Mutation ratios and African admixture with archaics

In my previous post I mentioned the TCC→TTC mutation anomaly, which is higher among Europeans than Africans or East Asians.

Gao, Zhang, Przeworski, and Moorjani, 2022 reported that there were several other mutation discrepancies between these three popilations.

They also looked into mutation raties that differ in "old polymorphisms that predate the out-of-Africa migration" and suggest that this case is due to the different proportion that the ancient archaic ancestors contributed to modern African and non-African people. They also point out that age of reproduction (generation time) can't explain their observations and suggest that "other factors —genetic modifiers or environmental exposures— must have had a non-negligible impact on the human mutation landscape".

Aging fathers tend to pass on to their children more T→C mutations, and mothers contribute more C→G mutations.

They noticed different T→C/T→G mutations among archaic populations (over 28,800 generations ago) compared to more recent ones in all three populations. They were surprised by this difference: (YRI is Yoruba African, CEU is Caucasian and CHB is Chinese from Beijing):

"Unexpectedly, we detected significant differences between YRI and the other two populations, CEU and CHB, in the mutation spectra of polymorphisms that are estimated to long predate the OOA migration. Specifically, the T>C/T>G mutation ratio is elevated in the very old allele age bins compared to more recent bins for all populations, with a significantly higher ratio seen in YRI than in CEU and CHB. We showed that the inter-population differences cannot be explained by differential gene flow from sequenced archaic hominins —Neanderthals or Denisovans— into the ancestors of non-Africans and such introgression alone cannot explain the shift in the older bins in all modern human populations.
Instead, we found evidence that the signals come from extremely old variants that emerged prior to the split of modern humans and archaic hominins at least ∼550,000 years ago (Prüfer et al. 2014). This suggests that the observed differences between contemporary populations could have arisen from the complex demographic history of ancestral populations. Based on observed polymorphism patterns in contemporary African populations and using simulations, several recent studies have suggested that one or more ghost archaic populations may have introgressed into the ancestors of Africans and possibly into the common ancestors of all modern humans (Hammer et al. 2011; Ragsdale and Gravel 2019; Speidel et al. 2019; Durvasula and Sankararaman 2020). After the ancestors of non-Africans migrated out of Africa, the ghost archaic group(s) may have continued interbreeding with remaining populations in Africa, leading to higher ancestry in YRI. An alternative model is deep population structure in modern humans. Under this model, two or more long-lasting, weakly differentiated ancestral populations contributed differentially to contemporary human populations through continuous gene flow or multiple merger events (Ragsdale et al. 2022). In both models, a greater contribution from a group with a higher T>C/T>G ratio to the ancestors of African individuals would explain differences between YRI and non-African population samples as well as the elevated ratio in old variants for all three contemporary human populations. Our analysis further showed that the T>C/T>G signal comes from T>C mutations rather than T>G mutations, suggesting that one or more of the remote ancestral populations had a higher T>C mutation rate relative to their contemporaries as well as to modern humans."

Time and time again we have evidence of archaic introgression into Africans that has not been passed on to Eurasians. These contriuted to their heterozygosity, diversity, and different mutation rates. This renders many conclusions based on molecular clocks and differences in alleles obsolete. It makes the Africans look more divergent but in fact this may be the outcome of swapping bodily fluids after the OOA event.

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Patagonian Monsters - Cryptozoology, Myths & legends in Patagonia Copyright 2009-2026 by Austin Whittall © 

Timeline, and age of the Y Chromosome

This post will look into the "age" or timeline of our Y chromosome. There are different methods used to estimate how this chromosome changes over time by accumulating mutations (*). This leads to a mutation rate expressed in mutations per site per year. It can be calculated by comparing the Y chromosomes of two different men, or a modern human and an archaic one, or another ape and humans, and count the mutations by which they differ, if we know when their lineages split.

(*) Actually, to be correct, a mutation is a chance change in a base pair, an error in the transcription of our genetic code. A substitution is when a mutation becomes fixed within a population, one that has not been erased by genetic drift, or natural selection. For instance, a chance mutation may lead to a "C" to appear instead of a "G", but and then another chance mutation could flip it to a "T" in that case looking at the original and the final versions we'd see one substitution, and imagine one mutation, but there were 2 mutations. Another example would be a flip back, to "C" (back-mutation or reversion), where we'd see no mutation or substitution, but in fact, there was one mutation.

The formula to calculate the Time to the Most Recent Common Ancestor (TMRCA) is shown below. Where the TMRCA in years; k is the number of base pair differences between both men, 2 is a factor added because the difference corresponds to the divergence in both men, half for each one and we don't want to count them twice, L is the total sequence length sampled in which k was detected; and μ is the mutation rate.

TMRCA (years) = k (mutations) / μ (mutations/site · year) · L (sites)· 2

From which the mutation rate can be calculated by shuffling terms as shown:

μ (mutations/site · year) = k (mutations) / TMRCA (years) · L (sites)· 2

A real life example comparing Chimpanzee and Human base pair divergences found in Shen et al., (2000) is the following: assumed TMRCA: 4,900,000 years, acutal values measured (the length sampled in base pairs) L: 38,568, (the number of divergent base pairs) k: 470, calculation of μ shown below:

μ (mutations/site · year) = 470k (mutations) / 4,900,000 (years) · 38,568 (sites)· 2

μ (mutations/site · year) = 1.25 × 10⁻⁹.

