dna

The Overall Migration of Humanity

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Earlier this week I introduced a new ancestry algorithm, that is really incredible. It’s based upon a previous algorithm I introduced a few years back in a paper called “A New Model of Computational Genomics” [1]. The core difference between the new algorithm, and the algorithm introduced in [1], is that the algorithm introduced in [1] is a necessary but not sufficient condition for ancestry. This new algorithm, is instead a necessary and sufficient condition for ancestry, with a clearly identifiable risk, that is discussed in the note linked to above. Specifically, the risk is that the dataset only contains Denisovan, Heidelbergensis, and Neanderthal genomes, and as a consequence, because the test assumes it is considering exactly one archaic genome at a time, if it encounters e.g., Homo Erectus mtDNA, it won’t be able to identify it. Because the list of archaic humans keeps growing, this is a real and unavoidable risk, but as a whole, the algorithm clearly produces meaningful results. Most importantly, it produces results that are consistent with my “Migration Back Hypothesis” [2], that humanity began in Africa, migrated to the Middle East, then to Asia, and then came back to Europe and Africa, and spread further out from Asia into South East Asia.

The narrative is that life begins in Africa, somewhere around Cameroon, and this is consistent with the fact that the modern people of Cameroon test as the ancestors of Heidelbergensis, Neanderthals, and archaic Siberian Denisovans. See [2] for details. Heidelbergensis is clearly the ancestor of the Phoenicians, and you can run the test to see this, or read [2], where I actually analyze the Phoenician and Heidelbergensis genomes, segment by segment, demonstrating a clear ancestry relationship. The Phoenicians are in turn the ancestors of the Old Kingdom Ancient Egyptians, and this is where things get complicated.

The Old Kingdom Ancient Egyptians are obviously Asian, and this is based upon archeology, where depictions of Ancient Egyptian leaders and others are obviously of Asian origin, in particular Nefertiti. This checks out with the Old Kingdom Ancient Egyptian genome in the dataset, as it is a 99% match to many South East Asians in Thailand, Korea, and Japan in particular. The Phoenicians are clearly the maternal ancestors of the Ancient Egyptians, and so the question is, did the Phoenicians travel to Asia, eventually producing the Ancient Egyptian maternal line? The answer according to the new test is again yes, specifically, the modern Sardinians (who are basically identical to the Phoenicians) test as the ancestors of the modern Sri Lankan people. Previously, I did exactly this test in [2], and in that case, the Phoenicians again tested as the ancestors of the Sri Lankan people. The problem in [2], is that it was a low confidence answer, whereas the updated test provides a high confidence answer, drawn from the entire dataset of genomes. Finally, I’ll note that many modern Scandinavians and some other Europeans (typically in the North) are 99% matches to the Ancient Egyptian line. Putting it all together, humanity begins somewhere around Cameroon, migrates to the Middle East, and then migrates to Asia, where it then spreads back to Northern Europe and Africa, and spreads further into South East Asia. This is not different from the thesis presented in [2], but that thesis is now supported by a single test that draws on every genome in the dataset, creating clear scientific evidence for what was presented in [2] as a mix of archeological, scientific, and common sense reasoning.

Updated Algorithmic Ancestry Test

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Introduction

In a previous post, I shared what I thought was a clever way of testing for ancestry, that turned out to be a failure empirically. I now understand why it doesn’t work, and it’s because I failed to consider an alternative hypothesis that is consistent with the purported facts. This produced a lot of bad data. I’ll begin by explaining how the underlying algorithmic test for ancestry works, and then explain why this instance of it failed, and close by introducing yet another test for ancestry that plainly works, and is simply amazing, allowing us to mechanically uncover the full history of mankind, using mtDNA alone.

Algorithmic Testing for Ancestry

Assume you’re given whole mtDNA genomes A, B, and C. The goal is to test whether genome A is the ancestor of both genomes B and C. It turns out, this is straight forward as a necessary (but not sufficient condition) for ancestry. Specifically, if we begin with genome A, and then posit that genomes B and C mutated independently away from genome A (e.g., groups B and C travelled to two distinct locations away from group A), then it is almost certainly the case that genomes B and C have fewer bases in common with each other, than they have in common with genome A.

For intuition, because we’ve assumed genomes B and C are mutating independently, the bases that mutate in each of B and C are analogous to two independent coins being tossed. Each mutation will reduce the number of bases in common with genome A. For example, if genome B mutates, then the number of bases that A and B have in common will be reduced. Note we are assuming genome A is static. Because B and C are mutating independently, it’s basically impossible for the number of bases in common between B and C to increase over time. Further, the rate of the decrease in common bases is almost certainly going to be higher between B and C, than between A and B, and A and C. For example, if there are 10 mutations in each of genomes B and C (i.e., a total of 20 mutations combined), then the match counts between A and B and A and C, will both decrease by exactly 10, whereas the match count between B and C should decrease by approximately 20. Let |AB| denote the match count between genomes A and B. We have then the following inequalities:

Case 1: If genome A is the common ancestor of both genomes B and C, then it is almost certainly the case that |AB| > |BC| and |AC| > |BC|. See, “A New Model of Computational Genomics” [1] for further details.

Even though this is only a necessary condition for ancestry, this pair of inequalities (coupled with a lot of research and other techniques), allowed me to put together a complete, and plausible, history of mankind [2], all the way back to the first humans in Africa.

