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Exploring and visualizing large-scale genetic associations by using PheWeb

Nature Genetics volume 52, pages 550–552 (2020)Cite this article

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To the Editor — Advances in genotyping and sequencing technologies, the growing availability of electronic health records for research use and the emergence of population-scale cohorts are enabling large studies to collect copious amounts of both phenotype and genotype data. Studies can collect thousands of traits measured across hundreds of thousands of individuals, each assessed at millions of genetic variants. These resources enable genome- and phenome-wide association studies (GWAS and PheWAS, respectively) at increasing scales and can generate high-dimensional results that can provide insights into many aspects of human genetics and biology. However, navigating these association results can be challenging and cumbersome. To aid in generating and testing hypotheses for the mechanisms underlying complex traits, these results should be organized in an intuitive, easily navigable manner. The current standards in the field are to use Manhattan1 and LocusZoom2 plots to review single-trait results and to use PheWAS3,4 plots to summarize results across many traits. The ability of investigators to explore their own data by alternating between these two view types, is an increasingly common feature of large-scale association analyses. Therefore, we developed PheWeb, an easy-to-use open-source web-based tool for visualizing, navigating and sharing GWAS and PheWAS results.

We have used PheWeb to explore association results for large datasets such as the UK Biobank5 (http://pheweb.org/MGI-freeze2/) and the Michigan Genomics Initiative (MGI; http://pheweb.sph.umich.edu/MGI-freeze2). The PheWeb instance populated with the UK Biobank summary statistics displays 28 million genetic markers assessed across 1,403 binary traits for 408,961 white British participants6 (Supplementary Note). Others have used PheWeb to explore large sets of association results, such as the Oxford Brain Imaging Genetics Project (http://big.stats.ox.ac.uk) and the new computationally efficient association tool fastGWA (http://fastgwa.info/ukbimp).

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Fig. 1: Interactive views of genetic associations in the UK Biobank instance of PheWeb.

Data availability

PheWeb fully embodies the philosophy of data and results sharing to advance scientific discoveries. PheWeb allows users to download full sets of summary statistics or subsets corresponding to the strongest association results. The summary statistics that populate the UK Biobank instance of PheWeb can be downloaded at ftp://share.sph.umich.edu/UKBB_SAIGE_HRC/.

The following links can be used to reproduce the images from Fig. 1: a, http://pheweb.sph.umich.edu/SAIGE-UKB/pheno/189.2; b, http://pheweb.sph.umich.edu/SAIGE-UKB/region/189.2/8:143552994-143952994; c, http://pheweb.sph.umich.edu/SAIGE-UKB/variant/8:143752994-T-C.

Code availability

PheWeb’s codebase is open source and hosted on GitHub at https://github.com/statgen/pheweb. Our pipeline for computing pairwise genetic correlations by using cross-trait LD Score regression10 or SumHer12 is scalable to large datasets and is available at https://github.com/statgen/pheweb-rg-pipeline. Additional information is provided in the Nature Research Reporting Summary.

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Acknowledgements

The UK Biobank genetic association summary statistics were generated previously by using the UK Biobank Resource through project ID number 24460. This research was supported by US National Institutes of Health grants HG009976 (M.B.) and HG007022 (G.R.A.).

Author information

Author notes

  1. These authors contributed equally: Sarah A. Gagliano Taliun, Peter VandeHaar.

Authors and Affiliations

  1. Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA

    Sarah A. Gagliano Taliun, Peter VandeHaar, Andrew P. Boughton, Ryan P. Welch, Daniel Taliun, Ellen M. Schmidt, Wei Zhou, Cristen J. Willer, Seunggeun Lee, Lars G. Fritsche, Michael Boehnke & Gonçalo R. Abecasis

  2. Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA

    Sarah A. Gagliano Taliun, Peter VandeHaar, Andrew P. Boughton, Ryan P. Welch, Daniel Taliun, Ellen M. Schmidt, Seunggeun Lee, Lars G. Fritsche, Michael Boehnke & Gonçalo R. Abecasis

  3. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA

    Wei Zhou & Cristen J. Willer

  4. Department of Internal Medicine, Division of Cardiology, University of Michigan Medical School, Ann Arbor, MI, USA

    Jonas B. Nielsen & Cristen J. Willer

  5. Regeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, NY, USA

    Gonçalo R. Abecasis

Authors
  1. Sarah A. Gagliano Taliun
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Contributions

G.R.A. conceived the project. P.V. implemented the PheWeb software. A.P.B., R.P.W. and D.T. contributed additional key features to the software. S.A.G.T. drafted the initial manuscript and revised it according to coauthor input. S.A.G.T., P.V., A.P.B., R.P.W., D.T., E.M.S., L.G.F., M.B. and G.R.A. contributed to study design and/or direction. W.Z., J.B.N., S.A.G.T., C.J.W. and S.L. generated the UK Biobank association summary statistics. All authors reviewed the manuscript and suggested revisions. All authors approved the final manuscript.

Corresponding author

Correspondence to Gonçalo R. Abecasis.

Ethics declarations

Competing interests

G.R.A. is an employee of Regeneron Pharmaceuticals; he owns stock and stock options for Regeneron Pharmaceuticals. The spouse of C.J.W. is employed at Regeneron Pharmaceuticals.

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Editorial Note: This article has been peer reviewed.

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Supplementary Information

Supplementary Note and Figs. 1–7

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Gagliano Taliun, S.A., VandeHaar, P., Boughton, A.P. et al. Exploring and visualizing large-scale genetic associations by using PheWeb. Nat Genet 52, 550–552 (2020). https://doi.org/10.1038/s41588-020-0622-5

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