RACER: a data visualization strategy for exploring the comparison between regional association from multiple datasets

Sabik, O.; Farber, C.

doi:10.1101/495366

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bioRxiv · 10.1101/495366

RACER: a data visualization strategy for exploring the comparison between regional association from multiple datasets

Sabik, O.·Farber, C.

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Abstract

Genome-wide association studies (GWASs) have identified thousands of loci associated with risk of various diseases; however, the genes responsible for the majority of loci have not been identified. One means of uncovering potential causal genes is the identification of expression quantitative trait loci (eQTL) that colocalize with disease loci. Statistical methods have been developed to assess the likelihood that two associations (e.g. disease locus and eQTL) share a common causal variant, however, visualization of the two loci is often a crucial step in determining if a locus is pleiotropic. While the current convention is to plot two associations side-by-side, it is difficult to compare across two x-axes, even if they are identical. Thus, we have developed the Regional Association ComparER (RACER) package, which creates \"mirror plots\", in which the two associations are plotted on a shared x-axis. Mirror plots provide an effective tool for the visual exploration and presentation of the relationship between two genetic associations.\n\nAvailability and ImplementationRACER is provided under the GNU General Public License version 3 (GPL-3.0). Source code is available at https://github.com/oliviasabik/RACER.\n\nContactols5fg@virginia.edu\n\nSupplementary informationSupplementary data are available online with the paper, see the Supplemental Data Manifest.

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Sabik, O., Farber, C.. 2018-12-13. RACER: a data visualization strategy for exploring the comparison between regional association from multiple datasets. https://doi.org/10.1101/495366

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