Publication | Open Access
Whole‐exome sequencing provides insights into monogenic disease prevalence in Northwest Russia
38
Citations
34
References
2019
Year
GeneticsGenetic EpidemiologyGenomicsMonogenic Disease PrevalenceGenetic MedicineGenome-wide Association StudiesClinical GeneticsGenome-wide Association StudyMendelian DisorderHuman PhenotypesLarge DatasetNorthwest RussiaWhole Genome StudiesPublic HealthPersonal GenomicsMonogenic DisordersVariant InterpretationQuantitative GeneticsInherited Metabolic DiseaseStatistical GeneticsGenetic VariationAllele FrequenciesSequencingEpidemiologyMolecular MedicineAllelic VariantNext-generation SequencingMedical GeneticsMedicine
Abstract 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.
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