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Sökning: WFRF:(Macarthur Daniel G) > (2020-2023) > The mutational cons...

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

Karczewski, Konrad J. (författare)
Massachusetts General Hospital,Broad Institute
MacArthur, Daniel G (författare)
Massachusetts General Hospital,Broad Institute
Groop, Leif (creator_code:cre_t)
Lund University,Lunds universitet,Genomik, diabetes och endokrinologi,Forskargrupper vid Lunds universitet,Genomics, Diabetes and Endocrinology,Lund University Research Groups,Institute for Molecular Medicine Finland (FIMM)
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Haiman, Christopher (creator_code:cre_t)
Lund University
Melander, Olle (creator_code:cre_t)
Lund University,Lunds universitet,Kardiovaskulär forskning - hypertoni,Forskargrupper vid Lunds universitet,Cardiovascular Research - Hypertension,Lund University Research Groups,Skåne University Hospital
Nilsson, Peter M (creator_code:cre_t)
Lund University,Lunds universitet,Internmedicin - epidemiologi,Forskargrupper vid Lunds universitet,Internal Medicine - Epidemiology,Lund University Research Groups,Skåne University Hospital
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 (creator_code:org_t)
 
2020-05-27
2020
Engelska 10 s.
Ingår i: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 581, s. 434-443
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
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  • 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.

Ämnesord

NATURVETENSKAP  -- Biologi -- Genetik (hsv//swe)
NATURAL SCIENCES  -- Biological Sciences -- Genetics (hsv//eng)
MEDICIN OCH HÄLSOVETENSKAP  -- Medicinska och farmaceutiska grundvetenskaper -- Medicinsk genetik (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Basic Medicine -- Medical Genetics (hsv//eng)

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