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ome of african origin i now have very little doubt about the out of africa hypothesis and specifically i think the modern day people of cameroon are related to the first humans since every test i ve come up with points to them as the ancestor of all the archaic genomes in the dataset since the modern day cameroon people test as the ancestors of the archaic genomes they are presumably even more archaic but somehow still alive you can read more about this here i should be done with the complete history of the neanderthal and denisovan lines shortly it s just a lot of information and much more complicated than heidelbergensis which was astonishingly simple and obvious the heidelbergensis maternal line january 29 2026 january 29 2026 erdosfan leave a comment introduction i m building up to a formal paper on human history that uses machine learning applied to mtdna you can find an informal but fairly rigorous summary that i wrote here 1 that includes the dataset in question and the code in this note i m going to treat the topic at the individual genome level whereas in 1 i generally applied algorithms to entire populations at a time i e multiple genomes of the same ethnicity and looked at genetic similarities across entire populations the goal here is to tell the story of the heidelbergensis maternal line which is the largest maternal line in the dataset accounting for 414 of the 644 genomes in the dataset i e 62 35 specifically 414 genomes are at least a 90 match to either heidelbergensis itself or one of the related genomes we ll discuss below the dataset the dataset consists of 644 whole mtdna genomes taken from the nih database there are therefore 644 rows and columns each column representing a base of the genome stored in that row i e each column entry is one of the bases a c g or t though there are some missing bases represented by 0 s said otherwise each genome contains bases and each row of the dataset contains a full mtdna genome i ve diligenced the genome provenance files see e g this norwegian genome s provenance file to ensure the ethnicity of the individual in question is e g a person that is ethnically norwegian as opposed to a resident of norway the dataset consists of 75 classes of genomes which are generally speaking ethnicities and column n 1 contains an integer classifier for each genome representing the ethnicity of the genome e g norway is represented by the classifier 7 the dataset also contains 19 archaic genomes that similarly have unique classifiers that are treated as ethnicities as a practical matter for example there are 8 neanderthal genomes each of which have a classifier of 32 and are for all statistical tests treated as a single ethnicity though as i noted previously neanderthals are decidedly heterogenous so big picture we have 644 full mtdna genomes each stored as a row in a matrix i e the dataset where each of the first columns contains a base of the applicable genome and an integer classifier in column n 1 that tells you what ethnicity the genome belongs to heidelbergensis and mtdna heidelbergensis is an archaic human that lived according to brittanica approximately 600 000 to 200 000 years ago when i first started doing research into mtdna i immediately noticed that a lot of modern mtdna genomes were a 95 or more match to heidelbergensis i thought at first i was doing something wrong though i recently proved both mathematically and empirically that this is definitely not the case and in fact there s only one way to compare whole mtdna genomes you can read the previous note linked to for details but the short story is mtdna is generally inherited directly from your mother i e there s no paternal dna at all in mtdna with no mutations though mutations can occur over long periods of time i e thousands of years or sometimes more as a result any method you use to compare an entire mtdna genome must be able to produce nearly perfect matches since a large enough dataset should contain a basically perfect match for a significant number of genomes given mtdna s extremely slow rate of mutation said otherwise if you have a large number of whole mtdna genomes there should be nearly perfect matches for a lot of the genomes in the dataset since mtdna mutates extremely slowly there are of course exceptions especially when you re working with archaic genomes that might not have survived to the present but the gist is mtdna mutates so slowly someone should have basically the same mtdna as you empirically there s exactly one method of whole genome comparison that accomplishes this which is explained in the previous link and contains the applicable code to test the hypothesis just in case it s not clear whole genome comparison means you take two entire genomes and compare them side by side rather than looking for individual sequential segments like genes which until recently was the more popular approach if you re curious i ve demonstrated that whole genome comparison and random base selection are categorically superior to relying on sequential bases e g genes for imputation at least as applied to mtdna see a new model of computational genomics 2 we will also discuss using genome segments in the final section below a heidelbergensis skull image courtesy of britannica whole genome comparison the method of comparison that follows from this observation is straight forward you simply count the number of matching bases between two genomes so for example if we re given genome and the number of matching bases is simply 2 because mtdna is circular it s not clear where to start the comparison for example we could start reading genome at the first rather than the first base however the previous link demonstrates that there s exactly one whole genome alignment otherwise known as a global alignment or starting index for mtdna the rest of them are simply not credible for the reasons discussed above this makes whole genome comparison super easy and incredibly fast and in fact my software can compare a given genome to all 644 genomes in the dataset in just 0 02 seconds running on an apple m2 pro producing a ton of statistics for the input genome not just the number of matching bases sure it s a great machine but it s not a super computer which means now everyone can do real genetic analysis on consumer devices once popularized these methods will probably make short work of the complete history of mankind and possibly the entire history of life itself since mtdna is not unique to humans further these methods and their results are rock solid empirical evidence for the theory of evolution which as you ll see below is not subject to serious criticism at least with respect to mtdna modern relatives of heidelbergensis as noted above many modern living humans have mtdna that is a 95 or more match to the single heidelbergensis genome in the dataset the genome was found at sima de los huesos and there is apparently some debate about whether it is actually a neanderthal but it is in all cases a very archaic genome from around 500 000 years ago as such though i concede this heidelbergensis genome is a 95 10 match to the third neanderthal genome in my dataset which is from around 100 000 years ago i think it s best to distinguish between the two given the huge amount of time between the two genomes and the fact that they re not exactly the same genome recall that we are comparing whole genomes by simply counting the number of matching bases which we ll call the match count