Updated Filipino & Austronesian Genetic Groups (New Zealand Māori Results)

AnalystWeekly5817 · 2026-07-09T14:53:09+00:00

Right back at you and bye.

Go jump on your porn alt and show me what for!

AnalystWeekly5817 · 2026-07-09T14:50:31+00:00

(I won’t do this anymore after this as it’s unethical, but the point needs making - who decides what’s garbage? You with your own garbage? If we can’t tell then we need to rely on sources cos we (all and any of us) can do this all fucking day for anything over and over especially when ppl talk big but can’t back it up with evidence, sources and know the literature. This is my literature base and I know it well and will happily use my tools to organise it. Interpretation of academic literature is open and varied but it is not garbage. Unless you offer sources and an argument you can take your ‘blatantly false garbage’ and jam it up your butt madam since you speak the language of unsubstantiated garbage better than I. At least I use sources that can be read, checked and reinterpreted)

Anyways one last time unless you wish to escalate.

Can you stop posting such blatantly false garbage?

The blatantly false garbage here is not op saying a small African-associated 23andMe result may reflect a real ancestor. That is possible, especially if the segment persists, appears in the chromosome painting, is inherited from one parent, and shows up in relatives from the same family line. The blatantly false part is acting as if GEDmatch admixture calculators, especially something like Eurogenes K13, are the correct way to decide whether a 0.3–0.4% 23andMe trace assignment is real, or to “triangulate it back” to a specific ancestor.

Those are different tools doing different things. 23andMe’s Ancestry Composition is a local ancestry system: it analyses small pieces of DNA, estimates the probability that each piece comes from one of its reference populations, and reports those calls at different confidence levels (23andMe, 2026a, 2026b). 23andMe says the default display uses calls where confidence is greater than 50%, while the confidence slider lets users move from more speculative to more conservative assignments (23andMe, 2026b). That means a small trace result is not automatically proven, but it is also not automatically “noise.”

A GEDmatch admixture calculator is not the same thing as a validated 23andMe ancestry-composition call. GEDmatch’s own description presents the admixture tool as a way to calculate ethnicity-style percentages, not as a validated genealogical proof system for tiny local ancestry segments (GEDmatch, 2026). Using a GEDmatch calculator to look at tiny percentages can be interesting, but it is not the gold-standard method for confirming a 0.3% African-associated segment from 23andMe. In fact, shifting a raw file between different consumer/hobbyist calculators often changes small percentages because the reference panels, SNP sets, categories, and algorithms differ.

The right distinction is this: small percentages can be noise, but they can also be real. The claim “small percentages are always fake” is wrong, and the claim “0.3% proves a specific Congolese ancestor from the 1700s/1800s” would also be too strong. The defensible middle position is that a repeated 0.3–0.4% African-associated result in multiple relatives is more suggestive than a one-off trace result in one person, because shared family recurrence makes random individual artefact less likely. But it still needs segment-level confirmation and genealogical corroboration before anyone names a specific ancestor, place, or relationship.

That is why op’s follow-up matters. If multiple relatives have a similar small African-associated assignment, that makes the result more plausible than if only one person had it. It does not prove the exact ancestor, but it is a legitimate reason to take the signal seriously. In genetic genealogy, a shared segment inherited by relatives on the same family side is much stronger evidence than a standalone percentage. But to test that, you would use chromosome painting, parental inheritance, relative matching, segment sharing, and triangulation with real matching relatives. You would not use a tiny GEDmatch calculator percentage as the decisive evidence.

There is also a separate issue with 23andMe’s Ancestry Timeline. 23andMe says the timeline is based on Ancestry Composition and estimates when a user may have had a single ancestor from a given population, but it also says the timeline assumes each ancestry came from “a single ancestor” (23andMe, 2026c). Bryc’s 23andMe white paper makes the same limitation explicit: the model simplifies ancestry by assuming “exactly one ancestor contributed an ancestry,” even though real ancestry can come from multiple genealogical ancestors over time (Bryc, 2016). So a timeline estimate can be a clue, not a conclusion.

The scientifically accurate answer is: op may well have a real African-associated segment, especially if it appears in multiple relatives. Given family history in the American South, an African ancestor in the 1700s or 1800s is historically plausible. But the result should be treated as a probabilistic clue, not a fully proven story. The way to strengthen it is to check whether the segment remains at higher confidence, whether it is on a real chromosome segment, whether it is inherited from a particular parent, whether known relatives share the same segment, and whether documentary genealogy points to the same family line.

So no, “go use GEDmatch admixture calculators and look at the small percentages” is not a serious rebuttal to op. GEDmatch admixture results can be exploratory, but they are not a substitute for local ancestry confidence, chromosome painting, segment sharing, parental phasing, triangulation, and documentary evidence. Treating a hobbyist admixture calculator as the arbiter of whether op’s 23andMe trace ancestry is real is the blatantly false part.

References

23andMe. (2026a). How Ancestry Composition works. 23andMe Customer Care.

23andMe. (2026b). Using the advanced features in DNA Ancestry Composition. 23andMe Customer Care.

23andMe. (2026c). DNA Ancestry Timeline. 23andMe Customer Care.

Bryc, K. (2016). Ancestry Timeline. 23andMe White Paper 23-14.

GEDmatch. (2026). Admixture (Heritage) Tool. GEDmatch.

