloid a person is? The only way I know is to do one to one comparison of person with E. Asians and Siberians and count how many genes they match with the oerson in IBS. If you do this the mystery of why some west Asians have Mongoloid phenotype but score a little mongoloid on calculators will be largely solved
I came up with a new way to estimate the amount of Mongoloid ancestry. I'm using qpGraph to model populations as a combination of the common ancestor of two Caucasoid populations and the common ancestor of two Mongoloid populations:Quote:
Originally Posted by Zoro
Result:Code:library(admixtools)
library(tidyverse)
p1=c("Lithuanian","Sardinian")
p2=c("Mongol","Han")
p3=c("Tharu","Finnish","Kurd","Kazakh","Russia_HG_Karelia","Turkey_N","Italy_North_Villabruna_HG","Russia_Samara_EBA_Yamnaya","Sherpa","Newar","Bahun","Rai","Tamang","Gurung","Magar","Japanese","Enets","Nganasan","French","Uyghur")
f2=f2_from_geno("v50.0_HO_public",pops=c(p1,p2,p3),maxmiss=1)
res=sapply(p3,function(x){
tree=rbind(c("R","A"),c("R","B"),c("A","C"),c("B","C"),cbind("A",p1),cbind("B",p2),c("C",x))
gr=qpgraph(f2,tree)
w=(gr$edges%>%filter(from=="B"&to=="C"))$weight
list(w,gr$score)
})%>%apply(1,unlist)
res=res[order(res[,1]),]
paste(sprintf("%.1f",100*res[,1]),rownames(res),round(res[,2]))%>%writeLines
# x="Tharu"
# tree=rbind(c("R","A"),c("R","B"),c("A","C"),c("B","C"),cbind("A",p1),cbind("B",p2),c("C",x))
# gr=qpgraph(f2,tree)
# plot_graph(gr$edges)
# ggsave("1.png",width=4,height=4)
0.0 Turkey_N 2144
0.0 Italy_North_Villabruna_HG 1311
0.5 French 1616
6.9 Finnish 1312
8.8 Kurd 1444
9.6 Russia_Samara_EBA_Yamnaya 1463
19.9 Russia_HG_Karelia 1454
39.1 Bahun 1345
55.7 Uyghur 1303
61.9 Newar 1949
66.2 Tharu 1816
70.2 Kazakh 1195
85.7 Tamang 2273
86.7 Magar 2257
86.8 Enets 1531
91.1 Gurung 2281
93.9 Rai 2231
97.0 Sherpa 2034
100.0 Japanese 3110
100.0 Nganasan 2433
For example the model for Tharu used a graph like this:
https://i.ibb.co/0yWq3gh/g.png
I like your open thinking and skills but to be fair to our E. European friends since they also have some Siberian and E. Asian ancestry I wouldn’t use Lithuanians as proxy for W. Eurasian. Use something that you’re more sure that doesn’t have any Siberian or E. Asian. Maybe some W. African population as non mongoloid proxyQuote:
Originally Posted by Komintasavalta
To be even more fair also use Siberian as Mongoloid and use a couple of different W. and E. Asian proxies and average the results
You can also try Basque as a W. Eurasian proxy. You can also do one run with 1000G only, the other with Simmons only since results will be different because they use different SNPs.
