Abstract

BackgroundOver the last 10 years, there have been over 3300 genome-wide association studies (GWAS). Almost every GWAS study provides a Manhattan plot either as a main figure or in the supplement. Several software packages can generate a Manhattan plot, but they are all limited in the extent to which they can annotate gene-names, allele frequencies, and variants having high impact on gene function or provide any other added information or flexibility. Furthermore, in a conventional Manhattan plot, there is no way of distinguishing a locus identified due to a single variant with very significant p-value from a locus with multiple variants which appear to be in a haplotype block having very similar p-values.ResultsHere we present a software tool written in R, which generates a transposed Manhattan plot along with additional features like variant consequence and minor allele frequency to annotate the plot and addresses these limitations. The software also gives flexibility on how and where the user wants to display the annotations. The software can be downloaded from CRAN repository and also from the GitHub project page.ConclusionsWe present a major step up to the existing conventional Manhattan plot generation tools. We hope this form of display along with the added annotations will bring more insight to the reader from this new Manhattan++ plot.

Highlights

  • ResultsWe present a software tool written in R, which generates a transposed Manhattan plot along with additional features like variant consequence and minor allele frequency to annotate the plot and addresses these limitations

  • Over the last 10 years, there have been over 3300 genome-wide association studies (GWAS)

  • Information in the plot, the Manhattan plot is losing its importance in more recent GWAS publications

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Summary

Results

The software is customizable and can generate a Manhattan of about 20 million variants on a 32 bit desktop personal computer using under 3.4 GB RAM and similar in Linux (Centos 7). The software has been tested to display annotation of 130 loci in a tabular format (Fig. 2a) with odds ratio, effect allele frequency, p-value & gene-name. If the number of loci to display goes beyond 130, we recommend using just the gene (or variant) names and the software will display the names in a force directed manner (Fig. 2b). The colours and the number of variants in each bin are customizable The user have the option to save the output as a PDF or a high-resolution TIFF file

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Conclusions

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