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bioRxiv · 10.1101/2022.03.05.483121

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

George, G.·Huang, Y.·Gan, S.·Nar, A. S.·Ha, J.·Venkatesan, R.·Mohan, V.·Wang, H.·Brown, A.·Palmer, C. N. A.·Doney, A.
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Abstract

Estimating 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.

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George, G., Huang, Y., Gan, S., Nar, A. S., Ha, J., Venkatesan, R., Mohan, V., Wang, H., Brown, A., Palmer, C. N. A., Doney, A.. 2022-03-07. iPheGWAS : an intelligent computational framework to integrate and visualise genome-phenome wide association studies. https://doi.org/10.1101/2022.03.05.483121

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