# Winston Ewert: The Dependency Graph of Life

**URL:** <https://discourse.peacefulscience.org/t/winston-ewert-the-dependency-graph-of-life/728>\
**Category:** Office Hours\
**Tags:** Design\
**Created:** [July 21, 2018, 2:51am UTC](https://discourse.peacefulscience.org/t/winston-ewert-the-dependency-graph-of-life/728 "2018-07-21T02:51:30Z")\
**Posts on this page:** 1\
**Showing post:** 28

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**Author:** ![Winston\_Ewert](https://avatars.discourse-cdn.com/v4/letter/w/9dc877/32.png) [@Winston\_Ewert](https://discourse.peacefulscience.org/u/Winston_Ewert)\
**Post date:** [July 21, 2018, 9:31pm UTC](https://discourse.peacefulscience.org/t/winston-ewert-the-dependency-graph-of-life/728/28 "2018-07-21T21:31:58Z")

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Firstly, I see that I need to clarify the nature of the argument I made in the paper.

If my hypothesis is correct, this predicts that a dependency graph ought to be a better fit to the biological data than a tree. This prediction is fulfilled, thus providing some level of evidence that my hypothesis was correct. My argument is **not** that because the dependency graph model beats the tree model that the dependency graph model is correct. Such an argument would not be valid. Instead, I’m merely arguing that this fulfills a prediction.

The challenge this leaves to common descent is explaining why this prediction worked.

> [@swamidass](#):
>
> I can see a few:
> 
> 1. Incomplete sorting (which seems to be at play in the human data).
> 2. Deletion and large scale genome rearrangement.
> 3. The Birthday paradox.
> 4. Introgression and/or hybridization after speciation.

I expected 1 and 4 as obvious candidates (and mentioned them in my paper).

As for 2, I do have deletions in my model. But I’m curious about how you see large scale genome rearrangement playing into this. Since I’m just looking at the presence or absence of gene families, I’d think a rearrangement wouldn’t do anything interesting there. But presumably you know something about that which I don’t.

As for 3, it seems to me that this should be taken care of by the probabilistic analysis. I assume what you are thinking here is that some genes could end up in a similar set of species and thus look a lot like a module, but by pure coincidence. But the Bayesian analysis, and in the particular the penalty for the dependency graph should prevent that happening.

What I’m surprised by is you not bringing up horizontal gene transfer. Do you not think it is a good candidate?

My thinking is that none of these mechanisms seem like good candidates to explain my successful prediction. Obviously, my intuition on this point is worth diddly squat. It has to be backed up by cold hard evidence which I don’t have (yet).

> [@swamidass](#):
>
> If I am right (and I may not be), it seems this is a direct falsification of the conceptual argument being put forward. It seems that “design modules” must be defined to be non-neutral, and you _ **may** _ have an explanation for why they almost fit in a nested tree. However, that _ **does not** _ explain why more neutral mutations (it is a relative term) might fit more tightly in a nested three than design modules. That seems to be a looming problem for your proposal. I don’t think you can make your case without dealing with this head one. It seems to be a direct falsification of your proposal, unless I’m missing something here.

So, yes, dealing with the exact sequence (instead of just gene family) and in particular the more neutral elements of that sequence is really key. If that can’t be done my proposal fails. It remains to be seen whether a model can be developed here.

> [@swamidass](#):
>
> So if your non-tree model fits better than a tree model, that is an important failed control. Without getting into the details yet, we _ **already** _ know that tree models fail on human diversity data. There have been several papers put out demonstrating this. We should get into the weeds on this I am sure, but that seems to indicate that common descent in the real world does not produce a tree, so your tests themselves are not demonstrating that your model is better than common descent. **Where am I going wrong in that reasoning?** And do you want to see some examples of what I am talking about?

It should be emphasized, the fact that human variability deviates from the expectations of tree does not automatically mean that it will fit a dependency graph better. So its very much an open question as to what the results will look like.

But more critically, I’m not sure what prediction I would make about the results of the test. Whether or not common descent produces a tree depends on various assumption you make about the evolutionary process. I suspect that neither a tree or a dependency graph is the right model in this case.

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