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Journal articles on the topic 'Classification ACMG'

Author: Grafiati
Published: 21 December 2024

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1

Lugeiro, Palloma C., Betsaida Urtremari, Lucas S. Santana, Elisangela P. S. Quedas, and Delmar Muniz Lourenco. "Comparative Analysis of Different International Criteria (ACMG-AMP vs. TENGEN) Applied to Classification of Missense Germline Allelic Variants in Patients With Multiple Endocrine Neoplasia Type 1 or Suspected to this Syndrome." Journal of the Endocrine Society 5, Supplement_1 (May 1, 2021): A1014. http://dx.doi.org/10.1210/jendso/bvab048.2074.

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Abstract Context: Multiple endocrine neoplasia type 1 (MEN1) is a rare autosomal dominant genetic syndrome caused by germline pathogenic allele variants (PAV) in the MEN1 tumor suppressor gene, which predispose MEN1 carriers to the increased risk of several endocrine neoplasms throughout life. The MEN1 gene (11q13), contains 10 exons encoding the MENIN protein. About 600 different PAVs have been reported, with 25% of them being missense variants. Of value, the definition of pathogenicity can be challenging, especially for missense variants. Thus, international guidelines for improving the classification of allele variants were recently defined by the ACMG-AMP (2015). Recently, applying ACMG-AMP criteria with inclusion of clinical features the TENGEN French group suggested modifications aiming to refine the classification of variants in MEN1 syndrome. Objective: To classify missense allelic variants found in the MEN1 gene by the ACMG-AMP guideline using VARSOME and by the TENGEN group to support a comparative analysis of the results obtained with these two methodologies (ACMG-AMP; TENGEN). Methods: the classification of 16 different missense allele variants identified in 17 index cases with or suspected to MEN1 syndrome was conducted according to ACMG-AMP criteria using the VARSOME software followed by the analysis defined by the TENGEN group. Results: Of the 16 variants, 6 were new, 1 was recurrent (2 unrelated index cases) and 9 of them occurred in codons with previous reports of different amino acid exchanges in the same region. Differences observed in the classification by ACMG-AMP and TENGEN were: pathogenic variant (6% vs. 65%); probably pathogenic (88% vs. 12%) and variants of uncertain significance (VUS) (6% vs. 23%). The four VUS classified by TENGEN (one of them for ACMG-AMP) were of sporadic cases without clinical diagnosis of MEN1 (2, for one MEN1-related tumor in early age; 1, for suspected MEN1) or with high risk of phenocopy (1, HPT + acromegaly). Conclusion: The difference observed in the classification of the pathogenicity of these variants, especially due to the higher occurrence of VUS in TENGEN, indicates that the criteria adopted by ACMG-VARSOME would have to be refined for clinical features. By other side, TENGEN apparently reinforce the classification of pathogenicity in cases with clinical diagnosis of MEN1 and reduce the definition of pathogenicity to variants found in MEN1-suspected cases without clinical criteria for the MEN1 diagnosis. These protocols apparently need to be investigate, validated and, probably, improved in other cohorts to reduce risks of misinterpretations and classifications that can, lately, interfere in genetic counseling and in the clinical management of patients. Finally, long-term outcome of cases classified as VUS, functional studies and, familial segregation may reinforce the initial impressions obtained with TENGEN classification.
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Cristofoli, Francesca, Muharrem Daja, Paolo Enrico Maltese, Giulia Guerri, Benedetta Tanzi, Roberta Miotto, Gabriele Bonetti, et al. "MAGI-ACMG: Algorithm for the Classification of Variants According to ACMG and ACGS Recommendations." Genes 14, no. 8 (August 8, 2023): 1600. http://dx.doi.org/10.3390/genes14081600.

