Reassessment of Genomic Sequence Variation to Harmonize Interpretation for Personalized Medicine

Garber, Kathryn B.; Vincent, Lisa M.; Alexander, John J.; Bean, Lora Jh; Bale, Sherri J.; Hegde, Madhuri

doi:10.1016/j.ajhg.2016.09.015

Cited by 52 publications

(83 citation statements)

References 23 publications

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“…Also uniform access to the most recent information on sequenced data is needed, in order to avoid differences in classification and need for reassessment over short periods of time. This is particularly important for genes whose role in disease is newly established where data on functional and clinical consequence accumulate rapidly …”

Section: Future Perspectivesmentioning

confidence: 99%

Distinguishing pathogenic mutations from background genetic noise in cardiology: The use of large genome databases for genetic interpretation

et al. 2017

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Advances in clinical genetic testing have led to increased insight into the human genome, including how challenging it is to interpret rare genetic variation. In some cases, the ability to detect genetic mutations exceeds the ability to understand their clinical impact, limiting the advantage of these technologies. Obstacles in genomic medicine are many and include: understanding the level of certainty/uncertainty behind pathogenicity determination, the numerous different variant interpretation-guidelines used by clinical laboratories, delivering the certain or uncertain result to the patient, helping patients evaluate medical decisions in light of uncertainty regarding the consequence of the findings. Through publication of large publicly available exome/genome databases, researchers and physicians are now able to highlight dubious variants previously associated with different cardiac traits. Also, continuous efforts through data sharing, international collaborative efforts to develop disease-gene-specific guidelines, and computational analyses using large data, will indubitably assist in better variant interpretation and classification. This article discusses the current, and quickly changing, state of variant interpretation resources within cardiovascular genetic research, e.g., publicly available databases and ways of how cardiovascular genetic counselors and geneticists can aid in improving variant interpretation in cardiology.

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Section: Future Perspectivesmentioning

confidence: 99%

Distinguishing pathogenic mutations from background genetic noise in cardiology: The use of large genome databases for genetic interpretation

et al. 2017

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“…Therefore, the variant may not have been reevaluated and reclassified without patient engagement. Although efforts to resolve discrepancies in classifications by clinical laboratories via standardized interpretation guidelines and data sharing have been successful (Garber et al 2016; Harrison et al 2017), concordant VUS interpretations would not be included in such studies and may never be otherwise prompted for reassessment.…”

Section: Discussionmentioning

confidence: 99%

Reclassification of the BRAF p.Ile208Val variant by case-level data sharing

Grant

Hemphill

Vincent³

et al. 2018

Cold Spring Harb Mol Case Stud

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The ClinVar database is a useful tool for patients and physicians to view variant interpretations submitted by clinical and nonclinical labs. However, variants of uncertain significance (VUS) in ClinVar can pose a significant burden on patients. If possible, it is important to resolve discrepancies and uncertainties surrounding interpreted variants. Here we highlight a case of a family who received a report of a variant (c.622A>G, p.Ile208Val) in BRAF following prenatal RASopathy testing. The variant had been previously classified by our laboratory as a VUS, so the mother contacted our laboratory via ClinVar for further information, which prompted reevaluation of the variant. Multiple sources of case-level data as well as the presence of the variant in the general population yielded sufficient evidence to reclassify the variant as likely benign. This reclassification alleviated significant concern for the family, and the child was born healthy with no clinical manifestations of Noonan syndrome or a RASopathy.

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“…Since pathogenicity assessment is a complex process and relies on different sources of information that can be updated over time, discrepancies in interpretation among different laboratories are frequent (Garber et al., ) and the adoption of a common framework is required to decrease the number of variants with conflicting pathogenicity assessments (Hoskinson, Dubuc, & Mason‐Suares, ).…”

Section: Introductionmentioning

confidence: 99%

CardioVAI: An automatic implementation of ACMG-AMP variant interpretation guidelines in the diagnosis of cardiovascular diseases

Nicora

Limongelli²,

Gambelli

et al. 2018

Human Mutation

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Variant interpretation for the diagnosis of genetic diseases is a complex process. The American College of Medical Genetics and Genomics, with the Association for Molecular Pathology, have proposed a set of evidence‐based guidelines to support variant pathogenicity assessment and reporting in Mendelian diseases. Cardiovascular disorders are a field of application of these guidelines, but practical implementation is challenging due to the genetic disease heterogeneity and the complexity of information sources that need to be integrated. Decision support systems able to automate variant interpretation in the light of specific disease domains are demanded. We implemented CardioVAI (Cardio Variant Interpreter), an automated system for guidelines based variant classification in cardiovascular‐related genes. Different omics‐resources were integrated to assess pathogenicity of every genomic variant in 72 cardiovascular diseases related genes. We validated our method on benchmark datasets of high‐confident assessed variants, reaching pathogenicity and benignity concordance up to 83 and 97.08%, respectively. We compared CardioVAI to similar methods and analyzed the main differences in terms of guidelines implementation. We finally made available CardioVAI as a web resource (http://cardiovai.engenome.com/) that allows users to further specialize guidelines recommendations.

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Reassessment of Genomic Sequence Variation to Harmonize Interpretation for Personalized Medicine

Cited by 52 publications

References 23 publications

Distinguishing pathogenic mutations from background genetic noise in cardiology: The use of large genome databases for genetic interpretation

Distinguishing pathogenic mutations from background genetic noise in cardiology: The use of large genome databases for genetic interpretation

Reclassification of the BRAF p.Ile208Val variant by case-level data sharing

CardioVAI: An automatic implementation of ACMG-AMP variant interpretation guidelines in the diagnosis of cardiovascular diseases

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