Back to Search
Start Over
Assessment of the ExAC data set for the presence of individuals with pathogenic genotypes implicated in severe Mendelian pediatric disorders.
- Source :
-
Genetics in medicine : official journal of the American College of Medical Genetics [Genet Med] 2017 Dec; Vol. 19 (12), pp. 1300-1308. Date of Electronic Publication: 2017 May 04. - Publication Year :
- 2017
-
Abstract
- PurposeWe analyzed the Exome Aggregation Consortium (ExAC) data set for the presence of individuals with pathogenic genotypes implicated in Mendelian pediatric disorders.MethodsClinVar likely/pathogenic variants supported by at least one peer-reviewed publication were assessed within the ExAC database to identify individuals expected to exhibit a childhood disorder based on concordance with disease inheritance modes: heterozygous (for dominant), homozygous (for recessive) or hemizygous (for X-linked recessive conditions). Variants from 924 genes reported to cause Mendelian childhood disorders were considered.ResultsWe identified ExAC individuals with candidate pathogenic genotypes for 190 previously published likely/pathogenic variants in 128 genes. After curation, we determined that 113 of the variants have sufficient support for pathogenicity and identified 1,717 ExAC individuals (~2.8% of the ExAC population) with corresponding possible/disease-associated genotypes implicated in rare Mendelian disorders, ranging from mild (e.g., due to SCN2A deficiency) to severe pediatric conditions (e.g., due to FGFR1 deficiency).ConclusionLarge-scale sequencing projects and data aggregation consortia provide unprecedented opportunities to determine the prevalence of pathogenic genotypes in unselected populations. This knowledge is crucial for understanding the penetrance of disease-associated variants, phenotypic variability, somatic mosaicism, as well as published literature curation for variant classification procedures and predicted clinical outcomes.
Details
- Language :
- English
- ISSN :
- 1530-0366
- Volume :
- 19
- Issue :
- 12
- Database :
- MEDLINE
- Journal :
- Genetics in medicine : official journal of the American College of Medical Genetics
- Publication Type :
- Academic Journal
- Accession number :
- 28471432
- Full Text :
- https://doi.org/10.1038/gim.2017.50