Ancestry from Archaic Genomes

The simple insight I had, was that if A is not archaic, and B is archaic, then A can’t credibly be the ancestor of B. That is, you can’t plausibly argue that a modern human is the ancestor of some archaic human, absent compelling evidence. Further, it turns out the inequality (since it is a necessary but not sufficient condition) is also consistent with linear ancestry in two cases. Specifically, if |AB| > |BC| and |AC| > |BC|, then we can interpret this as consistent with –

Case 2: B is the ancestor of A, who is in turn the ancestor of C.

Case 3: C is the ancestor of A, who is in turn the ancestor of B.

If you plug in A = Phoenician, B = Heidelbergensis, and C = Ancient Egypt, you’ll find the inequality is satisfied for 100% of the applicable genomes in the dataset. Note that the dataset is linked to in [1]. It turns out you simply cannot tell what direction time is running given the genomes alone (unless there’s some trick I’ve missed), and so all of these claims are subject to falsification, just like science is generally. That said, if you read [2], you’ll see fairly compelling arguments consistent with common sense, that Heidelbergensis (which is an archaic human), is the ancestor of the Phoenicians, who are in turn the ancestors of the Ancient Egyptians. This is consistent with case (2) above.

Putting it all together, we have a powerful necessary condition that is consistent with ancestry, but not a sufficient condition, and it is therefore subject to falsification. However, one of these three cases is almost certainly true, if the inequalities are satisfied. The only question is which one, and as far as I can tell, you cannot determine which case is true, without exogenous information (e.g., Heidelbergensis is known to be at least 500,000 years old). You’ll note that cases (1), (2), and (3) together imply that A is always the ancestor of either B or C, or both. My initial mistake was to simply set B to an archaic genome, and assert that since A cannot credibly be the ancestor of B, it must be the case that A is the ancestor of C. Note that because A cannot credibly be the ancestor of B, Cases (1) and (3) are eliminated, leaving Case (2), which makes perfect sense: B is archaic, and is the ancestor of A, who is in turn the ancestor of C. However, this is not credible if C is also archaic, producing a lot of bad data.

Updated Ancestry Algorithm

The updated algorithm first tests literally every genome in the dataset, and asks whether it is at least a 60% match to an archaic genome, and if so, it treats that genome as archaic for purposes of the test, so that we avoid the problem highlighted above. This will allow us to reasonably assert that all tests involve exactly one archaic genome B, and therefore, we must be in Case (2). Interestingly, some archaic populations were certainly heterogenous, which is something I discussed previously. As a result, there are three ostensibly archaic genomes, that do not match to any other archaic genomes in the dataset, and they are therefore, not treated as archaic, despite their archeological classification. You can fuss with this, but it’s just three genomes out of 664, and a total of 19,972,464 comparisons. So it’s possible it moved the needle in marginal cases, but the overall conclusions reached in [2] are plainly correct, given the data this new ancestry test produced.

There is however the problem that the dataset contains only Heidelbergensis, Denisovan, and Neanderthal genomes, leaving out e.g., Homo Erectus, and potentially other unknown archaic humans. There’s nothing we can do about this, since we’re constantly finding new archaic humans. For example, Denisovans were discovered in 2010, which is pretty recent, compared to Heidelbergensis, which was discovered in 1908. Moreover, the three genomes in question are possibly three new species, since they don’t match to Denisovan, Heidelbergensis, or Neanderthals. All of that said, taken as a whole, the results produced by this new algorithm, which makes perfect theoretical sense and must be true, are consistent with the results presented in [2]. Specifically, that humans began in Africa, somewhere around present day Cameroon, migrated to the Middle East, then Asia, producing the two most evolved maternal lines that I’ve identified, somewhere around Nepal. Those two maternal lines are both found around the world, and descend from Denisovans and Heidelbergensis, respectively, suggesting that many modern humans are a mix between the most evolved maternal lines that originated in two distinct archaic human populations, effectively creating hybrids. For your reference, you can search for the Pre Roman Ancient Egyptian genome (row 320, which descends from Heidelbergensis) and the Icelandic genome (row 464, which descends from Denisovans).

The Distribution of Archaic mtDNA

When I first started studying mtDNA, I quickly realized that a lot of modern humans have archaic mtDNA. See [1] for details. This is not surprising, since mtDNA is so stable, and inherited directly from a mother to its offspring, and modern humans carry at times significant quantities of archaic DNA generally. That said, 53.01% of the genomes in the dataset test as archaic, meaning that the genome is a few hundred thousand years old, without that much change. I’ve seen studies that say some humans contain around 7% to 10% archaic DNA (on the high end). This is not exactly the same statement, since those types of studies say that around 7% to 10% of someone’s DNA could be archaic. In contrast, my work suggests that a significant majority of living human beings contain outright archaic mtDNA.

That said, I’m using whole-genome sequencing, with a single global alignment, which maximizes the differences between genomes. See [2] for more details. So it’s possible that as techniques improve, studies in other areas of the human genome will produce results similar to mine, since most researchers are (as far as I know) still focusing on genes, which are a tiny portion of the whole genome. Generally speaking, my work shows that focusing on genes is probably a mistake, that was driven by necessity since genomes are huge, and computers were slow. See [1] for empirical results that demonstrate the superiority of whole-genome analysis. I did all of this on a Mac Mini, and it runs in about 3 hours, and requires comparing all triplets of genomes, drawn from a dataset of 664 genomes (i.e., rows), where each genome has 16,579 bases (i.e., columns). This works out to O(10^{21}) calculations, all done on a consumer device. I’d wager professional computers can now start to tackle much larger genomes using similar techniques. As a result, I think we’re going to find that a lot of people contain a lot of truly archaic DNA generally. Any argument to the contrary is sort of strange, because if people stopped selecting archaic female mates, then archaic mtDNA should have vanished, and it obviously didn’t, leading to the conclusion, that the rest of the genome likely does contain archaic DNA generally.

