PGENETICS-D-26-00069

Ultra-fast genetic colocalisation across millions of traits

PLOS Genetics

Dear Dr. Alasoo,

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Kind regards,

Lin S. Chen, Ph.D.

Academic Editor

PLOS Genetics

Michael P. Epstein

Section Editor

PLOS Genetics

Aimée Dudley

Editor-in-Chief

PLOS Genetics

Anne Goriely

Editor-in-Chief

PLOS Genetics

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Reviewers' comments:

Reviewer's Responses to Questions.

Reviewer #1: The authors introduced gpu-coloc, a GPU-accelerated re-implementation of the coloc algorithm that enables biobank-scale colocalisation. Through comprehensive computational benchmarking, they demonstrated the efficiency of gpu-coloc. They also investigated the colocalisation colliders and provided practical recommendations in real data analysis for users. I have some comments as follows.

1. Although the authors explained PP.H4 in detail in the Methods section, it would be helpful to briefly introduce its meaning when it is first mentioned. This would make the manuscript more accessible to a more general audience.

2. Line 153-156: Why is the gpu-coloc PP.H4 considered more conservative than that from coloc.bf_bf? According to Figure 2A, the PP.H4 values between the two methods are virtually identical, with only a few outliers where gpu-coloc reports lower values. Notably, these discrepancies are concentrated in the region where PP.H4 < 0.6, which may have limited impact on the false positive rate in practice. Furthermore, I don't think that being more conservative is necessarily superior.

3. Line 164-174: The major contribution of this paper lies in its highly efficient implementation. Therefore, I suggest providing more detailed information when benchmarking the computation time, such as the hardware of the high-performance computing system (GPU model, CPU model, number of cores, RAM), computation configuration (number of parallel processes), and the time spent on model fitting versus I/O operations. Additionally, how does the computation time of gpu-coloc scale with the dataset size? Identifying the primary computational bottleneck would be highly beneficial for potential users.

4. How does the computational efficiency of gpu-coloc compare with other popular efficient methods (e.g., HyPrColoc, https://doi.org/10.1038/s41467-020-20885-8)? I suggest including more competing methods for a more comprehensive comparison.

5. The empirical strategy to select the p12 prior and corresponding CLPP thresholds by quantifying the rate of colocalisation colliders is very interesting and valuable to users. However, relying solely on this metric may be limiting since the colocalisation colliders are not strictly equivalent to false positives. Have the authors considered other metrics?

6. It's interesting to see that the SuSiE signals that failed purity filtering may cause the increased rate of colocalisation colliders for gpu-coloc. In the real data analyses, did the authors observe any concrete examples of such collider events? For example, do these signals exhibit broader or multi-modal PIP distributions, or more complex LD structures that prevent them from passing the purity filtering?

7. Figure 3C: Does CS refer to credible set? I suggest explaining the abbreviation at first use.

Reviewer #2: In this manuscript, the authors introduced a scalable implementation of colocalization algorithm. They compared locus-level posteriors with variant-level overlap metrics such as CLPP and provided practical recommendations for choosing pipelines, thresholds, and priors in colocalization analyses. The work is likely to be impactful for the statistical genetics community, particularly for large-scale integrative analyses involving GWAS and molecular QTL datasets. However, several aspects of the study would benefit from clearer explanation, including implementation choices for handling missing values, the design of benchmarking, and parts of the practical recommendations. Detailed comments are provided below:

1. In the title, "across millions of traits" is potentially misleading. As presented in the following analyses, it appear to involve millions of colocalization tests or locus-trait or signal-trait pairs, rather than millions of distinct traits. Rephrasing the title to more precisely reflect the scale of the analyses would improve clarity.

2. The workaround of replacing missing LBF values with an extreme negative constant is a critical implementation step that enables the matrix-based computations in gpu-coloc. To improve transparency, It would be helpful to explicitly illustrate this step in Figure 1. In addition, please justify the choice of the value of -1e6. It is unclear whether this threshold should remain fixed across different datasets or analysis settings, or whether it should depend on factors such as sample size, effect size distributions, or prior choices. A brief sensitivity analysis assessing the robustness of posterior inferences to the choice of this constant would further strengthen confidence in the method.

3. The gpu-coloc replaces missing LBFs with an extreme negative value, so the difference in PP.H4 shown in Figure 2A may depend on the pattern and extent of missingness across datasets, traits, locus, and variants. Please clarify how frequently the replacement occurs in practice, and whether heterogeneity in missingness could influence the observed differences between methods.