Assumptions and Data

As you can see, the formula is logic, and straightforward, we take a sample of a DNA strand from a Y Chromosome with a length of L base pairs (my intro to Y Chromosome post explains the basic terms) in both subjects, and then count the differences we observe (k), knowing how long ago both individuals shared their last common ancestor (TMRCA) we calculate the mutation rate μ.

The same method can be applied to any chunk of nuclear DNA including the X chromosome and any of the non-sexual chromosomes or autosomal DNA, as well as the mtDNA. The interesting part is that they all mutate at different rates.

Y chromosome mutation rates (μ)

Over the course of the years, different studies using different methods have attempted to calculate the mutation rate of the Y chromosome in humans. The table below shows some of these studies. The values of μ are given in mutations per site per year. For non-scientists, note that expressing a number multiplied by 10^-9 is another way of writing: "divided by 10⁹" and 10 to the ninth power is 1000,000,0000. This means that 1.24 x 10^-9 = 1.24 / 1000,000,000 = 0.00000000124 (very small indeed!).

• 1.240 × 10^-9 Thompson et al., (2000)
• 1.500 × 10^-9 Kuroki et al., (2006)
• 1.000 x 10^-9 Xue et al., (2009)
• 0.617 × 10^-9 Mendez et al., (2013)
• 0.820 × 10^-9 Poznick et al., (2013)
• 0.530 × 10^-9 Francalacci et al., (2013)
• 0.716 × 10^-9 Trombetta et al., (2015)
• 0.871 × 10^-9 Helgason, (2015)
• 0.760 × 10⁻⁹ Fu, et al., (2016)

The difference between the first value reported by Thompson et al, and the figure given by Francalcci et al is 2.34 times!

There are big discrepancies in these μ values

Using one or the other to estimate the age of a given specimen would result in one figure being over twice the age of the other one.

The different methods employed to calculate μ involve different assumptions and they all have their shortcomings. In the following commentary I will follow the excellent work of Wang CC, Gilbert MT, Jin L, Li H. (2014) ( Evaluating the Y chromosomal timescale in human demographic and lineage dating. Investig Genet. 2014 Sep 10;5:12. doi: 10.1186/2041-2223-5-12. PMID: 25215184; PMCID: PMC4160915).

The data used for comparing divegencies in the Y chromosome's base pairs can come from different sources: Ancient DNA, samples taken from prehistoric human remains which have been dated by using radiocarbon or other methods. Genealogical, using samples from a certain family for which the genealogy has been confirmed and dated, these usually involve short time spans and few generations. Archaeological Events, taking an estimated date for an event, such as the peopling of America or the settlement in a given region in Europe, and applied to samples from that date.

Confounding Factors

As mentioned in my post on phylogenetic tree branch lengths (a factor that shows that mutation rates are not constant), the value of μ should be considered as an estimation, and not something written in stone. Trombetta et al., (2015) point out that "variants" (mutations) appear at different rates across Y chromosome haplogroups, geographic locations, and time: "... we observed a remarkable heterogeneity in the distribution of variants, not only across different regions, but also across lineages and different times. Hg A00 stands out for showing strong associations with almost all genomic features considered. In the rest of the tree Hg's A0, A1a, A2'3 and B differ from Hg's DE, FC and R, and ancient branches differ from recent ones. The two levels are not entirely independent, as far as recent branches are enriched in lineages belonging to Hg's DE and R. It is possible that different social habits, lifestyles and environmental conditions experienced by populations harbouring different haplogroups resulted in systematic variations of the generation time and average paternal age at conception." They attribute this to older or younger age of the fathers when their children are conceived —older dads have more mutated sperm, and the effects of environment on DNA mutations.

Chimpanzees and Men

When comparing human and chimp Y chromosomes, we are not only separated by a gulf of 5 to 7 million years of separate evoluton, the evolution itself has been different in both species. The chimpanzee Y chromosome is much smaller than that of humans. it lost roughly one-third of its genes in the MSY, or male-specific Y region of their Y chromosomes, compared to men.

Kuroki, Y., Toyoda, A., Noguchi, H. et al. (2006) noted that there is a greater divergence in the sequence of human Y chromosome vs. chimpanzee Y chromosome than between the whole genomes of both species (1.78% and 1.23% respectively).

The reliable dating of the Chimpanzee-Human split is still being debated, and figures range from 4.2 to 12.5 million years ago. A factor of three!

There are also structural differences in the shape of our and the chimp's Y chromosome which complicates the alignment of segments for comparison. Finally, chimpanzees and modern humans have different pair-bonding sexual behaviors (Hughes et al, 2013). Schaller et al., (2010) note that receptive females copulate with multiple male partners creating selective pressure towards the male fertility genes in the chimp's Y chromosome. Monogamous pair bonding in humans lacks this intense selective force. This alters the rate of the mutational clock. The pair bonding in hominins can be seen in the reduced sexual dimorphism in australopithecines and the loss of sperm competition adaptations, suggesting less male-to-male strife, and the growth of cooperation as a means for reproductive success (Gavrilets, 2012).