we can therefore set a minimum match count of say i e 90 of the genome and retrieve all genomes that are at least a 90 match to heidelbergensis this produces the chart below where the height of the bar provides the percentage of genomes in the applicable population that are at least a 90 match to the single heidelbergensis genome for example 100 of the iberian romani are at least a 90 match to the heidelbergensis genome producing a height of 1 0 in the chart below the population acronyms can be found at the back of 2 but just to highlight some of the obvious matches kz stands for kazakhstan ib stands for iberian romani it stands for italy and ru stands for russia a chart showing the percentage of each population that is at least a 90 match to heidelbergensis the plain takeaway is that many modern humans carry mtdna that is close to heidelbergensis peaking at a 96 69 match for a kazakh individual as noted above when working with modern genomes you ll often find a basically perfect match that exceeds 99 but when working with archaic genomes that s not always the case and it makes perfect sense since so much time has elapsed that even with the incredibly slow rate of mutation for mtdna a few percentage points of mutation drift is to be expected the phoenician people the phoenicians were a mediterranean people that existed from around 2500 bc to 64 ad though there could be other example genomes the phoenicians are a great case study because they are a partial match to heidelbergensis and a partial match to the pre roman ancient egyptian genome you can already appreciate the intuition that heidelbergensis evolved into the phoenicians and then the phoenicians evolved further into the ancient egyptians now the real story is more complicated and it doesn t look like all of this happened in the mediterranean instead it looks like human life begins in west africa migrates to roughly the mediterranean and eurasia migrates further to somewhere around northern india and then spreads back to europe and africa and further out into east asia you can read 2 for more on this topic this note will instead be focused on the evolution of the individual genomes and less so on claims regarding their historical geographies that is i m going to present you with a set of genomes that begin with heidelbergensis and end in the icelandic people who are almost certainly vikings but i m not going to argue too much about where these mutations happened outside of a few notes for context so that it s not all happening in a void returning to the phoenicians we want to show first that the phoenicians evolved from heidelbergensis all of these steps will involve epistemological reflections so that we can be comfortable that we re asserting reasonable claims that said as you ll see all of these claims are uncertain and plainly subject to falsification but that s science to begin note that there are 6 phoenician genomes in the dataset and that the first phoenician genome in the dataset row 415 is at least a 99 72 match to the other 5 phoenician genomes as such to keep things simple we will treat this first phoenician genome as a representative genome for the entire class of phoenicians further note that the first phoenician genome is a 41 17 match to heidelbergensis if we were comparing two random genomes then the expected match count is 25 of the genome since the distribution is given by the binomial distribution with a probability of success of that is at each base we have two random variables one for each genome and each of those variables can take on a value of a c g or t if it s truly random then there are possible outcomes and only 4 of those outcomes correspond to the bases being the same producing a probability of therefore we can conclude that the match count of 41 17 between heidelbergensis and the phoenician genome is probably not the result of chance the claim that the two genomes are truly related finds further support in the location of the matching bases which are concentrated in the first 3 500 bases which is shown in the chart below the chart below is produced by taking 500 bases at a time starting with the first 500 bases of each genome and counting how many bases within that 500 base segment match between the two genomes the maximum match count is of course 500 bases which would produce a height of 1 0 or 100 this process continues over the entire genomes producing the chart below as you can see the most significant matches are clustered in the first 7 segments representing the first 3 500 bases of the genomes the argument is because there is a significant contiguous segment within the genomes that are highly similar we can confidently rule out chance as the driver of the similarity you can never be totally certain but since it s probably not chance that s driving the similarity the logical conclusion is that heredity and mutation is what caused the similarity between the two genomes now we don t know the direction of time from this analysis alone i e either genome could have evolved into the other but because heidelbergensis is very archaic the logical conclusion is that heidelbergensis mutated eventually forming the phoenician maternal line a chart showing the percentage of matching bases between the heidelbergensis and phoenician genome broken into 500 base segments one important point to note is that even if a genome evolves it does not imply that all instances of that genome evolve for example as noted above 100 of the living iberian romani people are at least a 90 match to heidelbergensis demonstrating that at least some heidelbergensis genomes did not evolve into the phoenician line and instead remained roughly the same over time as such we can say confidently that mtdna is very slow to mutate as a general matter but the rates of mutation are heterogenous just to close this section with some context for modern humans that carry the phoenician line 80 of living sardinians and 33 33 of living vedda aboriginals are at least a 90 match to the phoenicians obviously it s a bit shocking that you d have phoenician mtdna in asia but if you read 2 you ll quickly learn that these are global maternal lines that often contain multiple disparate people two common sense explanations 1 the phoenicians really made it to asia or 2 there s a common ancestor for both the phoenician and vedda people presumably somewhere in asia hypothesis 2 finds support in the fact that 10 52 of mongolians are also at least a 90 match to the phoenicians this is a complicated topic and it s just for context the real point of this note is that you can plainly see that heidelbergensis evolved which is already interesting and compelling evidence for the theory of evolution and specifically it evolved into the phoenician maternal line the ancient egyptians introduction the ancient egyptians were a mediterranean civilization that lasted from around 3150 bc to 30 bc until it was ruled by rome from around 30 bc to 642 ad there are two ancient egyptian genomes in the dataset one from approximately 2000 bc before roman rule and another genome from approximately 129 to 385 ad during roman rule this is a huge amount of time and so it s not surprising that the demographics changed but the ancient egyptians present a shocking demographic shift from earlier rulers that were plainly of asian origin to rulers that looked and were known to be european for example see the panel of images below with nefertiti 1353 to 1336 bc on...
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