I’m planning on turning my work on mtDNA into a truly formal paper, that is more than just the application of Machine Learning to mtDNA, and is instead a formal piece on the history of humanity. As part of that effort, I revisited the global alignment I use (which is discussed here), attempting to put it on a truly rigorous basis. I have done exactly that. This is just a brief note, I’ll write something reasonably formal tomorrow, but the work is done.
First, there’s a theoretical question: How likely are we to find the 15 bases I use as the prefix (i.e., starting point) for the alignment, in a given mtDNA genome? You can find these 15 bases by simply looking at basically any mtDNA FASTA file in the NIH website, since they plainly use this same alignment. Just look at the first 15 bases (CTRL + F “gatcacaggt”), you’ll see them. Getting back to the probability of finding a particular sequence of 15 bases in a given mtDNA genome, the answer is not very likely, so we should be impressed that 98.34% of the 664 genomes in the dataset contain exactly the same 15 bases, and the remainder contain what is plainly the result of an insertion / deletion that altered that same sequence.
Consider first that there are sequences of bases of length 15, since there are 4 possible bases, ACGT. We want to know how likely it is that we find a given fixed sequence of length 15, anywhere in the mtDNA genome. If we find it more than once, that’s great, we’re just interested initially in the probability of finding it at least once. The only case that does not satisfy this criteria, is the case where it’s not found at all. The probability of two random 15 base sequences successfully matching at all 15 bases is . Note that a full mtDNA genome contains bases. As such, we have to consider comparing 15 bases starting at any one of the indexes available for comparison, again considering all cases where it’s found at least once as a success.
This is similar to asking the probability of tossing at least one heads with a coin over some number of trials. However, note in this case, the probability of success and failure are unequal. Since the probability of success at a given index is given by , the probability of failure at a given index is . Therefore, the probability that we find zero matches over all indexes is given by , and so the probability that we find at least one match is given by . That’s a pretty small probability, so we should already be impressed that we find this specific sequence of 15 bases in basically all human mtDNA genomes in the dataset.
I also tested how many instances of this sequence there are in a given genome, and the answer is either exactly 1, or 0, and never more, and as noted above, 98.34% of the 664 genomes in the dataset contain the exact sequence in full.
So that’s great, but what if these 15 bases have a special function, and that’s why they’re in basically every genome? The argument would be, sure these are special bases, but they don’t mark an alignment, they’re just in basically all genomes, at different locations, for some functional reason. We can address this question empirically, but first I’ll note that every mtDNA genome has what’s known as a D Loop, suggesting again, there’s an objective structure to mtDNA.
The empirical test is based upon the fact that mtDNA is incredibly stable, and offspring generally receive a perfect copy from their mother, with no mutations, though mutations can occur over large periods of time. As a result, the “true” global alignment for mtDNA should be able to produce basically perfect matches between genomes. Because there are 16,579 bases, there are 16,579 possible global alignments. The attached code tests all such alignments, and asks, which alignments are able to exceed 99% matches between two genomes? Of the alignments that are able to exceed 99%, 99.41% of those alignments are the default NIH alignment, suggesting that they are using the true, global alignment for mtDNA.
In a paper entitled, “A New Model of Computational Genomics” [1], I introduced an algorithmic test for ancestry using whole-genome mtDNA. I’ve since updated that test significantly, as described below. In this first of what will be a series of articles, I will present the results of this test as applied to specific regions of the world, in this case, to Scandinavia. Each of the articles will contain an independent summary of the algorithm and its overall results, and so you can read each independently.
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, [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 in the dataset, 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 three most evolved maternal lines that I’ve identified, somewhere around Nepal, specifically, the Ancient Egyptians, the Vikings, and the Ancient Romans. The first two maternal lines are both found around the world, and descend from Heidelbergensis and Neanderthals and / or Denisovans, respectively, suggesting that many modern humans are a mix between the most evolved maternal lines that originated in three distinct archaic human populations, effectively creating hybrids. The Ancient Roman maternal line no longer exists, and seems to have been deliberately annihilated. 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 either Neanderthals or Denisovans, or both, it’s not clear).