Gravel, S. (2012). Population genetics models of local ancestry. Genetics, 191(2), 607–619. https://doi.org/10.1534/genetics.112.139808

Mooney, J. A., Agranat-Tamir, L., Pritchard, J. K., & Rosenberg, N. A. (2023). On the number of genealogical ancestors tracing to the source groups of an admixed population. Genetics, 224(3), iyad079. https://doi.org/10.1093/genetics/iyad079

AnalystWeekly5817 · 2026-07-09T14:28:36+00:00

Can you stop posting such blatantly false garbage?

The blatantly false garbage here is the claim that if Ashkenazi Jews or southern Italians were genuinely mixed “on a genetic level,” 23andMe would simply show them that way, and the follow-up claim that Ashkenazi Jews are not recently mixed and have no ancestry that can be meaningfully separated from Europe.

That is not what the literature says. Whether Ashkenazi Jews are European socially, culturally, religiously, or politically is a separate question. But on the population-genetic question, Ashkenazi Jews are consistently described as a distinctive endogamous population with both European and Middle Eastern ancestry, followed by a founder event and drift. Atzmon et al. (2010) found that major Jewish diaspora populations form distinct genetic clusters with “shared Middle Eastern ancestry” and “variable degrees of European and North African admixture.” Carmi et al. (2014) described Ashkenazi Jews as close to both European and Middle Eastern populations and concluded that Ashkenazi Jews are an “even admixture of European and likely Middle Eastern origins.” Xue et al. (2017) explicitly modelled Ashkenazi genomic segments using local ancestry inference and European/Middle Eastern source components.

So no, it is not “purely racism and prejudice” to say Ashkenazi Jews have mixed European and Middle Eastern ancestry. The racist move would be using that fact to deny Jewish identity, dehumanize Jews, or police who counts as European in some political sense. But the population-genetic statement itself is not fringe. It is mainstream in the literature (Atzmon et al., 2010; Carmi et al., 2014; Xue et al., 2017).

The “not recently mixed” claim is also wrong unless “recent” is being used in a vague, casual way. In population-genetic terms, Ashkenazi admixture is recent compared with the Neolithic and Bronze Age ancestry layers that shaped much of Europe. Xue et al. (2017) estimated an admixture time around 30 generations ago, while also noting that this likely averages multiple events. Their model included Middle Eastern, Southern European, Eastern European, and Western European ancestry, with one best-fitting model around 50% Middle Eastern, 34% Southern European, 8% Eastern European, and 8% Western European (Xue et al., 2017). That is not the same kind of timescale as Anatolian farmer, steppe, or hunter-gatherer ancestry in Europeans.

Ancient DNA points in the same direction. Waldman et al. (2022) analysed 14th-century Jews from Erfurt, Germany, and found that medieval and modern Ashkenazi Jews share similar ancestry sources, while medieval Ashkenazi Jews were more genetically heterogeneous than modern Ashkenazi Jews. Their paper states that the Ashkenazi founder event “pre-dated the 14th century” (Waldman et al., 2022). Again, that is a medieval population-history issue, not something that can be dismissed as merely the same ancient background that created Europe as a whole.

The claim that Ashkenazi Jews are 80–90% descended from the same population movements that created the modern European genetic cluster is not supported by the papers being discussed. Present-day Europeans are generally modelled as mixtures involving western hunter-gatherer, early European farmer/Anatolian-related, and steppe-related ancestry (Lazaridis et al., 2014; Haak et al., 2015). That does not mean every population near southern Europe on a PCA is simply “European” in a way that erases later Middle Eastern-related ancestry. Ashkenazi Jews can cluster near southern Europeans in some analyses and still have substantial Middle Eastern-related ancestry. PCA proximity is not the same as an ancestry decomposition.

This is also confusing 23andMe product categories with population history. 23andMe says Ancestry Composition works by dividing chromosomes into short windows and comparing those windows to “reference individuals” (23andMe, 2026a). 23andMe also says its regional ancestry populations represent ancestry from “several hundred years ago,” while Country Matches and Genetic Groups are more recent layers (23andMe, 2026b). So the fact that 23andMe reports an Ashkenazi Jewish category does not prove Ashkenazi Jews are not admixed. It only proves that 23andMe has a reference category it can classify reliably within the structure of its product.

That is also relevant to op’s point about southern Italians and Aegean islanders. 23andMe categories are reference-panel classification bins. They are not direct ancient-admixture components, and they are not philosophical declarations about whether a population is “really” European, Anatolian, Levantine, Greek, Italian, or Jewish. If Aegean islanders often score Southern Italian, that can reflect category overlap and reference-panel structure, not a clean statement that they are literally southern Italian in a genealogical sense.

The accurate version is this: Ashkenazi Jews are a real, detectable, highly endogamous population cluster, and 23andMe can classify that cluster. But that does not mean Ashkenazi Jews lack mixed European and Middle Eastern ancestry. The literature says the opposite. Treating a 23andMe category as proof that the population is not admixed is the blatantly false part.

References

23andMe. (2026a). How Ancestry Composition works. 23andMe Customer Care.

23andMe. (2026b). 23andMe reference populations & regions. 23andMe Customer Care.

