u/strike978

I've made a browser extension called "Enhancer for Ancestry" that enhances how you view your AncestryDNA results (and your matches' too).

https://chromewebstore.google.com/detail/enhancer-for-ancestry/pfdfpebnpacojfibpkkajkgclbneigbg

What it does:

• Cleaner regions: See your ethnicity regions in a much cleaner layout, grouped by macro-regions, with percentages and confidence ranges.
• More details: Click on any region to learn more about it, including where it’s mainly found and a map showing the area.
• Journeys/communities: See your Journeys in an easier-to-read format, including how strong your connection is and any sub-Journeys.
• Match list: See the Journeys your DNA matches belong to directly under their names.
• Better match pages: When viewing a DNA match, you get the same improved regions and Journeys layout, and you can see all of their Journeys, not just the ones you share. Note: If a match has chosen to only share regions they have in common with you, you’ll only be able to see the shared regions and Journeys.
• Download as an image: Easily save your regions and Journeys, or those of any of your matches, as a high-quality image.

If anyone has any feedback or suggestions, I’m definitely open to hearing them.

Hi everyone!

I’m looking for 23andMe data from people from different countries.

If you’d like to help out, you can use the browser extension I created, MatchFetch

https://chromewebstore.google.com/detail/matchfetch/jlmmgekompcehbmpaemjafkcleadiomb

The extension now includes filtering options, so you don't have to extract all your matches.

https://preview.redd.it/1x9tsodi3rih1.png?width=1527&format=png&auto=webp&s=9fc2c1756b2c053f6a8c633ac73bcb2036a28a76

Once you’ve collected the data with the extension, you can use a Python script I developed to generate charts like the ones I’ve shared in the gallery. You just need to export the data generated by MatchFetch and use it with the script.

I’m looking for the resulting charts, not your personal data.

You can get the Python script here

https://drive.google.com/file/d/1OZyr1hOV92T1IL557O-BDYo5CrU__XcN/

Thanks!

¡Hola a todos!

Estoy buscando datos de 23andMe de personas Dominicanos.

Soy mitad dominicano, pero lamentablemente la mayoría de mis coincidencias en 23andMe son puertorriqueños 😭, así que intento recopilar más datos de personas que sean exclusivamente Dominicanos.

Si quieres colaborar, puedes usar la extensión para navegador que creé, MatchFetch:

https://chromewebstore.google.com/detail/matchfetch/jlmmgekompcehbmpaemjafkcleadiomb

La extensión ahora cuenta con opciones de filtrado, por lo que no es necesario extraer todas tus coincidencias.

https://preview.redd.it/f72fypc21rih1.png?width=1527&format=png&auto=webp&s=c85395cbf3c0cc30f9cd991cae8e7d83c43d4bb0

Una vez recopilados los datos con la extensión, puedes utilizar un script de Python que desarrollé para generar gráficos como los que he compartido en la galería. Solo tienes que exportar los datos generados por MatchFetch y usarlos con el script.

Lo que busco son los gráficos resultantes, no tus datos personales.

https://preview.redd.it/vex8ncb10rih1.png?width=1920&format=png&auto=webp&s=0f9b70ec91f06a4b3b28c9e6977e6f946ef5884d

Puedes obtener el script de Python aquí:

https://drive.google.com/file/d/1OZyr1hOV92T1IL557O-BDYo5CrU__XcN/

¡Gracias!

This is NOT "average." I'll stress this since certain people got really angry at my last post on ancestry composition when I used the word "average." This is simply based on the existing 23andMe data and nothing more.

The Python script I’m using to generate these 23andMe charts, as well as the ancestry composition charts, is available here

https://drive.google.com/file/d/1OZyr1hOV92T1IL557O-BDYo5CrU__XcN/

It requires the use of my MatchFetch browser extension (https://matchfetch.app) in order to work with the data.