Below I’ve set out a list of populations ordered according to the percentage of genomes within that population that test as archaic, starting at 0% archaic, and increasing up to 100% archaic, i.e., in increasing order. The test performed was to ask, for each population, what percentage of the genomes in that population are at least a 60% match to at least one archaic genome. Again, 53.01% of the full dataset tested as archaic, and as you’ll see below, several modern populations consist of only archaic mtDNA (i.e., 100% of the genomes are a 60% match to at least one archaic genome). One immediate takeaway, is that the classical world seems to have absolutely no archaic mtDNA. I’ve also noted that the Ancient Roman maternal line seems to have been annihilated, which was almost certainly deliberate.

Finally, I’ll note that preliminary results suggest that the Ancient Roman maternal line (which again, no longer exists, anywhere in the world) seems to be the most evolved maternal line in the entire dataset.

The code is attached to the bottom of the post.

0% Archaic

Ancient Egyptian
Ancient Roman
Basque
Phoenecian
Saqqaq
Thai
Igbo
Icelandic
Hawaiin
Dublin
Sri Lanka

10% to 49% archaic

Polish
Sardinian
Tanzania
Korean
German
Swedish
Scottish
Nepalese
Japanese
Sami
Filipino
Dutch
Spanish
Sephardic
Belarus
Norwegian
Egyptian
Finnish
Pashtun
Ukrainian
Irish
French
Portuguese
Danish
English

50% to 99% Archaic

Chinese
Maritime Archaic
Georgian
Munda
Nigerian
Ashkenazi
Mongolian
Hungarian
Ancient Finnish
Greek
Russian
Mexican
Vedda Abor.
Italian
Turkish
Chachapoya
Khoisan
Neanderthal
Uyghur
Kenyan
Indian
Saudi
Kazakh
Denisovan
Mayan
Taiwanese

100% Archaic

Iberian Roma
Heidelbergensis
Papau New Guinea
Ancient Bulgarian
Ancient Chinese
Sol. Islands
Indonesian
Andamanese
Iranian
Ancient Khoisan
Javanese
Jarkhand
Cameroon

Ancestry AlgoDownload

Algorithmic Testing for Ancestry

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In a paper I wrote entitled A New Model of Computational Genomics [1], I presented a simple test for ancestry that is impossible to argue with. Let |AB| denote the number of matching bases between two genomes A and B. Given genomes A, B, and C, if we assume that genome A is the common ancestor of genomes B and C, then it is almost certainly the case (see [1] for a discussion of the probabilities) that |AB| > |BC| and |AC| > |BC|. That is, genomes A and B, and A and C, almost certainly have more bases in common than genomes B and C. For intuition, beginning with genome A, and assuming independent mutations away from A to genomes B and C, this is like tossing two independent coins (i.e., the mutations within genomes B and C that deviate from A), which should not have more than chance in common. As such, B and C should deviate away from each other at a faster rate than they deviate from A individually.

Now this is already really powerful, and led me to a complete history of mankind, which is more than plausible. But that said, it’s a necessary condition, not a sufficient condition. That is, if genome A is the common ancestor of genomes B and C, then the inequalities above almost certainly hold, but it’s subject to falsification (i.e., it’s not a sufficient condition). I realized tonight, you can actually transform this into a necessary and sufficient condition.

Specifically, the inequality above can be represented as a graph where A is connected to B, A is connected to C, and B is connected to C, with the match counts labelling the edges of the graph. For example, the edge connected A to B would be labeled with |AB|, which will be some integer. If the inequalities are satisfied, only two such graphs out of six are plausible, for the same reasons that underly the inequality. Specially, if I assume A is the ancestor of B, which is in turn the ancestor of C, then A and C almost certainly have fewer bases in common than A and B.

The graphs that remain, imply that if the inequality is satisfied, then A is almost certainly the ancestor of either B or C, or both, as a necessary and sufficient condition. If we plug in an implausible genome for either B or C (e.g., assuming that the Norwegians = A are the ancestors of Heidelbergensis = B), then the inequality serves as a necessary and sufficient condition for the descendants of the Norwegians, i.e., genome C. I will write more about this tomorrow, including code and some testing.

UPDATE 10/19/25

I’ve implemented a new version of the ancestry algorithm, which so far seems to work. Code is attached below, more to come!

 

Generate Ancestry MatrixDownload

On the Classification of Archeological Finds

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I noticed a while back that individual subspecies of archaic humans were actually heterogenous, at least with regards to their mtDNA. In particular, the Neanderthal genomes in my dataset are actually 6 completely different maternal lines. There are 10 Neanderthal genomes in total, and the breakdown is (i) genomes 1, 2, and 10 are at least a 99.5% mutual match to each other, (ii) genomes 5 and 6 are a 63.4% match to each other, (iii) genomes 8 and 9 are a 99.9% match to each other, and (iv) genomes 3, 4, and 7 are unique, and have no meaningful match to each other or the rest of the Neanderthal genomes. Further, clusters (i), (ii), and (iii) have no meaningful match to each other. The plain result is that we actually have a heterogenous group of genomes, that have nonetheless been classified as Neanderthal.

Now I’m in no position to criticize archaeological work, but you can’t ignore the fact that we have 6 completely distinct classes of genomes. Because, by definition, there must be 6 distinct maternal lines in this population, it’s probably the case that the rest of the genome also differs meaningfully, though note the number of paternal lines could be larger or smaller than 6. But the point remains, the genomes probably differ generally, not just along the maternal line.