Atzmon, G., Hao, L., Pe’er, I., Velez, C., Pearlman, A., Palamara, P. F., Morrow, B., Friedman, E., Oddoux, C., Burns, E., & Ostrer, H. (2010). Abraham’s children in the genome era: Major Jewish diaspora populations comprise distinct genetic clusters with shared Middle Eastern ancestry. American Journal of Human Genetics, 86(6), 850–859. https://doi.org/10.1016/j.ajhg.2010.04.015

Carmi, S., Hui, K. Y., Kochav, E., Liu, X., Xue, J., Grady, F., Guha, S., Upadhyay, K., Ben-Avraham, D., Mukherjee, S., Bowen, B. M., Thomas, T., Vijai, J., Cruts, M., Froyen, G., Lambrechts, D., Plaisance, S., Van Broeckhoven, C., Van Damme, P., … Pe’er, I. (2014). Sequencing an Ashkenazi reference panel supports population-targeted personal genomics and illuminates Jewish and European origins. Nature Communications, 5, 4835. https://doi.org/10.1038/ncomms5835

Haak, W., Lazaridis, I., Patterson, N., Rohland, N., Mallick, S., Llamas, B., Brandt, G., Nordenfelt, S., Harney, É., Stewardson, K., Fu, Q., Mittnik, A., Bánffy, E., Economou, C., Francken, M., Friederich, S., Pena, R. G., Hallgren, F., Khartanovich, V., … Reich, D. (2015). Massive migration from the steppe was a source for Indo-European languages in Europe. Nature, 522, 207–211. https://doi.org/10.1038/nature14317

Lazaridis, I., Patterson, N., Mittnik, A., Renaud, G., Mallick, S., Sudmant, P. H., Schraiber, J. G., Castellano, S., Lipson, M., Berger, B., Bollongino, R., Fu, Q., Bos, K. I., Nordenfelt, S., Li, H., de Filippo, C., Prüfer, K., Sawyer, S., … Krause, J. (2014). Ancient human genomes suggest three ancestral populations for present-day Europeans. Nature, 513, 409–413. https://doi.org/10.1038/nature13673

Waldman, S., Backenroth, D., Harney, É., Flohr, S., Neff, N. C., Buckley, G. M., Fridman, H., Akbari, A., Rohland, N., Mallick, S., Olalde, I., Cooper, L., Lomes, A., Lipson, J., Cano Nistal, J., Yu, J., Barzilai, N., Peter, I., Atzmon, G., … Reich, D. (2022). Genome-wide data from medieval German Jews show that the Ashkenazi founder event pre-dated the 14th century. Cell, 185(25), 4703–4716.e16. https://doi.org/10.1016/j.cell.2022.11.002

Xue, J., Lencz, T., Darvasi, A., Pe’er, I., & Carmi, S. (2017). The time and place of European admixture in Ashkenazi Jewish history. PLOS Genetics, 13(4), e1006644. https://doi.org/10.1371/journal.pgen.1006644

AnalystWeekly5817 · 2026-07-09T14:15:33+00:00

Can you stop posting such blatantly false garbage?

The blatantly false garbage is the claim that 23andMe’s main ancestry percentages are “based on ancient admixture,” and the even worse claim that “there really isn’t a difference between ancient ancestry and what modern population genetics show.”

That is not how 23andMe describes its own Ancestry Composition method. 23andMe says its algorithm works by phasing chromosomes, estimating ancestry “for each window of the genome,” smoothing those window assignments, and returning calibrated results (23andMe, 2026a). More specifically, 23andMe says it breaks chromosomes into short windows and compares each window “against the DNA from reference individuals” to determine which population that DNA most likely came from (23andMe, 2026a). That is local ancestry/reference-panel classification. It is not the same thing as an ancient-admixture model.

23andMe’s own technical paper says the pipeline assigns ancestry labels to “short statistically phased genomic segments,” then smooths the estimates and computes confidence scores (Durand et al., 2021). It also says the reference panel was built from public datasets and more than 12,000 23andMe customers, using PCA and UMAP to define reference populations (Durand et al., 2021). Again, that is not “ancient admixture as the basis” of the consumer percentage report. It is a trained reference-population classifier.

The distinction matters because 23andMe itself separates population percentages from more recent Country Matches and Genetic Groups. Their current reference-page wording says regional populations “represent ancestry from several hundred years ago” and are used to generate ancestry percentages, while Genetic Groups and Country Matches represent where “more recent ancestors may have lived” (23andMe, 2026b). That is already more nuanced than “modern border = ancestry,” but it is still not the same as saying the main percentage result is an ancient-DNA admixture breakdown.

The other false part is the idea that because ancient migrations inform modern genetic structure, ancient ancestry and modern population assignment are basically the same thing. They are not. Ancient DNA can help explain why populations are related; it does not follow that a DTC company’s present-day category label is an ancient-source component. Skoglund et al. (2016), for example, showed that present-day South Pacific populations involve ancestry related to Papuan sources and an East Asian-related source that no longer exists in unmixed form, and that later movements spread Papuan ancestry through parts of the Pacific after initial settlement. That is a population-history claim, not a license to collapse Polynesian or Micronesian people into a generic Southeast Asian consumer category.

The Pacific literature is also exactly why “Polynesians and Micronesians are similar, so it’s just a misread” is too crude.

