DOI: 10.1101/495366Dec 13, 2018Paper

RACER: A data visualization strategy for exploring multiple genetic associations

BioRxiv : the Preprint Server for Biology
Olivia Sabik, Charles Farber

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.

Related Concepts

Genes
Fluorescent stain
Side
Pleiotropism
Quantitative Trait Loci
Genetic Loci
Comparative Analysis
BAT Loci
Protein Expression
Exploration Procedure

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