As a result, we have to ask whether we actually have a single subspecies. If we take that view, then the subspecies is the result of the mixing of these 6 distinct maternal lines. And this makes perfect sense, because the vast majority of human populations have heterogeneous maternal lines, and the only exceptions I’m aware of are the Romani People and the Papuans, who are almost perfectly homogenous on the maternal line. It’s worth noting that Romani mtDNA is basically identical to Papuan mtDNA, so there’s probably something to that.

We could instead take the view that the archeological classification is wrong, and that mtDNA controls the definition of a subspecies. I think this is a little aggressive, given that mtDNA is a very small portion of the overall human genome. But at the same time, mtDNA conveys a lot of information about heredity and even conveys information about paternal ancestry, which is amazing. That said, I think the better view is that a given group of people is (generally speaking) the result of a heterogenous group of people that is roughly stable over some period of time, in terms of its distribution of underlying genomes. This apparently applies to archaic humans as well, who seem to be (in at least this case) heterogenous.

Interestingly, the Denisovan genomes in the dataset are all a 97% match to each other, except one, which is totally unique. All of the genomes were (based upon the provenance files) taken from Denisova Cave in Siberia. Though we can’t know, it’s at least possible Denisovans were a more insular group of people than the Neanderthals. It’s possibly unscientific, but the Finns have a lot of Denisovan mtDNA, and they speak a language that is totally different from the Swedes, Norwegians, and Russians, despite sharing large borders with all three countries, suggesting the Finns really are an insular people.

Below are links to the genomes on the NIH website:

Neanderthal Genomes

1. https://www.ncbi.nlm.nih.gov/nuccore/OM062614.1

2. https://www.ncbi.nlm.nih.gov/nuccore/MT677921.1

3. https://www.ncbi.nlm.nih.gov/nuccore/MT795654.1

4. https://www.ncbi.nlm.nih.gov/nuccore/MT921957.1

5. https://www.ncbi.nlm.nih.gov/nuccore/MT576650.1

6. https://www.ncbi.nlm.nih.gov/nuccore/MK123269.1

7. https://www.ncbi.nlm.nih.gov/nuccore/KY751400.2

8. https://www.ncbi.nlm.nih.gov/nuccore/MK033602.1

9. https://www.ncbi.nlm.nih.gov/nuccore/MK033602.1

10. https://www.ncbi.nlm.nih.gov/nuccore/KU131206.2

Denisovan Genomes

1. https://www.ncbi.nlm.nih.gov/nuccore/KX663333.1

2. https://www.ncbi.nlm.nih.gov/nuccore/KT780370.1

3. https://www.ncbi.nlm.nih.gov/nuccore/MT576653.1

4. https://www.ncbi.nlm.nih.gov/nuccore/MT576652.1

5. https://www.ncbi.nlm.nih.gov/nuccore/MT576651.1

6. https://www.ncbi.nlm.nih.gov/nuccore/NC_013993.1

7. https://www.ncbi.nlm.nih.gov/nuccore/FR695060.1

8. https://www.ncbi.nlm.nih.gov/nuccore/FN673705.1

On the origins of modern human mtDNA

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In my paper, A New Model of Computational Genomics [1], I introduce a simple test for ancestry that cannot credibly be argued with. The argument is as follows: assume that we begin with genome A in location a, and that three groups of individuals with genome A all begin in location a. Now assume that two of those groups go to different locations, specifically, that one group goes to location b and the other group goes to location c. Because mtDNA is so stable, it could be the case that even over significant amounts of time, the populations in locations b and c, still have genome A, with basically no mutations. If however, any mutations occur, it cannot credibly be the case that genomes in location b (genome B) and location c (genome C) develop even more bases in common with each other. This becomes increasingly unlikely as a function of the number of new matching genomes between B and C, and is governed by the binomial distribution. As a consequence, if A is the common ancestor of genomes B and C, it must be the case that |AB| < |BC| and |AC| < |BC|, where |xy| denotes the number of matching bases between genomes x and y. That is, A must have more bases in common with B and C, than B and C have in common with each other, since B and C independently mutated away from genome A.

Applying this test, we find that the Old Kingdom Ancient Egyptians are the common ancestors of basically all Northern Europeans, many Africans, Asians, and in particular, South East Asians. I’ve also noted repeatedly that the Old Kingdom Ancient Egyptians appear to be Asian, which, superficially, makes no sense. Finally, I’ve noted that Heidelbergensis plainly evolved into Phoenicians, and then the Old Kingdom Ancient Egyptians. Phoenicians appear in Asia on the maternal line, in particular in Sri Lanka.

Putting it all together, tonight I tested which population is most likely to be the ancestor of the Old Kingdom Ancient Egyptians, and the clear answer is the Sri Lankans. The attached code runs the test, and produces a normalized score. The Sri Lankans scored 17.36, and the next best answer was the Vedda Aboriginals (also in Sri Lanka), with a score of 8.3064. The plain implication is that the mutation from the Phoenician maternal line, into the Old Kingdom Ancient Egyptian maternal line took place in Sri Lanka, or somewhere very close.

This completes the history of mankind, with the people of Cameroon the likely source population of all of mankind (including the Denisovans, Heidelbergensis, and Neanderthals), Heidelbergensis then evolving into the Phoenicians, the Phoenicians traveling to Asia, there evolving into the Old Kingdom Ancient Egyptian maternal line, who then migrated back to North East Africa, forming the cradle of modern human mtDNA all over the world, suggesting they were even more successful as a people than current history suggests.