Ideally you want to use whole genomes to reduce bias but I don’t know if you can find whole genomes for many ethnicities
The results don't change that much if you add more references. I also tried to use Serbia_IronGates_Mesolithic and Turkey_N as the Caucasoid references, but for some reason it caused Finns to get only 3% Mongoloid ancestry.Quote:
Originally Posted by Zoro
Another dead simple method to estimate the percentage of Mongoloid ancestry of is to first do a PCA of Eurasian populations, and to then apply min-max scaling to PC1 so the smallest value becomes 0 and the largest value becomes 100. It gave the figure of 66% for Tharu, which is exactly the same as the result of my qpGraph model:
Code:$ wget https://reichdata.hms.harvard.edu/pub/datasets/amh_repo/curated_releases/V50/V50.0/SHARE/public.dir/v50.0_HO_public.{anno,ind,snp,geno}
$ f=v50.0_HO_public;convertf -p <(printf %s\\n genotypename:\ $f.geno snpname:\ $f.snp indivname:\ $f.ind outputformat:\ PACKEDPED genotypeoutname:\ $f.bed snpoutname:\ $f.bim indivoutname:\ $f.fam)
$ igno()(grep -Ev '\.REF|rel\.|fail\.|\.contam|Ignore_|_dup|_contam|_lc|_father|_mother|_son|_daughter|_brother|_sister|_relative|_sibling|_twin|Neanderthal|Denisova|Vindija_light|Gorilla|Macaque|Marmoset|Orangutan|Primate_Chimp|hg19ref')
$ printf %s\\n AA Aleut Algerian Australian BantuKenya BantuSA BantuSA_Ovambo Biaka Bolivian Canary_Islander Datog Egyptian Eritrea Esan Ethiopia_BetaIsrael Gambian Hadza1 IBS_CanaryIslands Jew_Ethiopian Jew_Moroccan Jew_Tunisian Ju_hoan_North Karitiana Khomani Kikuyu Libyan Luhya Luo Malawi_Chewa Malawi_Ngoni Malawi_Tumbuka Malawi_Yao Mandenka Masai Mayan Mbuti Mende Mixe Mixtec Moroccan Mozabite Namibia_Bantu_Herero Nasioi Papuan Piapoco Pima Quechua Saharawi Somali Surui Tlingit Tunisian YRI Yemeni Yemeni_Desert2 Yoruba Zapotec>noneurasia
$ x=eurasia;awk -F\\t '$5==0&&$7!~/\./{print$2,$7}' v50.0_HO_public.anno|igno|grep -v _o|awk 'NR==FNR{a[$0];next}!($2 in a)' noneurasia ->$x.pick
$ plink --bfile v50.0_HO_public --keep <(awk 'NR==FNR{a[$1];next}$2 in a' $x.pick v50.0_HO_public.fam) --make-bed --out $x
$ plink --bfile $x --indep-pairwise 50 10 .1 --maf --out $x;plink --bfile $x --extract $x.prune.in --make-bed --out $x.p
$ plink --bfile $x.p --pca 20 --out $x
$ awk 'NR==FNR{a[$1]=$2;next}{p=a[$2];s[p]+=$3;n[p]++}END{for(i in s)print s[i]/n[i],i}' $x.{pick,eigenvec}|awk '{if(NR==1||$1>max)max=$1;if(NR==1||min>$1)min=$1;a[NR]=$1;n[NR]=$2}END{for(i=1;i<=NR;i++)print 100*(a[i]-min)/(max-min),n[i]}'|sort -n|awk '{$1=sprintf("%.0f",$1)}1'
0 Sardinian
0 Italian_Sardinian
2 Spanish_Lleida
2 Spanish_North
2 Basque
3 Italian_North
3 Cypriot
3 Spanish
3 Sicilian
3 Jew_Turkish
3 Italian_South
3 BedouinB
3 Jew_Libyan
3 Lebanese_Christian
3 Jew_Yemenite
3 Greek
4 Jew_Iraqi
4 Druze
4 Maltese
4 French
4 English
4 Albanian
4 Yemeni_Northwest
4 Yemeni_Highlands
4 Yemeni_Desert
4 Armenian_Hemsheni
4 Saudi
4 Croatian