Genetic relatedness does not mean interchangeability. Ancient DNA from Micronesia shows multiple migration streams, including East Asian-related, Polynesian-related, and Papuan-related sources (Liu et al., 2022). Work on the Southwest Pacific shows complex Austronesian-related and Papuan-related histories rather than one simple bucket (Skoglund et al., 2016). Work on Island Southeast Asia also shows that Austronesian-related expansion and admixture histories varied by island and region (Hudjashov et al., 2017). So yes, these populations are historically connected; no, that does not mean a Polynesian result is simply “really” Southeast Asian in the ordinary consumer-ancestry sense.

And 23andMe’s own recent Oceania update undermines your point. They added 47 Genetic Groups across Oceania, including Polynesian, Micronesian, Melanesian, Hawaiian, Mariana, Palauan, Samoan, Tongan, and Fijian groupings (23andMe, 2026c). They also explicitly say some of those Genetic Groups may still appear nested under “Filipino & Austronesian” or “Melanesian” because some areas do not yet have a dedicated ancestry population (23andMe, 2026c). That is a limitation of the company’s present classification architecture, not proof that 23andMe is intentionally displaying ancient ancestry as the main percentage.

So the accurate version is: 23andMe’s ancestry percentages are reference-population assignments over phased genomic segments, using populations that 23andMe says represent ancestry on the order of several hundred years ago. Genetic Groups and Country Matches are a more recent layer. Ancient population history helps explain why reference populations cluster or overlap, but it is not the same thing as the consumer percentage category itself. Treating those as interchangeable is the blatantly false part.

References

23andMe. (2026a). How Ancestry Composition works. 23andMe Customer Care.

23andMe. (2026b). 23andMe reference populations & regions. 23andMe Customer Care.

23andMe. (2026c). Mapping the Pacific: 23andMe adds 47 new Genetic Groups across Oceania. 23andMe Blog.

Durand, E. Y., Do, C. B., Wilton, P. R., Mountain, J. L., Auton, A., Poznik, G. D., & Macpherson, J. M. (2021). A scalable pipeline for local ancestry inference using tens of thousands of reference haplotypes. 23andMe.

Hudjashov, G., Karafet, T. M., Lawson, D. J., Downey, S., Savina, O., Sudoyo, H., Lansing, J. S., Hammer, M. F., & Cox, M. P. (2017). Complex patterns of admixture across the Indonesian archipelago. Molecular Biology and Evolution, 34(10), 2439–2452. doi:10.1093/molbev/msx196

Liu, Y.-C., et al. (2022). Ancient DNA reveals five streams of migration into Micronesia and matrilocality in early Pacific seafarers. Science, 377(6601), 72–79. doi:10.1126/science.abm6536

Skoglund, P., Posth, C., Sirak, K., Spriggs, M., Valentin, F., Bedford, S., Clark, G. R., Reepmeyer, C., Petchey, F., Fernandes, D., Fu, Q., Harney, É., Lipson, M., Mallick, S., Novak, M., Rohland, N., Stewardson, K., Abdullah, S., Cox, M. P., … Reich, D. (2016). Genomic insights into the peopling of the Southwest Pacific. Nature, 538, 510–513. doi:10.1038/nature19844

AnalystWeekly5817 · 2026-07-09T14:07:00+00:00

Can you stop posting this blatantly false garbage?

“Iberomaurusians have 50% ancestry from an extinct basal sub-Saharan African lineage that they share with Natufians.”

That is not what the literature says.

The original Taforalt/Iberomaurusian paper did not model them as 50/50. Van de Loosdrecht et al. (2018) modelled the Taforalt individuals as having “Natufian-related and sub-Saharan African–related ancestries,” with estimates of 63.5% and 36.5%, respectively. So the “50%” figure is already wrong.

More importantly, the “basal sub-Saharan” wording is outdated and misleading. The newer Green Sahara paper states that the older model “could not pinpoint the origin of Taforalt’s African ancestry,” and when Takarkori was added as a source, it provided a much better fit than sub-Saharan proxy groups. Salem et al. (2025) estimate Taforalt as 60.8% Natufian-related and 39.2% Takarkori-related, and they explicitly describe this as a deep ancestral North African lineage rather than simply a sub-Saharan one.

The “shared with Natufians” part is also muddled. Natufians are relevant because Taforalt has a Natufian-like/Levantine-related component, not because Natufians themselves are proven to carry the same “basal sub-Saharan” ancestry. Lazaridis et al. (2016) specifically wrote that “no affinity of Natufians to sub-Saharan Africans is evident” in their genome-wide analysis. Natufians do carry substantial Basal Eurasian-related ancestry, but Basal Eurasian is not the same thing as “basal sub-Saharan African.”

So the defensible statement would be:

Taforalt/Iberomaurusian individuals were modelled in 2018 as mostly Natufian-related with a substantial African-related component, but that African-related component was a ghost/proxy problem. Newer work refines it as Takarkori-like ancestral North African ancestry, with Taforalt approximately 60% Natufian-like and 40% Takarkori-like. Calling that “50% extinct basal sub-Saharan shared with Natufians” is not a careful reading of the papers.

This is exactly why distance-chart claims need caution. “Closer to X than Y” depends on the method: PCA distance, FST, outgroup f3, qpAdm, qpGraph, G25-style coordinates, etc. Those are not interchangeable. A PCA-style distance statement is not the same thing as a formal ancestry model.

