Dear Dr Albers,

Thank you for submitting your manuscript entitled "Dating genomic variants and shared ancestry in population-scale sequencing data" for consideration as a Methods and Resources paper by PLOS Biology.

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Dear Dr Albers,

Thank you very much for submitting your manuscript "Dating genomic variants and shared ancestry in population-scale sequencing data" for consideration as a Methods and Resources paper at PLOS Biology. Your manuscript has been evaluated by the PLOS Biology editors, an Academic Editor with relevant expertise, and by three independent reviewers.

You'll see that the reviewers are broadly positive about your study, but have a number of requests for clarifications, additional analyses and presentational improvements. The Academic Editor asked us to emphasise that s/he shares reviewer #2's uneasiness about your reliance on a single modal estimate (which is how most people will use the database), and thinks that this could be quite misleading in downstream analysis, since it discards information on the range of plausible estimates; s/he requests that you at least include some explicit discussion of this issue.

In light of the reviews (below), we are pleased to offer you the opportunity to address the comments from the reviewers in a revised version that we anticipate should not take you very long. We will then assess your revised manuscript and your response to the reviewers' comments and we may consult the reviewers again. IMPORTANT: Note that reviewer #2's comments are available in the downloadable attachment.

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Sincerely,

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Roland G Roberts, PhD,

Senior Editor

PLOS Biology

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REVIEWERS' COMMENTS:

Reviewer #1:

This paper describes a very useful and carefully built resource: an atlas of allele age estimates for the variants in a large database of publicly available genomes. The algorithm used to infer these allele ages is clearly described in the supplement and represents a cogent heuristic for tackling this difficult problem. GEVA has the potential to be a 1-stop shop for local ancestry inference and demographic inference, two important classes of methods that usually involve different assumptions and software.

I have no significant concerns about the technical soundness of this manuscript. I do think it could be cleaned up a bit to improve its “user-friendliness”—the supplement is a very rich source of information, but it is not very well organized or indexed. I think the presentation of supplementary figures before the supplement index is counterproductive, and would prefer the index to be on the first page of this file so readers can more easily refer back to it.

The discussion of the paper is very short, especially given the breadth of results being presented, and it could be usefully expanded to flesh out how GEVA fits into the existing landscape of similar methods. For example, GEVA does some of the same things as tsinfer, another method from the McVean group, including inferring local ancestry profiles. Can the authors recommend when one local ancestry method may be more appropriate than the other, and how they are likely to compare to methods like chromopainter, RFmix, etc? In addition, the paper includes many PSMC-like plots, but it isn’t clear whether the authors would recommend their method as an alternative to users who may be interested in running PSMC on new genomes from humans or other organisms. A biorxiv preprint by Leo Speidel, Simon Myers, et al. also estimates allele ages, and it would be useful to say something about the differences between the methods in performance and applicability.

The GEVA source code is available, but may or may not be as user-friendly as the database of variant ages that the authors obtained from a fixed set of publicly available genomes. Are the authors planning to keep updating the database as more genomes are sequenced, or are readers encouraged to run the method on other datasets themselves? Is it recommended for use on data from organisms other than humans? Do the authors expect that inferring demography directly from their coalescence time density functions might be more or less accurate than inferring demography using PSMC or other methods that incorporate site frequency spectrum information?

Minor comments:

-A PLoS Genetics paper recently published by Alexander Platt, Jody Hey, et al. also estimates allele ages. This should probably be referenced.

-The supplement presents a nice empirically calibrated error model that is trained on the discrepancies between the Illumina Platinum Genomes and less accurate sequences generated from the same cell lines. The model appears to generate posterior probability estimates as to whether a given variant is an error or not. These error probabilities are likely to be useful in downstream applications of the variant allele age database. Are they searchable within the database? When a variant is queried, will it be flagged as probably an error if appropriate?

