Yes, we covered this ad nausea the last time you were here. You still have not answered my challenge to you on this.
What you are describing is a case where the beneficial mutations must all be there to be beneficial, or come in a particular sequence. That isn’t what I’m talking about (parallel evolution), so this experiment is not relevant.
Yes, he’s big on word-salad. And he’s adopted fixed descriptions of everything that he will never change or explain, merely repeat at every opportunity. You should check out his previous publications, not a one of them ever refereed by anyone competent in population genetics.
One set of mutations involved the NOTCH2NL genes which produce large brain size. These mutations are found in humans, Denisovans, Neanderthals but not in chimps.
Paired involvement of human-specific Olduvai domains and NOTCH2NL genes in human brain evolution
Fiddes et al
Human Genetics volume 138, pages715–721(2019)Abstract: Sequences encoding Olduvai (DUF1220) protein domains show the largest human-specific increase in copy number of any coding region in the genome and have been linked to human brain evolution. Most human-specific copies of Olduvai (119/165) are encoded by three NBPF genes that are adjacent to three human-specific NOTCH2NL genes that have been shown to promote cortical neurogenesis. Here, employing genomic, phylogenetic, and transcriptomic evidence, we show that these NOTCH2NL / NBPF gene pairs evolved jointly, as two-gene units, very recently in human evolution, and are likely co-regulated. Remarkably, while three NOTCH2NL paralogs were added, adjacent Olduvai sequences hyper-amplified, adding 119 human-specific copies. The data suggest that human-specific Olduvai domains and adjacent NOTCH2NL genes may function in a coordinated, complementary fashion to promote neurogenesis and human brain expansion in a dosage-related manner.
This person’s writings in this conversation, by the way, do not describe a “minority position.” They describe a confused, inaccurate, shallow, uninformed position that apparently requires a strawman (the claim that you and others believe that all mutations in this lineage were neutral) to get in the door. No further conversation is warranted IMO.
When it comes to DNA evolution, the number of genome replications per offspring is two for diploid organisms. If polyploid, the number of DNA replications per offspring is the ploidy of the organisms so you would have to adjust your math accordingly. But you would also have to consider whether the genetic loci were homo- or heterozygous. If homozygous, the same beneficial mutation would be applicable to either gene. On the other hand, it is possible that a beneficial mutation for one gene might not be a beneficial mutation for the other gene. Haploid or diploid, it only changes the mathematical behavior slightly.
Recombination can also have a slight effect on DNA evolution for a single evolutionary adaptive step. If you have one parent that has a beneficial allele at one locus and another parent has a second beneficial allele at a different locus and they recombine to give an offspring with both beneficial alleles, you can have a step improvement in fitness. Here’s a paper that shows how to do that math.
Random recombination and evolution of drug resistance
And remember, Chimpanzees are diploid and do recombination.
For calculation’s sake, say we only have beneficial mutations of 1% benefit.
Kimura’s fixation rate formula from a paper entitled “On the Probability of Fixation of Mutant Genes in a Population”
For a diploid population of size N, and deleterious mutation of selection coefficient - s, the probability of fixation is equal to
P fixation = (1 - e-2s)/(1 - e-4Ns)
(if s =/= 0. If s = 0, then we simply use his equation 6, where probability fixation = 1/2N).
Formula (10) from On the Probability of Fixation of Mutant Genes in a Population
If s = 0.01 and N = 1000000, (ie beneficial mutation with 1% fitness advantage and population 1000000), probability of fixation is
(1-e-0.02)/(1-e-40000) = 0.01980132669
Each beneficial mutation of 1% benefit has a 2% chance of fixing in a population N=1000000.
Sqy the human chimpanzee divergence time is 6000000 years.
We need 250 beneficial mutants in that time span for 5 beneficial mutants to fix.
So 1 beneficial mutant needs to occur in the human lineage every 24000 years.
@Joe_Felsenstein if I have made any errors.
Imagine being a scientist and trying not to be understood. It isn’t already hard enough to explain things using the words people already know?
All true, but completely irrelevant to my point–you are ignoring recombination.
What about whether the mutant alleles were dominant or recessive?
Either gene? Don’t you mean either allele?
You’re not addressing recombination.
HIV is not a diploid organism, so it’s not applicable to the problem: you’re not considering meiotic recombination at all.
I’m well aware of that, thanks.
The calculations are correct. However people seem to be assuming that changes in phenotypes such as skull shape are all due to new mutations. If we start out with genetic variation in the population for those characters, then we don’t have to wait for new mutations. When we select on such a character, it will immediately be affected as the gene frequencies in the loci that affect the trait are changed by the selection.
Beneficial mutations must occur in a specific sequence, that of ever-increasing fitness. Here’s a paper that explains this:
Darwinian Evolution Can Follow Only Very Few Mutational Paths to Fitter Proteins
The reason for this is that unless that variant can increase in number, the probability of the next beneficial mutation occurring will remain low.
So when are you going to explain to us how evolution works in parallel? Joshua, you have a responsibility to do this, especially the evolution of drug resistance and why cancer treatments fail (because they fail by evolutionary processes). You’ve claimed that you can account for most of the genetic differences between humans and chimpanzees using neutral evolution but there are now over 7 billion humans and only 300,000 chimpanzees. And 99% of all humans have lived in the last 10,000 years. You are not doing the mathematics of evolution correctly. Neutral evolution does not explain these population or genetic differences.