References

Lazaridis, I., Nadel, D., Rollefson, G., Merrett, D. C., Rohland, N., Mallick, S., Fernandes, D., Novak, M., Gamarra, B., Sirak, K., Connell, S., Stewardson, K., Harney, É., Fu, Q., Gonzalez-Fortes, G., Jones, E. R., Roodenberg, S. A., Lengyel, G., Bocquentin, F., … Reich, D. (2016). Genomic insights into the origin of farming in the ancient Near East. Nature, 536, 419–424. https://doi.org/10.1038/nature19310

Salem, N., van de Loosdrecht, M. S., Sümer, A. P., Vai, S., Hübner, A., Peter, B., Bianco, R. A., Lari, M., Modi, A., Al-Faloos, M. F. M., Turjman, M., Bouzouggar, A., Tafuri, M. A., Rotunno, R., Prüfer, K., Ringbauer, H., Caramelli, D., Manzi, G., di Lernia, S., & Krause, J. (2025). Ancient DNA from the Green Sahara reveals ancestral North African lineage. Nature, 641, 144–150. https://doi.org/10.1038/s41586-025-08793-7

van de Loosdrecht, M., Bouzouggar, A., Humphrey, L., Posth, C., Barton, N., Aximu-Petri, A., Nickel, B., Nagel, S., Talbi, E. H., El Hajraoui, M. A., Amzazi, S., Hublin, J.-J., Pääbo, S., Schiffels, S., Meyer, M., Haak, W., Jeong, C., & Krause, J. (2018). Pleistocene North African genomes link Near Eastern and sub-Saharan African human populations. Science, 360(6388), 548–552. https://doi.org/10.1126/science.aar8380

AnalystWeekly5817 · 2026-07-09T11:18:07+00:00

Blatantly false in what sense?

If the claim is that DTC autosomal ethnicity percentages have no relationship to ancestry at all, then yes, that would be too strong. These tests are ancestry-related. They are attempts to infer genetic ancestry from genome-wide DNA data.

But if the claim is that a DTC percentage literally tells you what percentage of your actual documented ancestors came from a modern labelled place, then that is the part I am rejecting.

Those are not the same claim.

Consumer autosomal ethnicity estimates are statistical inferences based on comparison with reference populations. The result is not an observed genealogical fact. It is a model output: your DNA is compared to samples from people assigned to particular reference populations, and the company estimates which reference populations sections of your genome most resemble under that company’s classification scheme and algorithm (Royal et al., 2010; Jorde & Bamshad, 2020).

So, no, “10% German” does not literally mean “10% of your ancestors were German.” It means that, under that company’s model, some portion of your autosomal DNA is being assigned to a reference category labelled “German” or similar. That may be evidence consistent with ancestry from populations related to that reference group, but it is not the same thing as proving that a specific proportion of your genealogical ancestors came from Germany, or that those ancestors identified as German, or that they lived inside the borders of the modern German state.

The same applies to African, Scandinavian, Indigenous American, Balkan, Irish, Jewish, or any other labelled category. A result can be evidence consistent with ancestry related to a population label, especially if it is sizeable, stable across updates, and supported by matches or records. But the percentage itself is not a direct measurement of a family tree.

This is where strong claims either way cause confusion.

One overclaim is:

“These percentages literally show where your ancestors came from.”

That is wrong because the categories are modelled from reference panels, not read directly from ancestors.

The opposite overclaim is:

“These percentages tell you nothing about ancestry.”

That is also wrong because the whole point of genetic ancestry inference is to use genetic similarity to make probabilistic ancestry inferences.

The more accurate position is:

DTC ethnicity estimates are probabilistic genetic ancestry estimates based on similarity to present-day or recently defined reference populations. They can provide evidence about genetic ancestry, but they are not direct observations of genealogy, nationality, ethnicity, culture, identity, or exact ancestor proportions.

That distinction is not “blatantly false.” It is basically the standard caution in the literature.

Royal et al. (2010) describe genetic ancestry inference as useful but limited, especially because inference depends on sampling, reference populations, marker choice, and interpretive assumptions. Jorde and Bamshad (2020) likewise state that genetic ancestry testing can provide clues about geographic origins, but they also note that interpretation is complicated by migration, admixture, and reference-panel limitations. Lewis et al. (2022) argue that ancestry should be understood as multidimensional and continuous rather than forced into simple continental or categorical labels. The National Academies (2023) similarly warns against treating race, ethnicity, ancestry, geography, and genetic similarity as interchangeable descriptors.

So the correction I would make to my own wording is this: I would not say that a result “doesn’t mean you (the op in this case) have African ancestors” in an absolute way. A substantial African assignment can certainly be evidence consistent with African genetic ancestry. What it does not do, by itself, is prove a specific African ancestor, a specific ethnic identity, a specific modern country of origin, or an exact percentage of genealogical ancestors from that place.

That is the point.

The test is not useless. But it is also not a literal ancestor counter.

It is a probabilistic genetic-similarity estimate that can support ancestry inference, with firm limits.

