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Olle Melander

Principal investigator

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

Author

  • Konrad J. Karczewski
  • Daniel G MacArthur

Other contributions

  • Leif Groop
  • Christopher Haiman
  • Olle Melander
  • Peter M Nilsson

Summary, in English

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.

Department/s

  • Genomics, Diabetes and Endocrinology
  • EXODIAB: Excellence in Diabetes Research in Sweden
  • EpiHealth: Epidemiology for Health
  • Cardiovascular Research - Hypertension
  • Internal Medicine - Epidemiology

Publishing year

2020-05-27

Language

English

Pages

434-443

Publication/Series

Nature

Volume

581

Document type

Journal article

Publisher

Nature Publishing Group

Topic

  • Genetics
  • Medical Genetics

Status

Published

Research group

  • Genomics, Diabetes and Endocrinology
  • Cardiovascular Research - Hypertension
  • Internal Medicine - Epidemiology

ISBN/ISSN/Other

  • ISSN: 0028-0836