Phenome-wide association studies (PheWAS) across large "real-world data" population cohorts support drug target validation
Christopher S. Franklin,
Chris C. A. Spencer,
Michael E Weale,
Patrick M.A. Sleiman,
Dermot F Reilly,
Joseph C. Maranville,
Arnaub K Chatterjee,
the 23andMe Research Team,
Daniel G. MacArthur,
Mark J. Daly,
David A. Hinds,
Caroline S. Fox,
Robert M. Plenge,
Posted 13 Nov 2017
bioRxiv DOI: 10.1101/218875
Posted 13 Nov 2017
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.
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