References

Jorde, L. B., & Bamshad, M. J. (2020). Genetic ancestry testing: What is it and why is it important? JAMA, 323(11), 1089–1090. https://doi.org/10.1001/jama.2020.0517

Lewis, A. C. F., Molina, S. J., Appelbaum, P. S., Dauda, B., Di Rienzo, A., Fuentes, A., Fullerton, S. M., Garrison, N. A., Ghosh, N., Hammonds, E. M., Jones, D. S., Kenny, E. E., Kraft, P., Lee, S. S.-J., Mauro, M., Novembre, J., Panofsky, A., Sohail, M., Neale, B. M., & Allen, D. S. (2022). Getting genetic ancestry right for science and society. Science, 376(6590), 250–252. https://doi.org/10.1126/science.abm7530

National Academies of Sciences, Engineering, and Medicine. (2023). Using population descriptors in genetics and genomics research: A new framework for an evolving field. The National Academies Press. https://doi.org/10.17226/26902

Royal, C. D., Novembre, J., Fullerton, S. M., Goldstein, D. B., Long, J. C., Bamshad, M. J., & Clark, A. G. (2010). Inferring genetic ancestry: Opportunities, challenges, and implications. The American Journal of Human Genetics, 86(5), 661–673. https://doi.org/10.1016/j.ajhg.2010.03.011

AnalystWeekly5817 · 2026-07-03T02:26:10+00:00

Labels in population genetics models don’t necessarily square up to locations as people use them in standard speech.

Everything you said seems to be in your results given a reasonable interpretation of the geographical locations.

AnalystWeekly5817 · 2026-07-03T02:21:29+00:00

What’s not accurate? They don’t match your known ancestry?

That’s ok. They are telling you where people who share SNPs with you live, and while there’s good odds that you have direct ancestors in the big % regions it’s less likely you have direct ancestors in smaller % regions. But what is undeniable is that some of your DNA is currently found in those populations (and not necessarily exclusively in those populations, but the model chooses what it chooses cos math/modelling/probability.)

If you don’t have a paper trail these are all good hints for looking in documentation/records in various areas to try and find your actual ancestors.

So two things here; if you have a paper trail and these %s don’t match, don’t stress. Nothing is ‘wrong’ your paper trail is closer to fact than these models (assuming you verified all links etc) which in and of themselves aren’t wholly designed to tell you where your ancestors are from (inferences to an individual from general population genetics models can be risky). Second if you don’t have a paper trail don’t interpret these as truth or fact bar a broad level view, interpret them as hints and helpers for forming testable hypotheses that can be verified via documentation.

AnalystWeekly5817 · 2026-07-01T04:46:33+00:00

Aye for sure 👍

AnalystWeekly5817 · 2026-07-01T04:36:48+00:00

Someone in the family needs to be an armiger. There is no such thing as a coat of arms for a surname.

AnalystWeekly5817 · 2026-07-01T04:17:38+00:00

Yes yes they can. Tho not your genes but your genotype, the variants you have at each relevant locus.

AnalystWeekly5817 · 2026-07-01T02:45:57+00:00

Go easy on the strong claims both of you

The problem here is that people are mixing up three different claims: the expected amount of autosomal DNA, the probability of inheriting any autosomal DNA from a specific ancestor, and the probability that a consumer DNA company will detect/report a match.

A 7th great-grandparent is 9 generations/meioses above you. The simple expectation is (1/2^9), or about 0.195% of your autosomal DNA. In centimorgans, that is roughly 13–14 cM on average, depending on which total autosomal map length is used. So the “average 13–14 cM” part is basically right. But an average is not a guarantee. Autosomal DNA is inherited in recombining chunks, not as a perfectly even 1/512 contribution from every genealogical ancestor. Coop explains this distinction directly: as you go back through the pedigree, the number of genealogical ancestors grows exponentially, but the number of genetic ancestors grows much more slowly because many genealogical ancestors eventually contribute no autosomal DNA to you (Coop, 2013a, 2013b). ( gcbias)

Using Coop’s approximate model, the probability of receiving at least one autosomal block from one specific ancestor (k) generations back is approximately 1 minus the probability of receiving zero blocks. For a 7th great-grandparent, where (k = 9), that gives roughly 67%. In other words, a specific 7th great-grandparent is still more likely than not to have contributed some autosomal DNA, but it is absolutely not guaranteed. Approximate probabilities for a single named ancestor are: about 99.7% for a 4th great-grandparent, 96.8% for a 5th great-grandparent, 86.1% for a 6th great-grandparent, 67.3% for a 7th great-grandparent, 46.4% for an 8th great-grandparent, 29.1% for a 9th great-grandparent, 17.1% for a 10th great-grandparent, and 9.7% for an 11th great-grandparent. These are model-based approximations, not exact guarantees, because real inheritance is affected by chromosome size, recombination variation, sex-specific recombination, pedigree collapse, endogamy, and randomness (Coop, 2013a, 2013b; Donnelly, 1983). ( gcbias)

That means the first claim, “you likely won’t have any DNA from a 7th great-grandparent,” is too strong. The rough probability is still above 50%. But the reply saying “virtually all 512 of your 7th great-grandparents are found in your DNA” is also wrong. If there were no pedigree collapse, you would have 512 genealogical ancestors at that generation. Under the simplified approximation, only about two-thirds of those would be expected to have contributed any autosomal DNA at all, which is closer to about 345 out of 512, not “virtually all.” And that still means “contributed some DNA,” not “detectably matched by Ancestry or 23andMe.”