Genealogical Methods

Pedigree-based studies look at men belonging to the same family, sharing the same paternal lineage, and whose birth dates are known, or at least, how many generations separate them. This provides a well defined dating.

Xue et al., (2009) studied 13 generations of men in haplogroup O3a. However, there are some potential sources of error in this method: Since mutation rates are random, variable, and unpredictable (the statistical term for this is highly stochastic), are we sure that 13 generations (approx. 390 years) is a long enough interval.

Another is the haplogroup itself, we have mentioned that haplogroups accrue mutations at different rates. Is haplogroup O3a a good reference for all other haplogroups?

Finally, if selection and genetic drift act mutations eliminating some of them over longer timescales, the number of mutations found in a genealogical study will be smaller than the actual one detected in longer scales.

Mutation Rates adjusted for autosomal mutation rates

This method was developed by Mendez F., et al., (2013) when they dated the extremely ancient A00 haplogroup Y chromosome, found in the Mbo people of Cameroon, Africa. The μ used by Mendez team was based on a study conducted in Iceland that calculated the autosomal mutation rate by analyzing the divergence between parents and their children. This implies several unverified assumptions: autosomal and Y chromosome mutation rates are similar (they are not), substitution rates and mutation rates are equivalent (they are not). They also used a generation time that spanned from 20 to 40 years when life expectancy for men in Cameroon in 1950 was below 40! (Source). Other authors using genealogical data, like Boattini et al., (2019) found generation lengths of 33.57 years. Hunter-gatherer groups in Equatorial Africa 150,000 years ago probably began mating at the age of 15. How can we know for sure? (See Elhaik E, Tatarinova TV, Klyosov AA, Graur D., (2013) and their critique to Mendez et al.

Older generation times lead to more mutations, and an overestimated TRMCA. A00's age is far shorter than the one put forward by Mendez et al.

Archaeological Data

In my posts, I have mentioned many ancient DNA samples taken from the remains of prehistoric, ancestral human beings and hominins (Yana River, Mal'ta and Ust'-Ishim). They provide a certain age with reliable radiocarbon-dating, and if the DNA is not degraded or contaminated, a reliable count of mutations.

Fu, et al., (2016) compared the remains of the Ust'-Ishim man with those of men alive today, and looked for "missing" mutations, those that appeared in modern men after Ust'-Ishim died. The team calculated a mutation rate of 0.76 × 10^-9

Human Migration approximations

Assuming dates for certain migratory events, like the peopling of America, placed at around 15,000 years ago, the dates for certain haplogroups found exclusively in America can be set close to that event. This has been used to calculate μ for splits between Asian and Amerindian lineages, or between Amerindian groups within America, obtaining a μ of 0.820 × 10^-9 (Poznick et al., (2013)).

A similar method was applied by Francalacci et al., (2013) to men native to the island of Sardinia in Europe, peopled around 7,700 years ago, and using the mutations detected in a sample of Sardinian men to calculate μ for their haplogroup (I2a1a). The value obtained was very low compared to those shown in the Table further up: 0.530 × 10^-9.

Can we be certain that the haplogroup diversified in its current location in Sardinia or America? Could it have taken place earlier (European mainland, or Siberia, respectively). Do the current Sardininans belong to the group that reached the island 7.7 kya? Or did they arrive later?

Implications

The sum of these factors show that environment, generation times, sexuality (pair bonding), natural selection, and genetic drift can promote or reduce mutation rates. Studies have a 2.4-fold variability, ranging from 1.24 × 10^-9 Thompson et al., (2000) to 0.53 × 10^-9 Francalacci et al., (2013), and these are the mean values, the confidence intervals are even wider. Supposing a threefold difference, what some estimate as a divergence taking place between two Y chromosome haplogroups 50,000 years ago during the final Out of Africa migration, may have taken place within Africa 150,000 years ago! And the A00 split instead of taking place 250,000 years ago may reflect one that ocurred 750,000 years ago.

The possibility that our most ancient ancestors had more chimp-like behavior would have implied a faster mutation rate during that period, followed by a slower rate later on. Even pair-bonding during the patrilineal, sedentary, agricultural period of the past 8,000 years may have slowed down mutation rates in comparison to the matrilineal hunter-gatherer period that preceeded it.

Mutation Rates and their impact on TMRCA inference. Copyright © 2026 by Austin Whittall

The image above shows how mutation rates can influence the age of the TMRCA. For the same measured value of "m" mutations marked by the gray line, the use of different mutation rates μ influences the depth or age to the TMRCA. A quick mutation rate like the blue one (μ₁) accumulates mutations quicker and takes less time to reach the detected "m" mutations, so its TMRCA is younger (T1), a slower mutation rate (red line) like μ₂ takes longer to accumulate the "m" mutations and its T2 is longer. Finally, a variable mutation rate like μ₃ (green line) that was faster in the distant past, and slower later on will have an even longer and older timeline (T3).

