Abstract
MotivationPheGWAS was developed to enhance exploration of phenome-wide pleiotropy at the genome-wide level through the efficient generation of a dynamic visualization combining Manhattan plots from GWAS with PheWAS to create a 3D ‘landscape’. Pleiotropy in sub-surface GWAS significance strata can be explored in a sectional view plotted within user defined levels. Further complexity reduction is achieved by confining to a single chromosomal section. Comprehensive genomic and phenomic coordinates can be displayed.ResultsPheGWAS is demonstrated using summary data from Global Lipids Genetics Consortium GWAS across multiple lipid traits. For single and multiple traits PheGWAS highlighted all 88 and 69 loci, respectively. Further, the genes and SNPs reported in Global Lipids Genetics Consortium were identified using additional functions implemented within PheGWAS. Not only is PheGWAS capable of identifying independent signals but also provides insights to local genetic correlation (verified using HESS) and in identifying the potential regions that share causal variants across phenotypes (verified using colocalization tests).Availability and implementationThe PheGWAS software and code are freely available at (https://github.com/georgeg0/PheGWAS).Supplementary information Supplementary data are available at Bioinformatics online.
Highlights
The potential of personalized medicine has evolved extensively in the last decade with the development of genome-wide association studies (GWAS), which is a powerful method for exploring the genetic architecture underlying diseases and traits affecting humans
The genes and single nucleotide polymorphisms (SNPs) reported in Global Lipids Genetics Consortium were identified using additional functions implemented within PheGWAS
Regional GWAS are offered by LocusTrack (Cuellar-Partida et al, 2015) which is another tool which combines the features of LocusZoom (Pruim et al, 2010) and SNAP plot (Johnson et al, 2008) and allows to choose between plotting the P-values or linkage disequilibrium (LD) on the y-axis
Summary
The potential of personalized medicine has evolved extensively in the last decade with the development of genome-wide association studies (GWAS), which is a powerful method for exploring the genetic architecture underlying diseases and traits affecting humans. The ability to visualize complex data can significantly enhance its exploration and understanding (Li et al, 2012) Applying this to the exploration of many genetic variants over many diseases demands data visualization tools, which present the data in an intuitive way that is capable of efficiently handling very large volumes of data. The Manhattan plot is the most readily available and established way to visualize GWAS and provides instant appreciation of the underlying genetic structure of the disease or trait being studied. It comprises a scatter plot of the positions of the single nucleotide polymorphisms (SNPs) along each autosomal chromosome on the x-axis and the y-axis corresponding to the significance of the association [expressed as Àlog10(p)] with the particular phenotype in question. Regional GWAS are offered by LocusTrack (Cuellar-Partida et al, 2015) which is another tool which combines the features of LocusZoom (Pruim et al, 2010) and SNAP plot (Johnson et al, 2008) and allows to choose between plotting the P-values or linkage disequilibrium (LD) on the y-axis
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