Recommendation
based on reviews by 3 anonymous reviewers
The human genome not only encodes for biological functions and for what makes us human, it also encodes the population history of our ancestors. Changes in past population sizes, for example, affect the distribution of times to the most recent common ancestor (tMRCA) of genomic segments, which in turn can be inferred by sophisticated modelling along the genome.
A key framework for such modelling of local tMRCA tracts along genomes is the Sequentially Markovian Coalescent (SMC) (McVean and Cardin 2005, Marjoram and Wall 2006) . The problem that the SMC solves is that the mosaic of local tMRCAs along the genome is unknown, both in their actual ages and in their positions along the genome. The SMC allows to effectively sum across all possibilities and handle the uncertainty probabilistically. Several important tools for inferring the demographic history of a population have been developed built on top of the SMC, including PSMC (Li and Durbin 2011), diCal (Sheehan et al 2013), MSMC (Schiffels and Durbin 2014), SMC++ (Terhorst et al 2017), eSMC (Sellinger et al. 2020) and others.
In this paper, Sellinger, Abu Awad and Tellier (2020) review these SMC-based methods and provide a coherent simulation design to comparatively assess their strengths and weaknesses in a variety of demographic scenarios (Sellinger, Abu Awad and Tellier 2020). In addition, they used these simulations to test how breaking various key assumptions in SMC methods affects estimates, such as constant recombination rates, or absence of false positive SNP calls.
As a result of this assessment, the authors not only provide practical guidance for researchers who want to use these methods, but also insights into how these methods work. For example, the paper carefully separates sources of error in these methods by observing what they call “Best-case convergence” of each method if the data behaves perfectly and separating that from how the method applies with actual data. This approach provides a deeper insight into the methods than what we could learn from application to genomic data alone.
In the age of genomics, computational tools and their development are key for researchers in this field. All the more important is it to provide the community with overviews, reviews and independent assessments of such tools. This is particularly important as sometimes the development of new methods lacks primary visibility due to relevant testing material being pushed to Supplementary Sections in papers due to space constraints. As SMC-based methods have become so widely used tools in genomics, I think the detailed assessment by Sellinger et al. (2020) is timely and relevant.
In conclusion, I recommend this paper because it bridges from a mere review of the different methods to an in-depth assessment of performance, thereby addressing both beginners in the field who just seek an initial overview, as well as experienced researchers who are interested in theoretical boundaries and assumptions of the different methods.
References
[1] Li, H., and Durbin, R. (2011). Inference of human population history from individual whole-genome sequences. Nature, 475(7357), 493-496. doi: https://doi.org/10.1038/nature10231
[2] Marjoram, P., and Wall, J. D. (2006). Fast"" coalescent"" simulation. BMC genetics, 7(1), 16. doi: https://doi.org/10.1186/1471-2156-7-16
[3] McVean, G. A., and Cardin, N. J. (2005). Approximating the coalescent with recombination. Philosophical Transactions of the Royal Society B: Biological Sciences, 360(1459), 1387-1393. doi: https://doi.org/10.1098/rstb.2005.1673
[4] Schiffels, S., and Durbin, R. (2014). Inferring human population size and separation history from multiple genome sequences. Nature genetics, 46(8), 919-925. doi: https://doi.org/10.1038/ng.3015
[5] Sellinger, T. P. P., Awad, D. A., Moest, M., and Tellier, A. (2020). Inference of past demography, dormancy and self-fertilization rates from whole genome sequence data. PLoS Genetics, 16(4), e1008698. doi: https://doi.org/10.1371/journal.pgen.1008698
[6] Sellinger, T. P. P., Awad, D. A. and Tellier, A. (2020) Limits and Convergence properties of the Sequentially Markovian Coalescent. bioRxiv, 2020.07.23.217091, ver. 3 peer-reviewed and recommended by PCI Evolutionary Biology. doi: https://doi.org/10.1101/2020.07.23.217091
[7] Sheehan, S., Harris, K., and Song, Y. S. (2013). Estimating variable effective population sizes from multiple genomes: a sequentially Markov conditional sampling distribution approach. Genetics, 194(3), 647-662. doi: https://doi.org/10.1534/genetics.112.149096
[8] Terhorst, J., Kamm, J. A., and Song, Y. S. (2017). Robust and scalable inference of population history from hundreds of unphased whole genomes. Nature genetics, 49(2), 303-309. doi: https://doi.org/10.1038/ng.3748
I am satisfied with the authors' revisions.
