Tailoring the American College of Medical Genetics and Genomics and the Association for Molecular Pathology Guidelines for the Interpretation of Sequenced Variants in the
            <i>FBN1</i>
            Gene for Marfan Syndrome

Muiño-Mosquera, Laura; Steijns, Felke; Audenaert, Tjorven; Meerschaut, Ilse; Paepe, Anne De; Steyaert, Wouter; Symoens, Sofie; Coucke, Paul; Callewaert, Bert; Renard, Marjolijn; Backer, Julie De

doi:10.1161/circgen.117.002039

Cited by 23 publications

(21 citation statements)

References 39 publications

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“…The utility of establishing gene-specific thresholds has been already reported previously. Also, several computational methods, including REVEL and VEST3, have been demonstrated reliable even when tested on unbalanced datasets (i.e., unequal number of benign and pathogenic variants) [32,52]. It should be said that selecting a precise cutoff for pathogenicity is a subjective choice.…”

Section: Discussionmentioning

confidence: 99%

In-silico Analysis of NF1 Missense Variants in ClinVar: Translating Variant Predictions into Variant Interpretation and Classification

Accetturo¹,

Bartolomeo

Stella

2020

IJMS

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Background: With the advent of next-generation sequencing in genetic testing, predicting the pathogenicity of missense variants represents a major challenge potentially leading to misdiagnoses in the clinical setting. In neurofibromatosis type 1 (NF1), where clinical criteria for diagnosis may not be fully present until late infancy, correct assessment of variant pathogenicity is fundamental for appropriate patients’ management. Methods: Here, we analyzed three different computational methods, VEST3, REVEL and ClinPred, and after extracting predictions scores for 1585 NF1 missense variants listed in ClinVar, evaluated their performances and the score distribution throughout the neurofibromin protein. Results: For all the three methods, no significant differences were present between the scores of “likely benign”, “benign”, and “likely pathogenic”, “pathogenic” variants that were consequently collapsed into a single category. The cutoff values for pathogenicity were significantly different for the three methods and among benign and pathogenic variants for all methods. After training five different models with a subset of benign and pathogenic variants, we could reclassify variants in three sharply separated categories. Conclusions: The recently developed metapredictors, which integrate information from multiple components, after gene-specific fine-tuning, could represent useful tools for variant interpretation, particularly in genetic diseases where a clinical diagnosis can be difficult.

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Section: Discussionmentioning

confidence: 99%

In-silico Analysis of NF1 Missense Variants in ClinVar: Translating Variant Predictions into Variant Interpretation and Classification

Accetturo¹,

Bartolomeo

Stella

2020

IJMS

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“…Automated classifications using the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) published guidelines were obtained from InterVar v.201904/hg19 . For FBN1 variants disrupting disulfide‐bond‐forming cysteines, the ACMG/AMP criterion PM1 (moderate evidence for pathogenicity) was manually adjusted to PS3 (strong evidence for pathogenicity) as previously suggested . To avoid a bias caused by the ACMG/AMP criterion PM2 (moderate evidence for pathogenicity) fulfilled for variants absent from control populations, the automated classification was manually adapted by applying the criterion PM2 for extremely rare gnomAD variants as well (allele count ≤2).…”

Section: Methodsmentioning

confidence: 99%

“…Subsequently, categories I-II were applied to the entire gnomAD dataset, whereas categories III-VI were only applied to FBN1 (NM_000138.4, all exons) and FBN2 (NM_001999.3, CCA-mutationhotspot exons [23][24][25][26][27][28][29][30][31][32][33][34]. 24 Categories I-V were defined as sensu stricto selected, that is, sequence variants with clear evidence for pathogenicity, while category VI contains sensu lato selected sequence variants, that is, additional, potentially pathogenic variants requiring manual expert review and interpretation.…”

Section: Detection Of Pathogenic Variants In Gnomadmentioning

confidence: 99%

“…28 For FBN1 variants disrupting disulfide-bondforming cysteines, the ACMG/AMP criterion PM1 (moderate evidence for pathogenicity) was manually adjusted to PS3 (strong evidence for pathogenicity) as previously suggested. 29 To avoid a bias caused by the ACMG/AMP criterion PM2 (moderate evidence for pathogenicity) fulfilled for variants absent from control populations, the automated classification was manually adapted by applying the criterion PM2 for extremely rare gnomAD variants as well (allele count ≤2). Likewise, for the two families with FBN1/FBN2 dual F I G U R E 1 Overview of used workflow.…”

