Manhattan++: displaying genome-wide association summary statistics with multiple annotation layers

Area of Science:

• Genetics
• Bioinformatics
• Computational Biology

Background:

• Over 3300 genome-wide association studies (GWAS) have been published in the last decade.
• Manhattan plots are standard visualizations in GWAS, but conventional plots have limitations in annotating gene names, allele frequencies, and variant impacts.
• Distinguishing single significant variants from haplotype blocks in conventional Manhattan plots is challenging.

Purpose of the Study:

• To address the limitations of conventional Manhattan plots in genome-wide association studies.
• To introduce a novel software tool for generating enhanced Manhattan plots with greater flexibility and annotation capabilities.

Main Methods:

• Development of a software tool using the R programming language.
• Implementation of a transposed Manhattan plot with additional annotation features.
• Inclusion of variant consequence and minor allele frequency data for enhanced visualization.

Main Results:

• The developed software generates enhanced Manhattan plots, termed Manhattan++ plots.
• The tool offers user flexibility in displaying annotations on the plot.
• The software addresses limitations in annotating gene names, allele frequencies, and variant impacts.

Conclusions:

• Manhattan++ represents a significant advancement over existing Manhattan plot generation tools.
• The enhanced visualization and annotations provided by Manhattan++ offer deeper insights into GWAS results.
• The software is available for download from CRAN and GitHub.