Trumpet plots: Visualizing The Relationship Between Allele Frequency And Effect Size In Genetic Association Studies
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**Editors Assessment: **
This work presents a new standardized graphical approach for visualizing genetic associations across a wide range of allele frequencies. These proposed TrumpetPlots have a distinctive trumpet shape, hence the proposed name. With the majority of variants having low frequency and small effects, while a small number of variants have higher frequency and larger effects, this view can help to provide new and valuable insights into the genetic basis of traits and diseases, and also help prioritize efforts to discover new risk variants. The tool is provided as a novel R package and R Shiny application and to demonstrate its use the article illustrates the distribution of variant effect sizes across the allele frequency range for over 100 continuous traits available in the UK Biobank. After some problems in testing the package is now available and easy to deploy via CRAN.
*This assessment refers to version 1 of this preprint. *
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Abstract
Recent advances in genome-wide association study (GWAS) and sequencing studies have shown that the genetic architecture of complex diseases and traits involves a combination of rare and common genetic variants, distributed throughout the genome. One way to better understand this architecture is to visualize genetic associations across a wide range of allele frequencies. However, there is currently no standardized or consistent graphical representation for effectively illustrating these results.
Here we propose a standardized approach for visualizing the effect size of risk variants across the allele frequency spectrum. The proposed plots have a distinctive trumpet shape, with the majority of variants having low frequency and small effects, while a small number of variants have higher frequency and larger effects. These plots, which we call ‘trumpet plots’, can help to provide new and valuable insights into the genetic basis of traits and diseases, and can help prioritize efforts to discover new risk variants. To demonstrate the utility of trumpet plots in illustrating the relationship between the number of variants, their frequency, and the magnitude of their effects in shaping the genetic architecture of complex diseases and traits, we generated trumpet plots for more than one hundred traits in the UK Biobank. To facilitate their broader use, we have developed an R package ‘TrumpetPlots’ and R Shiny application, available at https://juditgg.shinyapps.io/shinytrumpets/ , that allows users to explore these results and submit their own data.
STATEMENT OF NEED
Visualizations are powerful tools that have helped the field of genetics to better understand and communicate complex findings. By using visual aids like Manhattan and Volcano plots, genetic variants identified through genome-wide association studies can be more easily pinpointed. With the advancement of genome-wide association and sequencing studies, a mounting number of significant genetic variants, both common and rare, are being discovered. To better understand the relationship between these variants, combining these findings into single visualizations help to observe the relationship between effect size and allele frequency, providing a clearer picture of the genetic architecture of different traits and diseases. However, there is currently no consistent method for illustrating these results . In this paper, we propose a standardized approach for visualizing the effect size of risk variants across the allele frequency spectrum, generate plots for over a hundred traits in the UK Biobank, and provide to the field a R package and R Shiny application to explore their own results.
Availability of supporting source code and requirements
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Project name:
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R package available in project ‘TrumpetPlots’ https://gitlab.com/JuditGG/trumpetplots
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R shiny app and analyses in the UK Biobank available in project ‘ freq_or_plots ’ https://gitlab.com/JuditGG/freq_or_plots
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Project home page: https://juditgg.shinyapps.io/shinytrumpets/
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Operating system(s): Platform independent.
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Programming language: R
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RRID: Not applicable
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License: MIT
Article activity feed
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**Editors Assessment: **
This work presents a new standardized graphical approach for visualizing genetic associations across a wide range of allele frequencies. These proposed TrumpetPlots have a distinctive trumpet shape, hence the proposed name. With the majority of variants having low frequency and small effects, while a small number of variants have higher frequency and larger effects, this view can help to provide new and valuable insights into the genetic basis of traits and diseases, and also help prioritize efforts to discover new risk variants. The tool is provided as a novel R package and R Shiny application and to demonstrate its use the article illustrates the distribution of variant effect sizes across the allele frequency range for over 100 continuous traits available in the UK Biobank. After some problems in testing the …
**Editors Assessment: **
This work presents a new standardized graphical approach for visualizing genetic associations across a wide range of allele frequencies. These proposed TrumpetPlots have a distinctive trumpet shape, hence the proposed name. With the majority of variants having low frequency and small effects, while a small number of variants have higher frequency and larger effects, this view can help to provide new and valuable insights into the genetic basis of traits and diseases, and also help prioritize efforts to discover new risk variants. The tool is provided as a novel R package and R Shiny application and to demonstrate its use the article illustrates the distribution of variant effect sizes across the allele frequency range for over 100 continuous traits available in the UK Biobank. After some problems in testing the package is now available and easy to deploy via CRAN.
