Evaluating drug targets through human loss-of-function genetic variation
journal contributionposted on 05.07.2021, 22:27 by EV Minikel, KJ Karczewski, HC Martin, BB Cummings, N Whiffin, D Rhodes, J Alföldi, RC Trembath, DA van Heel, MJ Daly, Genome Aggregation Database Production Team, Genome Aggregation Database Consortium, SL Schreiber, DG MacArthur
Naturally occurring human genetic variants that are predicted to inactivate protein-coding genes provide an in vivo model of human gene inactivation that complements knockout studies in cells and model organisms. Here we report three key findings regarding the assessment of candidate drug targets using human loss-of-function variants. First, even essential genes, in which loss-of-function variants are not tolerated, can be highly successful as targets of inhibitory drugs. Second, in most genes, loss-of-function variants are sufficiently rare that genotype-based ascertainment of homozygous or compound heterozygous ‘knockout’ humans will await sample sizes that are approximately 1,000 times those presently available, unless recruitment focuses on consanguineous individuals. Third, automated variant annotation and filtering are powerful, but manual curation remains crucial for removing artefacts, and is a prerequisite for recall-by-genotype efforts. Our results provide a roadmap for human knockout studies and should guide the interpretation of loss-of-function variants in drug development.
CitationNature volume 581, pages 459–464 (2020)
Author affiliationDepartment of Cardiovascular Sciences and NIHR Leicester Biomedical Research Centre
VersionVoR (Version of Record)
Pagination459 - 464
Genome Aggregation Database Production TeamGenome Aggregation Database ConsortiumHumansNeurodegenerative Diseasestau ProteinsArtifactsReproducibility of ResultsSample SizeConsanguinityGene FrequencyHeterozygoteHomozygoteGenes, EssentialExonsAutomationGene Knockdown TechniquesMolecular Targeted TherapyPrion ProteinsHuntingtin ProteinLeucine-Rich Repeat Serine-Threonine Protein Kinase-2Loss of Function MutationGain of Function Mutation