Single Genome Anc TestDownload

The Rate of Mutation of mtDNA

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I’ve written in the past on the topic of the rate of mutation of mtDNA, in an attempt to calculate the age of mankind. It turns out, there really isn’t a good single answer to the rate at which human mtDNA mutates, and as a result, you really can’t come to any clear answer using mtDNA alone. And in fact, I realized the other day, that it seems to vary by maternal line. Specifically, some modern humans carry archaic mtDNA, in particular Heidelbergensis, Denisovan, and Neanderthal mtDNA. Other modern humans carry  mtDNA that is basically identical to ancient mtDNA (e.g., 4,000 years old), but not archaic mtDNA (e.g., 100,000 years old). In particular, many modern humans globally carry Ancient Egyptian mtDNA, from about 4,000 years ago.

You can get an idea of the rate of mutation, by taking e.g., a modern human that has Denisovan mtDNA, and comparing that to a bona fide archaic Denisovan genome, count the number of changed bases, and then divide by the number of years since the archaic sample lived, which will produce a measure of the number of changed bases per year. This can of course be expressed as a percentage of the total genome size, which is what I’ve done below.

We can be a bit fancier about it, by comparing a given genome to many others, producing a distribution of the number of changed bases per year. The code below does exactly this, producing the average total percentage change, minimum total change, maximum total change, and standard deviation over all total changes. The comparison was made only to modern genomes, and so we can take the known (and plainly approximate) date of the archaic / ancient genome, and divide by the number of years to the present. This will produce a rate of change per year, which I’ve expressed as a rate of change per 1,000 years.

The results are as follows:

Genome TypeAvg. ChangeMin. ChangeMax. ChangeStd. DeviationGenome DateAvg. Change Per 1000 Years
Denisovan26.39%25.76%32.70%1.99%120,000 BP0.22%
Neanderthal3.74%2.79%36.60%3.27%80,000 BP0.047%
Heidelbergensis4.27%3.30%37.61%3.30%430,000 BP0.0099%
Ancient Egyptian3.74%0.17%35.23%8.32%4,000 BP.935%

Again, note that Denisovan, Neanderthal, and Heidelbergensis are all archaic humans. In contrast, the Ancient Egyptians are of course ancient, but not archaic. The dataset contains 664 rows, 76 of which are archaic or ancient, which leaves 588 rows for the comparisons produced above. As a result, even though the table above was produced using only 4 input genomes, the results were generated comparing each of the 4 input genomes to all 588 complete, modern human mtDNA genomes in the dataset. The plain implication is that modern human mtDNA is evolving faster than archaic human mtDNA, since, e.g., the Ancient Egyptian genome has an average total rate of change equal to that of the Neanderthals, despite having only 4,000 years to achieve this total change, in contrast to the roughly 120,000 years that have passed since the Neanderthal genome. Technically, we should only be testing genomes we believe to be descended from the archaic / ancient genomes, since e.g., it is theoretically possible that a modern person has mtDNA that predates the Ancient Egyptian genome, since mtDNA is so stable. That said, the bottom line is that this is a measure of the variability of a particular maternal line, and the amount of mutation cannot exceed that variability. For this and other reasons, more studies are required, but this is an interesting observation.

The code is below, the balance of the code can be found in my paper, A New Model of Computational Genomics.

Rate of MutationDownload

Ancestry Test Code

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As I’ve noted several times, I’ve devised an ancestry test that is impossible to argue with, using whole-genome mtDNA. See Section 6.1 of A New Model of Computational Genomics [1]. Specifically, given whole-genomes A, B, and C, if genome A is the ancestor of both genomes B and C, then it must be the case with near certainty that genomes A and B, and A and C, have more bases in common than genomes B and C. Again, see [1] for an explanation. The test in Section 6.1 of [1] is at the genome level, and as such, using a dataset of genomes, the number of tests required to compare whether an entire population is the ancestor of two other populations grows quickly as a function of population sizes. As a tractable approximation, the attached code uses the average match count between populations A and B, A and C, and B and C, which of course loses information, but should at least help you reduce the number of cases that you investigate exhaustively.

Applying the attached, it turns out, that yet again, the Denisovans test as the common ancestor of humanity (though I now think the Cameroon might be more modern than I first suspected), specifically, the common ancestor of both Heidelbergensis and Neanderthals. Further, the Phoenicians again test as the common ancestor of basically everyone alive today, including the modern Thai, Nigerians, Norwegians, Koreans, the Saqqaq (in B.C. Greenland!), the Swedes, Indians, and Chinese. As a result, I’m fairly convinced early Middle Eastern people settled a significant portion of Europe and Asia, and possibly America (given Greenland), but I can’t put a date on it. Ugarit goes back to 6,000 BC, which should leave enough time, but this is an ordinal test only, and therefore cannot be used to date the relationships. Moreover, I’ve recently cast serious doubt on the idea that mtDNA has a single, stable rate of mutation. The net point is, therefore, the ancestry test is real, and very difficult to argue with, but limited to ordinal testing; further, mtDNA doesn’t seem to have a single, stable rate of mutation; as a result, it looks plausible (1) that the Denisovans are the first humans and (2) that either the Phoenicians or people close to them (on the maternal line) we’re prolific settlers, but we don’t know when either got started.