For the haplogroup classification, I downloaded and used the 23andMe data. If anyone with more knowledge about haplogroups wants to chime in and correct any of the branches below, feel free to let me know. I've classified the branches like this:

YDNA_REGIONS = {

'A': 'Sub-Saharan African', 'B': 'Sub-Saharan African',

'C': 'East Asian', 'D': 'East Asian', 'E': 'Sub-Saharan African',

'F': 'South Asian', 'G': 'West Eurasian', 'H': 'South Asian',

'I': 'West Eurasian', 'J': 'West Eurasian', 'K': 'South Asian',

'L': 'South Asian', 'M': 'Oceanic', 'N': 'East Asian',

'O': 'East Asian', 'P': 'South Asian', 'Q': 'East Asian',

'R': 'West Eurasian', 'S': 'Oceanic', 'T': 'West Eurasian',

}

MTDNA_REGIONS = {

'A': 'East Asian', 'B': 'East Asian',

'C': 'East Asian', 'D': 'East Asian', 'E': 'East Asian',

'F': 'East Asian', 'G': 'East Asian', 'H': 'West Eurasian',

'HV': 'West Eurasian', 'I': 'West Eurasian', 'J': 'West Eurasian',

'K': 'West Eurasian', 'L': 'Sub-Saharan African', 'M': 'South Asian',

'N': 'West Eurasian', 'O': 'Oceanic', 'P': 'Oceanic',

'Q': 'Oceanic', 'R': 'West Eurasian', 'S': 'Oceanic',

'T': 'West Eurasian', 'U': 'West Eurasian', 'V': 'West Eurasian',

'W': 'West Eurasian', 'X': 'West Eurasian', 'Y': 'East Asian',

'Z': 'East Asian',

}

# Anchors within the ordered yDNA tree, in list order, mapped to a region.

# Every yDNA ID after an anchor inherits its region until the next anchor.

Y_ANCHORS = [

('A', 'Sub-Saharan African'),

('B-M181', 'Sub-Saharan African'),

('E-P147', 'Sub-Saharan African'), # E1a

('E-V38', 'Sub-Saharan African'), # E1b1a

('E-M215', 'West Eurasian'), # E1b1b

('E-M75', 'Sub-Saharan African'), # E2

('D-M174', 'East Asian'),

('C-M130', 'East Asian'),

('F-M89', 'South Asian'),

('G-M201', 'West Eurasian'),

('H-L901', 'South Asian'),

('I-M170', 'West Eurasian'),

('J-M304', 'West Eurasian'),

('K-M9', 'South Asian'),

('L-M20', 'South Asian'),

('T-M184', 'West Eurasian'),

('N-M231', 'East Asian'),

('O-M1359', 'East Asian'),

('S-M230', 'Oceanic'),

('M-P256', 'Oceanic'),

('P-P295', 'South Asian'), # P* paragroup: South/Southeast Asia, Oceania

('P-M45', 'East Asian'), # P1: Siberian / Central Asian

('Q-M242', 'East Asian'),

('Q-M3', 'Indigenous American'), # Indigenous American

('Q-L940', 'West Eurasian'), # Q1a: Europe / West, Central & South Asia

('Q-L275', 'West Eurasian'), # Q2: Europe / Central / South Asia

('R-M207', 'West Eurasian'),

]

# Anchors within the ordered mtDNA tree, in list order, mapped to a region.

MT_ANCHORS = [

('L0', 'Sub-Saharan African'),

('M', 'South Asian'),

('M1', 'West Eurasian'), # North/East African

('M51', 'South Asian'),

('M7', 'East Asian'),

('M8', 'East Asian'),

('CZ', 'East Asian'),

('C', 'East Asian'),

('C1', 'Indigenous American'), # Indigenous American

('C4', 'East Asian'),

('Z', 'East Asian'),

('M9', 'East Asian'),

('E', 'East Asian'),

('M10', 'South Asian'),

('G', 'East Asian'),

('M13', 'South Asian'),

('Q', 'Oceanic'),

('M31', 'South Asian'),

('D', 'East Asian'),

('D1', 'Indigenous American'), # Indigenous American

('D4a', 'East Asian'),

('D4h3', 'Indigenous American'), # Indigenous American (coastal Chile/Peru)

('D5', 'East Asian'),

('N', 'West Eurasian'),

('N9', 'East Asian'),

('A', 'East Asian'),

('A2', 'Indigenous American'), # Indigenous American

('A6', 'East Asian'),

('A5', 'East Asian'),

('O', 'Oceanic'),

('S', 'Oceanic'),

('X', 'West Eurasian'),

('X2a', 'Indigenous American'), # Indigenous American

('X2b', 'West Eurasian'),

('R', 'West Eurasian'),

('F', 'East Asian'),

('B7', 'East Asian'),

('B4', 'East Asian'),

('B2', 'Indigenous American'), # Indigenous American

('B5', 'East Asian'),

('P', 'Oceanic'),

('U', 'West Eurasian'),

('U6', 'West Eurasian'), # North African

('K', 'West Eurasian'),

]

The data above comes from 23andMe matches who have all 4 grandparents listed as being born in these countries. Usually, once we get to around 30 matches, we can get a pretty good idea of the average.

