Whole-exome sequencing provides insights into monogenic disease prevalence in Northwest Russia.
Mendelian disease
Russia
allele frequency
whole-exome sequencing
Journal
Molecular genetics & genomic medicine
ISSN: 2324-9269
Titre abrégé: Mol Genet Genomic Med
Pays: United States
ID NLM: 101603758
Informations de publication
Date de publication:
11 2019
11 2019
Historique:
received:
09
05
2019
accepted:
07
08
2019
pubmed:
5
9
2019
medline:
1
7
2020
entrez:
5
9
2019
Statut:
ppublish
Résumé
Allele frequency data from large exome and genome aggregation projects such as the Genome Aggregation Database (gnomAD) are of ultimate importance to the interpretation of medical resequencing data. However, allele frequencies might significantly differ in poorly studied populations that are underrepresented in large-scale projects, such as the Russian population. In this work, we leveraged our access to a large dataset of 694 exome samples to analyze genetic variation in the Northwest Russia. We compared the spectrum of genetic variants to the dbSNP build 151, and made estimates of ClinVar-based autosomal recessive (AR) disease allele prevalence as compared to gnomAD r. 2.1. An estimated 9.3% of discovered variants were not present in dbSNP. We report statistically significant overrepresentation of pathogenic variants for several Mendelian disorders, including phenylketonuria (PAH, rs5030858), Wilson's disease (ATP7B, rs76151636), factor VII deficiency (F7, rs36209567), kyphoscoliosis type of Ehlers-Danlos syndrome (FKBP14, rs542489955), and several other recessive pathologies. We also make primary estimates of monogenic disease incidence in the population, with retinal dystrophy, cystic fibrosis, and phenylketonuria being the most frequent AR pathologies. Our observations demonstrate the utility of population-specific allele frequency data to the diagnosis of monogenic disorders using high-throughput technologies.
Sections du résumé
BACKGROUND
Allele frequency data from large exome and genome aggregation projects such as the Genome Aggregation Database (gnomAD) are of ultimate importance to the interpretation of medical resequencing data. However, allele frequencies might significantly differ in poorly studied populations that are underrepresented in large-scale projects, such as the Russian population.
METHODS
In this work, we leveraged our access to a large dataset of 694 exome samples to analyze genetic variation in the Northwest Russia. We compared the spectrum of genetic variants to the dbSNP build 151, and made estimates of ClinVar-based autosomal recessive (AR) disease allele prevalence as compared to gnomAD r. 2.1.
RESULTS
An estimated 9.3% of discovered variants were not present in dbSNP. We report statistically significant overrepresentation of pathogenic variants for several Mendelian disorders, including phenylketonuria (PAH, rs5030858), Wilson's disease (ATP7B, rs76151636), factor VII deficiency (F7, rs36209567), kyphoscoliosis type of Ehlers-Danlos syndrome (FKBP14, rs542489955), and several other recessive pathologies. We also make primary estimates of monogenic disease incidence in the population, with retinal dystrophy, cystic fibrosis, and phenylketonuria being the most frequent AR pathologies.
CONCLUSION
Our observations demonstrate the utility of population-specific allele frequency data to the diagnosis of monogenic disorders using high-throughput technologies.
Identifiants
pubmed: 31482689
doi: 10.1002/mgg3.964
pmc: PMC6825859
doi:
Substances chimiques
Biomarkers
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e964Informations de copyright
© 2019 The Authors. Molecular Genetics & Genomic Medicine published by Wiley Periodicals, Inc.
Références
Bioinformatics. 2009 Jul 15;25(14):1754-60
pubmed: 19451168
Gigascience. 2015 Nov 13;4:53
pubmed: 26568821
Hum Mutat. 2003 Apr;21(4):387-93
pubmed: 12655548
Nature. 2015 Oct 1;526(7571):82-90
pubmed: 26367797
Genomics. 2020 Jan;112(1):442-458
pubmed: 30902755
Hum Mutat. 2000;15(6):489-96
pubmed: 10862079
Vestn Oftalmol. 2017;133(4):4-11
pubmed: 28980559
Genet Med. 2018 Mar;20(3):360-364
pubmed: 29155419
J Biol Chem. 1990 Oct 15;265(29):17792-7
pubmed: 2120217
Mol Genet Genomic Med. 2019 Nov;7(11):e964
pubmed: 31482689
J Med Genet. 2007 Nov;44(11):673-88
pubmed: 17717039
Fly (Austin). 2012 Apr-Jun;6(2):80-92
pubmed: 22728672
Nature. 2015 Oct 1;526(7571):68-74
pubmed: 26432245
Hum Genome Var. 2016 Jun 30;3:16016
pubmed: 27408750
Genomics. 2014 May-Jun;103(5-6):323-8
pubmed: 24703969
Genet Med. 2013 Mar;15(3):178-86
pubmed: 22975760
PLoS One. 2018 Jul 11;13(7):e0200423
pubmed: 29995946
N Engl J Med. 2014 Jun 19;370(25):2418-25
pubmed: 24941179
Hum Mutat. 2014 Jan;35(1):105-16
pubmed: 24123366
Am J Hum Genet. 2012 Feb 10;90(2):201-16
pubmed: 22265013
Nature. 2009 Sep 10;461(7261):272-6
pubmed: 19684571
Genes (Basel). 2018 Aug 17;9(8):
pubmed: 30126146
Nature. 2016 Aug 17;536(7616):285-91
pubmed: 27535533
Blood. 2004 Sep 1;104(5):1243-52
pubmed: 15138162
Nat Genet. 2011 May;43(5):491-8
pubmed: 21478889
Sci Rep. 2020 Feb 6;10(1):2057
pubmed: 32029882
Genet Med. 2015 May;17(5):405-24
pubmed: 25741868
Genet Med. 2017 Oct;19(10):1105-1117
pubmed: 28492532
Hum Mutat. 2016 Mar;37(3):235-41
pubmed: 26555599
Curr Protoc Bioinformatics. 2013;43:11.10.1-11.10.33
pubmed: 25431634
Nature. 2013 Jan 10;493(7431):216-20
pubmed: 23201682
Eur J Hum Genet. 2019 Feb;27(2):254-262
pubmed: 30275481