iPheGWAS: an intelligent computational framework to integrate and visualise genome-phenome wide association results

Abstract

AbstractEstimating the genetic correlations by LDSC is computationally demanding and visualising multiple GWAS results along with their genetic relationships is restricted. This study developed iPheGWAS, a novel approach which applied hierarchical clustering to GWAS summary statistics to (i) calculate their genetic relatedness, and (ii) enable three-dimensional visualisation of multiple ordered GWAS plots. Simulation and real-world data analysis demonstrated that when investigating genetic relationships among multiple phenotypes, iPheGWAS can deliver comparable results with LDSC but with 8 times faster computational speed. It can also provide novel findings in studying genetically-correlated comorbidities, such as mental illness and rheumatoid arthritis.