4. The results of computational performance on pages 5-6 would benefit from additional clarification and reorganization. (1) When reporting the running times, it would be helpful to clearly specify the computational settings, including allocated memory, number of cores, and the total number of colocalization tests performed. Reporting a normalized metric such as running time per test may facilitate more meaningful comparison across methods and computing environments. (2) The manuscript mentions running times on a personal Mac computer in both lines 168 and 179, which appear to convey similar messages regarding computational efficiency. Reorganizing these descriptions could improve clarity and avoid redundancy. (3) Finally, to better demonstrate scalability, it would be informative to show how running time scales with the number of colocalization tests, and to compare with existing methods.

5. The gpu-coloc is compared with coloc.bf_bf and with CLPP using different datasets. It would be helpful for the authors to clarify the rationale and criterion in choosing different datasets for different comparisons. In particular, please explain whether this choice reflects method-specific input requirements, data availability, or distinct benchmarking goals. Without such clarification, it is difficult to disentangle method differences from dataset-specific effects.

7. From "we leveraged our efficient gpu-coloc implementation to calculate both the CLPP and PP.H4 values", it seems the gpu-coloc method can be used as re-implementation for both standard coloc and CLPP. But in the method overview and Figure 1, it seems gpu-coloc is only a more efficient tool for standard coloc. Please clarify.

8. The mapping between CLPP and coloc posterior probability (page 6-7) is derived empirically using the eQTL Catalogue and FinnGen datasets. This mapping may not generalize naturally to other data.

9. In Figures 3D and S2D, the proportion for signals shared between gpu-coloc and CLPP is even higher than CLPP-only. It is not clear whether different denominators were used in these two proportions? Please justify.

10. In the section of "Recommendations for large-scale colocalisation analysis", reorganizing the three points may provide better practice guidance. Currently, Point 1 primarily discusses threshold selection for both coloc and CLPP methods. Points 2 and 3 focus on method choice under different data availability scenarios. This seems not a natural order of actions to perform in real data analysis. A more intuitive way may be to first recommend using CLPP when fine-mapping results and credible sets are available and reliable, together with guidance on the appropriate CLPP threshold. And then, when fine-mapping results are unavailable or too noisy, recommend using gpu-coloc, with corresponding guidance on posterior probability thresholds and prior choices.

Reviewer #3: The paper presents a new implementation of the coloc method that uses GPUs to greatly speed up analyses for very large datasets.

Overall, I think the paper is solid and the analyses are appropriate. In the end, the overlap with the original coloc results is close to perfect, which makes sense given that the underlying mathematics is essentially the same. Any discrepancies may be due to the grouping of loci into large regions based on genomic position, which may substantially enlarge loci and thus affect the final results.

I do have some comments:

1) The paragraph comparing coloc and CLPP is somewhat difficult to follow. I think the language could be made clearer.

2) I am not sure why the authors use CLPP as a “gold standard” when comparing coloc to it. On the one hand, CLPP is essentially a scaled version of PPH4: CLPP is the sum of the product of the two PIPs SNP by SNP, whereas PPH4 is based on the product of the ABFs. However, PIPs are ultimately derived by dividing ABFs by their sum. The key difference is that PPH4 is scaled relative to the evidence for the other hypotheses, while CLPP is not. In this context, I would consider coloc to be more statistically coherent. Therefore, if the goal is to provide recommendations regarding CLPP so that its results are more consistent with coloc, the paragraph should be framed in that way. At the moment, it almost appears to suggest the opposite.

3) As the authors state, coloc “colliders” arise from either a lack of power in fine-mapping or insufficient quality control. While coloc should not be applied in the second case, in the first case the correct result would in fact be to detect both colocalizations. Although this situation is a nuisance and difficult to interpret, it is nevertheless a correct result. Therefore, the fact that coloc detects such cases should be considered a positive outcome, as it is closer to the underlying truth.

4) The paragraph comparing colocalization with and without fine-mapping does not add much, as there is no clear reason to expect results that differ from what is already known.

If the authors would like to frame the paper as a guide on how to run and properly interpret colocalization analyses (which could be a valid option), more work would be needed to explore the impact of various parameters, perhaps using simulations. However, this may be beyond the scope of the current study.

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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: None

Reviewer #3: Yes

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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Dear Dr Alasoo,

We are pleased to inform you that your manuscript entitled "Ultra-fast genetic colocalisation across millions of association signals" has been editorially accepted for publication in PLOS Genetics. Congratulations!

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Yours sincerely,

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

www.plosgenetics.org

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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 addressed all of my concerns.

Reviewer #2: The authors have addressed all my comments.

Reviewer #3: I have no further comments

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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: None

Reviewer #3: None

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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