"I'm treating the mutation rate as a substitution rate" - Dr. Nathaniel Jeanson

Awesome, thanks a bunch. I find that it’s really difficult to parse out where people are missing each other when there is so much being said. Is there anything on the Howe diagram you feel isn’t explained by a combination of gene loss and gene gain?

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Still waiting.

Let’s use the lottery as an example. There is a single winner of the lottery. You calculate the odds of the winner you observed, and the odds are 1 in 150 million. And yet, it just took 1 drawing for that person to win. How do you explain this?

I already did that:


Figure 1.2.1. A plot of the CI values of cladograms versus the number of taxa in the cladograms . CI values are on the y-axis; taxa number are on the x-axis. The 95% confidence limits are shown in light turquoise. All points above and to the right of the turquoise region are statistically significant high CI values. Similarly, all points below and to the left of the turquoise region are statistically significant low values of CI. (reproduced from Klassen et al. 1991, Figure 6).

It’s no different than saying elliptical orbits are explained by gravity. All it requires is a basic understanding of how mutations and vertical inheritance works.

If you like, we can use populations of mice as an example. They are observed to produce nested hierarchies.

Or . . .

You will forget everything you see in about 12 hours.

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No, Bill. By “tree” we mean a graphical representation of the differences between organisms or sequences–the data themselves. It is never used to “organize” data.

Please try and learn (and remember) a lot more before pretending that you understand this stuff.

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I’m not 100% sure you have truly looked at the available evidence for common ancestry.

There are several papers that statistically test the empirical evidence for separate versus shared ancestry and any model that posits independent genetic origins for groups of organisms that creationists would say share no genetic ancestry is astronomically improbable.

From White et al. 2013 PLOS,

“We demonstrate quantitatively that, as predicted by evolutionary theory, sequences of homologous proteins from different species converge as we go further and further back in time. The converse, a non-evolutionary model can be expressed as probabilities, and the test works for chloroplast, nuclear and mitochondrial sequences, as well as for sequences that diverged at different time depths. Even on our conservative test, the probability that chance could produce the observed levels of ancestral convergence for just one of the eight datasets of 51 proteins is ≈1×10−19 and combined over 8 datasets is ≈1×10−132. By comparison, there are about 1080 protons in the universe, hence the probability that the sequences could have been produced by a process involving unrelated ancestral sequences is about 1050 lower than picking, among all protons, the same proton at random twice in a row. A non-evolutionary control model shows no convergence, and only a small number of parameters are required to account for the observations. It is time that that researchers insisted that doubters put up testable alternatives to evolution.”

and from Baum et al. 2016 Evolution on common ancestry among primates, including humans (SA = separate ancestry, CA = common ancestry)…

“Every test of species SA that we applied to the primates suggested that this model does a very poor job of explaining actual biological data as compared to CA (Table 3). Many of these datasets reject species SA strongly: the probability of obtaining a test statistic more extreme than the one observed under the species SA model being incredibly small, often approaching or greatly exceeding the probability of picking the correct atom at random among the estimated 1080 atoms in the known universe.”

It’s not simply that nested hierarchies exist in the comparative data but that the same hierarchies may be observed across completely independent lines of evidence. That and other lines of evidence like the congruence between the fossil record and phylogenetic trees as measured by methods such as gap excess ratios (see Wills et al. 2008 Systematic Biology).

The evidence for common ancestry is so overwhelming that the vast majority of working scientists examining the data for over a century have come to a clear and unambiguous consensus on this matter, our best model to explain the diversity of life rests on common ancestry. Virtually no one believes otherwise except for people with strong religious motivations to reject evolution.

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I am not sure you are saying anything new here.

Quick guide for the uninitiated: Just what the hell is Bill Cole, @colewd, droning on about?

For the most part Bill is simply blathering. Once again he name drops some scientist, or some subject, that isn’t really relevant to the Howe diagram(thread about that here) and what Bill is demanding to account for it.