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We have developed MAGI-ACMG, a classification algorithm that allows the classification of sequencing variants (single nucleotide or small indels) according to the recommendations of the American College of Medical Genetics (ACMG) and the Association for Clinical Genomic Science (ACGS). The MAGI-ACMG classification algorithm uses information retrieved through the VarSome Application Programming Interface (API), integrates the AutoPVS1 tool in order to evaluate more precisely the attribution of the PVS1 criterion, and performs the customized assignment of specific criteria. In addition, we propose a sub-classification scheme for variants of uncertain significance (VUS) according to their proximity either towards the “likely pathogenic” or “likely benign” classes. We also conceived a pathogenicity potential criterion (P_POT) as a proxy for segregation criteria that might be added to a VUS after posterior testing, thus allowing it to upgrade its clinical significance in a diagnostic reporting setting. Finally, we have developed a user-friendly web application based on the MAGI-ACMG algorithm, available to geneticists for variant interpretation.
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Mattivi, Connor L., J. Martijn Bos, Richard D. Bagnall, Natalie Nowak, John R. Giudicessi, Steve R. Ommen, Christopher Semsarian, and Michael J. Ackerman. "Clinical Utility of a Phenotype-Enhanced MYH7 -Specific Variant Classification Framework in Hypertrophic Cardiomyopathy Genetic Testing." Circulation: Genomic and Precision Medicine 13, no. 5 (October 2020): 453–59. http://dx.doi.org/10.1161/circgen.120.003039.

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Background: Missense variants in the MYH7 -encoded MYH7 (beta myosin heavy chain 7) represent a leading cause of hypertrophic cardiomyopathy (HCM). MYH7 -specific American College of Medical Genetics and Genomics (ACMG) variant classification guidelines were released recently but have yet to be assessed independently. We set out to assess the performance of the MYH7 -specific ACMG guidelines and determine if the addition of phenotype-enhanced criteria (PE-ACMG) using the HCM Genotype Predictor Score can further reduce the burden of variants of uncertain significance (VUS). Methods: Re-assessment was performed on 70 MYH7 -variants in 121 unique patients from Mayo Clinic, and an independent cohort of 54 variants in 70 patients from Royal Prince Alfred Hospital (Australia). Qualifying variants were re-adjudicated using both standard ACMG and MYH7 -ACMG guidelines, and HCM Genotype Predictor Score was used to provide a validated measure of strength of clinical phenotype to be incorporated into the MYH7 -ACMG framework. Results: Among Mayo Clinic identified variants, 11/70 (16%) were classified as pathogenic (P), 10/70 (14%) as likely pathogenic, and 49/70 (70%) as a VUS. A similar distribution was seen in the Australian patients (12/54 [22%] P, 12/54 [22%] likely pathogenic, and 30/54 [56%] VUS; P =not significant). Application of the MYH7 -ACMG resulted in a nonsignificant reduction of the VUS burden in both cohorts from 49/70 to 39/70 (56%; P =0.1; Mayo Clinic) and from 30/54 to 20/54 (37%; P =0.1; Australia). Using the combined PE-MYH7-ACMG framework, the VUS decreased significantly from 49 to 27 ( P <0.001, Mayo Clinic) and from 30 to 16 ( P <0.001; Australia). Conclusions: Use of the MYH7 -specific guidelines alone failed to significantly decrease VUS burden in 2 independent cohorts. However, a significant reduction in VUS burden was observed after the addition of phenotypic criteria. Using a patient’s strength of sarcomeric HCM phenotype for variant adjudication can increase significantly the clinical utility of genetic testing for patients with HCM.
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4

Cheng, Liting, Xiaoyan Li, Lin Zhao, Zefeng Wang, Junmeng Zhang, Zhuo Liang, and Yongquan Wu. "Reevaluating the Mutation Classification in Genetic Studies of Bradycardia Using ACMG/AMP Variant Classification Framework." International Journal of Genomics 2020 (February 26, 2020): 1–12. http://dx.doi.org/10.1155/2020/2415850.