Maternal Ancestry Among Scandinavians and Germans
Intuition suggests that the Sami People, who are indigenous Scandinavians, should as a general matter test as the ancestors of at least some Scandinavian people. At the same time, because all but the Finns and Sami speak Germanic languages, we would expect the Germans to test as the ancestors of at least some Scandinavian people. All of that said, during the Viking Age, the Scandinavians made use of a Phoenician-like alphabet, known as Runes, and so it’s at least possible we should see either Continental European ancestry (e.g., the Basque used similar scripts in antiquity), Middle Eastern ancestry, or some other form of ancestry that explains this otherwise anomalous alphabet. We will examine each of these questions below using the ancestry test.
Levänluhta
Levänluhta is an underwater gravesite in Finland that contains the remains of about 100 individuals from the Iron Age (c. 800 to 500 BC). Though Scandinavia has been occupied by humans since the Stone Age, common sense says that these individuals should test as the ancestor of at least some modern Scandinavians. This is indeed the case, and in fact, these individuals test as even more ancient than the Sami People, which you can see in the chart below. A positive number indicates that the population in question is a net ancestor, whereas a negative number indicates that the population in question is a net descendant. That is, if e.g., X is the number of times the ancestry test was satisfied from Sweden to Norway, and Y is the number of times the ancestry test was satisfied from Norway to Sweden, the chart below plots X – Y for each population. As you can see, all other Scandinavian groups test as the descendants of the individuals buried in Levänluhta. You can find the acronyms used below at the end of [1], but for now note that FN = Finland, NO = Norway, SW = Sweden, DN = Denmark, SM = Sami, IL = Iceland, and AF = Ancient Finland (i.e., Levänluhta). If you look at the ancestors of the individuals buried in Levänluhta (i.e., X – Y > 0), you’ll see HB = Heidelbergensis, AN = Andamanese, and other archaic populations, suggesting the individuals buried in Levänluhta are somewhere between archaic humans and modern humans, despite being a relatively recent Iron Age gravesite.
The Sami People
The Sami People are indigenous Scandinavians that speak an Uralic language and live in Northern Scandinavia, spanning Sweden, Norway, Finland, and Russia. For context, Uralic languages are spoken in regions around Finland, including Finland itself, Estonia, parts of Russia, as well Hungary. Uralic languages are to my knowledge not related to Germanic languages. As such, we should not be surprised if the Sami have a maternal ancestry that is distinct from the rest of the Scandinavians and Germans. This is in fact the case, and in particular, the Sami contain a significant amount of Denisovan mtDNA. See, [1] for more details. As noted above, Denisovans are a relatively recently discovered subspecies of archaic humans. The main archeological site where they were discovered is the Denisovan Cave in Siberia, and the dataset includes 8 Denisovan genomes from that site.
Above is the net maternal ancestry of the Sami people, where, again, a positive number indicates that the population in question is an ancestor of the Sami, and a negative number indicates that the population in question is a descendant of the Sami. As you can see above, all other living Scandinavian people test as the descendants of the Sami, making the Sami the most ancient among the living Scandinavian people.
The Finnish People
As noted above, the Finnish people speak an Uralic language, like the Sami, and as such, we should not be surprised if they have a distinct ancestry from the rest of the Scandinavians. This is in fact the case, though they are one step closer to modern Scandinavians than the Sami, and as you can see below, all Scandinavian people (other than the Sami) test as the descendants of the Finns.
Now this doesn’t mean that all the other Scandinavians descend directly from the Finns, which is too simple of a story, but it does mean that when comparing Finns to the rest of the Scandinavians (save for the Sami), it is more likely that a given Finn will test as the ancestor of a given Scandinavian, than the other way around. This is not terribly surprising since the Finns speak a completely different language that has (to my knowledge) an unknown origin, suggesting the language is quite ancient, and the Finns seem to be as well. The Finns also have a significant amount of Denisovan mtDNA from Siberia, which is again consistent with the claim that the Finns are, generally speaking, the second most ancient of the living Scandinavians.