For those interested in maths, the slope of each curve marks the mutation rate dm/dt = μ(t), steeper curves mean quicker mutation rates. It an analogy of speed as the differential of space over differential of time.

I believe that the μ₃, variable mutation rate is closer to reality, reflecting variable social-sexual-cultural patterns of the small and egalitarian promiscuous hunter-gatherer groups of early hominins and human evolution. Later settled societies with paternal monogamy and private property led to slower mutation rates. This of course would imply an older root for the human Y-chromosome haplogroups, in Africa, prior to our migration into Eurasia.

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Fall has begun here in Buenos Aires, in the Southern Hemisphere! Lucky you who are now entering Spring!

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Patagonian Monsters - Cryptozoology, Myths & legends in Patagonia Copyright 2009-2026 by Austin Whittall © 

Back Mutations are common & more frequent than previously stated

Reversions, also known as back mutations are considered rare in biology. Basically, what it means is an initial mutation in the geneome, reverses back to its original (wild-type) form through a second mutation that restores the base in the DNA sequence.

For example, a "chunk" of DNA could contain the following bases: ACGCTG and, a random, chance mutation replaces the cytosine (C) for an adenine (A) ACGATG and a second mutation restores (reverses) the original situation ACGCTG.

This has important consequences, first of all, if we look at the ancient sample and the most recent one, there is no way we will ever know that it mutated and reverse by back-mutating, (both have the same sequence: ACGATG so how can we know if there was a back and forth flip in between both samples?)... unless we find an sample of someone in the same line, or a parallel lineage with the first (derived) mutation, but not the reversion,which seems a very unlikely situation.

Assuming that all mutations are forward oriented and never reverse, may overlook mutations that were reversed. Since coalescence time and dates of lineage splits are based on mutations, if we overlook the reversions, we will miss out on the actual mutations (to and fro), counting zero when in fact there were two mutations.

So we will assume that mutation rates are lower than they really are by missing out these back-and-forth mutations.

If we overlook reversions we will assume there were only n mutations per a given amount of years, while there were actually m mutations: "n" that we see (for instance, there is a G instead of an A at a certain locus), and "p" mutations that flipped forth and another "p" that flipped back. n is therefore smaller than m; m = n+2p. So the mutation rate is higher than assumed.

However, the Neutral Theory of genetic evolution does not consider this alternative, it requires No back-mutations. Changes can only happen in one direction A → G. Which will never again flip back G → A, and No Recurrence there can't be multiple mutations at identical loci in different lineages.

Are they Common?

William Amos (2020) suggests that "back-mutations are far commoner than has been previously assumed" he adds that "Back-mutations are ‘silent' because they create the original ancestral allele, but can reasonably be assumed to occur about twice as often as triallelic SNPs are generated (two transitions are approximately twice as likely as one transition and one transversion). Triallelic SNPs are coded ‘MULTI-ALLELIC' rather than ‘SNP' in the 1000 genome data and are often ignored, but I counted 257,827 occurrences across all autosomes, implying over half a million sites carrying back-mutations. Moreover, this is probably an underestimate because the 1000 genomes data are low coverage and rely on extensive imputation which will often cause rare third alleles to go undetected. Equally, conservative curation will tend to remove third alleles that lack strong support. Note, triallelic sites are unlikely to be generated mainly by sequencing errors because only 1% of these sites carry a singleton as the rarest allele. This analysis is not intended to provide an accurate estimate of the back-mutation rate, but instead simply to demonstrate that large numbers of back-mutations do exist to the extent that models of evolution that rely on back-mutations occurring in appreciable numbers should not be dismissed a priori."

Research by Anke Fähnrich et al., (2023) on the North and Eastern African mtDNA shows that some mutations that serve as markers appear time and time again, the authors consider some as "Shared back mutations", others are simply repeat mutations. The paper shows them in a tree for "L0a1 and (b) L2a1" and clarifies that "We highlight with magenta, gray and turquoise boxes those variants that indicate that a different phylogenetic tree may better explain the samples from North and East Africa." These trees can be seen in the image below. The paper adds that "Shared back mutations (magenta) denote that parental haplotypes may be missing in PhyloTree. Mutations repeatedly observed in a subtree (gray) suggest that child haplogroups are missing. Variants that occur in multiple samples and differ from variants defining a parental haplogroup (turquoise) suggest that different variant combinations and haplogroup specifications may better explain North and East African mtDNA sequences". It also marks them with an "@" as "assumed back mutation or missing mutation". This goes to show that markers are shared across different haplogroups.