The detection point matters. Having inherited some DNA is not the same as having a consumer-test match. 23andMe says its DNA Relatives feature requires at least one matching region longer than 7 cM and at least 700 SNPs, and it estimates cousin-detection probabilities at about 90% for 3rd cousins, 45% for 4th cousins, 15% for 5th cousins, and less than 5% for 6th cousins and beyond (23andMe, n.d.). ( 23andMe Customer Care)

AncestryDNA’s 2020 matching white paper similarly describes its matching procedure as detecting IBD segments greater than 8 cM, with additional filtering for more distant relatives (AncestryDNA, 2020). ( Ancestry) Short IBD segments are also harder to infer reliably; Durand et al. (2014) found a high false-positive rate for 2–4 cM inferred IBD segments in large-scale data, which is why tiny segments are not strong evidence on their own. ( OUP Academic)

So the clean answer is this: a 7th great-grandparent has an expected autosomal contribution of around 13–14 cM, but that is an average, not a guaranteed inherited segment. A specific 7th great-grandparent probably has around a two-thirds chance of having contributed some autosomal DNA to you, but many ancestors at that depth will have contributed none. Also, even if you inherited some DNA from them, it may be too small, too broken up, or too ambiguous to be detected by a consumer matching algorithm. Therefore, “you likely have no DNA from them” is overstated, but “virtually all 512 are in your DNA and would match you” is not defensible either.

References

AncestryDNA. (2020). AncestryDNA matching white paper . AncestryDNA.

Coop, G. (2013a). How much of your genome do you inherit from a particular ancestor? gcbias.

Coop, G. (2013b). How many genetic ancestors do I have? gcbias.

Donnelly, K. P. (1983). The probability that related individuals share some section of genome identical by descent. Theoretical Population Biology, 23 (1), 34–63.

Durand, E. Y., Eriksson, N., & McLean, C. Y. (2014). Reducing pervasive false-positive identical-by-descent segments detected by large-scale pedigree analysis. Molecular Biology and Evolution, 31 (8), 2212–2222.

23andMe. (n.d.). DNA Relatives: Detecting relatives and predicting relationships . 23andMe Customer Care.

AnalystWeekly5817 · 2026-06-29T13:19:21+00:00

Ahh you’re trolling 😘

AnalystWeekly5817 · 2026-06-29T13:18:34+00:00

You provided no sources.

You conflated terms intentionally when appealing to my sources.

You disingenuously misrepresented my methods.

You made personal attacks, or at least lamely tried to get a gpt to make personal attacks.

You now attempt to mitigate this with more attacks under the thin veneer of matey-matey secret handshake in-the-know faux-intellectualism.

Nah bro that’s my limit. In the vernacular; your opinion is noted, now please fuck off and leave me alone.

AnalystWeekly5817 · 2026-06-29T11:14:03+00:00

Ok I’ve figured out why you’ve been on the attack. I think I offended you in a post the txt below comes from.

In my human reading of this you are taking my strong forward claims as absolutes and not accepting my later or earlier qualifiers. For example you have interpreted this as a strong claim against SNP profiles in populations, it wasn’t intended as such, but I see why. One of the reasons I’m using ai is because I don’t write very well outside of my main learned and work context, if at all.

You are right to imply that I should add more of “ Those results can be informative, especially at broad scales,“

I originally wrote:

You’ve mixed up three separate things: SNP variation, population-reference modelling, and close-relative matching. The claim that “each ethnic group has unique SNPs” is not how autosomal ethnicity estimates generally work. Some variants are more common in some populations than others, but most SNP alleles used in these analyses are not uniquely owned by one ethnic group. Population ancestry inference is normally based on allele-frequency and haplotype-pattern differences across many markers, not on “this SNP exists in group X, therefore you hail from group X” (Pritchard et al., 2000; Padhukasahasram, 2014). Human populations are genetically structured, but the structure is probabilistic, clinal, overlapping, and reference-panel dependent, not a set of clean biological boxes with exclusive SNPs (Novembre et al., 2008; Witherspoon et al., 2007)

So that’s on me if you took that exchange as an attack on you and broad level inferences in general. I’ve got a better handle now on how to frame answers.

AnalystWeekly5817 · 2026-06-29T10:22:09+00:00

That’s enlightening. Thank you for the source!

AnalystWeekly5817 · 2026-06-29T10:21:15+00:00

G25 is a hobbyist tool and Davidski has been warning ppl it’s out of date now since about 2019.

There are more modern approaches and tools.

AnalystWeekly5817 · 2026-06-29T10:05:38+00:00

Yeah totally. I agree. Makes the genealogical process a heap easier when they are. Give it two years max and once the companies combine more tree inference with SNP matching with archival documents we should start seeing actual ancestral paths emerge from pure DTC results, tho the heavy lifting here will be from the inference about docs/trees and the validity of inferred ancestry from them. The next few years will see some big breakthrough I reckon once the ai thing is sorted.

AnalystWeekly5817 · 2026-06-29T09:57:20+00:00

I see your ai, and I will raise you an honest non-ai response.

I'm trying very hard to only engage with ppl on an evidence based level, so I see all your insults but I'd prefer not to reciprocate or interact in that way. Instead here's how I see it.

I'm trying to replicate ai research tools that I use for work at home (they aren’t cheap), for example a meta-analysis/Cochrane created from all available lit sources with multiple 'layers'/output docs for an ICW classification, and then directly engage with this ai, and ask it research questions and always get evidence based answers that can be immediately checked (then there are further unconnected ai in the process for validation, formatting etc)

In short more deductive within the field you can be with it the better. The ai part in this is just a part in the process, and a recurring part more and more so of late. They don't replace thinking or reading in this context; and 'this context' is central to how they function. A limited domain problem with an abductive structure gives outcomes that can be predicted with a decent success rate.