4 Jew_Ashkenazi
4 Armenian
4 Moldavian
5 Lebanese_Muslim
5 Romanian
5 Bulgarian
5 Palestinian
5 Scottish
5 Assyrian
5 Orcadian
5 Gagauz
5 Icelandic
5 Georgian
5 Czech
5 Norwegian
5 Jew_Iranian
5 Jew_Georgian
5 BedouinA
6 Jordanian
6 Lebanese
6 Hungarian
6 Syrian
6 Lithuanian
6 Yemeni_Highlands_Raymah
6 Ukrainian_North
6 Ukrainian
7 Abkhasian
7 Belarusian
7 Russia_Abkhasian
8 Estonian
8 Kurd
8 Kaitag
8 Ezid
8 Kubachinian
9 Darginian
9 Chechen
9 Tabasaran
9 Lezgin
9 Avar
9 Lak
9 Russian
10 Ingushian
10 Adygei
10 Iranian
10 Turkish
11 Kumyk
11 Azeri
11 Ossetian
12 Circassian
12 Finnish
12 Russia_NorthOssetian
12 Mordovian
12 Balkar
13 Karachai
13 Iranian_Bandari
13 Karelian
13 Russian_Archangelsk_Krasnoborsky
13 Kabardinian
13 Abazin
14 Veps
15 Makrani
16 Russian_Archangelsk_Pinezhsky
17 Brahui
17 Turkish_Balikesir
17 Balochi
18 Russian_Archangelsk_Leshukonsky
19 Tatar_Mishar
20 Kalash
22 Pathan
22 Tajik
23 Tatar_Kazan
24 Sindhi_Pakistan
25 GujaratiA
25 Jew_Cochin
25 Chuvash
26 Besermyan
27 Nogai_Karachay_Cherkessia
29 GujaratiB
29 Udmurt
30 Burusho
31 GujaratiC
32 GujaratiD
34 Punjabi
34 Bashkir
36 Turkmen
39 Uzbek
41 BEB
42 Bahun
45 Yukagir_Forest
46 Tatar_Siberian
47 Nogai_Stavropol
49 Tatar_Siberian_Zabolotniye
50 Mansi
52 Nogai_Astrakhan
52 Uyghur
53 Hazara
53 Karakalpak
54 Altaian_Chelkan
58 Tubalar
59 Shor_Mountain
60 Shor_Khakassia
60 Kazakh
62 Selkup
63 Newar
63 Even
63 Khakass
63 Kyrgyz_Tajikistan
64 Ket
65 Kyrgyz_Kyrgyzstan
66 Tharu
67 Kyrgyz_China
69 Khakass_Kachin
69 Altaian
70 Enets
71 Kazakh_China
73 Kusunda
77 Kalmyk
78 Tuvinian
78 Evenk_FarEast
78 Tofalar
80 Dolgan
80 Todzin
81 Mongol
81 Eskimo_Naukan
81 Tamang
81 Buryat
82 Eskimo_ChaplinSireniki
82 Yakut
82 Burmese
83 Eskimo_Sireniki
83 Dongxiang
83 Magar
83 Malay
83 Chukchi
83 Khamnegan
83 Thai
84 Chukchi1
84 Dungan
84 Itelmen
85 Cambodian
85 Koryak
86 Yukagir_Tundra
86 Gurung
86 Salar
87 Evenk_Transbaikal
87 Nganasan
88 Tagalog
89 Tu
89 Visayan
90 Rai
91 Bonan
91 Mongola
91 Sherpa
91 Daur
92 Dusun
92 Oroqen
92 Xibo
92 Tibetan
92 Yugur
92 Ilocano
93 Hezhen
93 Murut
93 Tibetan_Yunnan
93 Ulchi
93 Nivh
93 China_Lahu
93 Negidal
93 Kinh
94 Naxi
94 Vietnamese
94 Dai
94 Nanai
94 Yi
94 Atayal
94 Japanese
95 Ami
95 Kankanaey
95 Zhuang
95 Miao
95 Gelao
96 Han
96 Tujia
96 Korean
96 She
96 Qiang
96 Maonan
98 Dong
98 Mulam
100 Li
Although you’re on the right track you’re still far from the truth. To get to the truth you can’t use PCA programs or Admixture, you have to use gene to gene IBS comparison?Quote:
Originally Posted by Komintasavalta
How do I know you’re still far away from the truth? Simply because I’ve done IBS and when I did Baloch and Kalash had less similarity with Mongols than Kurds. Whereas here you show Kurds 8.1 and Kalash 20.0
Try Plink IBS (—genome flag) one to one with Mongol and post list
Once you do IBS list you can normalize results by assigning top pop Han or Mongol 100% and bottom pop Yoruba as 0