Abstract
Context:Genetic testing is increasingly used for clinical diagnosis, although variant interpretation presents a major challenge because of high background rates of rare coding-region variation, which may contribute to inaccurate estimates of variant pathogenicity and disease penetrance.Objective:To use the Exome Aggregation Consortium (ExAC) data set to determine the background population frequencies of rare germline coding-region variants in genes associated with hereditary endocrine disease and to evaluate the clinical utility of these data.Design, Setting, Participants:Cumulative frequencies of rare nonsynonymous single-nucleotide variants were established for 38 endocrine disease genes in 60,706 unrelated control individuals. The utility of gene-level and variant-level metrics of tolerability was assessed, and the pathogenicity and penetrance of germline variants previously associated with endocrine disease evaluated.Results:The frequency of rare coding-region variants differed markedly between genes and was correlated with the degree of evolutionary conservation. Genes associated with dominant monogenic endocrine disorders typically harbored fewer rare missense and/or loss-of-function variants than expected. In silico variant prediction tools demonstrated low clinical specificity. The frequency of several endocrine disease‒associated variants in the ExAC cohort far exceeded estimates of disease prevalence, indicating either misclassification or overestimation of disease penetrance. Finally, we illustrate how rare variant frequencies may be used to anticipate expected rates of background rare variation when performing disease-targeted genetic testing.Conclusions:Quantifying the frequency and spectrum of rare variation using population-level sequence data facilitates improved estimates of variant pathogenicity and penetrance and should be incorporated into the clinical decision-making algorithm when undertaking genetic testing.
Highlights
Design, Setting, Participants: Cumulative frequencies of rare nonsynonymous single-nucleotide variants were established for 38 endocrine disease genes in 60,706 unrelated control individuals
Failure to identify pathogenic variants in an individual may result in missed opportunities for diagnosis and treatment, whereas misclassification of benign variants as pathogenic may result in inadvertent harm through unnecessary investigation and treatment [7]
The identification of all nonsynonymous single-nucleotide variant (SNV) in each of the 38 genes revealed that the overwhelming majority were rare, with ~60% occurring as singletons, whereas ~92% of gene-specific SNVs had an allele frequency (AF),0.05% (Fig. 1A)
Summary
A. ExAC Population, Genes, and Variant Classification. SNVs are described relative to the canonical transcript. This analysis was performed on the complete ExAC data set (n = 60,706). A separate subanalysis was performed on the data set with the 7601 germline samples from The Cancer Genome Atlas (TCGA) cohort removed (further details provided in Supplemental Table 1 and the Supplemental Materials and Methods). Insertions and deletions (indels) were excluded from the main analysis because of the reduced reliability of detection and an increased false discovery rate relative to SNVs [14], a separate analysis evaluated the frequency of LOF indels (i.e., resulting in a frameshift) in each of the 38 genes (further details are provided in the Supplemental Materials and Methods)
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