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Purpose. Next-generation sequencing (NGS) has become more accessible, leading to an increasing number of genetic studies of familial bradycardia being reported. However, most of the variants lack full evaluation. The relationship between genetic factors and bradycardia should be summarized and reevaluated. Methods. We summarized genetic studies published in the PubMed database from 2008/1/1 to 2019/9/1 and used the ACMG/AMP classification framework to analyze related sequence variants. Results. We identified 88 articles, 99 sequence variants, and 34 genes after searching the PubMed database and classified ABCC9, ACTN2, CACNA1C, DES, HCN4, KCNQ1, KCNH2, LMNA, MECP2, LAMP2, NPPA, SCN5A, and TRPM4 as high-priority genes causing familial bradycardia. Most mutated genes have been reported as having multiple clinical manifestations. Conclusions. For patients with familial CCD, 13 high-priority genes are recommended for evaluation. For genetic studies, variants should be carefully evaluated using the ACMG/AMP variant classification framework before publication.
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Brown, Angela, Mansour Zamanpoor, Donald R. Love, and Debra O. Prosser. "Determination of Pathogenicity of Breast Cancer 1 Gene Variants using the American College of Medical Genetics and Genomics and the Association for Molecular Pathology Guidelines." Sultan Qaboos University Medical Journal [SQUMJ] 19, no. 4 (December 22, 2019): 324. http://dx.doi.org/10.18295/squmj.2019.19.04.008.

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Objectives: Molecular diagnostic laboratories screen for mutations in disease-causing genes in order to confirm a clinical diagnosis. The classification of DNA variants as ‘pathogenic’ or ‘likely pathogenic’ mutations creates a workflow bottleneck, which becomes increasingly challenging as greater number of genes are screened. The classification challenge is also acute if there are conflicting reports regarding pathogenicity and differing classification criteria between laboratories. This study aimed to compare two procedures for the classification of variants in the breast cancer (BRCA)1 gene. Methods: This bioinformatic study was conducted at LabPLUS, Auckland, New Zealand, from February to June 2017. DNA was extracted from peripheral blood samples of 30 patients and gene library construction was carried out using a commercially available targeted panel for the BRCA1 and BRCA2 genes. The genes were subsequently sequenced and the sequence data analysed. The guidelines published by the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/ AMP) provides a comprehensive framework for the interpretation of variants in genes that are associated with Mendelian disorders. The use of these guidelines were compared to the variant classifications that were achieved by reference to those reported in the BRCA Exchange database. Results: The results showed concordance between the two classification protocols for a panel of 30 BRCA1 gene variants, although the transparency in following the ACMG/AMP guidelines provides a diagnostic laboratory with a generalisable approach that allows laboratorydirected revisions to be undertaken in light of new information. Conclusion: The ACMG/AMP-based guidelines were applied to a cohort of patients with BRCA1 gene variants. The use of these guidelines provides a system which creates consistency in variant interpretation and supports subsequent clinical management.Keywords: BRCA1 Gene; Bioinformatics; DNA Sequencing; Nonsense Codon; Splice Donor Site; New Zealand.
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Cristofoli, Francesca, Elisa Sorrentino, Giulia Guerri, Roberta Miotto, Roberta Romanelli, Alessandra Zulian, Stefano Cecchin, et al. "Variant Selection and Interpretation: An Example of Modified VarSome Classifier of ACMG Guidelines in the Diagnostic Setting." Genes 12, no. 12 (November 25, 2021): 1885. http://dx.doi.org/10.3390/genes12121885.

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Variant interpretation is challenging as it involves combining different levels of evidence in order to evaluate the role of a specific variant in the context of a patient’s disease. Many in-depth refinements followed the original 2015 American College of Medical Genetics (ACMG) guidelines to overcome subjective interpretation of criteria and classification inconsistencies. Here, we developed an ACMG-based classifier that retrieves information for variant interpretation from the VarSome Stable-API environment and allows molecular geneticists involved in clinical reporting to introduce the necessary changes to criterion strength and to add or exclude criteria assigned automatically, ultimately leading to the final variant classification. We also developed a modified ACMG checklist to assist molecular geneticists in adjusting criterion strength and in adding literature-retrieved or patient-specific information, when available. The proposed classifier is an example of integration of automation and human expertise in variant curation, while maintaining the laboratory analytical workflow and the established bioinformatics pipeline.
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Liu, Yichuan, Hui-Qi Qu, Adam S. Wenocur, Jingchun Qu, Xiao Chang, Joseph Glessner, Patrick Sleiman, Lifeng Tian, and Hakon Hakonarson. "Interpretation of Maturity-Onset Diabetes of the Young Genetic Variants Based on American College of Medical Genetics and Genomics Criteria: Machine-Learning Model Development." JMIR Biomedical Engineering 5, no. 1 (December 1, 2020): e20506. http://dx.doi.org/10.2196/20506.

