Serbian autosomal DNA #2

[Dušan]

I think this account belongs to Serb from Croatia, hence hr in mail-address. Kit I checked gets Serbian matches. You can check them in user lookup by typing this:

It is obviously milosh r
sh = š

No sane Serb will add "hr" next to his name.

[Jana]

It is obviously milosh r
sh = š

No sane Serb will add "hr" next to his name.

Ok
Some type of Serb anyway.

[Leto]

Must be a Serb (female). An equal amount of Baltic and North Atlantic

Admix Results (sorted):

# Population Percent
1 North_Atlantic 27.97
2 Baltic 27.35
3 East_Med 17.89
4 West_Med 17.07
5 West_Asian 7.49
6 East_Asian 0.77
7 Amerindian 0.66
8 Oceanian 0.52
9 Northeast_African 0.29

Single Population Sharing:

# Population (source) Distance
1 Serbian 3.87
2 Romanian 5.69
3 Bulgarian 7.74
4 Hungarian 9.52
5 Moldavian 9.65
6 Croatian 10.99
7 Austrian 12.09
8 East_German 13.79
9 Greek_Thessaly 15.32
10 West_German 16.2
11 North_Italian 16.4
12 French 17.24
13 South_Polish 17.5
14 Ukrainian_Lviv 17.56
15 South_Dutch 17.79
16 Tuscan 18.02
17 Ukrainian 18.44
18 Portuguese 19.56
19 Spanish_Galicia 20.11
20 Spanish_Cataluna 20.33

Mixed Mode Population Sharing:

# Primary Population (source) Secondary Population (source) Distance
1 55% Tuscan + 45% Southwest_Russian @ 3.44
2 50.6% Tuscan + 49.4% Ukrainian @ 3.5
3 55.9% South_Polish + 44.1% West_Sicilian @ 3.57
4 66% Bulgarian + 34% East_German @ 3.57
5 57.7% Tuscan + 42.3% Belorussian @ 3.58
6 93.7% Serbian + 6.3% Spanish_Valencia @ 3.59
7 62% Tuscan + 38% Lithuanian @ 3.59
8 55.5% Tuscan + 44.5% Ukrainian_Belgorod @ 3.61
9 50.8% South_Polish + 49.2% Tuscan @ 3.61
10 96.6% Serbian + 3.4% Sardinian @ 3.61
11 92.5% Serbian + 7.5% North_Italian @ 3.65
12 94.2% Serbian + 5.8% Spanish_Cataluna @ 3.67
13 94.8% Serbian + 5.2% Spanish_Murcia @ 3.69
14 57.3% Tuscan + 42.7% Estonian_Polish @ 3.69
15 95.2% Serbian + 4.8% Spanish_Andalucia @ 3.69
16 62.9% Bulgarian + 37.1% Austrian @ 3.69
17 95.7% Serbian + 4.3% Spanish_Aragon @ 3.7
18 95.4% Serbian + 4.6% Southwest_French @ 3.71
19 94.7% Serbian + 5.3% Portuguese @ 3.71
20 95.2% Serbian + 4.8% Spanish_Castilla_Y_Leon @ 3.72

[Nausevar]

Was I added to the list of serbian samples to make some average?
I posted my K13 and K15 in late august/early september. Now I also have G25.

[vbnetkhio]

Was I added to the list of serbian samples to make some average?
I posted my K13 and K15 in late august/early september. Now I also have G25.

Serbs from Dalmatia, 15 samples:

Serb_Dalmatia,25.69,28.77,16.89,9.22,15.58,1.30,0.60,0.31,0.63,0.45,0.41,0.10,0.06

[Nausevar]

Serbs from Dalmatia, 15 samples:

Serb_Dalmatia,25.69,28.77,16.89,9.22,15.58,1.30,0.60,0.31,0.63,0.45,0.41,0.10,0.06

Ok, thanks.
Is it better and/or easier to make a PCA with PAST or with R?
Because I'm trying to make a G25 PCA but it's complicated and it's difficult to add and remove rows, I don't know how to zoom in and so on.
I would like to have a PCA with modern averages (possibly some regional averages for our areas) and position myself, like with the famous K15 map.
I dislike big dispersive maps like the vahaduo one, I prefer a simple map with big squares/dots and names, easy to look at.

[vbnetkhio]

Ok, thanks.
Is it better and/or easier to make a PCA with PAST or with R?
Because I'm trying to make a G25 PCA but it's complicated and it's difficult to add and remove rows, I don't know how to zoom in and so on.
I would like to have a PCA with modern averages (possibly some regional averages for our areas) and position myself, like with the famous K15 map.
I dislike big dispersive maps like the vahaduo one, I prefer a simple map with big squares/dots and names, easy to look at.

vahaduo's pca is the easiest to use:
https://vahaduo.github.io/custompca/

but PAST PCAs are easier to look at.
for PAST it's best to prepare your data in Excel first, then just paste it into PAST.

[Nausevar]

vahaduo's pca is the easiest to use:
https://vahaduo.github.io/custompca/

What do projected and pca data mean?

[vbnetkhio]

What do projected and pca data mean?

source data - whenever you add or remove a sample from here, the shape of a PCA changes a bit.
projected data - doesn't influence the PCA, they are just projected, like the K15 map. when you are satisfied with the shape of the PCA, then continue to add samples here.
pca data - you don't add samples here, this is where the program outputs the results of the calculation (the PCA). you can copy it and project it as an XY graph in another program (like past)

[Dušan]

Position of Serbian TA members in PCA with north Slavic cluster (Russian, Polish, Ukrainian and Belarus regional averages), and pre-Slavic cluster (Greek and Italian averages)




As seen in the chart, we can be modeled with northern Slavs and southern Italians, but not with northern Italians.