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. 2020 Jun;52(6):550-552.
doi: 10.1038/s41588-020-0622-5.

Exploring and visualizing large-scale genetic associations by using PheWeb

Sarah A Gagliano Taliun #  1   2 , Peter VandeHaar #  1   2 , Andrew P Boughton  1   2 , Ryan P Welch  1   2 , Daniel Taliun  1   2 , Ellen M Schmidt  1   2 , Wei Zhou  1   3 , Jonas B Nielsen  4 , Cristen J Willer  1   3   4 , Seunggeun Lee  1   2 , Lars G Fritsche  1   2 , Michael Boehnke  1   2 , Gonçalo R Abecasis  5   6   7
Affiliations

Exploring and visualizing large-scale genetic associations by using PheWeb

Sarah A Gagliano Taliun et al. Nat Genet. 2020 Jun.
No abstract available

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Conflict of interest statement

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.

Figures

Figure 1.
Figure 1.
Interactive views of genetic associations in the UK Biobank instance of PheWeb. (a) Manhattan plot view for the GWAS on bladder cancer based on 2,427 cases and 404,796 controls using SAIGE (a generalized mixed model association test accounts for case-control imbalance) with sex, birth year and four principal components as covariates for the White British subset. (b) Regional view (LocusZoom) for variant rs2976384 (purple diamond) with variants present in the NHGRI-EBI GWAS Catalog displayed. (c) Phenome-wide view (PheWAS) for variant rs2976384 showing phenome-wide significant association with traits from the UK Biobank related to the urinary system or lower digestive tract. Traits are sorted and colored according to a meaningful set of biological categories (e.g. infectious diseases, neurological trait, metabolic). Direction of effect of the alternate allele’s association with each trait is exhibited by upward-facing (positive effect) or downward-facing (negative effect) triangle.
See this image and copyright information in PMC

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