paper/paper.md

In sinarueeger/ggGWAS: ggplot2 Extensions for Plotting GWAS Summary Statistics

title: 'ggGWAS: A ggplot2 extension for genomic summary statistics' tags: - R - visualisation - human genomics authors: - name: Sina Rüeger orcid: 0000-0003-2848-9242 affiliation: "1, 2" - name: Author 2 orcid: 0000-0000-0000-0000 affiliation: 2 affiliations: - name: Global Health Institute, School of Life Sciences, EPFL, Lausanne, Switzerland index: 1 - name: Swiss Institute of Bioinformatics, Lausanne, Switzerland index: 2 date: 30 August 2019 bibliography: paper.bib output: md_document: preserve_yaml: true

Summary

Genome-wide association studies (GWAS) estimate the association between each SNP and a trait of interest. The output of a GWAS is a matrix that contains the information and summary statistic for each SNP per line: SNP identifier, chromosome, position, effect size, standard error, p-value effect allele, and sometimes the minor allele frequency.

GWAS summary statistics are often computed for 1M+ SNPs. To summarise such large amounts of data and visualise the P-value distribution, two plots are typically done: - A Q-Q plot displays the theoretical against the observed p-value (McCarthy et al. 2008). A Q-Q plot helps to identify inflated P-values due to a mispecified GWAS model. Genomic inflation factors attempt the same. - A Manhattan plot displays the p-values along the chromosomal position (McCarthy et al. 2008).

While the GWAS computation itself is often done with command line tools (e.g. (???) or Yang et al. (2011)), the exploration and validation of the results is done in conventional statistical software, such as R (R Core Team 2018).

Q-Q plots might only be looked at once during the analysis process, but a Manhattan plots become primary figure of an article. For example, a prominent GWAS on anorexia (Watson et al. 2019) has a Manhattanplot as Figure 1, but the Q-Q plot is in the Supplementary.

Therefore, fast plotting is key to quickly summarise results during the analysis process and take decisions. But equally important is versatile plotting - being able to add layers and annotation to a plot to make it easily understandable for a reader.

ggplot2(Wickham 2016) offers such a versatile data visualisation package that is widely used. However, ggplot2 is known for its low speed (ref). One reason for this is that a ggplot2 object contains multiple versions of the data being plotted.

With ggGWAS we propose a ggplot2 extension for GWAS Q-Q plots (geom_gwas_qq) and Manhattan plots (geom_gwas_manhattan). For geom_qqplot there is additionally a hexagon version that reduces computing time by xx (geom_gwas_qq_hex). For both functions there is a filtering argument where points can be omitted.

Background

Q-Q plots and Manhattan plots are implemented in R with various functions. The most widely used package is qqman (Turner 2017), that has a function for Q-Q plots qq and Manhattan plots manhattan, but which is based on base R graphics. The ramwas bioconductor package (Shabalin et al. 2018) has two fast plotting functions too manPlotFast and qqPlotFast, also based on base R. There are many ggplot2 wrapper, e.g. mkanai/ggman (Kanai 2015), drveera/ggman (Rajagopal 2016), ggbio (Yin, Cook, and Lawrence 2012). But none of these is an extension that inherits the ggplot2 functionalities.

How can we speed up plotting in R? Two approaches are to 1) only plot a subset of points (called filtering later) or 2) use hexagons to represent clusters of points. There is a third option, to use rastering, but this will only affect the resulting image file size and not really the plotting speed in R. Hexagons are actually implemented in R and have been used to plot high dimensional data by others (Freytag 2019).

Obj Size Plotting speed File size Filtering x x x hexagons x x raster x

The ggGWAS R package

The ggGWAS package depends mainly on ggplot2, hexbin, scales. Additionally dplyr and magrittr are used for …

Brief example

Q-Q plot

ggplot(data = giant) + 
  geom_gwas_qq(aes(y = P)) + 
  geom_abline(intercept = 0, slope = 1, linetype = 2) + 
  theme(aspect.ratio = 1)

Manhattanplot

ggplot(data = giant) + geom_gwas_manhattan(aes(pos = POS, chr = CHR, y = -log10(P)))

• range of chromosomes can be passed on
• allow for the raster version (for faster plotting) and Pvalue thresholding (removing the high Pvalue SNPs from the plot)
• implement coloring (two alternating colors)

Discussion

• even though facetting could work, this is probably not possible when multiple GWAS with millions of SNps are present.

• implemented rastering, but that did not work (only dcreased file size)

• speed comparison qqman::manhattan(giant, chr = "CHR" , bp = "POS", p = "P")

Acknowledgements

• Flavia Hodel contributed parts of the Manhattan plot
• Fellay lab

References

Freytag, Saskia. 2019. Hexbin Plots for Single Cell Omics Data. https://github.com/SaskiaFreytag/schex.

Kanai, Masahiro. 2015. Ggman: Manhattan Plot Using Ggplot2. https://github.com/mkanai/ggman.

McCarthy, Mark I., Gonçalo R. Abecasis, Lon R. Cardon, David B. Goldstein, Julian Little, John P. A. Ioannidis, and Joel N. Hirschhorn. 2008. “Genome-Wide Association Studies for Complex Traits: Consensus, Uncertainty and Challenges.” Nature Reviews Genetics 9 (5): 356–69. https://doi.org/10.1038/nrg2344.

Rajagopal, Veera M. 2016. Ggman: R Package for Manhattan Plots. https://github.com/drveera/ggman.

R Core Team. 2018. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

Shabalin, Andrey A, Mohammad W Hattab, Shaunna L Clark, Robin F Chan, Gaurav Kumar, Karolina A Aberg, and Edwin JCG van den Oord. 2018. “RaMWAS: Fast Methylome-Wide Association Study Pipeline for Enrichment Platforms.” Bioinformatics. http://dx.doi.org/10.1093/bioinformatics/bty069.

Turner, Stephen. 2017. Qqman: Q-Q and Manhattan Plots for Gwas Data. https://CRAN.R-project.org/package=qqman.

Watson, Hunna J., Zeynep Yilmaz, Laura M. Thornton, Christopher Hübel, Jonathan R. I. Coleman, Héléna A. Gaspar, Julien Bryois, et al. 2019. “Genome-Wide Association Study Identifies Eight Risk Loci and Implicates Metabo-Psychiatric Origins for Anorexia Nervosa.” Nature Genetics, July, 1. https://doi.org/10.1038/s41588-019-0439-2.

Wickham, Hadley. 2016. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. https://ggplot2.tidyverse.org.

Yang, Jian, S. Hong Lee, Michael E. Goddard, and Peter M. Visscher. 2011. “GCTA: A tool for genome-wide complex trait analysis.” American Journal of Human Genetics 88 (1): 76–82. https://doi.org/10.1016/j.ajhg.2010.11.011.

Yin, Tengfei, Dianne Cook, and Michael Lawrence. 2012. “Ggbio: An R Package for Extending the Grammar of Graphics for Genomic Data.” Genome Biology 13 (8): R77.

sinarueeger/ggGWAS documentation built on Aug. 2, 2019, 4:03 p.m.