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Background Maturity-onset diabetes of the young (MODY) is a group of dominantly inherited monogenic diabetes, with HNF4A-MODY, GCK-MODY, and HNF1A-MODY as the three most common forms based on the causal genes. Molecular diagnosis of MODY is important for precise treatment. Although a DNA variant causing MODY can be assessed based on the criteria of the American College of Medical Genetics and Genomics (ACMG) guidelines, gene-specific assessment of disease-causing mutations is important to differentiate among MODY subtypes. As the ACMG criteria were not originally designed for machine-learning algorithms, they are not true independent variables. Objective The aim of this study was to develop machine-learning models for interpretation of DNA variants and MODY diagnosis using the ACMG criteria. Methods We applied machine-learning models for interpretation of DNA variants in MODY genes defined by the ACMG criteria based on the Human Gene Mutation Database (HGMD) and ClinVar database. Results With a machine-learning procedure, we found that the weight matrix of the ACMG criteria was significantly different between the three MODY genes HNF1A, HNF4A, and GCK. The models showed high predictive abilities with accuracy over 95%. Conclusions Our results highlight the need for applying different weights of the ACMG criteria in relation to different MODY genes for accurate functional classification. As proof of principle, we applied the ACMG criteria as feature vectors in a machine-learning model and obtained a precision-based result.
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Tavtigian, Sean V., Marc S. Greenblatt, Steven M. Harrison, Robert L. Nussbaum, Snehit A. Prabhu, Kenneth M. Boucher, and Leslie G. Biesecker. "Modeling the ACMG/AMP variant classification guidelines as a Bayesian classification framework." Genetics in Medicine 20, no. 9 (January 4, 2018): 1054–60. http://dx.doi.org/10.1038/gim.2017.210.

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9

Lattante, Serena, Giuseppe Marangi, Paolo Niccolò Doronzio, Amelia Conte, Giulia Bisogni, Marcella Zollino, and Mario Sabatelli. "High-Throughput Genetic Testing in ALS: The Challenging Path of Variant Classification Considering the ACMG Guidelines." Genes 11, no. 10 (September 24, 2020): 1123. http://dx.doi.org/10.3390/genes11101123.

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The development of high-throughput sequencing technologies and screening of big patient cohorts with familial and sporadic amyotrophic lateral sclerosis (ALS) led to the identification of a significant number of genetic variants, which are sometimes difficult to interpret. The American College of Medical Genetics and Genomics (ACMG) provided guidelines to help molecular geneticists and pathologists to interpret variants found in laboratory testing. We assessed the application of the ACMG criteria to ALS-related variants, combining data from literature with our experience. We analyzed a cohort of 498 ALS patients using massive parallel sequencing of ALS-associated genes and identified 280 variants with a minor allele frequency < 1%. Examining all variants using the ACMG criteria, thus considering the type of variant, inheritance, familial segregation, and possible functional studies, we classified 20 variants as “pathogenic”. In conclusion, ALS’s genetic complexity, such as oligogenic inheritance, presence of genes acting as risk factors, and reduced penetrance, needs to be considered when interpreting variants. The goal of this work is to provide helpful suggestions to geneticists and clinicians dealing with ALS.
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DeMille, Desiree, Jamie McDonald, Carmelo Bernabeu, Hilary Racher, Carla Olivieri, Claudia Cantarini, Anna Sbalchiero, et al. "Specifications of the ACMG/AMP Variant Curation Guidelines for Hereditary Hemorrhagic Telangiectasia Genes—ENG and ACVRL1." Human Mutation 2024 (May 18, 2024): 1–13. http://dx.doi.org/10.1155/2024/3043736.

