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Determinants of penetrance and variable expressivity in monogenic metabolic conditions across 77,184 exomes

Goodrich, J.K. ; Groop, Leif LU ; Nilsson, Peter M LU ; Tuomi, Tiinamaija LU and Udler, M.S. (2021) In Nature Communications 12(1).
Abstract
Hundreds of thousands of genetic variants have been reported to cause severe monogenic diseases, but the probability that a variant carrier develops the disease (termed penetrance) is unknown for virtually all of them. Additionally, the clinical utility of common polygenetic variation remains uncertain. Using exome sequencing from 77,184 adult individuals (38,618 multi-ancestral individuals from a type 2 diabetes case-control study and 38,566 participants from the UK Biobank, for whom genotype array data were also available), we apply clinical standard-of-care gene variant curation for eight monogenic metabolic conditions. Rare variants causing monogenic diabetes and dyslipidemias display effect sizes significantly larger than the top 1%... (More)
Hundreds of thousands of genetic variants have been reported to cause severe monogenic diseases, but the probability that a variant carrier develops the disease (termed penetrance) is unknown for virtually all of them. Additionally, the clinical utility of common polygenetic variation remains uncertain. Using exome sequencing from 77,184 adult individuals (38,618 multi-ancestral individuals from a type 2 diabetes case-control study and 38,566 participants from the UK Biobank, for whom genotype array data were also available), we apply clinical standard-of-care gene variant curation for eight monogenic metabolic conditions. Rare variants causing monogenic diabetes and dyslipidemias display effect sizes significantly larger than the top 1% of the corresponding polygenic scores. Nevertheless, penetrance estimates for monogenic variant carriers average 60% or lower for most conditions. We assess epidemiologic and genetic factors contributing to risk prediction in monogenic variant carriers, demonstrating that inclusion of polygenic variation significantly improves biomarker estimation for two monogenic dyslipidemias. © 2021, The Author(s). (Less)
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keywords
database, detection method, disease severity, epidemiology, gene expression, genetic variation, genotype, adult, Article, case control study, cohort analysis, controlled study, dyslipidemia, effect size, exome, genetic risk score, genetic variability, heredity, human, major clinical study, monogenic disorder, non insulin dependent diabetes mellitus, penetrance, phenotype, whole exome sequencing, biological variation, genetic predisposition, genetics, metabolism, multifactorial inheritance, risk assessment, biological marker, Adult, Biological Variation, Population, Biomarkers, Diabetes Mellitus, Type 2, Dyslipidemias, Exome, Genetic Predisposition to Disease, Genotype, Humans, Multifactorial Inheritance, Penetrance, Risk Assessment
in
Nature Communications
volume
12
issue
1
publisher
Nature Publishing Group
external identifiers
  • scopus:85107774343
  • pmid:34108472
ISSN
2041-1723
DOI
10.1038/s41467-021-23556-4
language
English
LU publication?
yes
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ce8387eb-c0b0-4853-8cd6-751ad3d2f760
date added to LUP
2021-12-28 09:59:21
date last changed
2022-04-27 07:00:45
@article{ce8387eb-c0b0-4853-8cd6-751ad3d2f760,
  abstract     = {{Hundreds of thousands of genetic variants have been reported to cause severe monogenic diseases, but the probability that a variant carrier develops the disease (termed penetrance) is unknown for virtually all of them. Additionally, the clinical utility of common polygenetic variation remains uncertain. Using exome sequencing from 77,184 adult individuals (38,618 multi-ancestral individuals from a type 2 diabetes case-control study and 38,566 participants from the UK Biobank, for whom genotype array data were also available), we apply clinical standard-of-care gene variant curation for eight monogenic metabolic conditions. Rare variants causing monogenic diabetes and dyslipidemias display effect sizes significantly larger than the top 1% of the corresponding polygenic scores. Nevertheless, penetrance estimates for monogenic variant carriers average 60% or lower for most conditions. We assess epidemiologic and genetic factors contributing to risk prediction in monogenic variant carriers, demonstrating that inclusion of polygenic variation significantly improves biomarker estimation for two monogenic dyslipidemias. © 2021, The Author(s).}},
  author       = {{Goodrich, J.K. and Groop, Leif and Nilsson, Peter M and Tuomi, Tiinamaija and Udler, M.S.}},
  issn         = {{2041-1723}},
  keywords     = {{database; detection method; disease severity; epidemiology; gene expression; genetic variation; genotype; adult; Article; case control study; cohort analysis; controlled study; dyslipidemia; effect size; exome; genetic risk score; genetic variability; heredity; human; major clinical study; monogenic disorder; non insulin dependent diabetes mellitus; penetrance; phenotype; whole exome sequencing; biological variation; genetic predisposition; genetics; metabolism; multifactorial inheritance; risk assessment; biological marker; Adult; Biological Variation, Population; Biomarkers; Diabetes Mellitus, Type 2; Dyslipidemias; Exome; Genetic Predisposition to Disease; Genotype; Humans; Multifactorial Inheritance; Penetrance; Risk Assessment}},
  language     = {{eng}},
  number       = {{1}},
  publisher    = {{Nature Publishing Group}},
  series       = {{Nature Communications}},
  title        = {{Determinants of penetrance and variable expressivity in monogenic metabolic conditions across 77,184 exomes}},
  url          = {{http://dx.doi.org/10.1038/s41467-021-23556-4}},
  doi          = {{10.1038/s41467-021-23556-4}},
  volume       = {{12}},
  year         = {{2021}},
}