The data was collected using my MatchFetch application (https://matchfetch.app), which can export the data in JSON format. I then used a Python script to parse the data and generate the results. If anyone wants the script, feel free to message me.

Obviously, this method won't work as well for countries like the United States on 23andMe, but it's much more useful for countries where people tend to have more similar ancestry.

My browser extension, MatchFetch, now supports 23andMe.

More features are on the way, including interactive maps with ancestry region descriptions, filtering by grandparent birth locations, the ability to fetch matches within a specific relationship range, and the option to fetch only a selected number of matches.

I've attached a few images showing some of the features currently available.

At the moment, MatchFetch is only available through the GitHub repository:
https://github.com/strike978/matchfetch

The updated version with 23andMe support should be available on the Chrome Web Store soon.

https://chromewebstore.google.com/detail/matchfetch/jlmmgekompcehbmpaemjafkcleadiomb?authuser=0&hl=en

MatchFetch is an independent project and is not affiliated with 23andMe.

Many people think you'll always look like your parents, or that your siblings can't look very different from you.

But that's simply not true, especially in more recently mixed, multi-generational populations like Dominicans.

This is why I always say not to put too much weight on your admixture percentages. Your haplogroups don't determine your appearance either. Obviously, my dad and all of his siblings share the same haplogroups (R-P311 from their Spanish ancestry and A2 from their Taíno ancestry), but they still look different from each other.

Your appearance is determined by the specific combination of alleles you inherit from your parents. The genetic variants that influence things like skin pigmentation, hair color, eye color, facial features, and other physical traits make up only a tiny fraction of your genome.

The first and second images are my Dominican dad. The next two was one of his older full siblings. The fifth and sixth images were my paternal grandparents (their parents). The next several images are more of his full siblings. He had a lot of them, and they vary quite a bit in pigmentation and facial features. My dad actually has the darkest skin of all of them. Some of his siblings are very light-skinned and also have noticeably different facial features.

The image after that is my parents, and the last image is me as a baby. Compared to my dad, I have lighter skin, thin lips, and a narrower nose. Overall, I look much more European than he does, although I'd say my eyes are probably the feature that gives away that I'm not European. But if you look at my dad's family, you can clearly see that they don't all look alike either. I don't think my mom (Puerto Rican) looked much like her mother either, and supposedly my mom's father looked very "Taíno."

This is also why I'm very much against labels like "white" and "black" and the idea of so-called "races." They are oversimplified categories that don't really reflect how human genetics works. No matter if our ancestry comes from Africa, Europe, Asia, or anywhere else, we are all about 99.6-99.9% genetically similar. Even within the remaining 0.1-0.4% that differs between us, only a very small number of genetic variants are responsible for many of the differences we see in appearance.

Remember that 23andMe and Ancestry only look at around 400,000 to 600,000 SNPs, which is roughly 0.1% of your genome. The traits that influence things like pigmentation, hair color, eye color, facial features, and other aspects of appearance come down to a very, very small number of variants within that.

A while back, I built a clustering chart using only around 90 appearance-related SNPs. Even with just those variants, I was able to broadly separate Northern Europeans, Southern Europeans, West Asians, Sub-Saharan Africans, and even separate African hunter-gatherer populations from other African populations, along with South Asians, Australasians, East Asians, and Indigenous Americans.

The point is that a relatively small number of genetic variants can have a big impact on the differences we see in appearance, while the rest of our genome is still overwhelmingly similar.

I saw a very ignorant comment the other day comparing the differences between Africans and Europeans to the differences between coyotes and wolves. 😔

No, we are not different species. Humans are all one species. Remember that even Neanderthals and Denisovans, our archaic human relatives, ultimately had ancestors that came from Africa, just much earlier than Homo sapiens. All of our direct paternal and maternal lineages trace back to African populations, and African hunter-gatherer groups carry the oldest Y-DNA and mtDNA lineages.