The Danish People
Like the Finns, the Danes also contain a significant but lesser amount of Siberian Denisovan mtDNA, and they similarly test as the ancestors of all other Scandinavians, other than the Finns and Sami, making them the third most ancient Scandinavian population. Note however that Danish is a Germanic language, suggesting independence between Uralic languages and Denisovan mtDNA, though there does seem to be some reasonable correlation.
The Norwegian People
The Norwegian people contain no meaningful quantity of Denisovan mtDNA, and they test as the fourth most ancient of the living Scandinavians. Note that the Sami, Finns, and Danes test as the net ancestors of the Norwegians, whereas the Swedes and Icelandic people test as the descendants of the Norwegians. Finally note that the Norwegians speak a Germanic language.
The Swedish People
The Swedes contain no meaningful quantity of Denisovan mtDNA, and they test as the fifth most ancient of the living Scandinavians, and are therefore more modern than the rest, save for the Icelandic (discussed below). The Swedes speak a Germanic language that is very similar to Norwegian, though the Swedes are notably distinct from the Norwegians in that they test as the descendants of the Germans, whereas the rest of the Scandinavians discussed thus far test as the ancestors of the Germans.
The Icelandic People
There is only one Icelandic genome in the dataset, but as you can see below, it is very similar to the Swedish population generally. Further, this genome tests as the descendant of all Scandinavian populations, and more generally, has only three descendants: the Ancient Romans, the Irish, and the Munda people of India. The Ancient Romans generally test as the descendants of the Northern Europeans, and are in fact the most modern population in the dataset according to this test. The Munda people of India are probably not Scandinavian, and instead, the Scandinavians and the Munda presumably have a common ancestor in Asia, consistent with the “Migration-Back Hypothesis” I presented in [2], that humanity begins in Africa, spreads to Asia, and then back to Northern Europe and Africa, as well as spreading into East Asia. Dublin was founded by the Vikings, so it is no surprise that some Irish test as the descendants of the Icelandic. However, there is only one Icelandic genome in the dataset, and so while we can’t say much about the Icelandic people in general on the basis of the dataset alone, because Iceland was (to my knowledge) uninhabited prior to the Vikings, it’s presumably the case that the people of Iceland are literally direct descendants of the Vikings, whereas in contrast, Scandinavia (as noted above) has been inhabited by humans since the Stone Age.
The Origins of the Runic Alphabet
Note that the Swedes and Icelandic are the only Scandinavians that test as a descendant as opposed to an ancestor of the Germans. This could explain why the majority of the Rune Stones are in Sweden, as opposed to the rest of Scandinavia. Specifically, the hypothesis is that Germanic people brought the Phoenician-like alphabet of the Runic Scripts to Sweden. As noted above, the Basque used a similar alphabet, who are also of course Continental Europeans, and so the overall hypothesis is that people of the Mediterranean (e.g., the Phoenicians themselves) brought their alphabet to the Continental Europeans, and the Germans brought that alphabet to the Swedes.
Asian and African Ancestors and Descendants of the Scandinavians
You’ll note in the charts above that some African and Asian people test as the ancestors and / or the descendants of the Scandinavians, in particular the Nigerians and Tanzanians, and the Koreans, Thai, and Japanese (though there are others). Though this might initially seem puzzling, it is instead perfectly consistent with the Migration-Back Hypothesis presented in [2], which asserts that many modern humans, in particular Northern Europeans, East Asians, and many Africans are the descendants of common ancestors from Asia.
The Ancient Mediterranean
The Ancient Romans are clearly descendants of the Northern Europeans, but I’ve found similar Italian genomes that are 35,000 years old. This implies that the most evolved genomes in the dataset are still at least 35,000 years old, and were already in Italy, long before Ancient Rome. The question is then, if the stage was set 35,000 years ago, in that the modern maternal lines were fully formed, why is that it took so long for civilization to develop? One possibility is that there was further evolution on the male line, or the rest of the genome, which is probably true given that mtDNA is, generally speaking, very slow to evolve.
