Identification of Misclassified ClinVar Variants via Disease Population Prevalence

Abstract

There is a significant interest in the standardized classification of human genetic variants. We used whole-genome sequence data from 10,495 unrelated individuals to contrast population frequency of pathogenic variants to the expected population prevalence of the disease. Analyses included the ACMG-recommended 59 gene-condition sets for incidental findings and 463 genes associated with 265 OrphaNet conditions. A total of 25,505 variants were used to identify patterns of inflation (i.e., excess genetic risk and misclassification). Inflation increases as the level of evidence supporting the pathogenic nature of the variant decreases. We observed up to 11.5% of genetic disorders with inflation in pathogenic variant sets and up to 92.3% for the variant set with conflicting interpretations. This improved to 7.7% and 57.7%, respectively, after filtering for disease-specific allele frequency. The patterns of inflation were replicated using public data from more than 138,000 genomes. The burden of rare variants was a main contributing factor of the observed inflation, indicating collective misclassified rare variants. We also analyzed the dynamics of re-classification of variant pathogenicity in ClinVar over time, which indicates progressive improvement in variant classification. The study shows that databases include a significant proportion of wrongly ascertained variants; however, it underscores the critical role of ClinVar to contrast claims and foster validation across submitters.

Clinical genetic testing in endocrinology: Current concepts and contemporary challenges

“…Although the ACMG guidelines provide a standardized approach to variant interpretation, there is considerable scope for variability and/or ambiguity in how specific criteria are applied, such that individual users may report different interpretations of the same variant . In addition, the appropriate application of individual criteria may be dependent on the availability of accurate information regarding disease prevalence and penetrance, the degree of genetic heterogeneity, knowledge of protein structure/function, and/or prior reports of variant classifications (eg from the literature or disease‐specific variant databases), and in many instances, these data may be absent or unreliable …”

“…For example, the ClinGen consortium (http://www.clinicalgenome.org) has established processes to standardize the implementation of variant interpretation criteria for specific monogenic disease genes . Variant repositories such as ClinVar, which have previously been reported to have high rates of conflicting interpretations and an inflated number of pathogenic variants, allow continual variant re‐evaluation by applying a rating system to the evidence supporting each submission . At the same time, high‐throughput functional assays are being developed that allow the evaluation of large numbers of variants.…”

“…71 In addition, the appropriate application of individual criteria may be dependent on the availability of accurate information regarding disease prevalence and penetrance, the degree of genetic heterogeneity, knowledge of protein structure/function, and/or prior reports of variant classifications (eg from the literature or disease-specific variant databases), and in many instances, these data may be absent or unreliable. 71,73 If a variant fails to meet the criteria for either a pathogenic/likely pathogenic or benign/likely benign categorization, it is stated to be of 'uncertain significance' (ie VUS). Variants in this category arise when there are either conflicting levels of evidence or incomplete/ inadequate information, and may be associated with a continuum of risk spanning a range of probabilities.…”

Clinotator: analyzing ClinVar variation reports to prioritize reclassification efforts

“…However it is important to underscore that other criteria, beyond the age of record, review of evidence and submitter, are important in the overall effort – in particular the use of disease prevalence 1 , and the consideration for penetrance of variants 2 . Given the current emphasis on machine learning in genetics and genomics, it is conceivable that a more comprehensive modeling of evidence and of biological basis of deleteriousness (eg, pathogenicity scores such as CADD 3 ) may contribute increasingly accurate ascertainment of variants.…”

Disease‐causing or benign? challenges in genetic variant interpretation and limitations of ClinVar

“…ClinVar (https://www.ncbi.nlm.nih.gov/clinvar/) is maybe the best known of these databases and sometimes considered a gold standard. To access the reliability of variant interpretation in ClinVar, Shah and colleagues carried out whole‐genome sequencing in > 10,000 unrelated individuals who were not ascertained for a specific health status and compared the frequency of rare, reportedly pathogenic, or likely pathogenic variants with the prevalence of the disease. They performed these analyses for a set of 59 well‐curated genes (ACMG‐59; genes in which incidental findings should be reported to patients) and a set of 463 less well‐curated genes previously linked to 256 different rare diseases (ORPHANET).…”