The impact on clinical success from the 23andMe cohort

Author:

Wang XinORCID,Karagounis Sotiris,Shringarpure Suyash S.,Srivas Rohith,Su Qiaojuan Jane, ,Vacic Vladimir,Pitts Steven J.,Auton Adam

Abstract

Abstract90% of therapeutic programs that enter clinical trials ultimately fail. Human genetic variation provides a set of “natural experiments” that can inform successful strategies for therapeutic discovery. Previous work has estimated that drug targets with human genetics supported mechanisms have a 2-3x increased likelihood of succeeding in the clinic compared to those without. 23andMe, Inc. is a direct-to-consumer genetics company that has created a human genetics dataset approximately an order of magnitude larger in sample size than current publically available cohorts. As of 2024, 23andMe has approximately 15 million individuals with genotype and phenotype data, of which ∼80% consent to participation in research. In this work, we explore how both the scale of the genetic data and improved methods to link genetic associations to putative causal genes impact the prediction of clinical success. Comparing the total number of target-indication pairs that have reached at least phase I that are also supported by genetic evidence, the number of target-indication pairs with support from 23andMe is 60% greater than that with support from all GWAS datasets in the public domain. Including 23andMe genetic evidence approximately doubles the number of target-indication pairs in the clinic that are supported by human genetics. Furthermore, we show that genetic associations derived from entirely self-reported phenotypes are 2-3x enriched for clinical success, just as for clinically derived phenotypes. In contrast to conclusions from the recent publication of Minikelet al., we found that minor allele frequencies and effect sizes from GWAS influence the relative success estimates for program approvals, and that drug programs supported by rare and large effect associations have greater (3-4x) likelihood to be approved compared to common variant associations with small effects. Finally, improved gene mapping to identify the likely causal genes underlying genetic associations can result in up to 4-5x enrichment for trial success. With the increased power and scale of the 23andMe genetic dataset, we identify an expansive set of opportunities that may be pursued in the clinic, emphasizing the importance of cohort size and gene mapping confidence in deriving clinical value.

Publisher

Cold Spring Harbor Laboratory

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