Trumpet plots: Visualizing The Relationship Between Allele Frequency And Effect Size In Genetic Association Studies

Abstract

AbstractRecent advances in genome-wide association study (GWAS) and sequencing studies have shown that the genetic architecture of complex diseases and traits involves a combination of rare and common genetic variants, distributed throughout the genome. One way to better understand this architecture is to visualize genetic associations across a wide range of allele frequencies. However, there is currently no standardized or consistent graphical representation for effectively illustrating these results.Here we propose a standardized approach for visualizing the effect size of risk variants across the allele frequency spectrum. The proposed plots have a distinctive trumpet shape, with the majority of variants having low frequency and small effects, while a small number of variants have higher frequency and larger effects. These plots, which we call ‘trumpet plots’, can help to provide new and valuable insights into the genetic basis of traits and diseases, and can help prioritize efforts to discover new risk variants. To demonstrate the utility of trumpet plots in illustrating the relationship between the number of variants, their frequency, and the magnitude of their effects in shaping the genetic architecture of complex diseases and traits, we generated trumpet plots for more than one hundred traits in the UK Biobank. To facilitate their broader use, we have developed an R package ‘TrumpetPlots’ and R Shiny application, available athttps://juditgg.shinyapps.io/shinytrumpets/, that allows users to explore these results and submit their own data.STATEMENT OF NEEDVisualizations are powerful tools that have helped the field of genetics to better understand and communicate complex findings. By using visual aids like Manhattan and Volcano plots, genetic variants identified through genome-wide association studies can be more easily pinpointed. With the advancement of genome-wide association and sequencing studies, a mounting number of significant genetic variants, both common and rare, are being discovered. To better understand the relationship between these variants, combining these findings into single visualizations help to observe the relationship between effect size and allele frequency, providing a clearer picture of the genetic architecture of different traits and diseases. However,there is currently no consistent method for illustrating these results. In this paper, we propose a standardized approach for visualizing the effect size of risk variants across the allele frequency spectrum, generate plots for over a hundred traits in the UK Biobank, and provide to the field a R package and R Shiny application to explore their own results.Availability of supporting source code and requirementsProject name:oR package available in project ‘TrumpetPlots’https://gitlab.com/JuditGG/trumpetplotsoR shiny app and analyses in the UK Biobank available in project ‘freq_or_plots’https://gitlab.com/JuditGG/freq_or_plotsProject home page:https://juditgg.shinyapps.io/shinytrumpets/Operating system(s): Platform independent.Programming language: RRRID: Not applicableLicense: MIT