Yamnaya-related ancestry proportions in present-day Poles

Modeling ancient ancestry proportions in present-day Europeans with the qpAdm software is now a lot more difficult. The reasons for this are updates to qpAdm as well as the availabiity of more useuful outgroups or right pops.

This isn't necessarily a bad thing, because users are forced to work harder to find successful models, which is likely to lead to some interesting discoveries. But it can be very frustrating.

I don't think that settling for poor statistical fits or using a small number of outrgoups are acceptable short cuts. Perhaps sequencing modern-day samples in exactly the same way as the ancient samples, and thus increasing the compatability between them, might help?

Limiting qpAdm runs to higher quality SNPs from transversion sites does help, but perhaps largely because of the significant reduction in markers?

In any case, I've now given up on running such analyses, at least until I see some serious pointers on the topic from Harvard's qpAdm experts. But before I put this project to bed for the time being, I'd like to share some new results for Poles from eastern and western Poland, respectively.

right pops:

CMR_Shum_Laka_8000BP
MAR_Taforalt
IRN_Ganj_Dareh_N
Levant_PPNB
GEO_CHG
TUR_Barcin_N
RUS_Piedmont_En
SRB_Iron_Gates_HG
WHG
RUS_Karelia_HG
MNG_North_N
RUS_Ust_Kyakhta

left pops:

Polish_East
CWC_Baltic_early 0.572±0.024
SWE_TRB 0.428±0.024
chisq 11.776
tail prob 0.300296
Full output

Polish_West
CWC_Baltic_early 0.587±0.021
SWE_TRB 0.413±0.021
chisq 11.165
tail prob 0.34478
Full output

Even using transversion sites, this is one of the very few combinations of ancient reference samples that works for the Poles with these right pops. That is, the combination of early Corded Ware samples from the East Baltic (CWC_Baltic_early) and Funnel Beaker samples from Scandinavia (SWE_TRB). The former are obviously the proxy here for Yamnaya-related ancestry.

Adding any sort of hunter-gatherer population to this model doesn't help or even makes things worse (for instance, see here and here). It is possible to add Baltic hunter-gatherers to a similar model after dropping CWC_Baltic_early in favor of closely related samples from the Early to Middle Bronze Age Pontic-Caspian steppe. Note, however, that the statistical fits are somewhat poorer.

Polish_East
Baltic_LTU_Narva 0.032±0.014
PC_steppe_EMBA 0.483±0.019
SWE_TRB 0.485±0.019
chisq 17.143
tail prob 0.0465198
Full output

Polish_West
Baltic_LTU_Narva 0.031±0.011
PC_steppe_EMBA 0.491±0.015
SWE_TRB 0.477±0.016
chisq 22.444
tail prob 0.00757421
Full output

Interestingly, but not surprisingly, the ancestry of many present-day Northwestern European populations can be modeled in basically the same way. That's because ancient ancestry proportions are more closely correlated with latitude than longitude across much of the European continent.

English_Kent
CWC_Baltic_early 0.527±0.024
SWE_TRB 0.473±0.024
chisq 13.042
tail prob 0.221357
Full output

Icelandic
CWC_Baltic_early 0.586±0.023
SWE_TRB 0.414±0.023
chisq 16.517
tail prob 0.085751
Full output

Scottish
CWC_Baltic_early 0.583±0.021
SWE_TRB 0.417±0.021
chisq 12.144
tail prob 0.275536
Full output

A zip file with the qpAdm output from this analysis and a list of the most relevant ancients is available here. I might try to run a few more populations over the next few days, but probably only from the northern half of Europe, so please check the zip file in a week or so to see what else is in there.

If anyone wants to challenge my results, note that these and very similar samples are freely available to the public via Harvard University here and here.

Update 22/08/2020: From Nick Patterson (Broad) in the comments:

My general advice for qpAdm is 1) Work on the right hand set. Don't include irrelevant population (except for one population as an outgroup); picking the best RHS can dramatically reduce s. errors on the admixture weights. 2) If qpAdm gives a very low p-value try and understand why, sometimes it is telling you that the target is not a mixture of the sources but sometimes the assumptions are violated, for example recent gene-flow from left pops -> right.

See also...

Ancient ancestry proportions in present-day Europeans

Awesome new toys from Vahaduo

Vahaduo now offers a 3D PCA experience. Check it out HERE and HERE. Below are a couple of screen caps of me messing around with the new tools.

Vahaduo says:

Hi everyone!

New tool - PCA 3D Viewer.

Global 25 version:

https://vahaduo.github.io/3d/g25

West Eurasia version:

https://vahaduo.github.io/3d/we


Usage:

Dots - ancients, circles - moderns.

Click X, Y, Z or COLOR tab and then click one of the PCx buttons to switch dimensions.

Click already active X, Y, Z or COLOR tab to temporarily reverse selected dimension. It will be restored to a default state when any of the dimensions will be switched to another one.

ADD CUSTOM POINTS - self-explanatory. Points will be added as "+". IMPORTANT - G25 version takes NON-SCALED coordinates. This will be true for any new tool dedicated to G25 and coordinates will be scaled automatically when needed or desired.

"Type parts of names." + TAG button - type parts of names to tag certain samples (try for example "KK1 Afon Pinar"). Search is Case Sensitive. Points will be redrawn as "x".

Next row - Labels and Annotations. Click the right button to cycle trough:

CLEAR LABELS + LABELS:AUTO
CLEAR ANNOTATIONS + ANNOTATIONS:CLICK
CLEAR ANNOTATIONS + ANNOTATIONS:AUTO
CLEAR ALL + LABELS AND ANNOTATION OFF
CLEAR LABELS + LABELS:CLICK

CLICK - click to add/delete labels/annotations.
AUTO - same as CLICK plus labels/annotations will be automatically added to newly plotted or tagged samples. Unfortunately adding new labels and annotations becomes very slow when there is too much of them, so there is a limit for the AUTO setting - 250 labels or 20 annotations at once.

Annotations are editable. They can be dragged to another place and text can be changed. Text can be also wrapped into an HTML SPAN element and some styles can be used, like "font-size" or "color". BR element (new line) works too.

HIGHLIGHT CLICK/OFF/HOVER - highlight all samples that belong to a single population. Set to HOVER to ignore clicks. Set to OFF to disable this feature and to remove highlight triggered by hover (highlights triggered by clicks will stay until they will be cleared or removed by a click). Click white dot to cycle trough available highlight colors.

Plotly buttons:

Default download is set to 1600x1200px PNG.

Custom Plotly buttons:
"Toggle projection: orthographic / perspective" - self-explanatory.
"Toggle background color" - cycles trough dark grey, black, white and light grey. Text color and white highlight will be switched to black when background will be set to white or light grey.
"Toggle color scheme" - cycles trough several gradients.
"Reverse color scheme" - reverses all gradients permanently.
"Download plot as png (custom size)" - default size is the size of the currently displayed plot.

See also...

New Global25 interpretation tools