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The mutational constraint spectrum quantified from variation in 141,456 humans

Karczewski, Konrad J. and MacArthur, Daniel G (2020) In Nature 581. p.434-443
Abstract
Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes1. Here we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence predicted loss-of-function... (More)
Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes1. Here we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence predicted loss-of-function variants in this cohort after filtering for artefacts caused by sequencing and annotation errors. Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare diseases. (Less)
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LU ; Haiman, Christopher ; LU orcid and LU
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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Nature
volume
581
pages
10 pages
publisher
Nature Publishing Group
external identifiers
  • scopus:85085542423
  • pmid:32461654
ISSN
0028-0836
DOI
10.1038/s41586-020-2308-7
language
English
LU publication?
yes
id
3e5af0e1-f985-4594-a50e-6eb5d4821baa
date added to LUP
2020-06-16 11:00:52
date last changed
2024-05-02 10:20:35
@article{3e5af0e1-f985-4594-a50e-6eb5d4821baa,
  abstract     = {{Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes1. Here we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence predicted loss-of-function variants in this cohort after filtering for artefacts caused by sequencing and annotation errors. Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare diseases.}},
  author       = {{Karczewski, Konrad J. and MacArthur, Daniel G}},
  issn         = {{0028-0836}},
  language     = {{eng}},
  month        = {{05}},
  pages        = {{434--443}},
  publisher    = {{Nature Publishing Group}},
  series       = {{Nature}},
  title        = {{The mutational constraint spectrum quantified from variation in 141,456 humans}},
  url          = {{http://dx.doi.org/10.1038/s41586-020-2308-7}},
  doi          = {{10.1038/s41586-020-2308-7}},
  volume       = {{581}},
  year         = {{2020}},
}