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 of 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].
I’ve noticed in the past that Finns have significantly higher IQ’s than the Swedes and Norwegians. This is in my opinion the group of people to study if you’re interested in the nature of intelligence, because they’re all very similar people, from roughly equally rich nations, in the same part of the world, which should allow innate ability to take control. One notable difference is that the Finns speak an Uralic language, whereas the Norwegians and Swedes speak a Germanic language. There could be something to this, but investigating the problem again today led me to what seems an inescapable conclusion, that whatever the connection is between mtDNA and intelligence, it simply cannot account for the distribution of IQ as it exists.
Instead I now believe that brain structure is the most important factor in intelligence, which simply cannot be controlled by mtDNA in any credible way. Specifically, my thinking is rooted in algorithmic complexity, that if you have two equally powered machines, running different algorithms that accomplish the same task, then the machine with the more efficient algorithm of the two will be the most powerful of the two. Translated to biology, if you have two brains that both consume the same amount of power per unit of time, and have the same “clock rate”, one brain could still be vastly more powerful than the other, due simply to different structure. This could explain e.g., the fact that some birds can talk, whereas some dogs will eat until they vomit, despite the fact that birds have brain volumes that are a small fraction of a dog’s brain volume.
mtDNA and Intelligence
Despite the apparent complexity of the subject, this is going to be a short note, because the idea that mtDNA controls for IQ is apparently nonsense, despite the scholarship on the topic (not picking on anyone, but here’s a decent article that runs through some credible arguments for the role of mtDNA in intelligence). But as you’ll see, whole-genome sequencing throws the argument in the garbage.
There’s no nice way to say this, but the Roma people have pretty low IQs, but what’s most interesting about them, is that they are basically identical to each other, and all other people of that maternal line, including about 100% of Papuans, 67% of Russians, and about 30% of Taiwanese people. If you want to test the results yourself, you can see my paper, “A New Model of Computational Genomics” [1], which includes all the software, and a detailed walkthrough to explain how I end up with these numbers. At a high level, the Papuans, Russians, and Taiwanese people in this group of Roma lineage, are all a 99% match to the Iberian Roma, with respect to their mtDNA. If mtDNA controlled intelligence, then all of those populations should have similarly low IQ’s, since they’re basically identical to the Roma. This is just not true, and instead the Taiwanese have around the highest and second highest IQ on Earth, and the Russians have roughly the same IQ as the Norwegians and Swedes, despite the fact that Russia is, quite frankly, poor and dysfunctional compared to Norway and Sweden.
One important note, though you’ll often hear that “humans are 98% monkey”, or some nonsense like that, the algorithms in [1] use what’s called a global alignment, and as a consequence, they’re extremely sensitive to changes in position, causing e.g., the Roma to have little more than chance in common with some people (i.e., about 25% of the mtDNA bases). This sensitivity is probably why the software in [1] is so powerful, and is able to predict ethnicity with about 80% accuracy, using mtDNA alone (which is pretty amazing). In contrast, NIH’s BLAST algorithm uses a local alignment, and so it deliberately seeks to maximize the number of matching bases, by shifting two genomes around, causing everyone to look the same, and therefore, throwing away valuable information about the genome.
Getting back to the core topic, if you pay attention to this limited set of facts, mtDNA is in the garbage as a driver of intelligence, and moreover, the role of poverty is not exactly clear either, since Russia is really poor compared to Norway and Sweden, and yet they have roughly the same IQs. So what is driving this? Cynically, I think IQ testing is really just testing for basic education (when you look at a map), which is absent in the truly poorest countries, but that doesn’t mean that we can’t debunk the connection between mtDNA and intelligence. But to be clear, I do think intelligence is genetic, and in anomalous cases like Finland, Cambodia, and Suriname, IQ becomes something interesting, because it’s at least a test. I just doubt it’s mtDNA driving the bus.
