Over a quarter of drugs that enter clinical development fail because they are ineffective. Growing insight into genes that influence human disease may affect how drug targets and indications are selected. However, there is little guidance about how much weight should be given to genetic evidence in making these key decisions. To answer this question, we investigated how well the current archive of genetic evidence predicts drug mechanisms. We found that, among well-studied indications, the proportion of drug mechanisms with direct genetic support increases significantly across the drug development pipeline, from 2.0% at the preclinical stage to 8.2% among mechanisms for approved drugs, and varies dramatically among disease areas. We estimate that selecting genetically supported targets could double the success rate in clinical development. Therefore, using the growing wealth of human genetic data to select the best targets and indications should have a measurable impact on the successful development of new drugs.
Access optionsAccess options
Subscribe to Journal
Get full journal access for 1 year
only $18.75 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
We would like to thank N. Srivastava for much of the manual mapping of GWASdb traits and Pharmaprojects indications to MeSH terms and P. Agarwal for many helpful conversations. GWASdb-related work was supported by the Research Grants Council, Hong Kong SAR, China (781511M, 17121414M) and the National Natural Science Foundation of China (91229105).
Integrated supplementary information
Supplementary Data Set 1: GWASdb entries with MeSH terms mapped for each trait and genes annotated as described in the Online Methods.
Disease: the name of the trait for the corresponding genetic association as provided by GWASdb, which is generally taken directly from the GWAS catalog or whatever source from which the association was derived. snp_id: the identifier (generally a dbSNP rs ID) of the SNP reported to be associated with disease. Link: the reference for the association. Most references are a PubMed ID for the published paper. pvalue: the P value reported for the association of snp_id with disease. Source: the origin of the association information. It may be the following: GWAS:A/B: results listed in the NHGRI GWAS Catalog. The publications the associations of GWAS:B are drawn from published tables and supplementary information. Omim: from Online Mendelian Inheritance in Man. All P values are zero. GWASCentral: from GWAS Central. dbGaP: associations from dbGaP. SNP.Trait.Cnt: the number of associations of the same snp_id with the same disease in the original data set. These have been reduced to a single row in this data set, and the minimum P value was selected. MSH: Medical Subject Heading for disease. Manually mapped by Computational Biology. MSH.Top: the MeSH term for the top level of the branch to which the trait is mapped. In most instances, there are many branches to which a single MSH may be mapped. When this occurs, the most common top-level term in GWASdb is selected. snp.ld: a SNP in linkage disequilibrium (LD) with snp_id that provides a plausible connection to a gene. Gene: a gene that snp.ld is within 5 kb of, is an eQTL for or sits in a DNase I hypersensitivity site that is correlated with, or is within the transcription start site of. r2: LD between snp_id and snp.ld. eqtl: indicates whether snp.ld is an eQTL for a gene. The eQTL data are drawn from eqtl.uchicago.edu. rdb: indicates whether snp.ld-gene mapping is the result of a DHS correlation (from Maurano et al. (2012), provided by J. Stamatoyannopoulos). Cat.rdb: RegulomeDB category of the SNP (if rdb is "yes"). Lower values indicate more lines of converging functional evidence. eCat: a derivative of Cat.rdb, filling in values where rdb is "no." If eqtl is "yes" but rdb is "no," then it gets a value of 2. If snp.ld is a missense variant (amino acid change), the value is 0. The value is 9 otherwise. AAEffect: amino acid effect of snp.ld. AAScore: Condel score from VEP for nonsynonymous variants. GeneScore: an overall assessment of the evidence that the associated variant has a causal effect on the gene in question, ranging from values of zero to eight. Higher scores imply higher weight of causal evidence. The contributions to GeneScore are summarized on a separate GeneScore Wiki page. Rank: the rank for the given gene for its strength of connection to snp_id. This takes LD and functional evidence into account.
Supplementary Data Set 2: Reduction of the genetic association data to a single row per gene and trait as used for most analyses described.
Variable names are as given for Supplementary Data Set 1.
Gene: drug target gene. MSH: MeSH term for drug indication. MSH.Top: top-level MeSH term. Phase.Latest: latest phase to which the target-indication pair progressed through the development pipeline. lApprovedUS.EU: indicator of whether a drug for the target-indication pair has been approved in the United States or European Union.
Row and column names correspond to each MeSH term for which a relative similarity could be computed.