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The 2015 ACMG/AMP standards and guidelines for interpretation of sequence variants are widely used by laboratories, including for variant curation of the hereditary hemorrhagic telangiectasia (HHT) genes. However, the need for gene- and disease-specific modifications and specifications of these general guidelines to optimize and standardize variant classification was recognized at the time of publication. With this goal, the ClinGen HHT variant curation expert panel was formed. Here, we describe our recommended HHT-specific variant classification criteria and the outcomes from pilot testing of 30 variants of the ENG and ACVRL1 genes. Eight of the original ACMG/AMP rules were determined to not be applicable for ENG- or ACVRL1-related HHT or were previously recommended by ClinGen for removal, two rules were unmodified, and the remaining 18 rules were modified according to HHT specifications or previous ClinGen general recommendations. This study demonstrates the importance of HHT-specific criteria in the optimization and standardization of HHT variant classification and conflicting classification resolution.
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Tavtigian, S. "Abstract ES3-1: Reclassifying VUS: New techniques can solve the puzzle once and for all." Cancer Research 82, no. 4_Supplement (February 15, 2022): ES3–1—ES3–1. http://dx.doi.org/10.1158/1538-7445.sabcs21-es3-1.

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Abstract Over the last 10 years, the scale of genetic testing for cancer predisposition has increased dramatically. Two simple facts drive this trend: (1) improving test technology has reduced the costs while increasing clinical yield, and (2) medical and surgical approaches to risk reduction can add years to the lives of carriers of pathogenic variants if these individuals are identified through cascade testing before they develop a metastatic tumor. Initial outcomes from this genetic testing fall into one of three categories: a pathogenic sequence variant(s) is found, no reportable sequence variant(s) is found, or a variant(s) of uncertain significance (VUS) is found. The observation and reporting of VUS remains problematic because (among other reasons) the inherent informational uncertainty can raise patient anxiety, and medical over-interpretation of VUS can lead to over-treatment of healthy individuals who carry that sequence variant. In 2008, an International Agency for Research on Cancer (IARC) Working Group on unclassified sequence variants published guidelines for classification of VUS in cancer susceptibility genes. Those guidelines were fundamentally quantitative, but were not efficient enough to keep up with the rate of VUS identification since then. In 2015, the American College of Medical Genetics (ACMG) published guidelines for classification of sequence variants in all Mendelian disease susceptibility genes. While the ACMG guidelines were qualitative, they had the advantage of being more efficient than the IARC guidelines. In 2018, we demonstrated that the qualitative ACMG guidelines are compatible with a quantitative Bayesian interpretation. Although the Bayesian re-interpretation validated the ACMG guidelines, it was not appropriate for day-to-day use because it requires users actually to execute the math of Bayes’ rule. In 2020, we derived a points-system for VUS evaluation from the Bayesian re-interpretation of the ACMG guidelines. The points system provides a number of advantages, including: (1) while quantitative, it faithfully reflects the ACMG qualitative guidelines; (2) using the points system requires no more than addition and subtraction, so there is no math barrier; and (3) it provides a route to calibrating ACMG “evidence criteria” (heretofore referred to as “data types”), which will become central to solving the VUS puzzle. Over the last few years, three additional strands are converging towards acceleration of VUS classification. The first strand is a community drive towards more accurate computational tools for VUS evaluation. In ACMG parlance, this data type is limited to “Supporting” evidence for or against pathogenicity, but will likely be shown to generate stronger (at least “Moderate”) evidence. The second strand is development of high-throughput functional assays. These assays can now be used to evaluate all possible missense substitutions encoded by smaller genes, and at least the key functional domains of large genes. In principle, these assays can generate “Strong” evidence for or against pathogenicity. The third strand is a clearer understanding of standards that need to be met when calibrating various data types toward variant classification - a direct benefit of the quantitative Bayesian re-interpretation of the ACMG guidelines. In the points system for VUS evaluation, Moderate evidence provides +/- 2 points, Strong evidence provides +/- 4 points, and the threshold for re-classifying a VUS as Likely Pathogenic is +6 points. Hence a large fraction of the VUS that are actually pathogenic may be classified as Likely Pathogenic through a combination of well calibrated computational data, well calibrated functional assay data, and minimal patient or population observational data. Hence a mantra for the next few years of “2 + 4 = 6. Better be sure that the 2 is correct, the 4 is correct, and addition is the correct operator”. Citation Format: S Tavtigian. Reclassifying VUS: New techniques can solve the puzzle once and for all [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr ES3-1.
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Спектор, М. А., Л. А. Ясько, and А. Е. Друй. "The interpretation of somatic genetic variants identified with high-throughput sequencing of DNA from paediatric solid tumors." Nauchno-prakticheskii zhurnal «Medicinskaia genetika, no. 3(224) (March 31, 2021): 3–25. http://dx.doi.org/10.25557/2073-7998.2021.03.3-25.

