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ich is that art in particular and aesthetics generally create an opportunity for information about a person s genome to be expressed and therefore facilitate mating decisions i ve seen some evidence that preferences are genetic and this should surprise no one since people clearly take aesthetic preferences seriously often motivating people to work harder just so they can live in a particular home and wear particular clothes the more formal conclusion i reached this morning is not on the empirical side it is instead a theoretical observation that the expression of preferences creates a language in which information about genomes can be conveyed there is still of course the empirical question of whether or not this is actually happening but as we ll see the opportunity to convey an enormous amount of information using preferences certainly exists let s begin by formalizing and limiting preferences as expressed with respect to a particular artifact e g a piece of music a painting a pair of pants etc as follows i hate it i don t like it it s ok i like it and i love it this creates an ordinal scale of 5 possibilities ranging from hate to indifference to love that we ll use to express preferences in reality people have written entire books about single paintings so the real world range of expression is much wider but as we ll see it doesn t matter you already have an incredibly expressive language using just these five possibilities specifically for any artifacts there will be possible rankings as a consequence two people discussing e g just 10 works of art creates 9 765 625 possible rankings and again all they have to say is i hate it i don t like it it s ok i like it or i love it 10 times and they ll convey about 23 binary bits of information which is genetic bases worth of information as you can see increasing the number of possible rankings simply increases the base of the exponent whereas the exponent dominates the counting function common sense says that people will primarily respond to appearance smell taste and other visceral information when selecting mates but there are still at least two potentially important roles that art and aesthetics could facilitate in mating decisions and thereby explain its otherwise anomalous importance to humanity 1 filtering large crowds of individuals on the basis of shared preferences and 2 marginal distinctions within homogenous populations understanding point 1 is straightforward if you e g put together an event that centers around aesthetic artifacts e g a concert a gallery exhibition etc then generally speaking people will attend that event only if they re sufficiently interested in the full set of artifacts and the particular combination of artifacts in question that is even if you like both keith haring and caravaggio it s a bit weird to combine both in a single exhibit therefore we can express the same ordinal rankings over combinations of artifacts and that drastically increases the amount of information conveyed specifically given artifacts there are possible combinations of artifacts i e the cardinality of the set of all subsets of artifacts each of which is capable of an ordinal ranking and therefore we have for any artifacts possible rankings over the set of all subsets of those artifacts if we set as we did above i e considering just 10 artifacts we find that which is approximately 1187 genetic bases worth of information or approximately 7 0 of an entire human mtdna genome now we re talking about a significant amount of information that can realistically be conveyed since and most people should be able to meaningfully consider around 1000 collections of artifacts at least over a significant period of time just imagine selecting an outfit to wear to a particular gallery exhibition this is exactly the kind of combination of preferences that we re considering which quickly start to cover large numbers of possible combinations in fact even having strong preferences of this sort could demonstrate genetic fitness in addition to conveying genetic information the net point being because it s possible and it s obviously something many people take very seriously it should have some biological function and i think it s the obvious you re conveying information about your genome through your preferences point 2 is not hard to understand given the above which is that in a genetically and morphologically homogenous society i e everyone is genetically and physically very similar marginal differences in genomes could be important in particular because you have a high risk of incest it s not clear to me that there s any real evidence of heightened selection for aesthetics in homogenous societies but the discussions above suggest it should happen because you can convey marginal differences in genetics through your preferences allowing people to maximize genetic diversity in an otherwise homogenous population an additional misclassified neanderthal genome february 8 2026 february 8 2026 erdosfan leave a comment i wrote previously that at least two neanderthal mtdna genomes in my dataset appear to have been misclassified by the authors of the articles that analyzed the genomes in question specifically it looks like the two genomes in question are actually denisovans not neanderthals i have since been working on assembling a history of the denisovan and neanderthal maternal lines having recently completed a history of the heidelbergensis maternal line all the way up to present day icelandic people i was stuck at neanderthal genome 8 in my dataset which you can find here on the nih website i simply could not construct a reasonable history for it using the rest of the neanderthal genomes however just like the other presumably misclassified neanderthal genomes in my dataset the provenance file i e the previous link for neanderthal genome 8 also contains a qualified entry in that the isolate field is set to denisova 15 suggesting again that this is actually a denisovan genome that is somehow associated with neanderthals to test this hypothesis i compared neanderthal genome 8 to all other genomes in my dataset to kick the tires from scratch and i noticed that neanderthal genome 8 is a 98 50 match to a modern african genome from cameroon that same cameroon genome also tests as denisovan specifically it is a 51 09 match to this denisovan genome the logical conclusion is that neanderthal genome 8 was similarly misclassified and is instead yet another denisovan genome 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 ...
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