PCOMPBIOL-D-24-02202

Sparse Multitask group Lasso for Genome-Wide Association Studies

PLOS Computational Biology

Dear Dr. Nouira,

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We look forward to receiving your revised manuscript.

Kind regards,

Adam Charles

Guest Editor

PLOS Computational Biology

Shihua Zhang

Section Editor

PLOS Computational Biology

Additional Editor Comments:

Dear Dr.'s Nouira and Agathe

Thank you for your patience in this review process. As you know it is increasingly difficult to solicit reviewers. Despite this we were able to receive sufficient reviewer feedback for your manuscript. Given the reviewer comments, the current manuscript lacks sufficient clarity, in particular int he details of the mathematical notation, selection of some of the model regularization, experimental results, and validation procedures to be published in its current form. However the reviewers did note the benefits of the approach and so I am suggesting a major revision, which should carefully address the specific reviewer feedback to improve the clarity of the manuscript.

Best,

-Adam

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At this stage, the following Authors/Authors require contributions: Asma Nouira, and Chloé-Agathe Azencott. Please ensure that the full contributions of each author are acknowledged in the "Add/Edit/Remove Authors" section of our submission form.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #1: In the manuscript, the authors extended the previous developed MuGLasso method to select population-specific risk variants with considering the selection of risk LD groups. The novel method, namely SMuGLasso, is based on a multitask group lasso framework. The manuscript is well-written, I have some major concerns related to the implementation details of the SMuGLasso:

1. The original MuGLasso requires additional post-processing steps to discern task-specific LD groups. What are the post-processing steps? And are they also be used for MuGLasso in the comparison experiments?

2. In the model, is X used to denote the raw genotypes of SNPs, or the normalized genotypes?

3. How to select hyperparameters lambda 1 and lambda 2?

4. How to pre-define the LD groups?

5. Are you using different variable selection methods on SNPs after LD pruning? How is the performance on SNPs without pruning?

6. Why different r2 thresholds are used for datasets in LD pruning?

7. In Figure 2, do runtimes of SMuGLasso and MuGLasso also incorporate the stability selection steps?

Minor issue:

Page 12, Line 363: For the Arabidopsis thaliana dataset (see ??)

Reviewer #2: I have uploaded my review in Word document, as it includes mathematical notations.

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Reviewer #1: Yes

Reviewer #2: No: At the time of my review, I did not have full access to the authors' simulation code or all simulation parameters. However, the real data components are clear.

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Reviewer #1: No

Reviewer #2: Yes: Seungjun Ahn

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Attachments

Attachment

Submitted filename: PLOS_CB_ReviewerComments.docx

Dear Nouira,

We are pleased to inform you that your manuscript 'Sparse Multitask group Lasso for Genome-Wide Association Studies' has been provisionally accepted for publication in PLOS Computational Biology. The reviewer with the main concerns has re-reviewed the manuscript and found that the authors have answered their concerns and made the necessary modifications to the manuscript. While the other reviewer was unable to re-review, I have reviewed the responses and found that they address the main concerns. 

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

Best regards,

Adam Charles

Guest Editor

PLOS Computational Biology

Shihua Zhang

Section Editor

PLOS Computational Biology

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Reviewer #2:

Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #2: Thank you for your careful revisions. I find that you have addressed my previous comments and concerns, and I have no additional comments at this stage.

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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 #2: None

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Reviewer #2: Yes: Seungjun Ahn