Mutations Confirm Common Descent

Thank you for providing evidence for common ancestry of primates.I have two questions for you. How much of our human DNA are shared among all people today? And how much of it is shared with other primates?I know I can look it up, but I'm asking you, so we can be sure that there is no disagreement on the numbers and you cannot accuse me of making anything up.

I found this Nonhuman Primate Genetic Variant Database. I was hoping to extract more exact percentages of shared DNA between nonhuman primates from raw data.Nonhuman Primate Genetic Variant DatabaseBut the access link gives error page for me.

I'm hoping to get a more accurate number and run different comparison methods. Also to understand the motivation behind different methods.Then, put the numbers in a mutation model, probably some kind of Monte Carlo system, and see what data can be expected and what not.

Why are you even talking when nothing but shit is coming out of your mouth?

All you do here is troll

Keep trolling. I do my research, I do not need your permission.

It seems like a good argument for common ancestry of primates, ngl. Though I haven't looked at all of your sources yet, you made a convincing argument.You got my attention, and if I can find data to verify independently as much as I can, we can discuss this further, if you wish.Right now, what I was thinking, is if we look at humans only, for example, there is a percentage that is shared among all humans.If we are in an ongoing evolution process, there is a portion of DNA in humans that is currently not shared among all individuals.
Depending on the part of DNA we are looking at, it can be anywhere between 0 and 1 fraction of total human population. For example DNA sequence for blue eyes may be around 8%, or perhaps more, as it may be present but not dominant.Some of these varying DNA could reach 100% at some point and would promote to being fixed, shared in whole population.From the data, we should be able to extract the rate of mutations and the fraction that fluctuate through the space between 0 and 1. We would need to make some assumptions on how much one sequence may have some beneficial advantages or not over other sequences. Then, hopefully, the model will give some predictions with margins of error, on how much different primate species should have in common. But I need to work out the model first and put in our data.

Well, I found sources from around 2018-2021, stating that 99.9% was shared among all humans. But you are suggesting it's lower, that in fact, for the vast majority of all DNA parts, if we'd pick one, there is likely to be one or a few individuals having a mutated sequence for that part?Besides changes in mutation rates, population sizes are also changing. I hope I can find some rough estimates of population sizes throughout most of primate history.

So the 99.9% is if we would pick two random and unrelated individuals, they would share about 99.9% of their genetic markup.Population size does matter for fixation rate, I suppose.Maybe it's easiest to start a model for fixed population size n and mutation rate mu. And even start with a population where all DNA is identical. Then one question would be, after m generations, how many mutated bases (or genes) would have spread through 0.1 and 0.2 fraction of the population? And then same for 0.2 and 0.3, or more generally, between p and q.
Should we only consider point mutations for simplification? As I don't think it would affect the results much. But there are several types of mutations, and model may get too complicated.

It would be easiest to consider a model with a single DNA base, I suppose. In a population of size N, with zero mutations for this base, it may to to 1 mutation at rate mu * N per generation.When n individuals have this mutation, it will go to n + 1 with probability p, and to n - 1 with probability 1 - p. For beneficial mutations, p would be greater than 1/2.Numerically, this is east to iterate to find the distribution after a certain number of generations, for fixed p = 1/2 for example. Or draw a random p from a probability distribution, where we use estimates of beneficial mutation, neutral mutation, bad mutation ratios. But it gets complicated fast, as we would need some continuous probability distribution for p values between 0 and 1.

Well, feel free to join the conversation, I suppose.

That is the correct math for one dimensional random walk between two fixed boundaries. If the data would show different rates outside of the models confidence ranges, we may need to see if near extinction events could explain higher fixation for example, as fixation becomes more likely in smaller populations, with boundaries closer together.

Equlibrium for different popularion sizes.I made these graphs. The model is for a single "neutral" DNA base, that can be A or B, so two possibilities instead of the usual four.The first graph shows equilibrium distribution for the DNA for different population sizes N, for a "mutation rate" of mu = 10^-7.It appears that when N * mu = 1, the distribution is a flat line.For larger populations, the distribution seems to approximate the normal distribution, as the central limit theory predicts.For the graphs below, each individual in the initial population has base A. Population size is fixed, each step consists of picking one random individual and copying it, with mu probability of a mutation happening for the copy. At the same time, another random individual dies to keep the population size fixed.
N steps counts as 1 generation.The gray lines are for the distribution after 1, 2, 4, 8, 16, etc generations until equilibrium has been reached. 1 2 3 4I tried to plot the average similarity between two random individuals against time. But this requires a lot of computer memory for large populations and too long computation time on my computer.

I'm showing the data from the model. We can use it for whatever.One of the things that I see is that under this model, in very large populations, practically none of the "neutral" DNA is fixed (at 0 or 1).The gray lines in the graphs are actually more precisely for generations 1, 3, 7, 15, 31, 63 (2^n - 1).What possible predictions do you think we can make from here?

Do you see images or links to images now?I described the model simplifications that I used.Starting from a fixed DNA base, it takes millions of generations to reach equilibrium.Depending on how much of DNA is junk or neutral, comparing DNA of two individuals and finding 99.9% similarity, seems very high, compared to my model for such large population.
Is it because we are still far from equilibrium?