PGENETICS-D-25-00986

mFABIO: An integrative multi-tissue TWAS fine-mapping approach to prioritize potentially causal genes and tissues underlying binary traits

PLOS Genetics

Dear Dr. Zhang,

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We look forward to receiving your revised manuscript.

Kind regards,

Lin S. Chen, Ph.D.

Academic Editor

PLOS Genetics

Michael Epstein

Section Editor

PLOS Genetics

Aimée Dudley

Editor-in-Chief

PLOS Genetics

Anne Goriely

Editor-in-Chief

PLOS Genetics

Additional Editor Comments:

Both reviewers find the manuscript methodologically sound, recognizing its potentials in multi-tissue TWAS fine-mapping for binary traits. They commend its improved power but request clarification on handling pleiotropic SNP effects, correlations among GReX, covariate adjustment, computational scalability, and use of heterogeneous eQTL references. Minor revisions to equations, figures, and tables are also suggested for clarity. We hope to see these points being addressed in the revision.

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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: The manuscript presents a multi-tissue TWAS fine-mapping method designed for binary traits, accounting for correlations in genetically regulated expression (GReX) of genes. Simulation studies demonstrate that the method provides substantial power gains while maintaining robust control of false discovery rates. Although the proposed method shows good potential, it could be further improved by more clearly addressing how to account for pleiotropic effects from SNPs. In addition, it seems how to account for the correlation among GReX of genes in a LD region is not clearly described in the fine mapping.

Minor comments.

It would be helpful add page number and line numbers.

In model (1)-(4), do the models include all SNPs in a region under consideration. It would helpful to explain how to account for the correlation among SNPs or how to select SNPs in a region to be included in a model. Is it reasonable to assume the effects of SNPs in a region follow iid as in the models?

It seems the function in model (7) do not depend on observed data G_hat and X. It would be helpful to explain how to use the observed data in the parameter estimation.

In the simulation studies, when generating a binary trait via latent variables, only contribution from genes are considered; how about contribution from SNPs? Is it possible some SNP affect gene expression and also have effects on the latent variables not through the genes.

Reviewer #2: The authors present mFABIO, a multi-tissue TWAS fine-mapping framework specifically developed for binary outcomes. Methodologically, mFABIO (i) models a multi-gene, multi-tissue GReX design within genomic loci, (ii) employs a probit latent-liability model suitable for case-control traits, and (iii) adopts a SuSiE-style sum-of-single-effects prior with a hierarchical Dirichlet structure to estimate posterior inclusion probabilities (PIPs) at both the gene and gene-tissue levels. The use of variational Bayes ensures computational scalability. Through extensive simulations and applications to six binary disease traits from the UK Biobank, the method demonstrates improved calibration and higher power compared with single-tissue fine-mapping tools, as well as performance gains over TGFM, while producing more concise and biologically supported candidate sets. The manuscript is clearly written and technically sound. I have the following comments:

1. Equations (1) and (3) incorporate gene-tissue effect sizes and horizontal pleiotropic effects, thereby capturing SNP influences not mediated through gene expression. Could the authors clarify whether mFABIO can also account for covariates such as age, sex, and principal components? Are these effects explicitly modeled within the probit framework, or should users adjust for them externally before applying mFABIO? In addition, it would be interesting to discuss whether the authors plan to extend mFABIO to a mixed-effects framework in the future, which could model correlations among individuals (e.g., related samples or population structure).

2. In the Simulations section, the authors use 50,000 individuals of European ancestry from the UK Biobank for GWAS data and 500 distinct individuals for eQTL mapping. Given the complexity of the Bayesian inference procedure, readers would benefit from a discussion of runtime and memory usage. A comparison of computational performance and statistical power across varying GWAS sample sizes (e.g., 50K, 100K, 150K, 200K, 250K) and eQTL sample sizes (e.g., 500, 1,000, 5,000, 10,000), alongside existing methods such as TGFM, FABIO, FOCUS, GIFT, and cTWAS, would be informative for understanding the scalability, efficiency, and practical utility of mFABIO for large biobank-scale analyses.

3. Both the simulation study and the UK Biobank application rely on eQTL reference data from a single source (500 individuals for the simulations and GTEx for the real data analysis). In many practical settings, eQTL models across tissues may come from different studies or consortia (e.g., GTEx, CMC, ROSMAP, Braineac, CAGE, eQTLGen) (see Methods of doi: 10.1038/s41467-018-04558-1). It would be helpful if the authors could clarify whether mFABIO remains applicable when eQTL reference panels across tissues originate from independent cohorts. For example, does the method require harmonized LD and allele frequencies across tissues, or is it sufficient to provide SNP prediction weights for each gene-tissue pair? A short discussion on this would improve readers’ understanding of mFABIO’s robustness and generalizability.

Other comments:

In Figure 1, the notation “SNP prediction weights for each of the M genes, which can be obtained from eQTL mapping cohorts across different tissues” may be slightly ambiguous. For example, the same notation (w11, …, w1S1) for gene 1 in both tissue 1 and tissue K, which could give the impression that SNP weights are shared across tissues. Clarifying that these weights are tissue-specific would avoid possible misunderstanding.

