Dataset Information

Reduced penetrance of pathogenic ACMG variants in a deeply phenotyped cohort study and evaluation of ClinVar classification over time.

ABSTRACT:

Purpose

We studied the penetrance of pathogenically classified variants in an elderly Dutch population from the Rotterdam Study, for which deep phenotyping is available. We screened the 59 actionable genes for which reporting of known pathogenic variants was recommended by the American College of Medical Genetics and Genomics (ACMG), and demonstrate that determining what constitutes a known pathogenic variant can be quite challenging.

Methods

We defined "known pathogenic" as classified pathogenic by both ClinVar and the Human Gene Mutation Database (HGMD). In 2628 individuals, we performed exome sequencing and identified known pathogenic variants. We investigated the clinical records of carriers and evaluated clinical events during 25 years of follow-up for evidence of variant pathogenicity.

Results

Of 3815 variants detected in the 59 ACMG genes, 17 variants were considered known pathogenic. For 14/17 variants the ClinVar classification had changed over time. Of 24 confirmed carriers of these variants, we observed at least one clinical event possibly caused by the variant in only three participants (13%).

Conclusion

We show that the definition of "known pathogenic" is often unclear and should be approached carefully. Additionally variants marked as known pathogenic do not always have clinical impact on their carriers. Definition and classification of true (individual) expected pathogenic impact should be defined carefully.

SUBMITTER: van Rooij J

PROVIDER: S-EPMC7605437 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Publications

Reduced penetrance of pathogenic ACMG variants in a deeply phenotyped cohort study and evaluation of ClinVar classification over time.

van Rooij Jeroen J Arp Pascal P Broer Linda L Verlouw Joost J van Rooij Frank F Kraaij Robert R Uitterlinden André A Verkerk Annemieke J M H AJMH

Genetics in medicine : official journal of the American College of Medical Genetics 20200715 11

<h4>Purpose</h4>We studied the penetrance of pathogenically classified variants in an elderly Dutch population from the Rotterdam Study, for which deep phenotyping is available. We screened the 59 actionable genes for which reporting of known pathogenic variants was recommended by the American College of Medical Genetics and Genomics (ACMG), and demonstrate that determining what constitutes a known pathogenic variant can be quite challenging.<h4>Methods</h4>We defined "known pathogenic" as class ...[more]

PMID: 32665702

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Project description:BackgroundDNA copy number variants (CNVs) are found in 15% of subjects with ID but their association with phenotypic abnormalities has been predominantly studied in smaller cohorts of subjects with detailed yet non-systematically categorized phenotypes, or larger cohorts (thousands of cases) with smaller number of generalized phenotypes.MethodsWe evaluated the association of de novo, familial and common CNVs detected in 78 ID subjects with phenotypic abnormalities classified using the Winter-Baraitser Dysmorphology Database (WBDD) (formerly the London Dysmorphology Database). Terminology for 34 primary (coarse) and 169 secondary (fine) phenotype features were used to categorize the abnormal phenotypes and determine the prevalence of each phenotype in patients grouped by the type of CNV they had.ResultsIn our cohort more than 50% of cases had abnormalities in primary categories related to head (cranium, forehead, ears, eye globes, eye associated structures, nose) as well as hands and feet. The median number of primary and secondary abnormalities was 12 and 18 per subject, respectively, indicating that the cohort consisted of subjects with a high number of phenotypic abnormalities (median De Vries score for the cohort was 5). The prevalence of each phenotypic abnormality was comparable in patients with de novo or familial CNVs in comparison to those with only common CNVs, although a trend for increased frequency of cranial and forehead abnormalities was noted in subjects with rare de novo and familial CNVs. Two clusters of subjects were identified based on the prevalence of each fine phenotypic feature, with an average of 28.3 and 13.5 abnormal phenotypes/subject in the two clusters respectively (P?<?0.05).ConclusionsOur study is a rare example of using standardized, deep morphologic phenotype clustering with phenotype/CNV correlation in a cohort of subjects with ID. The composition of the cohort inevitably influences the phenotype/genotype association, and our studies show that the influence of the de novo CNVs on the phenotype is less obvious in cohorts consisting of subjects with a high number of phenotypic abnormalities. The outcome of phenotype/genotype analysis also depends on the choice of phenotypes assessed and standardized phenotyping is required to minimize variability.

Project description:Since next-generation sequencing (NGS) has become widely available, large gene panels containing up to several hundred genes can be sequenced cost-efficiently. However, the interpretation of the often large numbers of sequence variants detected when using NGS is laborious, prone to errors and is often difficult to compare across laboratories. To overcome this challenge, the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) have introduced standards and guidelines for the interpretation of sequencing variants. Additionally, disease-specific refinements have been developed that include accurate thresholds for many criteria, enabling highly automated processing. This is of particular interest for common but heterogeneous disorders such as hearing impairment. With more than 200 genes associated with hearing disorders, the manual inspection of possible causative variants is particularly difficult and time-consuming. To this end, we developed the open-source bioinformatics tool GenOtoScope, which automates the analysis of all ACMG/AMP criteria that can be assessed without further individual patient information or human curator investigation, including the refined loss of function criterion ("PVS1"). Two types of interfaces are provided: (i) a command line application to classify sequence variants in batches for a set of patients and (ii) a user-friendly website to classify single variants. We compared the performance of our tool with two other variant classification tools using two hearing loss data sets, which were manually annotated either by the ClinGen Hearing Loss Gene Curation Expert Panel or the diagnostics unit of our human genetics department. GenOtoScope achieved the best average accuracy and precision for both data sets. Compared to the second-best tool, GenOtoScope improved the accuracy metric by 25.75% and 4.57% and precision metric by 52.11% and 12.13% on the two data sets, respectively. The web interface is accessible via: http://genotoscope.mh-hannover.de:5000 and the command line interface via: https://github.com/damianosmel/GenOtoScope.

Dataset Information

Reduced penetrance of pathogenic ACMG variants in a deeply phenotyped cohort study and evaluation of ClinVar classification over time.

Purpose

Methods

Results

Conclusion

Publications

Reduced penetrance of pathogenic ACMG variants in a deeply phenotyped cohort study and evaluation of ClinVar classification over time.

Similar Datasets

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