This is currently in the early testing stage. If anyone would like to try it out and provide feedback, it should be mostly functional.

Since it now works entirely through a browser extension, it is compatible with any operating system, including Windows, macOS, and Linux.

You can get it here for now:
https://github.com/strike978/matchfetch_ext

I plan to publish it on the official web stores once I feel it is ready and no longer in testing. I am looking for any feedback and reports of any issues or bugs you encounter.

All data is stored locally in your browser's IndexedDB. The data can also be exported and imported, allowing you to back up or transfer your data.

Filtering is fully implemented, which I think is one of the biggest benefits of this application. I have also added map views for different regions and journeys. Some journey names or maps may still be missing, but I will work on improving this in the future.

You can also customize the matches you want to fetch by setting a specific centimorgan range.

I've added Country Matches to what was previously called the 23andMe Genetic Groups Browser, which you can view at https://admixr.com/23gg. I'll be adding haplogroup search to this new version shortly.

I put together this image compilation with ancestry results and photos to better illustrate this point.

This page also goes over the same idea and shows examples from several other populations as well.
https://admixr.com/estimates

My father is listed as Dominican #3 and I am the one beside him (Puerto Rican/Dominican). I look much more European than him (lighter skin, fine facial features).

I will also note that my father has full siblings who look very different from him in both phenotype and pigmentation. His own father also looked much different than him as well.

The truth is that the amount of European (Spanish & Portuguese) ancestry we carry does not determine what we look like in Latin America. You can see that throughout this entire compilation.

Look at the Cubans. There is no real correlation there either between their ancestry estimates and appearance.

Also look at the Puerto Rican in this compilation with the highest European ancestry percentage. He is actually the darkest-skinned person in the entire image despite having the most European ancestry.

Then look at the Dominicans with light eyes and compare their percentages. One of them even has light blonde hair despite having more African ancestry than I do.

This is why I think people put far too much emphasis on ancestry percentages when trying to predict appearance, especially in Latin America. What matters is the specific DNA variants a person inherits, not just the overall ancestry percentages. Those variants can be inherited in very different combinations, even among full siblings.

The Cubans in this compilation are from this community:
https://www.americanindianmagazine.org/story/indigenous-cuba-hidden-plain-sight

Overall, I don't see any meaningful correlation between ancestry percentages and physical appearance. If anything, this compilation shows just how much variation there is among Latin Americans in particular, even among people with very similar ancestry results.

Hi all,

I've updated and rebuilt the 23andMe Genetic Groups Browser, which you can access here.

I'm looking for any feedback or suggestions. The current population browsing is still a work in progress. I plan to bring back the previous hierarchy so populations can be viewed by continent and region (for example: Europe → Spanish & Portuguese → Portuguese & Galician), which should make navigation much easier.

I'll also be improving the search functionality so that you can once again filter results by haplogroup.

Also, some populations don't list ethnicities or the number of reference samples used (for example, Melanesian), so you may notice differences there. In cases where no genetic group data is available like these, I've included country-level data instead.

This is the first version of the Admixture Atlas for Europe, which you can view here: https://admixr.com/atlas/europe_map.html
Any feedback or suggestions are welcome.

I want to stress that these source populations are not "pure," and no population can ever really be "pure." Some of them share ancestry with each other, while others are more distinct. The point here isn’t to draw hard boundaries, but to show finer-scale differences between European populations using source populations from similar time periods that still capture real structure.

I also want to talk about the elevated Anatolian Bronze Age-like ancestry in Italians. This is a real signal, but it doesn’t come from the Roman Empire as some people assume. It goes back much earlier. During the Bronze Age, there were major movements from Anatolia into the Aegean, Mediterranean islands, and the Italian Peninsula, bringing additional West Asian-related ancestry into southern Europe. In broad terms, this ancestry is closer to populations from regions like present-day Armenia, Georgia, and Iran than to earlier European farmer groups.

We can also see this broader West Asian-related ancestry across parts of Western Europe, including Spain and France. In some places it likely increased during later periods like the Roman era, when increased mobility within the empire led to more mixing between populations. It is basically absent in the Basques, who kept more continuity after the Iron Age, and it also shows up to a lesser extent in southern Germany.