Figure 10, haplo tree mtDNA L. Source

A similar situation was reported by Neil Howell, Joanna L Elson, D M Turnbull, and Corinna Herrnstadt (2004) who were investigating the oldest mtDNA haplogroups L0 and L2. Besides finding oddities in the trees, ages, etc., they noted multiple reversions: "The L0a outgroup sequence carries C alleles at nucleotides 16189 and 16192, whereas the L2a ancestral sequence is predicted to carry C and T, respectively, at these sites. The 16189 site subsequently undergoes mutation on four occasions (three forward and one reverse relative to the outgroup sequence), whereas the 16192 site undergoes reversion on five occasions. Thus, both sites appear to have relatively high rates of mutation, a result that has been observed in previous studies (Excoffier and Yang 1999; Meyer, Weiss, and von Haeseler 1999; Howelland Bogolin Smejkal 2000) and in the L2a networks of Salas et al. (2002)... The ancestral L2a sequence carries a C:T transitional nucleotide 16519, which undergoes reversion on three occasions. These results are not surprising and this site has long been recognized to have a high mutation rate."

This seems to define a "mutation hotspot", that I mentioned in a previous post (Laguna de los Pampas 10,000 BP remains in Argentina).

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Patagonian Monsters - Cryptozoology, Myths & legends in Patagonia Copyright 2009-2026 by Austin Whittall © 

Mutation rate is Faster in Africa

Mutation rate, the speed at which mutations occur in the human genome plays an important role in generating diversity, and is also used as a way to calculate the dates on which different lineages split from a basal one. It also has its problems because mutations are random, and don't take place at a constant rate, which makes the molecular clock based on them, erratical and unreliable.

While researching about the diversity in African human populations, I came across research by William Amos published in 2020, with a very intriguing title: "Signals interpreted as archaic introgression appear to be driven primarily by faster evolution in Africa", it questioned the validity of the admixture events with Neanderthals and Denisovans (I posted about this a few days ago, here), and considered it an artifact caused by the rapid rate of evoultion found in Africa:

"A model where Africans are unusually different from Neanderthals through accelerated divergence rather than non-Africans being unusually similar to Neanderthals though carrying introgressed fragments requires both a large number of back-mutations and variation mutation rate between human populations. Specifically, the mutation rate in Africa would have to have been higher than the mutation rate outside Africa since the out of Africa event, causing significantly more back-mutations in Africans. By counting triallelic sites, I show that very large numbers of back-mutations are indeed present, estimated at more than half a million...
In conclusion, I present a simple analysis that reveals an unexpected pattern in which non-zero human D statistics are unambiguously dominated by heterozygous African genotypes. These sites invariably cause the African to be less closely related to archaics and so appear to carry signatures of increased divergence from our common ancestor. More work is needed to reconcile these results with those of previous studies that conclude most non-African humans carry 1–2% archaic sequences.
Putting these elements together suggests a model where large D is driven by a higher mutation rate in Africans causing relatively greater divergence from Neanderthals. Individuals not carrying heterozygous African sites, or who carry fewer than the individual against whom they are being compared, therefore appear closer to the ancestral state and, hence, closer to related taxa such as Neanderthals."

Modern Africans have evolved since the Out of Africa event and done so at a faster rate than other Eurasian and American humans, this shows even greater "diversity" difference, and cline between Africa and the rest of the world.

Heterozygosity modulates Mutation Rates

Another paper by W. Amos (2013) suggests that heterozygosity increases mutation rates, the chart below shows how Africans with high heterozygosity in comparison to other populations, has a higher mutation rate:

Mutation rate and Heterozygosity. Fig. 1 in Source

Amos suggests that "The “heterozygote instability” (HI) hypothesis suggests that gene conversion events focused on heterozygous sites during meiosis locally increase the mutation rate... As humans left Africa they lost variability, which, if HI operates, should have reduced the mutation rate in non-Africans... For humans, HI implies a reduction in mutation rate as we left Africa with the counter-intuitive result that non-Africans will appear more closely related than Africans to other hominid lineages such as Neanderthals, a trend that has been observed and used as evidence of introgression."

Furthermore, Amos posits that HI promotes genetic diversity by favoring recombination and mutation hotspots: "Phenomena like mutation hotspots might also be seen in a different light, as should variation in recombination rate, since both are likely to some extent to be exaggerated or even caused by HI: the gene conversion-like events attracted by heterozygous sites likely in some cases to be resolved by recombination." Recombination has been shown to be linked with higher heterozygosity, and genetic diversity (Source). So, is this a self-reinforcing feedback loop with heterozygosity pushing up mutation rate which will create higher heterozygosity?

The matter had been brought up in the past by J. H. Relethford (1997), who pointed out that "Global studies of within-group genetic variation have revealed a tendency for some traits, but not all, to show higher heterozygosity in sub-Saharan African populations. Although excess African diversity has been interpreted as reflecting a greater "age" of sub-Saharan African populations, more recent research has shown that this excess is more likely a consequence of a larger African long-term effective population size... Here, I examine another possible factor: that excess African heterozygosity is in part a function of mutation rate...The results indicate that there is little excess African heterozygosity for traits with low mutation rates and greater excess heterozygosity for traits with moderate to high aggregate mutation rates."

Let's look into Relethford's suggestion:

The larger effective population or Ne is a clear driver of diversity because being large, there is risk of loss of heterozygous variants (more of them initially, and more chances of at least some carriers of them, having offspring). They also accumulate new allelles that arise due to chance mutations in the population, and there is less inbreeding.

