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. 2023 Sep 1;2023:gigabyte89. doi: 10.46471/gigabyte.89
Reviewer name and names of any other individual's who aided in reviewer Wentian Li
Do you understand and agree to our policy of having open and named reviews, and having your review included with the published manuscript. (If no, please inform the editor that you cannot review this manuscript.) Yes
Is the language of sufficient quality? Yes
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Is there a clear statement of need explaining what problems the software is designed to solve and who the target audience is? Yes
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Is the source code available, and has an appropriate Open Source Initiative license <a href="https://opensource.org/licenses" target="_blank">(https://opensource.org/licenses)</a> been assigned to the code? Yes
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As Open Source Software are there guidelines on how to contribute, report issues or seek support on the code? Yes
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Is the code executable? Yes
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Is installation/deployment sufficiently outlined in the paper and documentation, and does it proceed as outlined? Yes
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Is the documentation provided clear and user friendly? No
Additional Comments Many aspects of Fig.1 are not explained.
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Is there a clearly-stated list of dependencies, and is the core functionality of the software documented to a satisfactory level? Yes
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Have any claims of performance been sufficiently tested and compared to other commonly-used packages? Not applicable
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Are there (ideally real world) examples demonstrating use of the software? Yes
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Any Additional Overall Comments to the Author Plots with allele frequency as x axis and effect size (e.g. odds ratio) as y axis is a very common display of the contribution from both common and rare alleles to genetic association. A schematic form of this plot is practically on almost everybody's presentation slides when introduce this topic (to see an example, see, e.g. Science (23 Nov 2012), vol 338(6110), pp.1016-1017 ). Considering how many people have already been familiar with this type of plot, I feel that very little new is added in this paper: maybe only a new name ("trumpet"), and/or the power lines. The other methods contributions (log-x, one variant per LD, avoiding gene-level statistics) are rather straightforward. People without experience with "shiny" (R package) can still use ggplot2 or plot in R to get the same result. Generally speaking, I think the paper is weak, though OK as a program/package announcement. Major comments: * I think the trumpet shape (increase of "effect size" for rare variant) is probably a direct consequence of using odds-ratio as a measure of effect size. If the allele frequency in normal population is p0, that in disease population is p1, [p1/(1-p1)]/[p0/(1-p0)] ~ p1/p0 tends to be large for small p0's, simply because the denominator is small. On the other hand, if population attributable risk (p0*(RR-1)/(1+p0*(RR-1))) is used as the y-axis, I am uncertain what the shape of the plot would be. * A risk allele has these pieces of information: 1. allele frequency, 2. effect size (e.g. odds ratio), 3. type-I error/p-value, 4. type-II error/power. The plot in this paper show #1 vs #2 and #4 being added as extra. In another publication with a proposal to plot genetic association results (Comp Biol. and Chem. (2014), 48:77-83 doi: 10.1016/j.compbiolchem.2013.02.003), #2 is against #3 with #1 being an added extra. I'm sure using other combinations could lead to other types of plots. The authors should discussion/compare these possibilities. Minor comments: In Fig.1, the size of the dots, the brown vs cyan color, the discontinuity of scatter dots around 0.01, are not explained.
Recommendation Major Revisions