Phenome-wide association studies (PheWAS) across large "real-world data" population cohorts support drug target validation
By
Dorothée Diogo,
Chao Tian,
Christopher S. Franklin,
Mervi Alanne-Kinnunen,
Michael March,
Chris C. A. Spencer,
Ciara Vangjeli,
Michael E Weale,
Hannele Mattsson,
Elina Kilpeläinen,
Patrick M. Sleiman,
Dermot F Reilly,
Joshua McElwee,
Joseph C. Maranville,
Arnaub K Chatterjee,
Aman Bhandari,
the 23andMe Research Team,
Mary-Pat Reeve,
Janna Hutz,
Nan Bing,
Sally John,
Daniel G. MacArthur,
Veikko Salomaa,
Samuli Ripatti,
Hakon Hakonarson,
Mark J. Daly,
Aarno Palotie,
David Hinds,
Peter Donnelly,
Caroline S. Fox,
Aaron Day-Williams,
Robert M. Plenge,
Heiko Runz
Posted 13 Nov 2017
bioRxiv DOI: 10.1101/218875
Phenome-wide association studies (PheWAS), which assess whether a genetic variant is associated with multiple phenotypes across a phenotypic spectrum, have been proposed as a possible aid to drug development through elucidating mechanisms of action, identifying alternative indications, or predicting adverse drug events (ADEs). Here, we evaluate whether PheWAS can inform target validation during drug development. We selected 25 single nucleotide polymorphisms (SNPs) linked through genome-wide association studies (GWAS) to 19 candidate drug targets for common disease therapeutic indications. We independently interrogated these SNPs through PheWAS in four large real-world data cohorts (23andMe, UK Biobank, FINRISK, CHOP) for association with a total of 1,892 binary endpoints. We then conducted meta-analyses for 145 harmonized disease endpoints in up to 697,815 individuals and joined results with summary statistics from 57 published GWAS. Our analyses replicate 70% of known GWAS associations and identify 10 novel associations with study-wide significance after multiple test correction (P<1.8x10-6; out of 72 novel associations with FDR<0.1). By leveraging directionality and point estimate of the effect sizes, we describe new associations that may predict ADEs, e.g., acne, high cholesterol, gout and gallstones for rs738409 (p.I148M) in PNPLA3; or asthma for rs1990760 (p.T946A) in IFIH1. We further propose how quantitative estimates of genetic safety/efficacy profiles can be used to help prioritize candidate targets for a specific indication. Our results demonstrate PheWAS as a powerful addition to the toolkit for drug discovery.
Download data
- Downloaded 3,755 times
- Download rankings, all-time:
- Site-wide: 5,271
- In genetics: 169
- Year to date:
- Site-wide: 132,577
- Since beginning of last month:
- Site-wide: 198,441
Altmetric data
Downloads over time
Distribution of downloads per paper, site-wide
PanLingua
News
- 27 Nov 2020: The website and API now include results pulled from medRxiv as well as bioRxiv.
- 18 Dec 2019: We're pleased to announce PanLingua, a new tool that enables you to search for machine-translated bioRxiv preprints using more than 100 different languages.
- 21 May 2019: PLOS Biology has published a community page about Rxivist.org and its design.
- 10 May 2019: The paper analyzing the Rxivist dataset has been published at eLife.
- 1 Mar 2019: We now have summary statistics about bioRxiv downloads and submissions.
- 8 Feb 2019: Data from Altmetric is now available on the Rxivist details page for every preprint. Look for the "donut" under the download metrics.
- 30 Jan 2019: preLights has featured the Rxivist preprint and written about our findings.
- 22 Jan 2019: Nature just published an article about Rxivist and our data.
- 13 Jan 2019: The Rxivist preprint is live!