qpAdm thread

[Zoro]

I think the outgroup set EurasianDNA used is good because they are high quality and have high SNP overlap with your test subjects plus they can differentiate streams of ancestry well

For Serbs you can start out with higher quality neolithic farmer samples + Iron gates Whg + relevant MLBA steppe + Iron age steppe/turkic

Like I mentioned before you want your pright references to be differentially related to the dources you’re proposing. Here’s a qpAdm guide. I strongly recommend anyone interested in qpAdm familiarize themselves with it

https://www.biorxiv.org/content/10.1...664v1.full.pdf

In fact, if all 'right' populations are symmetrically related to all ‘left’ populations in this way, qpAdm will not produce meaningful results. The method requires differential relatedness, meaning that at least some 'right' populations must be more closely related to a subset of 'left' populations than to the other 'left' populations.

[andre]

qpAdm it's that type of thing that fascinating me, but, c'mon it's yet too "nerd" for normal human beings .

The better thing for us (amateurs) in my opinion, it's just a good improve of G25 or Vahaduo.. maybe with adding a function that detect overlapping; it would be nice.

[Lucas]

maybe with adding a function that detect overlapping; it would be nice.

That would be very good.

[JamesBond007]

QpAdm is a powerful tool in modelling present-day pops as admixtures of other present-day pops except perhaps in situations of continuous geneflow over an extended period of time

Yes, I agree, I know he was asking about QpAdm, specifically, but I was talking in the broader context of of all functions including all functionality of Qpwave and qpgraph which I have not explored completely in depth as I just set the thing up the other day.

[JamesBond007]

qpAdm it's that type of thing that fascinating me, but, c'mon it's yet too "nerd" for normal human beings .

It was extremely nerdy to get working and one big reason is apparently in this scientific field they use a weird idiosyncratic format for SNPs, genotype or genetic data and have nerdy CLI tools to convert between them that work best in nerd shell or perl scripts so you are a nerd bashing away in the terminal and also because installing the program in R and extracting the Data in R is extremely nerdy. Using the shiny_tools_GUI or graphical user interface to it is not that much nerdier than G25 IMHO .

The better thing for us (amateurs) in my opinion, it's just a good improve of G25 or Vahaduo.. maybe with adding a function that detect overlapping; it would be nice.

The better thing for amateurs is not to get involved. I hate G25 man. The crap is retarded it says I'm closest to the Dutch and a mix of Norwegian with a little bit of Spanish mostly. That crap is trash. It is too fine-grained, especially for modern populations and it uses non-academic sources for many modern samples.

[vbnetkhio]

qpAdm it's that type of thing that fascinating me, but, c'mon it's yet too "nerd" for normal human beings .

The better thing for us (amateurs) in my opinion, it's just a good improve of G25 or Vahaduo..

the new version is much easier to use. you can ask here if you get stuck anywhere.

maybe with adding a function that detect overlapping; it would be nice.

what do you mean? like genetic similarity?

[vbnetkhio]

Like I mentioned before you want your pright references to be differentially related to the dources you’re proposing. Here’s a qpAdm guide. I strongly recommend anyone interested in qpAdm familiarize themselves with it

https://www.biorxiv.org/content/10.1...664v1.full.pdf

Papuan, Nganassan, etc... are redundant since there will be no stream of ancestry from these pops to pleft.

what do you think, is it ok to merge similar populations into clusters and use those as outgroups, to improve the SNP count? e.g. and ANE group including mal'ta, Afontova Gora and Botai.

because of this problem: (quote from anthrogenica)

You want a higher snp count for your models, try removing the less important low coverage samples in the right popslist like Natufian.

[Zoro]

what do you think, is it ok to merge similar populations into clusters and use those as outgroups, to improve the SNP count? e.g. and ANE group including mal'ta, Afontova Gora and Botai.

because of this problem: (quote from anthrogenica)

No it wouldn't make sense

Yes you do want the highest SNP overlap possible. One thing I have learned from Eurasian dna is that accuracy should always be a priority. By using the highest quality samples you do 2 things you increase SNP overlap and you end up with higher accuracy because you filter out lower coverage samples.

Don't try to reinvent the wheel. Just use the high quality references Eurasian DNA uses they are optimized for quality as well as the ability to differentiate more closely related populations.https://eurasiandna.com/?p=2432

The following pright references were used in the qpAdm analysis:

Jo-Hoan-Simmons
Devils-Gate-Neolithic-WGS
Iran-GanjDareh-N
Anatolia-Neolithic
EHG-I0061-DIPLOID
Morocco-Iberomaurusian
Loschbour-DIPLOID
Kolyma-Mesolithic-WGS
Russia-Sunghir6
Botai-EN-DIPLOID
Yana-UP-WGS

Depending on the Scythian/Sarmatian samples used we were able to maintain an overlap of 220,000 to 400,000 SNPs between the samples.

In fact accuracy is so important to Eurasian DNA that they went ahead and diploid genotyped some of the published pseudo-haploid samples to get to a higher level than what the papers were using https://eurasiandna.com/?p=345

[Token]

what do you think, is it ok to merge similar populations into clusters and use those as outgroups, to improve the SNP count? e.g. and ANE group including mal'ta, Afontova Gora and Botai.

because of this problem: (quote from anthrogenica)

SNP count is not crucial, qpAdm deals very well with missing data. From Harney et al:

Each simulation contains an average of ~30 million SNPs. In order to understand the performance of qpAdm with less data, we randomly down-sample the complete dataset to produce analysis datasets of 1 million, 100 thousand, and 10 thousand sites. In all cases, the average admixture proportion estimate generated is extremely close to the simulated α, although we do observe an increase in the amount of variance in the individual estimates as the amount of data analyzed decreases (Figure 3A; Supplementary Table 3). In order to increase computational efficiency and to better approximate typical analysis datasets, all subsequent analyses are performed on the data that has been randomly down-sampled to 1 million sites. We observe similar results when using non-random ascertainment schemes to select sites for analysis (Supplementary Table 4).
The impact of non-random ascertainment schemes on qpAdm analyses are described in more detail in a later section.
We find that qpAdm is robust to missing data, where data from randomly selected sites in each individual is considered missing with rate 10%, 25%, 50%, 75% or 90%

I generally prefer stick with diploid genomes but pseudo-haploidity has little effect on qpAdm too.

[andre]

Could anyone who knows to use qpAdm test some balkanic populations? (bulgarians,romanians,serbians)

I'm very interest in how extra near eastern admix they get.

The model which i think it will works good is Barcin_N, WHG, some EBA steppe source (Yamnaya Samara i thing it's good) and Iran_N or CHG for show the extra Near eastern admix.

I don't think it would be necessary due to the low percent. but in case it's ok some Mongolia_N or Devil_Cave_N for extra North/East asian admix.