@article{3286,
  keywords = {Artifacts, Automation, Consanguinity, Exons, Gain of Function Mutation, Gene Frequency, Gene Knockdown Techniques, Genes, Essential, Heterozygote, Homozygote, Humans, Huntingtin Protein, Leucine-Rich Repeat Serine-Threonine Protein Kinase-2, Loss of Function Mutation, Molecular Targeted Therapy, Neurodegenerative Diseases, Prion Proteins, Reproducibility of Results, Sample Size, tau Proteins},
  author = {Eric Vallabh Minikel and Konrad J. Karczewski and Hilary C. Martin and Beryl B. Cummings and Nicola Whiffin and Daniel Rhodes and Jessica Alföldi and Richard C. Trembath and David A. van Heel and Mark J. Daly and Genome Aggregation Database Production Team and Genome Aggregation Database Consortium and Stuart L. Schreiber and Daniel G. MacArthur},
  title = {Evaluating drug targets through human loss-of-function genetic variation},
  abstract = {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.},
  year = {2020},
  journal = {Nature},
  volume = {581},
  pages = {459-464},
  month = {2020-05},
  issn = {1476-4687},
  doi = {10.1038/s41586-020-2267-z},
  language = {eng},
}