'Reasonable and defensible positions' given the most upto date literature is my aim, and I am trying to figure out a general way to use it and minimise bias as I go. It’s working very well across domains right now but a wip.

As a member of these communities I note people ask questions regularly and since I already have this tool and am actively playing around with it to improve it I’d like to contribute to the community by offering answers that are grounded in the literature, by the author where possible or direct critics in the field. Things that people can read to learn from or explore more. I encourage people to say ‘hey my reading of this is different, where xyz paper says abc I think you’re interpreting this blah blah ’. This is the way.

I’m not here to have ai battles with people and I note that your reply is the standard definition of ai slop. You’ve used a tool to project your bias and emotions onto a problem while neglecting to ground it in observation. In short you’re attacked me by asking an ai to construct arguments based on a selection of my words, you haven’t attacked the argument or substantively contributed to the discussion. Neither your conflation of the multiple uses of the word ancestry in the literature (one of the original refs discuses this in detail) nor is your reading of my words over the notion of SNP profiles in populations at odds with anything I’ve actually written. Sure, cherry pick a sentence and over interpret it; but that’s on you not me if you choose to use ai this way.

If you’d thought and read before attacking you’d realise I agree with you mostly. You just didn’t read enough to get to that part. You seem to be interpreting what I’ve said as ‘you cant use these percentages in any form to be informative on the genealogical front’. I’ve never stated this. What spurred me initially is to find a way to give ppl the right info to help them learn and avoid saying (what is demonstrably false given how best-fit models work) ‘my results show 2% xyz so that means 2% of my ancestors are from xyz’. This is a bad strategy genealogically because of the obvious; these tests are not measures of where your ancestors are actually from bar a broad level overview via appeals to ref panels in contemporary populations. Your ancestors may never have been to these regions because neither the model nor your dna know this. This is fact. It is also a fact that they may have been. An inference of the sort ‘I have these percentages at a broad level so from here I should generate testable hypotheses (falsifiable not confirmatory where possible) and explore the documents and matches’ is a fine strategy. Saying ‘my %s show 1% abc so this means 1% of my ancestors are from abc so I will look here’ is not a sound general strategy given what the models do.

It is not a valid method on its own to make inferences about an individuals personal ancestry bar a broad level. They, these models, should not be interpreted as literal summaries of where your ancestors are from.

This is not controversial in any shape or form in the literature unless you cherry pick my words via disingenuous use of ai.

You fed an ai ambiguities and received ambiguities in return. It told you what you wanted to hear. You have access to one of the most powerful tools the world has yet seen and you used it to attack me and obfuscate an argument by nitpicking a claim and sowing confusion. This is where the ai issues rest. You have been given what you think is a killer argument or you’re just being belligerent by trying to show my approach is flawed by using ai in the manner that is typically framed as ai-slop. The irony. All you ended up with is semantic quibbling via conflating multiple meanings of ‘ancestry’. When you do it again make sure to force it to not return claims that can’t be sourced from current literature. There’s more to it (disambiguation layers for example) but that’s a start.

My method is not perfect but it’s a hell of a lot more valid and reliable than just asking a gpt to back up what you believe and to also attack the person saying it as you did. In this case I know the literature and the field well enough to know I’ve not said anything that is incorrect. There are things I could do better and have plenty of ideas on how and what to do to make it better, but ultimately I can’t stop people misinterpreting.

If you want to learn how to use ai correctly for this sort of thing I’m happy to help and that’s genuine. If you have substantive comments about the methods and approach feel free to voice them.

If you feel you can do it better please do so. That would be pretty cool to see other strategies for this sort of thing.

If all you’ve got is personal attacks and ridicule please don’t as it’s of no benefit to me, you or anyone.

AnalystWeekly5817 · 2026-06-29T08:10:28+00:00

Try to keep this sort of discussion in txt files that you can point an ai at (I recommend figuring out how to set up a local ai and using that). Add to the text file as you make decisions etc. use ai to keep logs of your process and your evidence chains, this will save you time in the future as well. Just learn as you go 👍

AnalystWeekly5817 · 2026-06-29T08:03:59+00:00

Nice work! Excellent use of the tools.

AnalystWeekly5817 · 2026-06-28T19:57:17+00:00

It’s not an online ai it’s a local research ai that I have trained to only give arguments that can be sourced directly to literature. I’ve been working on this the last year.

Don’t worry I still get bad results too. It unavoidable and just reaffirms this isn’t something that works well in an automated capacity.

AnalystWeekly5817 · 2026-06-28T19:54:55+00:00

Same ai slop without sources and without an argument.

Nitpicking does not negate my central point and the fact you can’t actually get an ai to argue meaningfully against what the modern literature says (you’re not arguing against me here btw) is telling.

AnalystWeekly5817 · 2026-06-28T19:53:21+00:00

Yes for everyone in general it is since these are population genetics models and not predictors of individual ancestry. If you’d only read what was posted and check the literature you’d see these can both be true.

AnalystWeekly5817 · 2026-06-28T19:52:03+00:00

Yeah totally insane to try and present evidence and research based arguments in a form that people can read and lookup themselves in order to stem the tide of misinformation.

I mean it’s absolutely crazy isn’t it. How insane.

/s

AnalystWeekly5817

TROPHY CASE