Some Answers from Computer Science
Even if we posit arguendo (which is not very nice) that there’s something wrong with Roma mtDNA, this would simply imply that they have low energy per unit of time, perhaps as a function of fixed caloric intake and environment. To make this less abstract, let’s fix a Norwegian guy (not Roma) and a Russian guy (Roma), and give them the same food, education, climate, environment, clothes, etc., over a lifetime. Under this assumption, the Russian guy will produce less energy over his lifetime, and therefore, his brain has a lower output. But this is garbage as an argument, for mechanical reasons: if the Russian guy has a more efficient brain, then he doesn’t need as much power to run his brain. As a consequence, his output over a lifetime could in fact be higher.
To make things completely concrete, if you use a brute force method to sort a list of 10 letters, you’ll have to perform 10! = 3,628,800 calculations. If you instead use my parallel method, you’ll have to make between 3 and 4 calculations. As you can plainly see, there is an ocean between these two approaches to solving even the simple problem of sorting a list. As a consequence, the most sensible answer is, in my opinion, that brain structure controls for intelligence, for the simple reason, that it encodes the algorithms we use to solve the problems we face every day. Some people have fast ones, some people have dumb ones, and then there’s (probably) most people in the middle.
Returning to the birds versus dogs analogy, I think it’s not ridiculous to argue that birds have vastly more efficient brains than dogs, that something along the lines of computational efficiency is taking place in the brain of a bird, that allows it to perform complex tasks, with a smaller, presumably lower-energy brain. For the same reasons, this could explain the obvious fact that some people are wildly more intelligent than others, despite (possibly) having the same maternal line. Because intelligence varies within a given ethnicity, I can tell you that you are e.g., Norwegian, with high accuracy using just your mtDNA, but there’s no way of knowing (to my knowledge) whether you’re one of the dumb ones. This doesn’t preclude identifying deficiencies in mtDNA that will make you dangerously ill, and therefore not very bright, but it just doesn’t make sense that the means of power-production controls the most complex structure in the Universe –
It’s a single bean, in an ocean of genetic information.
I came to the conclusion last night, that I may have wasted a lot of time, thinking about time as an actual dimension of space. In my defense, I’m certainly not the first physicist or philosopher to do so. Specifically, my entire paper, A Computational Mode of Time-Dilation [1], describes time as a measurement of physical change, not a dimension. Nonetheless, it produces the correct equations for time-dilation, without treating time as a dimension, though for convenience, in a few places, I do treat it as a dimension, since a debate on the corporeal nature of time is not the subject of the paper, and instead, the point is, you can have objective time, and still have time-dilation.
As a general matter, my view now is that reality is a three-dimensional canvas, that is updated according to the application of a rule, effectively creating a recursive function. See Section 1.4 of [1]. Because [1] is years old at this point, this is obviously not a “new” view, but one that I’ve returned to, after spending a lot of time thinking about time as an independent dimension, that could, e.g., store all possible states of the Universe. The quantum vacuum was one of the primary drivers for this view, specifically, that other realities temporarily cross over into ours, and because that’s presumably a random interaction, you should have a net-zero charge (i.e. equal representation from all charges), momentum, etc, on average, creating an otherwise invisible background to reality, save for extremely close inspection.
I don’t think I’m aware of any experiment that warrants such an exotic assumption, and I’m not even convinced the quantum vacuum is real. As such, I think it is instead rational to reject the idea of a space of time, until there is an experiment that, e.g., literally looks into the future, as opposed to predicting the future using computation.
I’ll concede the recursive function view of reality has some problems without time as a dimension, because it must be implemented in parallel, everywhere in space, otherwise, e.g., one system would update its states, whereas another wouldn’t, creating a single reality with multiple independent timelines. This is not true at our scale, and I don’t think there’s any experiment that shows it’s true at any scale. So if time doesn’t really exist as a dimension, we still need the notion of syncopation, which is in all fairness, typically rooted in time. But that doesn’t imply time is some form of memory of the past, or some projection of the future.
This is plainly an incomplete note, but the point is to reject the exotic assumptions that are floating around in modern physics, in favor of something that is far simpler, yet works. Reality as a recursive function makes perfect sense, taking the present moment, transforming it everywhere, producing the next moment, which will then be the present, with no record of the past, other than by inference from the present moment.