It is also worth noting that Early European Farmers and Western Steppe Herders already carried substantial ancestry ultimately derived from populations related to Anatolia and the Caucasus. A lot of Western Steppe Herder ancestry is also linked to Paleolithic Siberian-related populations. So a large share of modern European ancestry ultimately traces back to groups that expanded into Europe from outside Europe after the Holocene, rather than being purely from long-term local continuity.

For Baltic and East Slavic populations, we also see ancestry components that are less common in many other northern Europeans, including higher levels of Mesolithic European forager-related ancestry. These are the populations that lived in Europe before the spread of Anatolian-derived early farmers. There is still debate about how this ancestry became more prominent in the groups that later formed Baltic and Slavic populations, including how much comes from local continuity versus later admixture events. Either way, it remains one of the main features that sets them apart from many other northern European populations.

One last thing I want to mention is Sardinians. They really stand out on the European cline. They’re basically the only population here without Western Steppe Herder ancestry. When I looked at them, I also noticed their allele frequencies at certain markers look quite different from other Southern Europeans.

I'll bring up Basques as a comparison point because they’re another population in Europe that sits off the main European gradient, so they’re a useful reference for contrast. The key difference is that Basques still have Western Steppe Herder ancestry, while Sardinians do not. Instead, Sardinians show Anatolian Bronze Age-related ancestry, even though both groups have similar levels of Early European Farmer ancestry.

In Japan, just as I've discussed previously for Europe, populations were not the same as they are today. People often assume modern populations have looked the same for thousands of years, but ancient DNA has shown that simply isn't true.

The Jomon, the original hunter-gatherers of the Japanese Islands, looked noticeably different from modern Japanese. Their ancestry wasn't erased, though. It was carried forward into later populations, with the Ainu of Hokkaido retaining the highest proportion of Jomon ancestry of any living population. Ancient DNA has also identified Jomon-related ancestry in ancient Korea, although it largely disappeared there over time and did not persist to the extent as it did in the Japanese Islands. The Ryukyu Islanders also retained more Jomon ancestry than mainland Japanese.

It's also important to point out that the Jomon were not some completely separate population. They were still very closely related to other East Asians and simply represented an earlier-diverging East Asian lineage. What changed the ancestry of Japan after the Jomon period was the arrival of new populations from the Asian mainland, beginning with the Yayoi migrations around 3,000 years ago and followed by additional migration during the Kofun period. These incoming groups mixed with the existing Jomon inhabitants and gradually gave rise to the ancestors of modern mainland Japanese.

That's why modern Japanese still cluster closely with other East Asians, while the Ainu and Ryukyu Islanders, who retained more Jomon ancestry, preserve a greater proportion of this ancient heritage but are still genetically closest to other Asian populations.

I've attached additional slides showing the ancestry and population history of Northeast Asia. They also illustrate that the Ancient Paleo-Siberians were the populations that later gave rise to Indigenous Americans, and that this ancient ancestry is still found in many populations across northern Asia today.

The broader lesson here is that human populations have changed massively even in just the last few thousand years. Europe changed dramatically, Asia changed including Japan and Korea, the Middle East changed, South Asia changed, Central Asia changed, Southeast Asia changed, the Pacific Islands changed, and many parts of Africa also underwent major demographic shifts. Ancient DNA shows that repeated waves of migration, admixture, and population turnover reshaped the genetic makeup and in many cases the physical appearance of populations across all these regions. The idea that human populations have remained genetically and physically unchanged for thousands of years is simply not supported by the evidence. Human history is defined by constant movement, interaction, and change.

Just something I’m experimenting with in terms of visual design and modeling. If anyone has suggestions for the source populations or the visual aspect, let me know.

Based on this, I think it is interesting to see the variation across Siberian populations. Ancient Beringian is essentially a good proxy for Ancient Paleo-Siberian. I also want to note that I used the same set of source populations for each population shown here.

The Yakut showing Sarazm-related ancestry also makes sense since this seems fairly common in Turkic-speaking populations. With the Nganasan, I was surprised by the amount of Ancient Paleo-Siberian ancestry they carry, but it lines up with what has been reported in studies. Overall, Uralic-speaking groups appear to have absorbed a substantial amount of this APS-related ancestry.

https://en.wikipedia.org/wiki/Nganasan_people#Genetics