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Активное внедрение высокопроизводительного секвенирования в клиническую практику требует общего подхода к интерпретации обнаруженных генетических вариантов, в частности, вариантов с соматическим статусом. В 2017 году Ассоциация молекулярной патологии США (AMP), Американская коллегия медицинской генетики и геномики (ACMG), Американское общество клинической онкологии (ASCO) и Коллегия американских патологов (CAP) опубликовали руководство по интерпретации соматических генетических вариантов и выдаче заключений по результатам высокопроизводительного секвенирования опухолевой ДНК. Данный обзор посвящен специфике применения руководства AMP/ACMG/ASCO/CAP для интерпретации результатов генетических исследований детских солидных опухолей. В статье приводятся критерии, на которых основана классификация соматических генетических вариантов, обсуждаются проблемы оценки клинической значимости генетических находок и приводятся примеры классификации генетических вариантов, выявленных в различных типах детских солидных опухолей. Active clinical implementation of high-throughput DNA sequencing requires a common approach to the interpretation of detected genetic variants, including variants with somatic status. In 2017, the United States Association of Molecular Pathology (AMP), the American College of Medical Genetics and Genomics (ACMG), the American Society of Clinical Oncology (ASCO), and the College of American Pathologists (CAP) published the guidelines for interpreting and reporting the somatic genetic variants in cancer identified using high-throughput sequencing analysis. This review focuses on the specific application of the AMP/ACMG/ASCO/CAP guidelines in the field of genetic research on paediatric solid tumors. In particular, the review provides the criteria for classification of somatic genetic variants, discusses the problems of evaluating the clinical significance of genetic findings in paediatric tumors, and provides examples of classification of genetic variants specific for certain types of childhood solid malignancies.
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Hatton, Jessica N., Megan N. Frone, Hannah C. Cox, Stephanie B. Crowley, Susan Hiraki, Noriko N. Yokoyama, Noura S. Abul-Husn, et al. "Specifications of the ACMG/AMP Variant Classification Guidelines for Germline DICER1 Variant Curation." Human Mutation 2023 (March 29, 2023): 1–15. http://dx.doi.org/10.1155/2023/9537832.

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Germline pathogenic variants in DICER1 predispose individuals to develop a variety of benign and malignant tumors. Accurate variant curation and classification are essential for reliable diagnosis of DICER1-related tumor predisposition and the identification of individuals who may benefit from surveillance. Since 2015, most labs have followed the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) sequence variant classification guidelines for DICER1 germline variant curation. However, these general guidelines lack gene-specific nuances and leave room for subjectivity. Consequently, a group of DICER1 experts joined ClinGen to form the DICER1 and miRNA-Processing Genes Variant Curation Expert Panel (VCEP) to create DICER1-specific ACMG/AMP guidelines for germline variant curation. The VCEP followed the FDA-approved ClinGen protocol for adapting and piloting these guidelines. A diverse set of 40 DICER1 variants were selected for piloting, including 14 known pathogenic/likely pathogenic (P/LP) variants, 12 known benign/likely benign (B/LB) variants, and 14 variants classified as variants of uncertain significance (VUS) or with conflicting interpretations in ClinVar. Clinically meaningful classifications (i.e., P, LP, LB, or B) were achieved for 82.5% (33/40) of the pilot variants, with 100% concordance among the known P/LP and known B/LB variants. Half of the VUS or conflicting variants were resolved with four variants classified as LB and three as LP. These results demonstrate that the DICER1-specific guidelines for germline variant curation effectively classify known pathogenic and benign variants while reducing the frequency of uncertain classifications. Individuals and labs curating DICER1 variants should consider adopting this classification framework to encourage consistency and improve objectivity.
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Lesmann, Hellen, Hannah Klinkhammer, and Prof Dr med Dipl Phys Peter M. Krawitz. "The future role of facial image analysis in ACMG classification guidelines." Medizinische Genetik 35, no. 2 (June 1, 2023): 115–21. http://dx.doi.org/10.1515/medgen-2023-2014.