We’re still left with the peculiar fact that all of mathematics seems immutable (e.g., all theorems of combinatorics govern reality, in a manner that is more primary than physics, since they can never change or be wrong), but that doesn’t imply time is a dimension, and instead, my view, is that mathematics is beyond causation, and simply an aspect of the fabric of reality, whereas physics is a rule, that is applied to the substance contained in reality, specifically, energy. Physics doesn’t seem to change, but it could, in contrast, mathematics will never change, it’s just not possible.
In my paper, A New Model of Computational Genomics [1], I presented an algorithm that can test whether one mtDNA genome is the common ancestor of two other mtDNA genomes. The basic theory underlying the algorithm is straightforward, 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 [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.
By iteratively applying the population-level test for different values of M, we can also generate a measure of uncertainty associated with our observation. That is, not only can we test whether the inequalities are satisfied, we can also generate a measure of uncertainty associated with the test.
Specifically, fix M to some minimum value, which we select as 30% of the full genome size N, given that 25% is the expected matching percentage produced by chance, and 30% is meaningfully far from chance (again, see Footnote 16 of [1]). Further, note that as M increases, our confidence that the matches between A and B and A and C, are not the result of chance, increases. For intuition, note that as we increase M, the set of matching genomes can only grow smaller. Similarly, our confidence that the non-matching genomes between B and C will not be the result of chance decreases as a function of M. For intuition, note that as we increase M, the set of non-matching genomes can only grow larger.
As a result the minimum value for which the inequalities are satisfied informs our confidence in the B to C test, and the maximum value of M for which the inequalities are satisfied informs our confidence in the A to B and A to C tests. Specifically, the probability the B to C test is the result of chance is informed by the difference between the minimum M – N25%, whereas the A to B and A to C tests are informed by the difference N – M, where M is the maximum M. Note this difference is literally some number of bases, that is in turn associated with a probability (see again, Footnote 16 in [1]), and a measure of Uncertainty (see Section 3.1 of [1]). This allows us to first test whether or not a given population is the common ancestor of two other populations, and then further, assign a value of Uncertainty to that test.
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.
Large leaps in evolution seem to require too much time to make sense. Consider the fact that about 500 bases separate human mtDNA from that of a gorilla or a chimp. That’s a small percentage of the approximately 16,000 bases that make up human mtDNA, but the number of sequences that are 500 bases in length is 4^ 500, which has approximately 300 digits. As a consequence, claiming that reproductive selection, i.e., the birth of some large number of children, that were then selected for fitness by their environment, is the driver of the change from ape to man, makes no sense, as there’s simply not enough time or offspring for that to be a credible theory, for even this small piece of its machinery, which is the evolution of mtDNA.
However, if we allow for evolution at the cellular level in the individual, over the lifetime of the individual, then it could explain how e.g., 500 extra bases end up added to the mtDNA of a gorilla, since there are trillions of cells in humans. That is, floating bases are added constantly, as insertions, in error, and when lethal, the cell in question dies off. However, if not lethal, and instead beneficial, this could occur throughout the body of the organism, causing the organism to evolve within its own lifetime, by e.g., changing its mtDNA through such a large scale, presumably beneficial insertion, like the one that divides apes from humanity.
This implies four corollaries:
1. It is far more likely that any such benefits will be passed on from the paternal line, since men constantly produce new semen. In contrast, women produce some fixed number of eggs by a particular age. As a result, men present more opportunities to pass down mutations of this type, if those mutations also impact their semen.
2. There must be some women who are capable of producing “new eggs” after a mutation, otherwise the mutation that caused gorilla mtDNA to evolve into human mtDNA, wouldn’t persist.
3. If you argue instead that such drastic mutations occur in the semen or the eggs, then you again have the problem of requiring too much time, since it would require a large number of offspring, that are then selected for lethal and non-lethal traits. This is the same argument we dismissed above. That is, the number of possible 500 base insertions is too large for this to be a credible theory. As a consequence, drastic mutations cannot be the result of reproductive selection, period, and require another explanation, for which cellular mutations within the individual seem a credible candidate.
4. If true, then it implies the astonishing possibility of evolution within the lifetime of an individual. This sounds far fetched, but cancer is a reality, and is a failure at the cellular level, that causes unchecked growth. The argument above implies something similar, but beneficial, that occurs during the lifetime of an individual, permeating its body, and thereby impacting its offspring.
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.
Information Overload 
