Development of a human genetics-guided priority score for 19365 genes and 399 drug indications – Nature.com

Plenge, R. M., Scolnick, E. M. & Altshuler, D. Validating therapeutic targets through human genetics. Nat. Rev. Drug Discov. 12, 581594 (2013).

Article CAS PubMed Google Scholar

Cook, D. et al. Lessons learned from the fate of AstraZenecas drug pipeline: a five-dimensional framework. Nat. Rev. Drug Discov. 13, 419431 (2014).

Article CAS PubMed Google Scholar

Dowden, H. & Munro, J. Trends in clinical success rates and therapeutic focus. Nat. Rev. Drug Discov. 18, 495496 (2019).

Article CAS PubMed Google Scholar

Nelson, M. R. et al. The support of human genetic evidence for approved drug indications. Nat. Genet. 47, 856860 (2015).

Article CAS PubMed Google Scholar

Ochoa, D. et al. Human genetics evidence supports two-thirds of the 2021 FDA-approved drugs. Nat. Rev. Drug Discov. 21, 551 (2022).

Article CAS PubMed Google Scholar

King, E. A., Davis, J. W. & Degner, J. F. Are drug targets with genetic support twice as likely to be approved? Revised estimates of the impact of genetic support for drug mechanisms on the probability of drug approval. PLoS Genet. 15, e1008489 (2019).

Article PubMed PubMed Central Google Scholar

Ghoussaini, M., Nelson, M. R. & Dunham, I. Future prospects for human genetics and genomics in drug discovery. Curr. Opin. Struct. Biol. 80, 102568 (2023).

Article CAS PubMed PubMed Central Google Scholar

Fang, H. et al. A genetics-led approach defines the drug target landscape of 30 immune-related traits. Nat. Genet. 51, 10821091 (2019).

Article CAS PubMed PubMed Central Google Scholar

Duffy, A. et al. Tissue-specific genetic features inform prediction of drug side effects in clinical trials. Sci. Adv. 6, eabb6242 (2020).

Article CAS PubMed Google Scholar

Nguyen, P. A., Born, D. A., Deaton, A. M., Nioi, P. & Ward, L. D. Phenotypes associated with genes encoding drug targets are predictive of clinical trial side effects. Nat. Commun. 10, 1579 (2019).

Article PubMed PubMed Central Google Scholar

Yao, J., Hurle, M. R., Nelson, M. R. & Agarwal, P. Predicting clinically promising therapeutic hypotheses using tensor factorization. BMC Bioinformatics 20, 69 (2019).

Article PubMed PubMed Central Google Scholar

Han, Y. et al. Empowering the discovery of novel target-disease associations via machine learning approaches in the open targets platform. BMC Bioinformatics 23, 232 (2022).

Article CAS PubMed PubMed Central Google Scholar

Ochoa, D. et al. Open Targets Platform: supporting systematic drug-target identification and prioritisation. Nucleic Acids Res. 49, D1302D1310 (2021).

Article CAS PubMed Google Scholar

Landrum, M. J. et al. ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Res. 46, D1062D1067 (2017).

Article PubMed Central Google Scholar

Cook, C. E. et al. The European Bioinformatics Institute in 2016: data growth and integration. Nucleic Acids Res. 44, D20D26 (2015).

Article PubMed PubMed Central Google Scholar

Stenson, P. D. et al. The Human Gene Mutation Database (HGMD): optimizing its use in a clinical diagnostic or research setting. Hum. Genet. 139, 11971207 (2020).

Article PubMed PubMed Central Google Scholar

Hamosh, A. et al. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 30, 5255 (2002).

Article CAS PubMed PubMed Central Google Scholar

Sveinbjornsson, G. et al. Weighting sequence variants based on their annotation increases power of whole-genome association studies. Nat. Genet. 48, 314317 (2016).

Article CAS PubMed Google Scholar

Karczewski, K. J. et al. Systematic single-variant and gene-based association testing of thousands of phenotypes in 394,841 UK Biobank exomes. Cell Genom. 2, 100168 (2022).

Article CAS PubMed PubMed Central Google Scholar

Pan-UKB team. https://pan.ukbb.broadinstitute.org (2020).

Mountjoy, E. et al. An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci. Nat. Genet. 53, 15271533 (2021).

