izabelcavassim
commented
2 years ago We have made some decisions in terms of the manuscript's scope (ping me, correct me if I am wrong) based on the discussion we had today (02/22/22) during our biweekly meeting:
PART I
For the demography inference with flavors of selection (background selection)
- Using three different software:
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- mscm, update it to mscm2 (recombination information based), inclusion by @izabelcavassim
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- stairwaiplot (SFS based inference), inclusion by @izabelcavassim
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- GONE (LD based inference?), inclusion by @chriscrsmith
These analyses are halfway implemented in our current analyses2 repository specifically in n_t.snake workflow
We also want the multi-population analyses
- Using (up to) three different software:
- fastsimcoal
- dadi
- momi2 (conditional on someone being interested in including it into our workflow). Species for these analyses: Human, and drosophila (?)
Part II
For the DFE inference excluding the positive portion
- Using four different software:
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- dadi, inclusion by @xin-huang and @chriscrsmith
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- polyDFE, inclusion by @xin-huang and @chriscrsmith
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- DFE-alpha, inclusion by @xin-huang and @chriscrsmith
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- GRAPES**, inclusion by @xin-huang and @chriscrsmith
I think implementations are almost finished, see analyses2 repository for details, thanks to @andrewkern @petrelharp @mufernando, and others...
Part III
Understanding/simulating beneficial mutations as in a sweep using the positive portion of a DFE
This is still a work in progress, but two things could be evaluated here methods inference:
- "Back-of-the-envelop" power analyses to detect sweeps
- How is dadi inference of the negative portion affected by the inclusion of the positive portion
- How do the currently/standard used methods predict the simulated sweeps?
- Could we think in the multi-population level and simulate across pops and do an Fst analysis on top of it?
As @andrewkern @petrelharp have pointed out, there are multiple features to be added in terms of positive selection, that could either be included in the discussion of this manuscript and implemented in the next paper, or that could be implemented for this paper but not trivial. I would personally vote to simplify and leave it as future work just so we don't lose momentum.
chriscrsmith
RyanGutenkunst
nspope
petrelharp
andrewkern
Hey all-- I'm opening up an issue for us to start bashing away at an outline for the second paper. a particular goal is to have a solid list of the analyses we want to do and then later, delegation of those analyses to particular individuals/groups.
We have a google doc going for the outline here , but it might be preferable to just use this issue and so I've copied that text below
Selection & PopSim Paper 2
Timeline for selection papers: Late summer early fall Companion papers on 1) sweeps & 2) rescaling. Also similar timeline.
Outline of main analyses for main paper: Comparison of different DFE methods like FitDadi polyDFE, GRAPES (Ryan G’s group & Izabel can work on this). How is demography dealt with? Sample size? Sweeps! (will be its own companion paper that Andy is leading, but some key results in the main paper). Implement sweep models from literature. Make a model in StdPopSim “recurrent_sweeps”. Can put this model with different demographics & rec rates, etc. Look at summary stats & power to detect sweeps in human genomes under different demographic models. Look at power of ML methods Confounders. Multiple sweeps. Sweeps & BGS. How do DFE methods perform when sweeps are included? Selection confounding demographic inference (can recycle a lot of pipelines from paper 1, just running them on models with selection).
What we need to do: Decide what models to do: DFE Sweep https://github.com/popsim-consortium/analysis2 Implement models QC Analyses
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Brainstorming of ideas for PopSim Selection paper form the call on 6/15 (not all will be in paper):
Comparison of different DFE methods (Ryan G’s group can work on this). How is demography dealt with? Sample size?
Scaling (maybe merits its own paper delving into theory of scaling...might be too ambitious for PopSim paper) Ideally, PopSim paper will point to this companion paper. PopSim paper will have to mention scaling in some way. PopSim paper could connect it with applications...use guidelines from theory paper to do stuff for a particular organism 3)Do current models of DFEs/annotations in humans predict summaries of genetic variation (spatial pattern of pi, SFS, LD?)? (strength: leverage demographic models from before...annotations, DFE...all the fancy stuff together. Great way to showcase the whole resource! Guidance for how well the field is doing in terms of model adequacy) What if synonymous (or “neutral sites”) are actually under selection? Does that confound things. Sweeps! (may be its own paper, but could put some key results in the main paper). Implement sweep models from literature. Make a model in StdPopSim “recurrent_sweeps”. Can put this model with different demographics & rec rates, etc. Look at summary stats & power to detect sweeps in human genomes under different demographic models. Look at power of ML methods Confounders. Multiple sweeps. Sweeps & BGS. How do DFE methods perform when sweeps are included? Selection confounding demographic inference In paper say how stdpopsim can be used to test “your new method” for detecting selection. No one perfect statistic--depends on biology, data, etc. Try to show an example in the paper from a non-human example.