The code is below, the balance can be found in [1]. One modification I plan to make is to use Monte-Carlo probing on the data that informs the averages. This will allow you to test a fixed portion of the genome-level data that you can scale given the power of your machine. BTW I just bought a Mac Mini running the M2 Pro chip, and I cannot recommend this machine enough, it is more than 10 times faster than my windows laptop. Running the ancestry test described above over 673 full mtDNA genomes takes about 0.5 seconds. I cannot believe this is a retail machine.

class-based-ancestry-algorithmDownload

On the Age of Humanity

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Introduction

My research shows unequivocally, that archaic humans are still alive today, in that many living humans carry archaic mtDNA. The obvious question is, how did archaic humans survive for so long? The answer is, they probably didn’t, but their mtDNA did, just like the widely accepted fact that many living humans carry archaic DNA generally. What makes mtDNA unique, is that it is so stable, passed from a mother to its offspring, with basically no mutations at all, even over thousands of years. One estimate claims that one mutation occurs roughly every 7,990 years, though this estimate is qualified and plainly subject to doubt. I show below that assuming this is correct, Denisovan mtDNA existed about 38,000,000 years ago.

This is obviously way earlier than anyone thinks, but it’s not totally absurd, especially in light of relatively recent finds, including Graecopithecus, which was dated to 7.2 million years ago, in Greece, not Africa, which of course implies it’s possible the species emerged much earlier in Africa itself. Also note that we’re only discussing mtDNA, not the full genome. As a result, the claim is limited to the existence of Denisovan mtDNA, not the full genome. The discussion below of course considers the case that the estimate of 7,990 years per mutation is simply wrong, which is arguably the point of this note. Specifically, not all systems have stable averages over time, and a system as complex as the human genome of course might not behave in a predictable, stable manner.

Alignment, Insertions, and Deletions

Assume you have two copies of the exact same genome, and call them A and B. Note that mtDNA is N = 16,579 bases long, and as a result, the match count between genomes A and B is 16,579 bases, or 100% of the genome. Now insert a random base in genome B, at index 2. This will shift every base after the first index in B, by 1 position. This should cause the remaining N-1 bases to match to genome A about 25% of the time. That is, because we’ve shifted one of the otherwise identical genomes by one base, whatever bases that happen to match post insertion, should be the result of chance, and because there are four possible bases, the probability of a match is 1/4. Note that a deletion will cause an analogous reduction to chance. As a result, a single insertion or deletion will cause the match count to drop to around chance, after the index of the insertion or deletion.

The work I present in, “A New Model of Computational Genomics” [1], makes use of a global alignment, which means that when comparing two genomes, you assign each base an index, and the comparisons are made by testing whether the bases are equal at each index. The match count is simply the total number of matching bases. See [1] generally. In contrast, local alignments take segments from a given genome A (e.g., bases 1 through 100), and attempt to find the highest match count anywhere in genome B (e.g., bases 100 through 200). This would therefore, ignore insertions and deletions, since e.g., in the example above, a local alignment would search all of genome A for the best match, which would produce a match count of N (i.e., 100% of the genome), with one “gap” to account for the insertion. In contrast, a global alignment (i.e., just counting matching corresponding bases) would produce a match count of 1 + approximately 0.25*(N-1) (i.e., the first matching base, plus approximately 25% of the remaining N-1 bases).

Insertions and deletions are, at least anecdotally, very impactful in terms of the affect they have, since, e.g., Williams Syndrome, Down Syndrome, and many others, are caused by insertions and deletions. As a result, it’s not surprising that local alignments don’t seem terribly useful in terms of predictive power, because they effectively ignore insertions and deletions, creating very high match counts across all human mtDNA. In contrast, the software in [1], makes use a global alignment, which ultimately allows ethnicity to be predicted with approximately 80% accuracy.

Application to Data

As noted in [1], and many other research notes I’ve written, there are plenty of modern living humans with archaic mtDNA, in particular, Denisovan mtDNA. Denisovans test as the common ancestor of all archaic humans, suggesting that they are in fact the first humans. Though technically the modern people of Cameroon test as the ancestors of the Denisovans, which is again possible because mtDNA is so stable, I’ll work instead with the actual Denisovan genomes in my dataset, which were all taken from the NIH database.  The goal of this section is to approximate the date of the first Denisovans, given the genomes of modern living humans that carry Denisovan mtDNA, and the actual Denisovan genomes recovered from Siberia. There are 8 such Denisovan genomes in the dataset, out of a total of 664 genomes. All genomes are complete mtDNA genomes, again taken from the NIH database.

If we fix a minimum match threshold of 50% of the genome, we find that 82 non-Denisovan genomes are at least a 50% match to at least one Denisovan genome. These are living, modern humans that carry Denisovan mtDNA. The average match count over all such genomes is 11,779.32 bases, or 71.05% of the full genome. This means that since the Denisovan cave, 100% – 71.05% = 28.95% of the genome has mutated. This is 4,799.62 bases.

Though the rate at which mtDNA mutates is still a subject of discussion, as noted above, one cited figure is one mutation per 7,990 years. This would put the age of the Siberian Denisovans at 38,348,963.80 years before the present. This is way out of the ballpark for the low-end of what I’ve seen regarding the dates of these finds, which is around 300,000 years ago. As noted above, it’s at least possible that the modern living Denisovans instead carry the mtDNA of the ancestors of the Siberian Denisovans, which would again force us to reject the date of 38,348,963.80 years before the present. However, the data suggests this is not the case. See Section 6 of [1] generally.

