Dear Assistant Professor Ament,

Thank you very much for submitting your manuscript "RWAS: Identify enhancer properties associated with genetic risk for complex traits" for consideration at PLOS Computational Biology.

As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. In light of the reviews (below this email), we would like to invite the resubmission of a significantly-revised version that takes into account the reviewers' comments.

We cannot make any decision about publication until we have seen the revised manuscript and your response to the reviewers' comments. Your revised manuscript is also likely to be sent to reviewers for further evaluation.

When you are ready to resubmit, please upload the following:

[1] A letter containing a detailed list of your responses to the review comments and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.

[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

Important additional instructions are given below your reviewer comments.

Please prepare and submit your revised manuscript within 60 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email. Please note that revised manuscripts received after the 60-day due date may require evaluation and peer review similar to newly submitted manuscripts.

Thank you again for your submission. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

Sincerely,

Teresa M. Przytycka

Associate Editor

PLOS Computational Biology

Sushmita Roy

Deputy Editor

PLOS Computational Biology

***********************

Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #1: This is a high quality study, well-written, with very interesting findings!

Three comments:

1. The authors used a highly comprehensive approach to define enhancers, particularly for the brain. But almost all data used were from ChIP-seq. I understand that those are good indicators of regulators overall, but the precision at individual genes might still be questionable. Given gene expression is the ultimate product of the regulation, why gene expression information was not considered.

I would love to see one additional analysis to show how well the expression activity is related to the states of the predicted regulators. It might be a challenge to combine regulators across a long genomic region to predict expression. I'd like to know how the authors address this issue or their thoughts about this problem.

In the end, I really hope to see that the enhancers are meaningful in relation to gene expression instead of using one histone pattern to prove another. I am not completely convinced about the current way defining enhancers.

2. The authors used "enhancer" for the functional elements they defined in the paper. Are they really all enhancers? Please clarify. Would the other types of regulators be relevant too? or they just used enhancers to represent all regulators, including silencers and insulators.

3. On page 14, the authors used MPRA and eQTL data to support a few top RWAS signals. I think it is more important to have an overall evaluation, like an overlap or enrichment test, to show how many or % of RWAS signals can be validated. eQTL is particularly interesting.

Again, I just want to congratulate the authors for the wonderful work. Hope it can be published soon.

Reviewer #2: In their paper “RWAS: Identify enhancer properties associated with genetic risk for complex traits,” Casella et. al present an alternative usage of MAGMA for scoring enhancers for enrichment in GWAS studies. While the general idea of something like RWAS isn’t completely novel, and the paper doesn’t feature any major methods development, the application here is well done and thorough. I have the following, mostly minor, items the authors should address:

1. I find the title a little confusing. It would be easier to read if it specified that RWAS is a method for identifying those properties. Additionally, the paper seems much more focused on downstream findings than the RWAS method itself.

2. Figures S1 E&F should say average enhancer length.

3. Is there a good reason why fetal female and fetal male don’t cluster together (Figure 2)? Would be good to discuss this in the text.

4. Looking at Figure 3b, there appear to be brains with nearly identical levels of significance which is surprising. Is there a good reason for this?

5. Figures 4a and b are informative, but they are not well described in the text (also I think the first mention of 4b should be 4a bottom).

6. It is claimed that “Examination of specific risk loci indicated that risk-associated enhancers capture the genetic risk signal at many of the SNP-based risk loci in a tissue-specific manner” [line 197] but In this example I also see significant schizophrenia risk in enhancers in primary t-cells and not temporal lobe. Is this enhancer also a true positive? Furthermore, it would be good to have some summary statistics of hits in enhancers across tissues (e.g. fraction of genome-wide significant hits captured by each enhancer set).

7. The MPRA analysis is quite ad-hoc. It needs numbers for how many overlapping hits were observed and how many would be expected.

8. In Figure 5a, do stars represent significance? Also, how was this subset of the 64 gene sets selected? It would be good for some gene sets that would be expected to be non-significant to be shown for comparison as negative controls.

