Hutz, J. | bioRxiv Science

bioRxiv2019

Target Gene Notebook: Connecting genetics and drug discovery

Target Gene Notebook was developed to enable more efficient linking of genetic associations to functional biological information. This process is essential to translating genetic insights into therapeutic hypotheses and, eventually, drug discovery. Although many public databases provide access to unfiltered genome annotations and genetic results, there was no existing tool to maintain group curation and integration with proprietary experimental data. We provide Target Gene Notebook freely via the MIT open-source license for the purposes of assisting therapeutic target evaluation and the creation of durable institutional and public knowledge bases. Implemented as a Java backend serving mainly Javascript content derived from gene-specific SQLite databases, Target Gene Notebook enables automated access to the most widely used sources of genetic association, expression and protein QTL data, provides intuitive interfaces to credible set and colocalization information, and enables comprehensive literature review and annotation by multiple users simultaneously to create a consistent target knowledgebase within an organization or across a consortium. TargetGeneNotebook is freely available from GitHub https://github.com/targetgenenotebook/tgn.git under the MIT open-source end-user license agreement and a live version of the interface is provided at http://tgn.broadinstitute.org/.

genomics↗

bioRxiv2017

Phenome-wide association studies (PheWAS) across large "real-world data" population cohorts support drug target validation

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.\n\nOne Sentence SummaryMatching genetics with phenotypes in 800,000 individuals predicts efficacy and on-target safety of future drugs.

genetics↗