It could also be the case that a single insertion or deletion is causing the match count to drop to around 70% of the genome when comparing the Siberian Denisovans to modern living humans. That is, there’s a single insertion or deletion further down the genome that causes the balance of the genome match count to drop to around 70%. This would not require that much time, since it is technically a single mutation. We can however rule this out by looking at the distribution of the matching bases along the genome. This can be done by grouping sequential bases (i.e., bases 1 through K, K+1 through 2K, etc), and then counting the percentage of matching bases in those segments. If the matching percentage of bases in each segment is always significantly above 25%, then it simply cannot be the case that the resultant match count is due to a single insertion or deletion within a given segment. The chart below shows the average percentage of matching bases for all 8 of the Siberian Denisovan genomes when compared to all other genomes that have at least a 50% match, breaking the full genome into 100 segments of 165 bases each.

You can plainly see that it’s not the result of a single insertion or deletion, since the match count is always above 40% of the bases in each segment. That said, there is still plainly a portion of the genome from around segment 5 to segment 40, that seems to have been impacted by insertions and deletions, but this is distinct from a single trivial insertion or deletion. As a result, we have an enormous amount of change to account for when comparing Siberian Denisovan mtDNA to the mtDNA carried by some modern, living humans. This again implies that either the estimated rate of mutation is wrong (probably correct) or the dates associated with the Siberian cave are way off (not as convincing). The software for this is below, and the balance of the software can be found in [1].

analyze-match-distributionpdfDownload

Phoenicians as Common Ancestor

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In a previous article, I showed that the people of Cameroon test as the ancestors of Heidelbergensis, Neanderthals, and Denisovans, with respect to their mtDNA. The obvious question is, how is it that archaic humans are still alive today? The answer is that they’re probably not truly archaic humans, but that their mtDNA is truly archaic. This is possible for the simple reason that mtDNA is remarkably stable, and can last for thousands of years without changing much at all. However, there’s still the question of where modern humans come from, i.e., is there a group of people that test as the common ancestors of modern human populations. The answer is yes, and it’s the Phoenicians, in particular, a group of mtDNA genomes found in Puig des Molins. Astonishingly, the Phoenicians test as the common ancestor of the Pre-Roman Egyptians (perhaps not terribly astonishing), and the modern day Thai and Sri Lankans, the latter two being simply incredible, and perhaps requiring a reconsideration of purported history.

The overall test is straight forward, and cannot be argued with: Given genomes A, B, and C, if genome A is the ancestor of genomes B and C, then it must be the case that genomes A and B, and A and C, have more bases in common than genomes B and C. This is a relatively simple fact of mathematics, that you can find in my paper, A New Model of Computational Genomics [1], specifically, in footnote 16. However, you can appreciate the intuition right away: imagine two people tossing coins simultaneously, and writing down the outcomes. Whatever outcomes they have in common (e.g., both throwing heads), will be the result of chance. For the same reason, if you start with genome A, and you allow it to mutate over time, producing genomes B and C, whatever bases genomes B and C have in common will be the result of chance, and as such, they should both mutate away from genome A, rather than developing more bases in common with each other by chance. This will produce the inequalities |AB| > |BC| and |AC| > |BC|, where |AB| denotes the number of bases genomes A and B have in common.

For the same reason, if you count the number of matches between two populations at a fixed percentage of the genome, the match counts between populations A, B, and C, should satisfy the same inequalities, for the same reason. For example, fix the matching threshold to 30% of the full genome, and then count the number of genomes between populations A and B that are at least a 30% match or more to each other. Do the same for A and C, and B and C. However, you’ll have to normalize this to an [0,1] scale, otherwise your calculations will be skewed by population size. My software already does this, so there’s nothing to do on that front.

In this case, I’ve run several tests, all of which use the second population-level method described above. We begin by showing that the Phoenicians are the common ancestor of the modern day Sri Lankans and Sardinians. For this, set the minimum match count to 99.65% of the full genome size. This will produce a normalized score of 0.833 between the Phoenicians and Sri Lankans, and 0.800 between the Phoenicians and Sardinians. However, the score between the Sri Lankans and the Sardinians is 0.200, which plainly satisfies the inequality. This is consistent with the hypothesis that the Phoenician maternal line is the ancestor of both the modern day Sri Lankans and Sardinians. Setting the minimum match count to 88.01% of the genome, we find that the score between the Phoenicians and the Pre-Roman Egyptians is 0.500, and the score between the Phoenicians and the Sri Lankans is 1.000. The score between the Pre-Roman Egyptians and the Sri Lankans is instead 0.000, again satisfying the inequality. This is consistent with the hypothesis that the Phoenicians are the common ancestor of both the Pre-Roman Egyptians and the modern day Sri Lankans.

This seems peculiar, since the Phoenicians are Middle Eastern people, and the genomes in question are from Ibiza. However, the Phoenicians in particular were certainly sea-faring people, and moreover, civilization in the Middle East goes back to at least Ugarit, which could date as far back as 6,000 BC. Though not consistent with purported history, this at least leaves open the possibility that people from the Middle East traveled to South Asia. This might sound too ambitious for the time, but the Phoenicians made it to Ibiza from the Middle East, which is roughly the same distance as the Middle East to Sri Lanka, both of which are islands. Once you’re in South Asia, the rest of the region becomes accessible.

