Reviewer name and names of any other individual's who aided in reviewer |
Wentian Li |
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Yes |
Is the language of sufficient quality? |
Yes |
Please add additional comments on language quality to clarify if needed |
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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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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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Yes |
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Not applicable |
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Are there (ideally real world) examples demonstrating use of the software? |
Yes |
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Additional Comments |
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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 |