Dear Dr Jankowiak,

Thank you very much for submitting your Research Article entitled 'Inferring selection effects in SARS-CoV-2 with Bayesian Viral Allele Selection' to PLOS Genetics.

The manuscript was fully evaluated at the editorial level and by independent peer reviewers. The reviewers appreciated the attention to an important topic but identified some concerns that we ask you address in a revised manuscript.

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Yours sincerely,

Takashi Gojobori

Academic Editor

PLOS Genetics

Bret Payseur

Section Editor

PLOS Genetics

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: A most interesting, well written and presented paper. I have only a few minor comments and suggestions.

Line 28: Add computationally before expensive (assuming this is what you mean) and remove from line 30.

Line 53: Give a brief I sentence definition of neutral/non-neutral alleles as this is an important aspect of the paper.

Lines not given: In methods the sentence begging ‘Since SARS-CoV2 and SARS-CoV-2 are know…’ Split into two sentences as is somewhat hard to read in its current format.

Lines 390-391: Spurious alleles you mean not SARS-CoV-2, just be more specific.

Line 444/Table1: BA4 does not actually appear in the table so remove from the sentence or add to the table.

Table3/Lines469: would be useful to have a column specifying, for those mutations in spike, where in the spike protein they are located. Either as simple as RBD/non-RBD or more specific as you pick up on the FC site later.

Line 500: does ‘S-gene’ include RBD or is it exclusive of RBD given that RBD has it’s own category.

4.4 Backtesting: Would be interesting to include omicron BA.4 and BA.5 analysis here, if it is possible to do in a timely manner. This would also make the paper more relevant to the current COVID-19 landscape.

Reviewer #2: In this manuscript, Jankowiak et al. present a scalable probabilistic method to identify the relative fitness and growth rate of viral lineages that can be applied to a global scale of SARS-CoV-2 sequence data. For a statistical method, BVAS can handle an incredible scale while maintaining high accuracy. The authors did a commendable job in rigorously developing and evaluating this method. The scalability of BVAS is owed to simple and realistic assumptions that most alleles are neutral and can be modeled using a discrete time branching process. On simulated data, BVAS outperforms state-of-the-art in both scale and accuracy. On real SARS-CoV-2 data, it is able to rank variants that match well with experimental data. With backtesting, the authors show that BVAS can be useful to identify concerning SARS-CoV-2 lineages early. The authors also present intriguing results in evaluating the epistatic interactions of mutations using their approach.

In my opinion, the manuscript is well-written and publishable in its current form. I have the following minor comments and questions for the authors:

1. Sequencing rates and surveillance efforts are highly non-uniform across different nations. Perhaps the author should discuss how this may affect BVAS and how realistic is its assumption of an i.i.d. sampling rate.

2. Simulated datasets use a sampling rate between 1-64%. It is likely that the COVID-19 sampling rate is a lot lower than 1%. Why not evaluate the results for smaller sampling rate values as well?

3. Simulated dataset use a small reproduction number range of 0.9 to 1.1. The range of R0 for SARS-CoV-2 is much higher. How does BVAS perform when this range is increased?

4. What is the value of sigma-Laplace for data in Fig. 1.?

5. For Fig. 4, why is the hit rate not optimal at #non-neutral sites=10?

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Have all data underlying the figures and results presented in the manuscript been provided?

Large-scale datasets should be made available via a public repository as described in the PLOS Genetics data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes: Alexander Stewart

Reviewer #2: No

Dear Dr Jankowiak,

We are pleased to inform you that your manuscript entitled "Inferring selection effects in SARS-CoV-2 with Bayesian Viral Allele Selection" has been editorially accepted for publication in PLOS Genetics. Congratulations!

Before your submission can be formally accepted and sent to production you will need to complete our formatting changes, which you will receive in a follow up email. Please be aware that it may take several days for you to receive this email; during this time no action is required by you. Please note: the accept date on your published article will reflect the date of this provisional acceptance, but your manuscript will not be scheduled for publication until the required changes have been made.

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Thank you again for supporting open-access publishing; we are looking forward to publishing your work in PLOS Genetics!

Yours sincerely,

Takashi Gojobori

Academic Editor

PLOS Genetics

Bret Payseur

Section Editor

PLOS Genetics

www.plosgenetics.org

Twitter: @PLOSGenetics

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Comments from the reviewers (if applicable):

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #2: The authors have addressed my questions and concerns satisfactorily. It might be worth including the author response to my question on the reproduction number range used in simulations in the manuscript.

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Have all data underlying the figures and results presented in the manuscript been provided?

Large-scale datasets should be made available via a public repository as described in the PLOS Genetics data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.

Reviewer #2: Yes

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PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: No

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