DOI or URL of the preprint: https://doi.org/10.1101/2020.07.23.217091
Dear Recommender,
Please find attached our revised manuscript entitled “Limits and Convergence properties of the Sequentially Markovian Coalescent” by Thibaut Sellinger, Diala Abu Awad and Aurélien Tellier, which we would like to be considered for recommendation in PCI Evolutionary Biology.
First, we would like to thank you for giving us the opportunity to resubmit this manuscript and your positive comments. We would also like to thank all reviewers for appreciating the importance of our work and for their useful comments.
We paid close attention to answering all the reviewers’ comments and have modified the manuscript accordingly. We believe that we have improved its readability, as we rewrote some sections of the manuscript that the reviewers felt were unclear. We have also included new Supplementary Figures (seven in total) corresponding to the requested analyses and six Supplementary Tables, containing the mean square error of past demographic inferences of all the figures in the manuscript. These measures have helped us, and hopefully will help the readers, to better understand our results. However we found that the MSE alone cannot precisely measure the performances of the methods (see the reply to the reviewers' comments for more detail).
In addition to what reviewers requested we made some additional corrections. First, we realised that the time window of the theoretical convergence analyses (now called best-case convergence) was ill-defined by a factor 2. All analyses were therefore run again to fix this. Secondly, there were minor errors in the msprime command lines when simulating data for SMC++, which required that we re-simulate all data using msprime and have re-run all analysis of SMC++. Slightly different results are observed for Figure 4 and for Supplementary Figure 14 compared to the first version of the manuscript, but all other SMC++ results are identical. We noticed that the section concerning transposable elements was confusing, and thus rewrote the section while adding two supplementary Figures (35 and 36). We hope our motivations and our results now appear in a clearer way. Lastly, we fixed a plotting issue in Supplementary Figures 15 and 22.
We hope that this revised version fulfils the criteria for recommendation in PCI,
Many thanks in advance,
Yours sincerely,
On behalf of the authors, Thibaut Sellinger.
, posted 25 Aug 2020This preprint by Sellinger et al. describes several analyses around the Sequentially Markovian Coalescent, a methodological framework used heavily in the field of demographic inference from genomic data.
The preprint has now been read by three anonymous reviewers. I have also read the paper carefully, and I agree with the reviewers' generally positive assessment. As reviewer #3 noted, while some of these results are probably already scattered around in the literature (also in Supplements), a systematically conducted and concisely summarised analysis of these various important caveats for SMC methods is still missing. So I definitely think this will be a useful and relevant contribution.
As you can see, all three reviewers have some comments for improving clarity, and possibly expanding the study a bit. I personally find two suggestions for adding analysis to be particularly worth considering: First, reviewer #1 proposed to add a constant population size scenario as a “basic” model to supplement the more complex demographic scenarios you currently have. Second, reviewer #3 suggests to add error quantification in small tables in all analyses using the mean square error.
I’m in principle happy to recommend this paper after a revision addressing the raised points by the reviewers. Please give good reasons if you believe some suggestions should not be followed.
Thanks again for submitting this interesting paper and I look forward to receiving the revised version.
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The authors conducted a simulation study of the strengths and weaknesses of some demographic inference packages based on the sequentially Markov coalescent, under various data and parameter regimes. SMC methods are now widely used, in an increasingly diverse array of settings, and it is important to understand what causes them to succeed and fail. Although several of the conclusions reached here are scattered about in the literature, this is a more systematic attempt to organize them into a coherent set of recommendations for practitioners. So, it seems like a useful contribution.
I don't have any major concerns or objections, but I think the paper could be improved a bit, and perhaps expanded in a few related directions.
stdpopsim package can simulate directly from this demography in a few lines of code.