Section: Automated Classifications Using the American College Of Medicalmentioning

confidence: 99%

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Variant filtering, digenic variants, and other challenges in clinical sequencing: a lesson from fibrillinopathies

et al. 2019

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Genome-scale high-throughput sequencing enables the detection of unprecedented numbers of sequence variants. Variant filtering and interpretation are facilitated by mutation databases, in silico tools, and population-based reference datasets such as ExAC/gnomAD, while variants are classified using the ACMG/AMP guidelines. These methods, however, pose clinically relevant challenges. We queried the gnomAD dataset for (likely) pathogenic variants in genes causing autosomal-dominant disorders. Furthermore, focusing on the fibrillinopathies Marfan syndrome (MFS) and congenital contractural arachnodactyly (CCA), we screened 500 genomes of our patients for cooccurring variants in FBN1 and FBN2. In gnomAD, we detected 2653 (likely) pathogenic variants in 253 genes associated with autosomal-dominant disorders, enabling the estimation of variant-filtering thresholds and disease predisposition/prevalence rates. In our database, we discovered two families with hitherto unreported co-occurrence of FBN1/FBN2 variants causing phenotypes with mixed or modified MFS/CCA clinical features. We show that (likely) pathogenic gnomAD variants may be more frequent than expected and are challenging to classify according to the ACMG/AMP guidelines as well as that fibrillinopathies are likely underdiagnosed and may co-occur. Consequently, selection of appropriate frequency cutoffs, recognition of digenic variants, and variant classification represent considerable challenges in variant interpretation. Neglecting these challenges may lead to incomplete or missed diagnoses. K E Y W O R D S congenital contractural arachnodactyly, digenic variants, genome sequencing, Marfan syndrome, variant interpretation

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“…To address these limitations, organizations are working to build and standardize multi-step protocols for variant classification such as the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG-AMP), who, in 2015, published a series of guidelines for the interpretation of germline genetic variants for genes causative of hereditary human disorders (Richards et al 2015 ). These guidelines have been adopted, refined, and tested in multiple institutions for several genetic diseases including cancer, Marfan Syndrome, and diabetes among others (Amendola et al 2016 ; Muino-Mosquera et al 2018 ; Richards et al 2015 ; Santana et al 2017 ; Sukhai et al 2016 ). Similarly, the International Society for Gastrointestinal Hereditary Tumors (InSiGHT) has developed a standardized classification scheme for variants occurring in genes associated with hereditary gastrointestinal tumors such as Lynch Syndrome (Thompson et al 2014 ).…”

Section: Challenges In Cancer Genomics Data Interpretationmentioning

confidence: 99%

Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives

et al. 2019

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In the field of cancer genomics, the broad availability of genetic information offered by next-generation sequencing technologies and rapid growth in biomedical publication has led to the advent of the big-data era. Integration of artificial intelligence (AI) approaches such as machine learning, deep learning, and natural language processing (NLP) to tackle the challenges of scalability and high dimensionality of data and to transform big data into clinically actionable knowledge is expanding and becoming the foundation of precision medicine. In this paper, we review the current status and future directions of AI application in cancer genomics within the context of workflows to integrate genomic analysis for precision cancer care. The existing solutions of AI and their limitations in cancer genetic testing and diagnostics such as variant calling and interpretation are critically analyzed. Publicly available tools or algorithms for key NLP technologies in the literature mining for evidence-based clinical recommendations are reviewed and compared. In addition, the present paper highlights the challenges to AI adoption in digital healthcare with regard to data requirements, algorithmic transparency, reproducibility, and real-world assessment, and discusses the importance of preparing patients and physicians for modern digitized healthcare. We believe that AI will remain the main driver to healthcare transformation toward precision medicine, yet the unprecedented challenges posed should be addressed to ensure safety and beneficial impact to healthcare.

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Tailoring the American College of Medical Genetics and Genomics and the Association for Molecular Pathology Guidelines for the Interpretation of Sequenced Variants in the FBN1 Gene for Marfan Syndrome

Cited by 23 publications

References 39 publications

In-silico Analysis of NF1 Missense Variants in ClinVar: Translating Variant Predictions into Variant Interpretation and Classification

In-silico Analysis of NF1 Missense Variants in ClinVar: Translating Variant Predictions into Variant Interpretation and Classification

Variant filtering, digenic variants, and other challenges in clinical sequencing: a lesson from fibrillinopathies

Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives

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