9. Figures 6a and 6b, should show some other GO terms for comparison. A negative control would also be good.

10. In Figure 6d, is this the full set of TFs that recognize positively associated motifs? If not, how were they selected? Also, it's hard to tell by looking at the figure if expression levels are significant.

11. The GitHub for RWAS is very limited, I would like to see code for some downstream analyses.

Reviewer #3: In this paper by Casella et al. author propose an approach denoted RWAS for predicting enhancer associated with complex traits and disorders. The proposed approach uses the tool MAGMA that was originally built for gene based GWAS study. Predicting the enhancer associated and contributing to each complex disorder is an important problem. I do have several major comments:

1. There seems to be no new code/method developed here (https://github.com/casalex/RWAS), simply commands to run MAGMA for this application. Please rewrite the paper to make it clear this is not a novel computational method but just a new application of MAGMA.

2. The proposed approach is based on running MAGMA. However, MAGMA is developed for studying genes. Utilizing same approach for application to enhancers might have some unforeseeable complications. For example, the fact that multiple enhancers can have complicated non-linear relationships with gene expression of multiple genes. How would this impact the result of the method?

3. There are several methods developed that also calculated the association of enhancer with diseases (e.g., FENRIR or ABC model). Please do a comparison with other computational methods.

4. There are well known CNVs correlated with schizophrenia. Is there an enrichment of these CNVs impacting the predicted enhancer?

5. There are studies that have shown significant enrichment of denovo coding variants in SCZ cases (Gulsuner et al. 2013). Can the author find some sequencing data that shows significant enrichment of rare variants in affected cases impacting these enhancers?

**********

Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: None

Reviewer #3: Yes

**********

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 #1: Yes: Chunyu Liu

Reviewer #2: No

Reviewer #3: No

Figure Files:

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org.

Data Requirements:

Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. For an example in PLOS Biology see here: http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.

Reproducibility:

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Dear Assistant Professor Ament,

Thank you very much for submitting your manuscript "Identifying enhancer properties associated with genetic risk for complex traits using regulome-wide association studies" for consideration at PLOS Computational Biology. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. The reviewers appreciated the attention to an important topic. Based on the reviews, we are likely to accept this manuscript for publication, providing that you modify the manuscript according to the review recommendations.

Please prepare and submit your revised manuscript within 30 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email.

When you are ready to resubmit, please upload the following:

[1] A letter containing a detailed list of your responses to all review comments, and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out

[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

Important additional instructions are given below your reviewer comments.

Thank you again for your submission to our journal. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

Sincerely,

Teresa M. Przytycka

Associate Editor

PLOS Computational Biology

Sushmita Roy

Deputy Editor

PLOS Computational Biology

***********************

A link appears below if there are any accompanying review attachments. If you believe any reviews to be missing, please contact ploscompbiol@plos.org immediately:

[LINK]

Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #1: All my questions have been well addressed.

Reviewer #2: The authors have addressed all major points raised in the initial review and added several interesting analyses based on reviewer comments. I only have a few minor comments:

- The authors raise an interesting point about the fetal female brain sample. Given the technical differences, I wonder if it may be worth excluding that sample. I leave it up to the authors.

- line 222 - “male fetal brain” is missing the word “enhancers”:

- Regarding figure 5a, I still don't see how a subset of the 64 were selected.

Reviewer #3: The authors have adequately addressed my comments.

**********

Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

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 #1: Yes: Chunyu Liu

Reviewer #2: No

Reviewer #3: No

Figure Files:

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org.

Data Requirements:

Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. For an example in PLOS Biology see here: http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.

Reproducibility:

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

References:

Review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript.

If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Dear Assistant Professor Ament,

We are pleased to inform you that your manuscript 'Identifying enhancer properties associated with genetic risk for complex traits using regulome-wide association studies' has been provisionally accepted for publication in PLOS Computational Biology.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us now if you or your institution is planning to press release the article. All press must be co-ordinated with PLOS.

Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology. 

Best regards,

Teresa M. Przytycka

Associate Editor

PLOS Computational Biology

Sushmita Roy

Deputy Editor

PLOS Computational Biology

***********************************************************