Evaluating drug targets through human loss-of-function genetic variation
(2020) In Nature 581. p.459-464- 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... (More)
- 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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/b6b6e8e1-e1ce-47be-ba79-abe72c95af87
- author
- Vallabh Minikel, Eric and MacArthur, Daniel G
- contributor
- Groop, Leif
LU
; Haiman, Christopher
; Melander, Olle
LU
and Nilsson, Peter M
LU
- author collaboration
- organization
- publishing date
- 2020-05-27
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- automation, cell, drug, genetic variation, genotype
- in
- Nature
- volume
- 581
- pages
- 6 pages
- publisher
- Nature Publishing Group
- external identifiers
-
- scopus:85085564434
- pmid:32461653
- ISSN
- 0028-0836
- DOI
- 10.1038/s41586-020-2267-z
- language
- English
- LU publication?
- yes
- id
- b6b6e8e1-e1ce-47be-ba79-abe72c95af87
- date added to LUP
- 2020-06-16 09:49:23
- date last changed
- 2024-04-03 07:57:33
@article{b6b6e8e1-e1ce-47be-ba79-abe72c95af87,
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.}},
author = {{Vallabh Minikel, Eric and MacArthur, Daniel G}},
issn = {{0028-0836}},
keywords = {{automation; cell; drug; genetic variation; genotype}},
language = {{eng}},
month = {{05}},
pages = {{459--464}},
publisher = {{Nature Publishing Group}},
series = {{Nature}},
title = {{Evaluating drug targets through human loss-of-function genetic variation}},
url = {{http://dx.doi.org/10.1038/s41586-020-2267-z}},
doi = {{10.1038/s41586-020-2267-z}},
volume = {{581}},
year = {{2020}},
}