Regarding mutation rate (μ) is seems reasonable that a population with a higher mutation rate will produce new variants. A reason for this seems to be that heterozygous loci cause Heterozygote instability during meiosis (the process during which the chromosomes split and sparate, halving their number in the gametes -sperm in men and ovum in women), this instability reduces the effectiveness of DNA repair mechanisms. Also, if mutations are related to adaptative benefits, selection will promote them.

In a population that is in equilibrium there is a formula that calculates the average expected heterozygosity "H". It involves the following terms: the neutral mutation rate or μ, and the effective population size or Ne (Source).

H = 4Ne μ / (4⁢Ne μ +1)

I calculated values of heterozygosity (y-axis) for different Ne sizes (x-axis) for two mutation rates, 10^-5 and twice that value (2*10^-5), the graph below shows the outcomes:

H as a function of Ne for two different μ values. Copyright © 2026 by Austin Whittall

As Ne increases, so does heterozygosity. But, with a same effective population size and a higher mutation rate, H increases too! Africans with a higher mutation rate (μ) would have increased their heterozygosity due to that effect alone, compared to slower mutating Eurasians.

Finally, a very interesting paper by Amos, Flint, and Xu (2008). states that "our analysis suggests that a feedback loop can operate causing heterozygosity to increase over time, each increase also increasing the mutation rate which in turn raises heterozygosity." Could this have happened, and still ocurr in Africa?

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Patagonian Monsters - Cryptozoology, Myths & legends in Patagonia Copyright 2009-2026 by Austin Whittall © 

Bitter Taste genes

The ability to taste bitterness may help us avoid poisoning from eating toxixc plants. Animals in general, and our ancestors, the primates developed this trait. The PTC gene is responsible for our perception of bitter taste.

All nonhuman primates have only one variant of the PTC gene known as PAV, which is therefore considered as the ancestral or original gene form, these apes are homozygous for it (meaning that the two copies they carry, one from each parent, are identical). This is known as the "taster" allele, it allows them to taste bitterness.

Genetics

PTC bitter gene, is formally known as the TAS2R38 gene. It comes in eight different variants but two of them are prevalent, the ancestral "taster" allele, and a "non-taster" allele which comprise 96% of the human population. These encode a specific protein, which contains 333 aminoacids, the 7-transmembrane domain G-protein-coupled receptor which responds to bitterness.

The different alleles cause tiny variations in the position of some aminoacids in this protein and cause the "taster" and "non-taster" variants and four other intermedieate "less-taster" types.

The variants are named after the positions of these amino acids the "original" or "ancestral" form is the PAV form (because it contains proline at position 49, alanine at position 262, and valine at position 296), this is the "taster" form.

Neanderthals and Denisovans are also PAV tasters (Source).

The second mayor form is the "non-taster" one, known as AVI, because it contains alanine, valine and isoleucine aminoacids in those three positions, respectively.

Further down we will look into why do the "non-taster" alleles survive, and account for roughly half of the human population, who can't taste bitter flavors. If tasting bitterness protects against plant toxins, why do so many of us carry the non-taster variant?

The other six variants are AAV, AVV, AAI, PAI, PVI, AAI, and PVV and are found at relatively low frequencies.

The prevalence found in one study was the following: 42.3% PAV (ancestral, taster), 53.1% (derived, non-taster) and the intermediate taster ones (2.5% AAV, 1.2% AAI, 0.8% PAI, and 0.1% PVI, no AVV or PVV were detected). (Source).

People who inherited at least one copy of the PAV allele from their parents are able to taste bitterness.

These alleles also have a geographic distribution, PAV and AVI are the most frequent, and make up the vast majority of European and Asian alleles. They are also found in Africa, but there, AAI is found at a relatively high frequency. The table below, (Table 1 from Risso, D., Mezzavilla, M., Pagani, L. et al. (2016) Global diversity in the TAS2R38 bitter taste receptor: revisiting a classic evolutionary PROPosal. Sci Rep 6, 25506), shows data from 5,589 individuals sampled across 105 populations around the World, and it highlights the slight variations in different populations.

Table 1: Detailed distributions of TAS2R38 haplotypes in the studied populations. . Source

The "Americans" in the table shown in the image are from the following groups (the number is the individuals in each sample): North America Maya Mexico 42, North America Puerto Ricans Puerto Rico 110, South America Colombians Colombia 134, South America Karitiana Brazil 28, South America Mexicans Mexico 160, South America PEL Peru 170, and South America Surui Brazil 16.

American Natives and Bitter Taste Genes

Looking at the data, we see that PVV is exclusively European, and does not appear in any other population, including Amerindians. It didn't admix into them despite the large-scale intermingling that took place after 1492, which is quite surprising.

AAI is definitively African where it reaches 13.22%, and in small amounts among Europeans and Americans (perhaps due to African genetic mixing into Southern Europeans and slave trade into America).

PVI is extremely rare, and is found at higher frequencies among Native Americans, with 0.19%, followed by Africans by 0.15%. None in Asia, and only 0.03% in Europe. We could suppose that African slave trade brought it into America, but why is the prevalence 26.6% higher in the Americas than in Africa? Being absent in Asia it surely didn't arrive via Beringian migrants.