ID-proponent and biochemist Michael Behe and David Snoke(Behe & Snoke 2004) had an exchange with biologist Michael Lynch (Lynch 2005) about divergence of duplicate genes, in which Behe & Snoke made different starting assumptions from Lynch. These assumptions made a huge difference to the rate at which duplicate genes could evolve and diverge. This is not directly related to the Howe diagram.

Bill one day sees the Howe diagram, in which it is implied that four species have evolved different numbers of novel genes (that are presumed to mostly not derive from divergence of duplicates) since they split off from their common ancestors.
These genes are not detected as orthologous to genes in the other three species by various criteria, which means they’re probably not diverged duplicates. So they’re “novel” genes in a certain sense. That means they’re probably the result of other forms of novel gene formation than duplication and divergence: fusions of fragments of other genes, frameshift mutations, HGT, genes evolving de novo from non-coding DNA and stuff like that.

To Bill this is a huge problem. Because Bill thinks new genes evolving is miraculously improbable (he rejects all the above mechanisms as being capable of generating any significant number of genes on the available timescales), so when he sees this is nevertheless implied to have occurred by the Howe diagram he thinks this means the shared relationship of the species in the diagram couldn’t possibly be true. That is, if the genes couldn’t have evolved, then the species that have them can’t have evolved from common ancestor either. Never mind that this doesn’t logically follow even if the premise is true, Bill isn’t strong on logic.

On a related note, and a very important one, it isn’t actually known how many of the putative novel genes in the Howe diagram are actually genuine novel genes. Nobody knows what any of them do, if anything. Nobody knows whether they affect fitness or whether they’re even expressed. There can exist an ORF in a piece of DNA that never gets expressed, or is only expressed at a very low level that doesn’t have a function. Bill doesn’t know of or understand any of this. He just seems a number on a figure and goes with it.

Anyway. He demands a “population genetics model” to account for the new genes in the diagram having evolved. It’s not really clear what he means by that.

Bill now name-drops Behe and Lynch, for reasons. The exchange they had has next to nothing to do with the Howe diagram, because the novel genes in that diagram are generally not though to owe to divergence of duplicate genes. So neither of their models or assumptions are really relevant to what Bill is asking for. All Bill knows is that Lynch is a population geneticist, he did something that has something to do with the evolution of new genes or new functions or… something, and he thinks he can score points by mentioning him and the fact that he interacted with Michael Behe.

I suppose Bill might think that because it is very unlikely that the novel genes in the Howe diagram are diverged duplicates, somehow in Bill’s mind Behe and Lynch’s exchange is relevant in showing this, with what Bill calls their “stochastic models” (Bill thinks saying those two terms in a sentence sounds clever and technical).

Bill understands nothing. He doesn’t understand the evidence for common descent. He doesn’t understand the logic of historical inference. He doesn’t know what models are. He doesn’t know what population genetics is. These ideas and concepts just exist in this strange hazy state in his mind and he somehow thinks they’re all related in ways he can’t articulate. So he always just writes these incoherent responses that we, correctly, tell him amount to word salad. Perhaps somewhere deep in Bill’s subconscious there’s a coherent idea he wants to express, but he lacks the capacity to put it into text.

Believe me when I say we’ve all been trying for years to get Bill to understand what the words he uses mean. And how these topics relate (and how they don’t) and how and why he doesn’t make sense.

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I agree the pattern could be partially explained by common descent but you need to recognize that common descent may have nothing to do with the pattern. Until a change mechanism is identified that can be modeled by population genetics mathematics common descents contribution is highly tentative.

colewd

Hi Matt
What needs to be understood is if the frequency of random gene loss and gene gain can pivot to a new functional animal with unique features given the waiting time of fixing changes in a population.

Since the animals in the Howe diagram(zebra fish, chickens, mice and humans) are unique functioning animals the genes appear purposefully arranged and not the product of accidental gene loss and gain.

I’m definitely not, but I’m certainly disagreeing with your description.

So what’s your reason for misrepresenting what the trees are? You also described them as models, which also is objectively false.

