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Thanks, neither me.Quote:
Originally Posted by MCMXCV
I've none of my grandparents tested (all deceased), but by comprehensive chromosome painting, resulting in 80% of the DNA getting painted and extrapolation to 100%, I found out that the DNA from one pair of grandparents was inherited as skewed as abt. 35%:15%. I regard that borderline and assess it to be within the 1% most extreme cases.
My grandparents are 90 and 89. The other two have been deceased for quite some time, so there was never an option to have them tested. Fortunately, my tested grandparents come from each of my parents, so it enables phasing for both of them.Quote:
Originally Posted by rothaer
I’m glad you were able to find out your inheritance percentages. 15% does seem low. Do you know whether there’s a tool which calculates an estimate of the missing (grand)parent’s DNA? Eurogenes values or something similar. Something along the lines of deducting the tested parent’s DNA from the child’s and predicting the untested parent’s DNA. I’m not sure if it exists or if it’s even possible. 23andMe does show which ethnicity you got from each parent after phasing, but you cannot just multiply that by two to get the parent’s estimate.
In my opinion, this is simply genetic similarity to populations/balanced modeling, and likely not indicative of actual ancestry, in your case. So while the results might suggest a Galician or Csangos ancestor, only a paper trail can add validity to that. Otherwise, it is just as likely to be fully attributable to genetic similarity or because of a deviation that you have from the average. It is important to remember that the averages are made up of a number of samples, those sample form a normal range for an ethnicity. My guess is that all family members you posted fall within the normal range of results for full Slovene, even if not right on the average. On an additional note you also are using 23andMe data, which is not quite as good for use with GED/Vahaduo and can skew individual components by 2%.Quote:
Originally Posted by MCMXCV
As far as comparing your K13 and K15.. K13 has been more extensively updated with quality samples across the board, so that could explain the difference. K15 is good for someone like me as it seems to handle my East-West split of German and Hungarian very well, and some people say it handles Western Europe better than K13, but in your case I would suggest K13 is the more reliable of the two. Still, your K15 is quite reasonable, imo.
When you have distance between parental ancestries (let's say Irish and Romanian, or German and Hungarian), you can make a somewhat reliable theoretical result for an untested parent using GED components. The further the distance between the parents, the better. However, when they (the parents) are from the same ethnicity, this method does not work well.Quote:
Originally Posted by MCMXCV
For example, I use a theoretical kit to represent my father (he was never tested), his kit was developed assuming that I plot exactly between my parents. The problem for an individual like you, who's parents are the same ethnicity, is that your family will tend to plot right around each other in an unpredictable way. The further the parents plot from each other, the more reliable the mid point method of creating an untested parent's kit becomes.
European 100%
Eastern European 72.0%
Slovenia Likely Match
1. Lendava
Hungary Possible Match
1. Budapest
Northwestern European 11.7%
French & German 11.6%
Broadly Northwestern European 0.1%
Southern European 7.8%
Greek & Balkan 6.2%
Croatia Possible Match
Broadly Southern European 1.6%
Ashkenazi Jewish 0.2%
Broadly European 8.3%
Don’t mind the above (re)post. I want to have it linked in my signature, but couldn’t edit the old one. I guess I’ll drop my Eurogenes K36 map here so this thread isn’t bumped for absolutely nothing.
https://uc3b14911afaed4a96e42a78b014...1HrOrz2w/p.png
A Slovene I've recently found. Probably already known to some of you
Admix Results (sorted):
# Population Percent
1 North_Atlantic 31.64
2 Baltic 30.64
3 West_Med 15.22
4 East_Med 9.86
5 West_Asian 8.33
6 South_Asian 1.32
7 Red_Sea 1.07
8 Oceanian 1.04
9 East_Asian 0.87
Single Population Sharing:
# Population (source) Distance
1 Hungarian 3.41
2 Austrian 5.43
3 Croatian 6.42
4 East_German 6.43
5 Moldavian 7.14
6 Serbian 7.31
7 South_Polish 11.45
8 Romanian 11.48
9 Ukrainian_Lviv 11.88
10 West_German 12.53
11 Ukrainian 13.1
12 South_Dutch 13.91
13 Bulgarian 14.24
14 Polish 15.15
15 North_German 15.34
16 French 15.94
17 North_Swedish 16.88
18 Southwest_Russian 17.26
19 Ukrainian_Belgorod 17.44
20 Swedish 17.5
Mixed Mode Population Sharing:
# Primary Population (source) Secondary Population (source) Distance
1 72.7% Moldavian + 27.3% Southeast_English @ 1.74
2 57.4% Austrian + 42.6% Moldavian @ 1.77
3 64.3% Moldavian + 35.7% West_German @ 1.91
4 74.7% Moldavian + 25.3% Southwest_English @ 1.92
5 66.7% Moldavian + 33.3% South_Dutch @ 1.95
6 72.2% Croatian + 27.8% French @ 2.05
7 53.5% East_German + 46.5% Serbian @ 2.05
8 69.8% East_German + 30.2% Bulgarian @ 2.06
9 67.1% Croatian + 32.9% West_German @ 2.1
10 65.1% East_German + 34.9% Romanian @ 2.27
11 75.8% Moldavian + 24.2% West_Scottish @ 2.34
12 74.1% Moldavian + 25.9% Orcadian @ 2.35
13 69.7% Croatian + 30.3% South_Dutch @ 2.4
14 75.1% Moldavian + 24.9% Irish @ 2.42
15 73% Moldavian + 27% North_Dutch @ 2.47
16 72.7% Moldavian + 27.3% Danish @ 2.51
17 65.1% South_Polish + 34.9% North_Italian @ 2.55
18 95.2% Hungarian + 4.8% Sardinian @ 2.57
19 59.1% Ukrainian_Belgorod + 40.9% Spanish_Andalucia @ 2.64
20 69.5% Moldavian + 30.5% North_German @ 2.65