Article CAS PubMed PubMed Central Google Scholar

Ferkingstad, E. et al. Large-scale integration of the plasma proteome with genetics and disease. Nat. Genet. 53, 17121721 (2021).

Article CAS PubMed Google Scholar

Denny, J. C. et al. Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nat. Biotechnol. 31, 11021111 (2013).

Article CAS PubMed PubMed Central Google Scholar

Kuhn, M. et al. The SIDER database of drugs and side effects. Nucleic Acids Res. 44, D1075D1079 (2016).

Article CAS PubMed Google Scholar

Hingorani, A. D. et al. Improving the odds of drug development success through human genomics: modelling study. Sci. Rep. 9, 18911 (2019).

Article CAS PubMed PubMed Central Google Scholar

Stein, D. et al. Genome-wide prediction of pathogenic gain- and loss-of-function variants from ensemble learning of a diverse feature set. Preprint at bioRxiv https://doi.org/10.1101/2022.06.08.495288 (2022).

Estrada, K. et al. Identifying therapeutic drug targets using bidirectional effect genes. Nat. Commun. 12, 2224 (2021).

Article CAS PubMed PubMed Central Google Scholar

Chen, B. & Altman, R. B. Opportunities for developing therapies for rare genetic diseases: focus on gain-of-function and allostery. Orphanet J. Rare Dis. 12, 61 (2017).

Article CAS PubMed PubMed Central Google Scholar

Kok, B. P. et al. Discovery of small-molecule enzyme activators by activity-based protein profiling. Nat. Chem. Biol. 16, 9971005 (2020).

Article CAS PubMed PubMed Central Google Scholar

Kobayashi, K. et al. Class B1 GPCR activation by an intracellular agonist. Nature 618, 10851093 (2023).

Article CAS PubMed PubMed Central Google Scholar

Okuyama, R. Chronological analysis of first-in-class drugs approved from 2011 to 2022: their technological trend and origin. Pharmaceutics 15, 1794 (2023).

Article CAS PubMed PubMed Central Google Scholar

Bodenreider, O. The Unified Medical Language System (UMLS): integrating biomedical terminology. Nucleic Acids Res. 32, D267D270 (2004).

Article CAS PubMed PubMed Central Google Scholar

Pendlington, Z. M et al. EBISPOT/EFO-UKB-mappings. GitHub. https://github.com/EBISPOT/EFO-UKB-mappings (2019).

Bento, A. P. et al. The ChEMBL bioactivity database: an update. Nucleic Acids Res. 42, D1083D1090 (2014).

Article CAS PubMed Google Scholar

Wishart, D. S. et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 46, D1074D1082 (2018).

Article CAS PubMed Google Scholar

Davies, M. et al. ChEMBL web services: streamlining access to drug discovery data and utilities. Nucleic Acids Res. 43, W612W620 (2015).

Article CAS PubMed PubMed Central Google Scholar

Gaulton, A. et al. The ChEMBL database in 2017. Nucleic Acids Res. 45, D945D954 (2016).

Article PubMed PubMed Central Google Scholar

Santos, R. et al. A comprehensive map of molecular drug targets. Nat. Rev. Drug Discov. 16, 1934 (2017).

Article CAS PubMed Google Scholar

Cunningham, F. et al. Ensembl 2022. Nucleic Acids Res. 50, D988D995 (2021).

Article PubMed Central Google Scholar

Khler, S. et al. The Human Phenotype Ontology in 2021. Nucleic Acids Res. 49, D1207d1217 (2021).

Article PubMed Google Scholar

Kuhn, R. M., Haussler, D. & Kent, W. J. The UCSC genome browser and associated tools. Brief. Bioinform. 14, 144161 (2012).

Article PubMed PubMed Central Google Scholar

Aguet, F. et al. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 13181330 (2020).

Article CAS Google Scholar

Karczewski, K. J. et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581, 434443 (2020).

Article CAS PubMed PubMed Central Google Scholar

Stekhoven, D. J. & Bhlmann, P. MissForestnon-parametric missing value imputation for mixed-type data. Bioinformatics 28, 112118 (2011).

Article PubMed Google Scholar

R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2022).

Duffy, A. & Do, R. Development of a human genetics-guided priority score for 19,365 genes and 399 drug indications. Zenodo https://doi.org/10.5281/zenodo.10095684 (2023).

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Development of a human genetics-guided priority score for 19365 genes and 399 drug indications - Nature.com

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