AAV, absent in Asians is also found among Americans (2.26%), slightly lower than Europeans (3.56%) and much higher than Africans (0.61%). If it introgressed into Amerindians through Europeans, then, why didn't the European AVI do so in a similar proportion? (AVI among Americans is 26.69% while it is 49.22% among Europeans and roughly 33% in Africans and Asians).

Regarding the ancestral PAV, original allele, it is highest among Native Americans with 68.8%. The other populations have a lower frequency of it.

Interestingly, according to Kim et al. (See: Kim UK, Jorgenson E, Coon H, Leppert M, Risch N, Drayna D., Science. 2003 Feb 21;299(5610):1221-5. doi: 10.1126/science.1080190. PMID: 12595690. Positional cloning of the human quantitative trait locus underlying taste sensitivity to phenylthiocarbamide), "The common nontaster AVI haplotype was observed in all populations except Southwest Native Americans, who were exclusively homozygous for the PAV haplotype" (these natvies were almost 100% tasters).

Another article (Flores SV, Roco-Videla A, Aguilera-Eguía R. Variation in haplotype frequencies of the TAS2R38 gene, associated with the perception of bitter taste. Salud, Ciencia y Tecnología. 2025 Jan. 1;5:1026.) notes taster prevalence among Peruvian Andean people: "A particularly interesting case is the Peruvian population, which stands out for its high frequency of bitter taste perception diplotypes. In this population, only 1 % has the AVI/AVI diplotype, indicating an almost total prevalence of bitter taste perception (PAV/PAV and PAV/AVI). This exception suggests a specific dietary adaptation in the Andean region, or well the result of genetic drift."

Why hasn't natural selection erased the non-taster alleles?

For human beings, nearly all naturally occurring plant toxins poisons taste bitter. But, not all bitter tasting foods are poisonous. Many bitter tasting foods are harmless.

One interesting paper suggests that excluding all bitter flavored plants would mean lost calories and nutrients, because many bitter veggies are healthy and have no harmful effect (citric fruits, bitter melon, or kale, as well as the other cruciferous vegetables), some foods like beer, green tea, or coffee are bitter yet pleasurable. Also, bitterness may also mean medical properties such as quinine (the bitter ingredient of tonic water) used to combat malaria, or the pain-killing properties of salicin, found in willow leaves, on which the aspirin was based.

Humans also have cognition, and curiosity, they may try a bitter food, which if it doesn't cause harm, can then be safely added to the diet.

A paper (T2R38 taste receptor polymorphisms underlie susceptibility to upper respiratory infection Robert J. Lee,… , Danielle R. Reed, Noam A. Cohen. Published October 8, 2012. Citation Information: J Clin Invest. 2012;122(11):4145-4159. https://doi.org/10.1172/JCI64240.) found that bitter taste receptors also act upon the tissue lining the upper respiratory tract, and those carrying at both PAV alleles (tasters) are better protected from microbes than those carrying one or none: "these individuals are more likely to be infected with gram-negative bacteria such as P. aeruginosa than those with 2 functional receptor alleles." It also suggests that " humans with more non-taster alleles live in the colder climates, where the evolutionary pressure for the taster genotype may be relaxed, as there are fewer pathogens than in warmer climates."

But why the AVI non-taster allele is still carried at around 50% levels in humans. Could it be functional for other reasons and offer an evolutionary advantage that we have not yet identified?

A 2012 paper (Campbell MC, Ranciaro A, Froment A, Hirbo J, Omar S, Bodo JM, Nyambo T, Lema G, Zinshteyn D, Drayna D, Breslin PA, Tishkoff SA. Evolution of functionally diverse alleles associated with PTC bitter taste sensitivity in Africa. Mol Biol Evol. 2012 Apr;29(4):1141-53. doi: 10.1093/molbev/msr293. Epub 2011 Nov 29. PMID: 22130969; PMCID: PMC3341826) says it does, but isn't yet fully understood:

"the selective force maintaining common AAV, AAI, and AVI haplotypes for extraordinarily long periods of time remains unclear. Although both AAV and AAI are associated with intermediate bitter taste sensitivity, the AAI haplotype is more common in Africa than AAV. Intriguingly, the AAV haplotype may represent a “stepping stone” to other more advantageous haplotype variation, such as AAI and AVI. We suggest that common PAV, AAI, and AVI haplotype variation may be maintained at high frequencies in response to selective pressures unrelated to diet. Indeed, recent studies have shown that bitter taste receptors are expressed in a variety of cell types in the human gastrointestinal tract (Rozengurt and Sternini 2007) and lungs (Shah et al. 2009; Deshpande et al. 2010), where they influence insulin and glucose levels (Dotson et al. 2008), eliminate harmful inhaled substances (Shah et al. 2009), and stimulate the relaxation of airways for improved breathing (Deshpande et al. 2010). These studies demonstrate that bitter taste loci have a number of different functions and raise the possibility that common variants at TAS2R38 may be under selection due to their physiological roles in human health beyond oral gustatory function. Though we cannot conclusively distinguish the selective forces maintaining common variation at TAS2R38, it is clear that genetic variation at this locus is diverse and has been functionally important long before modern Homo sapiens existed."