Joe, the error you are making is trying to use a fixation equation to describe adaptation. And just because fixation occurs doesn’t mean that adaptation also occurs. In fact, if an evolutionary adaptation process is operating in a competitive environment where fixation must occur, it only slows the adaptation process. This is what the Lenski experiment is demonstrating. He wrote (edited) this in this paper:
Distribution of fixed beneficial mutations and the rate of adaptation in asexual populations
Competition is a simple 1st law of thermodynamics process. Lenski limits the glucose which is the energy source for replication. The more effective users of glucose slowly drive the less effective users of glucose to extinction but while that happens, the less effective users of glucose are depriving the more effective users of that energy necessary to replicate and do the trial (replication) for the next beneficial mutation. On the other hand, the Kishony experiment has a much larger carrying capacity allowing for much larger populations and eliminating the need for fixation in order for adaptation to occur. The colonies can grow to sufficient size to have a reasonable probability of the next beneficial mutation occurring without fixation occurring. Any of the drug-sensitive variants happily continue to grow as long as the resources are available in the drug-free region.
In fact, you don’t even need the drug selection conditions for resistant variants to appear in a population. If the population is large enough, these variants will appear simply due to the mathematical behavior of DNA evolution. An example of this was found in the Lenski experiment where his founder bacteria showed resistance to many drugs they had never been exposed to. This was most likely due to the fact his founders were grown in much larger colonies than he used in his experiment. Here’s the paper where he wrote about this:
Changes in Intrinsic Antibiotic Susceptibility during a Long-Term Evolution Experiment with Escherichia coli
Interestingly, those drug-resistant variants were, for the most part, less fit replicators than the drug-sensitive variants in the experiment and were selected out during the course of the experiment. This gives a little encouragement to the medical field that cessation of the use of an antibiotic that shows widespread resistance pattern may become useful again.
And since I have your attention Joe, I looked at the Jukes-Cantor and derivative models of DNA evolution, one of which you wrote. There is a fundamental error in these models The problem is that the Markov transition matrix for DNA evolution is not a stationary matrix. What I mean by this is that the transition probabilities in a DNA evolution process change with population size and these models don’t include population size. What this means is that the process does not readily reach equilibrium. I’ve presented what I think is the correct model and it does correlate very well with the Kishony experiment. It happens that evolutionary adaptation is a 2nd law of thermodynamics process. Here’s the paper if you are interested:
The Kishony Mega-Plate Experiment, a Markov Process
Most of the differences between Chinese and Aboriginal Australians is due to neutral evolution, yet there are 1.5 billion Chinese and only 800000 Australian Aborigines.
What explains this large population discrepancy?
Oh my.
@kleinman please consider who you are addressing? Joe has written textbooks on population genetics. I’m pretty sure you (and all of us) could learn something.
That’s not the model of fitness I’m working from, and I’m sure that is not good model. Sorry. 
As within any population, you will have different fitnesses for different variants depending on the selection conditions. You know, there are only about 6,700,000 Jews and about 200,000 Eskimos, all part of the human race and any of them can learn how to farm. Give them the correct farmland (adequate water, nutrients, tools, etc.), and these smaller population size subsets of the total population would grow. Chimpanzees, on the other hand, have figured out how to use a stick to retrieve ants and termites out of their nests. Do you think that’s going to cause a population explosion for those primates? How many beneficial mutations would have to occur in a chimpanzee lineage to give them the cognitive power to understand how to do farming? It certainly appears that neutral evolution hasn’t done it.
The extent to which people, including practicing scientists who should know better, ignore the far role of existing polymorphisms over that of de novo mutations in evolution never ceases to amaze me.
Kleinman, unfortunately, is a crank in the classic style. To him, all scientists are idiots who refuse to recognize his genius. You will get nowhere by trying to get him to feel shame, of which he is incapable.
He seems to know nothing about neutral evolution, which Jukes-Cantor assumes for all sites. In particular, he doesn’t know that population size is not a relevant parameter. Nor does he have a clue about standing variation in quantitative traits, as Joe mentioned.
That should have been:
Agreed. The extent to which people, including practicing scientists who should know better, ignore the far larger role of existing polymorphisms over that of de novo mutations in evolution never ceases to amaze me.
Um, yes, you should consider who you are addressing. Not because of my high or low status in a relevant field, but because I didn’t write the comment that you are replying to me. It wasn’t me! It was a comment by “Witchdoc”, who at the end put “@Joe_Felsenstein” there to call my attention to his argument and ask me to correct it if it was wrong. That’s all.
I am innocent!!!
@kleinman said
The reason for this is that unless that variant can increase in number, the probability of the next beneficial mutation occurring will remain low.
The population genetics maths indicate that beneficial mutations have a suprisingly good chance of increasing in number.
It sounds like beneficial mutations fixing is the answer to your problem, n’est ce pas?
Of course. But the variants including human specific genes like SRGAP2C, ARHGAP11B or specific beneficial variants like CCR5d32 must have been novel at some point.