Evaluating drug targets through human loss-of-function genetic variation

Eric Vallabh Minikel (), Konrad J. Karczewski, Hilary C. Martin, Beryl B. Cummings, Nicola Whiffin, Daniel Rhodes, Jessica Alföldi, Richard C. Trembath, David A. Heel, Mark J. Daly, Stuart L. Schreiber and Daniel G. MacArthur ()
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Eric Vallabh Minikel: Broad Institute of MIT and Harvard
Konrad J. Karczewski: Broad Institute of MIT and Harvard
Hilary C. Martin: Wellcome Sanger Institute
Beryl B. Cummings: Broad Institute of MIT and Harvard
Nicola Whiffin: Broad Institute of MIT and Harvard
Daniel Rhodes: Queen Mary University of London and Barts Health NHS Trust
Jessica Alföldi: Broad Institute of MIT and Harvard
Richard C. Trembath: King’s College London
David A. Heel: Queen Mary University of London
Mark J. Daly: Broad Institute of MIT and Harvard
Stuart L. Schreiber: Broad Institute of MIT and Harvard
Daniel G. MacArthur: Broad Institute of MIT and Harvard

Nature, 2020, vol. 581, issue 7809, 459-464

Abstract: 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.

Date: 2020
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DOI: 10.1038/s41586-020-2267-z

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