Dear Dr Ritchie,

Thank you very much for submitting your Research Article entitled 'Tissue specificity-aware TWAS (TSA-TWAS) framework identifies novel associations with metabolic, immunologic, and virologic traits in HIV-positive adults' to PLOS Genetics. Your manuscript was fully evaluated at the editorial level and by independent peer reviewers. The reviewers appreciated the attention to an important problem, but raised some substantial concerns about the current manuscript. Based on the reviews, we will not be able to accept this version of the manuscript, but we would be willing to review again a much-revised version. We cannot, of course, promise publication at that time.

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[LINK]

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Yours sincerely,

John S Witte

Guest Editor

PLOS Genetics

Hua Tang

Section Editor: Natural Variation

PLOS Genetics

Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #1: Li et al. proposed a novel framework to integrate and maximize the performance of multiple TWAS methods.

1. In Figure 2, why the statistical power decreases with the gene expressing tissues increase? Do those genes have the same genetic heritability in different scenarios?

2. In line 265-266, the authors claimed that “if trait-related tissue(s) are uncertain, it may be better to stratify genes based on the number of tissues in which the genes are predicted to be expressed”, but it is not clear how the number of tissues can affect the choice of TWAS frameworks.

3. The authors claimed that the novel framework considers the potential false positive rates of tissue identifications. In real-data analysis of 37 baseline laboratory values, authors didn’t show the tissue-trait association results. At least, for those identified trait-associated genes, it is not clear in which tissues those genes were identified.

4. In line 323 and 324, the authors claimed that some of those pleotropic genes were located on the MHC region of chromosome 6, while the MHC region has a rather complicated LD structure and thus those identified results could possibly be false positives. However, the authors didn’t discuss about this point.

5. In line 431, “Genotypes were simulated as biallelic SNPs”, were LD structures considered when simulating genotypes?

6. To show the power/false discovery rate of the proposed framework, it will be better if the author can conduct simulation analysis using the proposed framework or at least summarize the potential performance of the proposed framework based on the current simulation results.

Reviewer #2: Binglan Li and colleagues present a comprehensive evaluation of single-tissue and cross-tissue transcriptome-wide association study methods using simulations with a focus on two methods PrediXcan and UTMOST. They develop a tissue-specificity aware analytic framework and apply their results GWAS data for laboratory traits from AIDS clinical trials. Overall this is an important methodological contribution to the growing field of transcriptome-wide association studies that helps further clarify the role of tissue specificity and cross-tissue and pleiotropic effects in influencing TWAS results. I do however have a few concerns and these are elaborated on below.

Major comments:

In their application of the TSA-TWAS framework to the ACTG data set, the authors identify several gene expression-trait associations where for the same trait multiple genes in the same genomic region are implicated. For example, UGT1A6-MROH2A-UGT1A1-UGT1A7 and total bilirubin in Table 3 and CD2AP and RP11-385F7.1 and absolute basophil count in Table 4, to name a couple of such instances. The current implementations of PrediXcan and its variations recommend coupling the analysis with enloc (reference 57 in this manuscript by Li et al). Why do the authors choose not to evaluate colocalisation between the GWAS signal and the eQTL signal to potentially filter associations at such multi-gene association regions?

Page 26, line 515, under Phenotype: How was linkage disequilibrium (LD) structure modelled, if at all, in the GWAS data set for the Phenotype in the simulations? There seem to be two forms of correlations that affect TWAS results that aren't clearly addressed in the simulation -- (1) correlation between SNPs due to LD in the phenotype or trait GWAS and (2) expression correlation between genes that are located physically near each other on the genome (a known phenomenon, see for example, Michalak, Genomics, Volume 91, Issue 3, March 2008, Pages 243-248). The authors highlight UGT1A1 as the target gene in the Abstract but rightly suggest the potential for multi-gene involvement on page 19, line 363 but this presents a paradox that is not addressed. What is the evidence from GWAS that there is a single causal gene at a locus or multiple causal genes at a locus for complex traits? How is this accounted for in TSA-TWAS and its underlying simulation framework? How does this affect the power of the approaches?

Page 18, line 318: are the pleiotropic genes expressed across tissues or are they tissue specific?

Minor comment:

Page 15, lines 311 to 313 (ATF6B...) - "For instance, ATF6B is a ... and it has been associated

with HIV-associated neurocognitive disorders in previous research." - please provide a citation to support this statement.

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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: None

Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: No

* 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. *

Dear Dr Ritchie,

Thank you very much for submitting your Research Article entitled 'Tissue specificity-aware TWAS (TSA-TWAS) framework identifies novel associations with metabolic, immunologic, and virologic traits in HIV-positive adults' 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

We therefore ask you to modify the manuscript according to the review recommendations. Your revisions should address the specific points made by each reviewer.

In addition we ask that you:

1) Provide a detailed list of your responses to the review comments and a description of the changes you have made in the manuscript.

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We hope to receive your revised manuscript within the next 30 days. If you anticipate any delay in its return, we would ask you to let us know the expected resubmission date by email to plosgenetics@plos.org.

If present, accompanying reviewer attachments should be included with this email; please notify the journal office if any appear to be missing. They will also be available for download from the link below. You can use this link to log into the system when you are ready to submit a revised version, having first consulted our Submission Checklist.

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool. 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.

Please be aware that our data availability policy requires that all numerical data underlying graphs or summary statistics are included with the submission, and you will need to provide this upon resubmission if not already present. In addition, we do not permit the inclusion of phrases such as "data not shown" or "unpublished results" in manuscripts. All points should be backed up by data provided with the submission.

PLOS has incorporated Similarity Check, powered by iThenticate, into its journal-wide submission system in order to screen submitted content for originality before publication. Each PLOS journal undertakes screening on a proportion of submitted articles. You will be contacted if needed following the screening process.

To resubmit, you will need to go to the link below and 'Revise Submission' in the 'Submissions Needing Revision' folder.

[LINK]

Please let us know if you have any questions while making these revisions.

Yours sincerely,

John S Witte

Guest Editor

PLOS Genetics

Hua Tang

Section Editor: Natural Variation

PLOS Genetics

Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #1: The authors have addressed most of previous concerns, but some still need clarifications:

1. In responses to “In Figure 2, why the statistical power decreases with the gene expressing tissues increase? Do those genes have the same genetic heritability in different scenarios?”, the author claimed that the decreasing power is due to the increased number of tests, but it shouldn’t be case for the Group Lasso-GBJ since it is a burden test considering all tissues together. This figure is also contradictory to Line 173-175

2. In Figure 6, Group-Lasso GBJ showed comparably lower power than other methods when a higher percentage of genes are tissue-specific genes, while in Fig 2, all methods have lower power for genes expressed in multiple tissues. It is understandable for Group-Lasso GBJ to have higher type-I error rates for tissue-specific genes, but it will be better to discuss about the lower power of Group-Lasso GBJ method

Reviewer #2: Thank you for the clear and thoughtful responses and for revising the manuscript.

**********

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: None

Reviewer #2: 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: No

Reviewer #2: Yes: Siddhartha Kar

Dear Dr Ritchie,

We are pleased to inform you that your manuscript entitled "Tissue specificity-aware TWAS (TSA-TWAS) framework identifies novel associations with metabolic, immunologic, and virologic traits in HIV-positive adults" has been editorially accepted for publication in PLOS Genetics. Congratulations!

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Yours sincerely,

John S Witte

Guest Editor

PLOS Genetics

Hua Tang

Section Editor: Natural Variation

PLOS Genetics

www.plosgenetics.org

Twitter: @PLOSGenetics

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Comments from the reviewers (if applicable):

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