Neanderthals

Our relatives, the Neanderthals had bitter taste perception (source) the El Sidrón individual, was heterozygous, carrying the ancestral PAV and the derived allele with an alanine in position 49, the study didn't clarify the other two positions, so this Neanderthal could have carried the common non-taster AVI, or the more rare variants AAI or AAV.

This means that the non-taster variant dates to before humans and Neanderthals split around 500,000 years ago. Of course, genetic flow between both groups could have introduced the derived non-taster variant into the 48,000-year-old Sidrón individual (meaning it originated among our H. sapiens), but the study considers this unlikely and affirms that "our results indicate that the non-taster alleles were already present in the ancestral human populations from which both Neanderthals and modern humans diverged."

The rise of non-tasters took place long ago. Studying African populations Tishkoff et al. (2012) found the following evolution and timeline for this gene from the PVA to the AVI form:

The PAV → AAV variant arose when P was replaced by A at site 49 1.3 million ± 242,211 years ago. Then the AAV → AAI shift took place when V was replaced with I at position 296, 1.0 million ± 267,268 years ago. These changes predate the split between Neanderthal-Denisovans and our ancestral H. sapiens lineage. Another mutation was the A for V at position 262, causing the AAI → AVI shift. This one took place 336,000 ± 89,845 years ago. The other low-frequency alleles are much younger than 200,000 years. Below is and years old, respectively. The lower frequency variants, including those that are associated with decreased PTC sensitivity, appear to be much younger in age, occurring within the last 200,000 years. Below is Fig. 4, from this paper, we added in red letters, each allele.

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Patagonian Monsters - Cryptozoology, Myths & legends in Patagonia Copyright 2009-2025 by Austin Whittall © 

Unique Amerindian Genetic Trait

My previous post dealt with the anomalous prevalence of Alzheimer's Disease among American Natives, today's deals with another "unique" Amerindian genetic trait, that extends to what in USA are known as Latinos (people with mixed ancestry that includes Native Americans): one that protects against breast cancer.

Breast Cancer incidence by Race USA. From [1]

The table above clearly shows how American Natives and Latinos have the lowest incidence of Breast Cancer among American women.

The cause according to a paper [2] by Laura Fejerman et al.,(2014) is a mutation in chromosome 6: "Here we carry out a genome-wide association study of breast cancer in Latinas and identify a genome-wide significant risk variant, located 5′ of the ​Estrogen Receptor 1 gene (​ESR1; 6q25 region). The minor allele for this variant is strongly protective (rs140068132: odds ratio (OR) 0.60, 95% confidence interval (CI) 0.53–0.67, P=9 × 10−18), originates from Indigenous Americans and is uncorrelated with previously reported risk variants at 6q25."

This mutation is the reason that "Latina women, those with a high proportion of Indigenous American ancestry are at a lower risk of developing breast cancer..." [2].

This mutation must have appeared in America otherwise the purported ancestors of Amerindians (as per the Out of Africa theory) would also carry this variant. By the way, the prevalence of Cancer among Amerindians is almost 1/3 of that found among White American women and half of that found among African American women. The Asian Americans' ratio is also almost twice that of American Natives. (these are supposedly the closest genetic relatives to Amerindians).

Is this also due to a bottleneck? or is did it appear during the "Beringian standstill"?

What does the genome of Neanderthal or Denisova tell us about this mutation? I have tried to find information but have not found anything. It may be a mutation inherited from them. Found only in America.

But what about Papuans, who have a high proportion of Denisovan genes? I found two papers (here) and (here) which inform extremely low levels of cance: roughly 8 to 20 times lower than the ratio among Ameridians"!: from 1958 to 1988, the incidence of breast cancer was betwenn 6.9 and 2.4 per 100,000 women.

Do Papuan women have a genetic mutation that protects them too? or is it just lifestyle? Or are these numbers not adjusted by age?

I found another interesting source (global Cancer atlas) which lists cancer prevalence among all human populations. I selected Breast Cancer Incidence and got this map:

Clearly this differs from the other information: dark blue= EU, Australia, America and NZ, Argentina... countries with a high prevalence of White Europeans that eat beef. And low prevalence in "poor" countries where fatty foods are not so common... Asia, Africa, Bolivia. The quality of the data is also variable, ranging from "A" in the US to "C" in China or "G" in Bolivia (19.2 per 100,000 cases) so it makes me wonder how reliable this information is.

Anway, the intersting point is the mutation in Chromosome 6 found among Native American women.

Sources

[1] Zhang and Olopade in Hereditary Breast Cancer. Edited by Caludine Isaacs, T.Rebbeck. pp.234
[2] Laura Fejerman, et al.,(2014). Genome-wide association study of breast cancer in Latinas identifies novel protective variants on 6q25, Nature Communications 5, Article number: 5260 doi:10.1038/ncomms6260

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Patagonian Monsters - Cryptozoology, Myths & legends in Patagonia Copyright 2009-2014 by Austin Whittall ©