However, civilization has geography to it, and it is simply impossible to ignore the Mediterranean, which produced the Ancient Egyptians, Mesopotamians, Ancient Greeks, and Ancient Romans, as well as others. Why did these people so drastically outperform literally all other humans? I think the answer is written language, and in turn, mathematics. That is, my hypothesis is that the genetics only gets you so far, and that you’ll find people very similar to e.g., the Phoenicians and Ancient Egyptians in other parts of the world that simply didn’t produce on the scale that the Mediterraneans did, and that the gap was driven by written language, which in turn allows for written mathematics, and everything that follows, from accurate inventories and contracts, to predictions about the future. That said, of all the Ancient and Classical people in the dataset, none of them contain any archaic mtDNA, suggesting maternal evolution really did play a role in intelligence and human progress.
This is difficult for modern people to appreciate, but imagine having no idea what happened a few weeks ago, and how that could leave you at a loss, or even put you at risk. At a minimum, written records reduce the risk of a dispute. Now imagine having no written system of mathematics, and trying to plan the construction of a structure, or travel over a long period of time. You’d have no means of calculating the number of days, or the number of individuals required, etc. Once you cross this milestone, it becomes rational to select mates on the basis of intelligence, which is a drastic shift from what happens in nature, which is selection for overall fitness. This seems to create a feedback loop, in that as civilizations become more sophisticated, intelligence becomes more important, further incentivizing selection for intelligence, thereby creating a more intelligent people.
This is not to diminish the accomplishments of other people, but it’s probably the case that the Mediterranean people of the Ancient and Classical periods were the most intelligent people in the world, at the time, which forces the question, of what happened to them? There’s unambiguous evidence that they were literally exterminated, at least in the case of the Romans. The thesis would therefore be that the Romans were slowly and systematically killed to the point of extinction, by less evolved people, creating the societal collapse and poverty that followed for nearly 1,000 years, until the Renaissance.
Unfortunately, it seems plausible the same thing is happening again. Specifically, consider that there have been no significant breakthroughs in physics since Relativity, which we now know is completely wrong. Also consider the fact that the most powerful algorithm in Machine Learning is from 1951. Not surprisingly, microprocessors have been designed using what is basically A.I., since the 1950s. So what is it then that these ostensible A.I. companies do all day? They don’t do anything, it’s impossible, because the topic began and ended in 1951, the only thing that’s changed, is that computers became more powerful. They are with certainty, misleading the public about how advanced A.I. really is, and it’s really strange, because scientists during the 1950s and 1960s, weren’t hiding anything at all. Obfuscation and dishonesty are consistent with a nefarious purpose, and companies like Facebook probably are criminal and even treasonous enterprises, working with our adversaries, and are certainly financed by backwards autocracies like Saudi Arabia.
If you’re too intelligent and educated, then you will know that the modern A.I. market is literally fake, creating an incentive to silence or even kill the most intelligent people, which is consistent with the extremely high suicide rate at MIT. It suggests the possibility that again, intelligent people are being exterminated, and having a look around at the world, it’s obvious that civilization is again declining, arguably when compared to the turn of the 20th Century, and certainly since the end of World War II. I think we all know who’s responsible, and it’s probably not Scandinavians.
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.
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 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
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!
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:
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.
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:
GenomeType
Avg. Change
Min. Change
Max. Change
Std. Deviation
Genome Date
Avg. Change Per 1000 Years
Denisovan
26.39%
25.76%
32.70%
1.99%
120,000 BP
0.22%
Neanderthal
3.74%
2.79%
36.60%
3.27%
80,000 BP
0.047%
Heidelbergensis
4.27%
3.30%
37.61%
3.30%
430,000 BP
0.0099%
Ancient Egyptian
3.74%
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.
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.
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].
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.
Information Overload 