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Okay, so what I’m hearing you say is a combination of gene loss and gene gain does explain the diagram, however you think that this is unlikely in an unguided evolutionary context because of the functionality and uniqueness of the animals. Do I have that right?

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Maybe, but Bill hasn’t bothered to look at their actual sequences to have even a marginally informed opinion on whether they have homologs or not. He just keeps on mentioning the diagram as though it’s some sort of magic spell.

Really unclear, since he labels trees as models.

How do frequencies pivot, exactly? I thought that pivoting was something done by people and hinges. Am I wrong?

How can you say any of that without conducting the analysis that you demand I provide? Why can’t I just say that given the data, it seems very likely to me that there’s no problem? You feel free to come to a certain conclusion based on nothing yet complain about a lack of rigor in others. Isn’t that a problem for you?

Not clear on that parenthetical note. They’re counts of orthologous genes, and paralogous genes are not considered the same gene. So where’s the assumption that they’re not from divergence of duplicates?

Not so. We know what many of them do. I couldn’t give a percentage off-hand, but I expect it’s fairly high. I suspect the percentage of real, not spurious, genes is also high.

I don’t think any of that really affects your point, though.

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Why are you pretending like you haven’t been repeatedly given such a mechanism?

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Well it comes from the method by which the orthologoues are determined in that “Ensembl Compara 63” database, which I understand to be based in part on both sequence similarity and synteny.

So if these genes are detected as species-specific they must have failed some sort of threshold for synteny and similarity, so what should cause us to think they’re still likely paralogous instead of orthologous?

I’d be really interested to see some examples if anyone knows how to extract that information? I confess I don’t know how to do that. If the majority of these species-specific genes really are paralogs it should be easy enough to find that they still have significant sequence similarity to other genes.

Hi Matt
Gene gain and loss is a possible explanation (by a yet to be defined mechanism) but so is separate creation or starting points for the 4 species in the Howe diagram.

John, You are welcome to come to your own conclusion. It just does not appear to me you are connecting all the dots.

  1. The waiting time as illustrated in the Behe/Lynch discussion.
  2. The enormous sequence space genes/proteins live in
  3. The magnitude of gene changes in the Howe diagram.

Is there a realistic window of conditions wheret stochastic processes can be the cause of the change?

I don’t understand what you’re saying. One presumes that paralogous genes would be detected by examining synteny, i.e. similar sequence, different location. This obviously works best if we have a good taxon sample. Visual aid:

A xxxMxxxxxx
B xxxMxxNxx
C xxxxxxxNxx

Supposing that A, B, and C are three species, M and N are similar sequences, and the x bits are flanking sequences that can be aligned among species. M and N are obviously paralogs, if only based on their simultaneous presence in species B or, even if species B were not noticed, their different locations in species A and C.

I don’t know how the Ensembl database worked, but one must assume that an attempt was made to sort orthologs from paralogs, with orthologs being counted as one gene and paralogs as two. Is that not the case?

Don’t understand that. These are relationships, not singular characters. Paralogous to what? Orthologous to what? What is called the same gene in different species is considered orthologous. What is called a different gene in different species is considered non-orthologous, which may be paralogous or non-homologous; the paper doesn’t separate the two. My suspicion would be that most “new” genes in that diagram result from duplication and that most lost genes still have paralogs in the taxa they’ve been lost from. Do you think that’s not true?

I don’t know how to extract the information. I’m just going by the ubiquity of gene duplication in evolution. Consider teleosts alone: they have a whole-genome doubling in their history, and diploidization must have proceeded quite rapidly.

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That doesn’t account for the nested hierarchical structure of the data. Sorry, mate.

What waiting time? How is this relevant?
What does sequence space have to do with it?
What magnitude of gene changes? You don’t know how big the changes are, unless you mean the number of gains and losses rather than the magnitude of changes.

Is there not? What do you even mean by “conditions” here?

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Ok, excellent, so gene gain and gene loss explains the diagram. Your point of contention with all this is the mechanism by which genes are gained and lost, which you think is undefined to date. Would that be accurate?

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