In Tables 1 and 2, adding a “Total” row summarizing the overall number of significant gene-level and gene-tissue pair-level associations across the six binary disease traits would help readers interpret the aggregate findings more easily. This would also make clearer the meaning of the 1,427 (1,355 unique) GWAS risk genes and 479 (470 unique) TWAS risk genes described in the main text.

In the paragraph describing the third example, “The third example is CCR6 …”, the gene name should be corrected from GCR6 to CCR6 and italicized.

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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: No: The manuscript should provide links to the data used

Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: Yes: Xihao Li

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PGENETICS-D-25-00986R1

mFABIO: An integrative multi-tissue TWAS fine-mapping approach to prioritize potentially causal genes and tissues underlying binary traits

PLOS Genetics

Dear Dr. Zhang,

Thank you for submitting your manuscript to PLOS Genetics. The reviewers are largely satisfied with the revised manuscript, though one reviewer still have minor suggestions and concerns. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised.

Please submit your revised manuscript within by Apr 29 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosgenetics@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pgenetics/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

* A letter that responds to each point raised by the editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. This file does not need to include responses to formatting updates and technical items listed in the 'Journal Requirements' section below.

* A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

* An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, competing interests statement, or data availability statement, please make these updates within the submission form at the time of resubmission. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

We look forward to receiving your revised manuscript.

Kind regards,

Lin S. Chen, Ph.D.

Academic Editor

PLOS Genetics

Michael Epstein

Section Editor

PLOS Genetics

Aimée Dudley

Editor-in-Chief

PLOS Genetics

Anne Goriely

Editor-in-Chief

PLOS Genetics

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1) We notice that your supplementary figures are uploaded with the file type 'Figure'. Please amend the file type to 'Supporting Information'. Please ensure that each Supporting Information file has a legend listed in the manuscript after the references list.

Note: If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

Reviewers' comments:

Reviewer's Responses to Questions

Reviewer #1: In this revision, the authors have been very responsive to the issues raised by the reviewers. I have only minor comments:

1. Page 1, lines 115, it says “These GreX values are assumed to have been pre-computed using standard software such as SuSiE”. Can you double check this is true? My understanding is SuSiE is SNP-based fine mapping method, not for building prediction models for gene expression.

2. When analyzing multiple tissues, gene expression levels (or their genetically predicted values) across tissues may be highly correlated or even identical, leading to a singular covariance matrix. How do you address this issue in such extreme cases?

3. In a single LD region, the number of SNPs analyzed can exceed 1,000. Does this pose any computational challenges?

4. In model (1) or (3), the authors assume both the intercept mu and the residue follow normal distributions. How do they use difference information from the data?

Page 10, line 181: Assuming that the vector γ follows a multinomial distribution, Multinomial(1,π), implies that there is at least one causal gene in the region under consideration. If mFABIO is applied to all regions without pre-screening (e.g., using marginal TWAS to identify candidate regions likely to contain causal genes), it may identify putative causal genes in regions that do not truly harbor any causal genes. Please clarify how mFABIO avoids such false positives.

Reviewer #2: I appreciate the efforts made by the authors in addressing my comments thoroughly, which have further enhanced the manuscript. I would like to recommend this manuscript for publication.

Reviewer #3: This manuscript presents mFABIO, a multi-tissue TWAS fine-mapping framework for binary traits that jointly models gene–tissue effects and accounts for correlations in genetically regulated expression. Using a probit model and a SuSiE-style prior, the method enables efficient inference at both gene and gene-tissue levels. Simulation studies and applications to UK Biobank data demonstrate improved power while maintaining appropriate control of false discoveries compared with existing approaches. Overall, the manuscript is well written and methodologically sound. I recommend this manuscript for publication.

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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

Reviewer #3: Yes

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Reviewer #1: No

Reviewer #2: Yes: Xihao Li

Reviewer #3: No

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While revising your submission, we strongly recommend that you use PLOS’s NAAS tool (https://ngplosjournals.pagemajik.ai/artanalysis) to test your figure files. NAAS can convert your figure files to the TIFF file type and meet basic requirements (such as print size, resolution), or provide you with a report on issues that do not meet our requirements and that NAAS cannot fix.

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To enhance the reproducibility of your results, we recommend that authors deposit 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 Dr Zhang,

We are pleased to inform you that your manuscript entitled "mFABIO: An integrative multi-tissue TWAS fine-mapping approach to prioritize potentially causal genes and tissues underlying binary traits" has been editorially accepted for publication in PLOS Genetics. Congratulations!

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Lin S. Chen, Ph.D.

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PLOS Genetics

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PLOS Genetics

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PLOS Genetics

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Editor-in-Chief

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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 #1: The authors have responded well to the issues raised by the reviewer. I have one minor question:

In Model/Equation (1), some covariates C may have fixed effects (e.g., sex); is it appropriate to treat their effects as random?

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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

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Reviewer #1: No

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