*This assessment refers to version 1 of this preprint. *
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AbstractRecent advances in genome-wide association study (GWAS) and sequencing studies have shown that the genetic architecture of complex diseases and traits involves a combination of rare and common genetic variants, distributed throughout the genome. One way to better understand this architecture is to visualize genetic associations across a wide range of allele frequencies. However, there is currently no standardized or consistent graphical representation for effectively illustrating these results.Here we propose a standardized approach for visualizing the effect size of risk variants across the allele frequency spectrum. The proposed plots have a distinctive trumpet shape, with the majority of variants having low frequency and small effects, while a small number of variants have higher frequency and larger effects. These plots, …
AbstractRecent advances in genome-wide association study (GWAS) and sequencing studies have shown that the genetic architecture of complex diseases and traits involves a combination of rare and common genetic variants, distributed throughout the genome. One way to better understand this architecture is to visualize genetic associations across a wide range of allele frequencies. However, there is currently no standardized or consistent graphical representation for effectively illustrating these results.Here we propose a standardized approach for visualizing the effect size of risk variants across the allele frequency spectrum. The proposed plots have a distinctive trumpet shape, with the majority of variants having low frequency and small effects, while a small number of variants have higher frequency and larger effects. These plots, which we call ‘trumpet plots’, can help to provide new and valuable insights into the genetic basis of traits and diseases, and can help prioritize efforts to discover new risk variants. To demonstrate the utility of trumpet plots in illustrating the relationship between the number of variants, their frequency, and the magnitude of their effects in shaping the genetic architecture of complex diseases and traits, we generated trumpet plots for more than one hundred traits in the UK Biobank. To facilitate their broader use, we have developed an R package ‘TrumpetPlots’ and R Shiny application, available at https://juditgg.shinyapps.io/shinytrumpets/, that allows users to explore these results and submit their own data.
This work has been published in GigaByte Journal under a CC-BY 4.0 license (https://doi.org/10.46471/gigabyte.89) and has published the reviews under the same license. These are as follows.
**Reviewer 1. Clara Albiñana **
As Open Source Software are there guidelines on how to contribute, report issues or seek support on the code?
No. Although there are no explicit guidelines for contribution in the manuscript or website, it is true that by placing the project on gitlab it is possible to contribute to the project / open issues.
Is the code executable?
No. Unfortunately, I wasn't able to install the R package. I have now opened an issue on the gitlab page so that it can hopefully get solved.
Is installation/deployment sufficiently outlined in the paper and documentation, and does it proceed as outlined?
Yes. It is very common for new R packages to just use devtools for installation.
Is the documentation provided clear and user friendly?
Yes. The requirements for generating a trumpet plot just involve providing a set of GWAS summary statistics with column-specific names, together with the GWAS sample size. This is very common for GWAS summary statistics-based tools. I think it is fine for the R package to require re-naming the columns to fit the format, as one already needs to upload the file into R. However, I find it inconvenient to have to re-save the summary statistics file with different name-columns for the shinyapp tool. Providing e.g. column indexes alone would be much more user-friendly.
Is there enough clear information in the documentation to install, run and test this tool, including information on where to seek help if required?
No. I cannot answer this question until I can install the tool.
Have any claims of performance been sufficiently tested and compared to other commonly-used packages?
Not applicable. There are no existing comparable tools.
Is automated testing used or are there manual steps described so that the functionality of the software can be verified?
Yes. I can see there is a toy dataset included with the R package.
Additional Comments:
I think the manuscript is very clear and good at making the point of the utility of the software. The proposed trumpet plots are very visually appealing and can be useful to characterise the genetic variation of diverse phenotypes. The novelty of the trumpet plots, as compared to previously proposed effect size vs. allele frequency plots, is the use of positive and negative effect sizes, making it look like a trumpet. I also appreciate the style decisions in the standard generated plots, with a nice visually-appealing color scheme and design.
On the use of the software, I have focused my testing on the R package, which I was not able to install. The shinyapp is very useful for visualising the existing, pre-computed trumpet plots, but I do not find it very useful for generating user-uploaded summary statistics for the reasons I mentioned above. Another comment on the ShinyApp is that I appreciate the possibility to download the plots but it would be very useful to include the name of the visualized phenotype as the plot title, for example, to avoid confusion when downloading multiple plots.
I also found an incorrect sentence in the abstract, which is think should be reversed: " The proposed plots have a distinctive trumpet shape, with the majority of variants having low frequency and small effects, while a small number of variants have higher frequency and larger effects".
**Reviewer 2. Wentian Li **
Is the documentation provided clear and user friendly?
No. Many aspects of Fig.1 are not explained.
Overall Comments: 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 introducing 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:
- allele frequency,
- effect size (e.g. odds ratio),
- type-I error/p-value,
- 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.
Re-review:
I have read authors' response and I'm mostly satisfied. Only two minor comments:
- Witte 2014 Nature Rev. Genet. article summarizes the point I tried to make well. I understand that rare variants should have a relatively higher effect from an evolutionary perspective, but since these are rare, their individual or even collective contribution to a disease in the population is still small. A casual reader may not realize this point and I think it would be helpful to cite Witte's article.
- My minor comment on Fig.1 is still not addressed: there seem to be more points on the right side of p=0.01 line than the left side. Why this discontinuity? (the added text in Revision is about the color and size of the dots, not about this discontinuity)
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