PheGWAS: A new dimension to visualize GWAS across multiple phenotypes

Gittu George; Sushrima Gan; Yu Huang; Philip Appleby; A. S. Nar; Radha Venkatesan; Viswanathan Mohan; Colin N. A. Palmer; Alex S. F. Doney

doi:10.1093/bioinformatics/btz944

PheGWAS: A new dimension to visualize GWAS across multiple phenotypes

Gittu George (Lead / Corresponding author), Sushrima Gan, Yu Huang, Philip Appleby, A. S. Nar, Radha Venkatesan, Viswanathan Mohan, Colin N. A. Palmer, Alex S. F. Doney (Lead / Corresponding author)

Population Health and Genomics

Research output: Contribution to journal › Article › peer-review

10 Citations (Scopus)

148 Downloads (Pure)

Abstract

Motivation: PheGWAS 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. Results: PheGWAS 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).

Original language	English
Pages (from-to)	2500-2505
Number of pages	6
Journal	Bioinformatics
Volume	36
Issue number	8
Early online date	20 Dec 2019
DOIs	https://doi.org/10.1093/bioinformatics/btz944
Publication status	Published - 15 Apr 2020

ASJC Scopus subject areas

Statistics and Probability
Biochemistry
Molecular Biology
Computer Science Applications
Computational Theory and Mathematics
Computational Mathematics

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10.1093/bioinformatics/btz944Licence: CC BY

Final Published VersionFinal published version, 2.15 MBLicence: CC BY

Cite this

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title = "PheGWAS: A new dimension to visualize GWAS across multiple phenotypes",

abstract = "Motivation: PheGWAS 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. Results: PheGWAS 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).",

author = "Gittu George and Sushrima Gan and Yu Huang and Philip Appleby and Nar, {A. S.} and Radha Venkatesan and Viswanathan Mohan and Palmer, {Colin N. A.} and Doney, {Alex S. F.}",

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AU - Gan, Sushrima

AU - Huang, Yu

AU - Appleby, Philip

AU - Nar, A. S.

AU - Venkatesan, Radha

AU - Mohan, Viswanathan

AU - Palmer, Colin N. A.

AU - Doney, Alex S. F.

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Y1 - 2020/4/15

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PheGWAS: A new dimension to visualize GWAS across multiple phenotypes

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ASJC Scopus subject areas

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Scotland India Diabetes Health Informatics Unit (joint with Madras Diabetes Research Foundation)

Health Informatics Centre

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PheGWAS: A new dimension to visualize GWAS across multiple phenotypes

Abstract

ASJC Scopus subject areas

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Scotland India Diabetes Health Informatics Unit (joint with Madras Diabetes Research Foundation)

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Health Informatics Centre

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