If this is true, then it shouldn’t be limited to Sri Lanka, and this is in fact the case. In particular, the Thai also test as the descendants of the Phoenicians, using the same analysis. Even more interesting, both the modern day Norwegians, Swedes, and Finns test as the descendants of the Thai, again using the same analysis. Putting it all together, it seems plausible that early Middle Eastern civilizations not only visited but settled South Asia, and that some of them came back, in particular to Egypt, and Scandinavia. This could explain why the Pre-Roman Egyptians are visibly Asian people, and further, why Thai-style architecture exists in early Scandinavia. Though the latter might sound totally implausible, it is important to note that some Thai and Norwegian people are nearly identical on the maternal line, with about 99.6% of the genome matching. Something has to explain that. Also note that the Sri Lankan maternal line was present throughout Europe around 33,000 BC. This suggests plainly that many Europeans, and the Classical World itself, descend from the Phoenicians. That somewhat remote populations also descend from them is not too surprising, in this context.

Further, there are alarming similarities between the Nordic religions and alphabet, and the Canaanite religions and alphabet, in particular, the gods El / Adon and Odin, with their sons, Baal and Baldur, respectively. Once you place greater emphasis on genetic history, over written history, this story sounds perfectly believable. Further still, if people migrated back from South Asia to the West, then this should again not be limited to Scandinavia, and this is in fact the case. Astonishingly, the Pre-Roman Egyptians test as the descendants of the Thai people, using the same analysis. Obviously the Pre-Roman Egyptians were not the first Africans, and in fact, everything suggests they’re South Asian, and for the same reason, none of this implies that modern day Scandinavians are the first Scandinavians, and instead, again, it looks like many Norwegians and Finns are instead, again, South Asian.

Finally, this is all consistent with the obvious fact that the most advanced civilizations in the world, i.e., the Classical World, are all proximate to the Middle East, suggesting that the genesis of true human intelligence, could have come from somewhere near Phoenicia.

Denisovan as Common Ancestor, Revisited

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In a previous note, I showed that the Denisovans appear to be the common ancestor of both Heidelbergensis and Neanderthals, in turn implying that they are the first humans. Since writing that note, I’ve expanded the dataset significantly, and it now includes the people of Cameroon. I noticed a while back that the people of Cameroon are plainly of Denisovan ancestry. Because it’s commonly accepted that humanity originated in Africa, the Cameroon are therefore a decent candidate for being related to the first humans.

It turns out, when you test Cameroon mtDNA, it seems like they’re not only related to the first humans, they are in fact the first humans, and test as the ancestors of the Denisovans, Heidelbergensis, and the Neanderthals. You might ask how it’s possible that archaic humans survived this long. The answer is, mtDNA is remarkably stable, and so while the people of Cameroon are almost certainly not a perfect match to the first humans, it seems their mtDNA could be really close, since they predate all the major categories of archaic humans with respect to their mtDNA.

The overall test is straight forward, and cannot be argued with: Given genomes A,B, and C, if genome A is the ancestor of genomes B and C, then it must be the case that genomes A and B, and A and C, have more bases in common than genomes B and C. This is a relatively simple fact of mathematics, that you can find in my paper, A New Model of Computational Genomics [1], specifically, in footnote 16. However, you can appreciate the intuition right away: imagine two people tossing coins simultaneously, and writing down the outcomes. Whatever outcomes they have in common (e.g., both throwing heads), will be the result of chance. For the same reason, if you start with genome A, and you allow it to mutate over time, producing genomes B and C, whatever bases genomes B and C have in common will be the result of chance, and as such, they should both mutate away from genome A, rather than developing more bases in common with each other by chance. This will produce the inequalities |AB| > |BC| and |AC| > |BC|, where |AB| denotes the number of bases genomes A and B have in common.

For the same reason, if you count the number of matches between two populations at a fixed percentage of the genome, the match counts between populations A, B, and C, should satisfy the same inequalities, for the same reason. For example, fix the matching threshold to 30%, and then count the number of genomes between populations A and B that are at least a 30% match or more to each other. Do the same for A and C, and B and C. However, you’ll have to normalize this to an [0,1] scale, otherwise your calculations will be skewed by population size. My software already does this, so there’s nothing to do on that front.

If it is the case that populations B and C evolved from population A, then the number of matches between A and B and A and C, should exceed the number of matches between B and C. The mathematics is not as obvious in this case, since you’re counting matching genomes, rather than matching bases, but the intuition is the same. Just imagine beginning with population A, and replicating it in populations B and C. In this initial state, the number of matching genomes between A and B, A and C, and B and C, are equal, since they’ve yet to mutate away from A (i.e., they are all literally the same population). As populations B and C mutate, the number of matching genomes between B and C should only go down as a function of time, since the contrary would require an increase in the number of matching bases between the various genomes, which is not possible at any appreciable scale. Again, see [1] for details.

In the first note linked to above, I show that the Denisovans are arguably the common ancestors of both Heidelbergensis and the Neanderthals. However, if you use the same code to test the Cameroon, you’ll find that they test as the common ancestor of the Denisovans, Heidelbergensis, and the Neanderthals. This is just not true of other populations that are related to Denisovans. For example, I tested the Kenyans, the Finns, and the Mongolians, all of which have living Denisovans in their populations (at least with respect to their mtDNA) and they all fail the inequalities. Now, there could be some other group of people that are even more archaic than the Cameroon, but the bottom line is, this result is perfectly consistent with the notion that humans originated in Africa, migrated to Asia, and then came back to both Europe and Africa, since e.g., about 10% of Kenyans are a 99% match to South Koreans and Hawaiians, and the Pre-Roman Ancient Egyptians were visibly Asian people, and about 40% of South Koreans are a 99% match to the Pre-Roman Ancient Egyptians.

The updated dataset that includes the Cameroons, and others, is available here. You’ll have to update the command line code in [1] to include the additional ethnicities, but it’s a simple copy / paste exercise, which you’ll have to do anyway to change the directories to match where you save the data on your machine.