On the improbability of reproductive selection for drastic evolution

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.

Denisovan as Common Ancestor, Revisited

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.