Reviewer #2:

IMPORTANT! Please see attached PDF

Reviewer #3:

[identifies himself as Aylwyn Scally]

Just some quick comments from me. I like this paper, found it very interesting, and look forward to seeing it published. No major issues to raise, but I do have a couple of observations regarding the emphasis of the text which I think the authors should consider addressing.

Firstly, it seems a bit odd that the first section of the paper, and indeed the first figure, places such emphasis on the comparison with using PSMC to estimate allele ages. This feels like inside baseball -- interesting from the perspective of algorithm development but not so important for understanding how it works. Perhaps I am missing something, but there didn't seem to be anything particularly surprising; using PSMC the method performed similarly, as one would expect, barring discretisation, in terms of accuracy if not speed, and the take-away message is that the authors' tailored approach was faster without adding bias or error. This is good but I think the simulation comparison alone without PSMC would be more meaningful for most readers, in terms of convincing the reader that the method does a good job at recovering allele age, which I think is the main task at this point in the paper. I'd put the PSMC aspect in the supplement.

Secondly, and perhaps more importantly, I'd like to see the paper give a bit more focus to the comparison with allele frequencies. A lot of people will approach this study and the resources presented as a more accurate way to incorporate allele age into their analyses than simply using allele frequency as a proxy. Figures 4A and S6 give some useful information, but I think the presentation and discussion is a bit limited. One thing to note, for example, is how contingent allele age is on population of ascertainment. For example the rarest alleles (<1%) if ascertained in Africa are on average about twice the age of equivalent alleles ascertained in Europe or East Asia. More pertinently, are there any cases where using frequency as a proxy might be misleading or biased, relative to using the ages estimated here? My overall impression is that frequency is a pretty good proxy for age if ascertained straightforwardly in a single G1k population - but the mapping is nonlinear and does not transfer between populations.

There are also some interesting signals in figure S6. The text points out the striking signal of recent African admixture in America, but there is also an apparent sign of recent gene flow from Africa into Europe, manifesting as an excess of old but very rare alleles there. At least, that is how I read the plot in the fourth panel of the top row -- do the authors concur? A consequence of this is presumably also seen in figure 4A, where European variants older than ~25000 years are apparently more likely to be in the rarest frequency bin than the next most common.

Aylwyn Scally

Attachments

Attachment

Submitted filename: GEVA_review (1).pdf

Dear Dr Albers,

Thank you for submitting your revised Methods and Resources entitled "Dating genomic variants and shared ancestry in population-scale sequencing data" for publication in PLOS Biology. I have now obtained advice from one of the original reviewers and have discussed their comments with the Academic Editor.

We're delighted to let you know that we're now editorially satisfied with your manuscript. However before we can formally accept your paper and consider it "in press", we also need to ensure that your article conforms to our guidelines. A member of our team will be in touch shortly with a set of requests. As we can't proceed until these requirements are met, your swift response will help prevent delays to publication. Please also make sure to address the Data Policy-related requests noted at the end of this email.

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Sincerely,

Roli Roberts

Roland G Roberts, PhD,

Senior Editor

PLOS Biology

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DATA POLICY:

You may be aware of the PLOS Data Policy, which requires that all data be made available without restriction: http://journals.plos.org/plosbiology/s/data-availability. For more information, please also see this editorial: http://dx.doi.org/10.1371/journal.pbio.1001797

Note that we do not require all raw data. Rather, we ask that all individual quantitative observations that underlie the data summarized in the figures and results of your paper be made available in one of the following forms:

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REVIEWERS' COMMENTS:

Reviewer #2:

[identifies himself as Yuval Simons]

I thank the authors for taking the time to address all the issues raised in the previous round of reviews in such meticulous detail.

I think this elegant and exciting work is ready for publication.

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Dear Dr Albers,

On behalf of my colleagues and the Academic Editor, Nick H. Barton, I am pleased to inform you that we will be delighted to publish your Methods and Resources in PLOS Biology.

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on behalf of

Roland Roberts,

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