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Abstract The use of next-generation sequencing (NGS) has dramatically improved the diagnosis of rare diseases. However, the analysis of genomic data has become complex with the increasing detection of variants by exome and genome sequencing. The American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) developed a 5-tier classification scheme in 2015 for variant interpretation, that has since been widely adopted. Despite efforts to minimise discrepancies in the application of these criteria, inconsistencies still occur. Further specifications for individual genes were developed by Variant Curation Expert Panels (VCEPs) of the Clinical Genome Resource (ClinGen) consortium, that also take into consideration gene or disease specific features. For instance, in disorders with a highly characerstic facial gestalt a “phenotypic match” (PP4) has higher pathogenic evidence than e.g. in a non-syndromic form of intellectual disability. With computational approaches for quantifying the similarity of dysmorphic features results of such analysis can now be used in a refined Bayesian framework for the ACMG/AMP criteria.
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Hirotsu, Yosuke, Udo Schmidt-Edelkraut, Hiroshi Nakagomi, Ikuko Sakamoto, Markus Hartenfeller, Ram Narang, Theodoros G. Soldatos, et al. "Consolidated BRCA1/2 Variant Interpretation by MH BRCA Correlates with Predicted PARP Inhibitor Efficacy Association by MH Guide." International Journal of Molecular Sciences 21, no. 11 (May 29, 2020): 3895. http://dx.doi.org/10.3390/ijms21113895.

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BRCA1/2 variants are prognostic biomarkers for hereditary breast and/or ovarian cancer (HBOC) syndrome and predictive biomarkers for PARP inhibition. In this study, we benchmarked the classification of BRCA1/2 variants from patients with HBOC-related cancer using MH BRCA, a novel computational technology that combines the ACMG guidelines with expert-curated variant annotations. Evaluation of BRCA1/2 variants (n = 1040) taken from four HBOC studies showed strong concordance within the pathogenic (98.1%) subset. Comparison of MH BRCA’s ACMG classification to ClinVar submitter content from ENIGMA, the international consortium of investigators on the clinical significance of BRCA1/2 variants, the ARUP laboratories, a clinical testing lab of the University of UTAH, and the German Cancer Consortium showed 99.98% concordance (4975 out of 4976 variants) in the pathogenic subset. In our patient cohort, refinement of patients with variants of unknown significance reduced the uncertainty of cancer-predisposing syndromes by 64.7% and identified three cases with potential family risk to HBOC due to a likely pathogenic variant BRCA1 p.V1653L (NM_007294.3:c.4957G > T; rs80357261). To assess whether classification results predict PARP inhibitor efficacy, contextualization with functional impact information on DNA repair activity were performed, using MH Guide. We found a strong correlation between treatment efficacy association and MH BRCA classifications. Importantly, low efficacy to PARP inhibition was predicted in 3.95% of pathogenic variants from four examined HBOC studies and our patient cohort, indicating the clinical relevance of the consolidated variant interpretation.
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Huang, Yingzhao, Bowen Liu, Jile Shi, Sen Zhao, Kexin Xu, Liying Sun, Na Chen, Wen Tian, Jianguo Zhang, and Nan Wu. "Landscape of Secondary Findings in Chinese Population: A Practice of ACMG SF v3.0 List." Journal of Personalized Medicine 12, no. 9 (September 14, 2022): 1503. http://dx.doi.org/10.3390/jpm12091503.

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Clinical exome sequencing (CES) has shown great utility in the diagnosis of Mendelian disorders. CES can unravel secondary findings (SFs) unrelated to the primary diagnosis but with potential health implications. The American College of Medical Genetics and Genomics (ACMG) has published a guideline for reporting secondary findings and recently updated an ACMG SF v3.0 list comprising 73 genes. Several studies have been performed to explore the prevalence of SFs. However, the data were limited in the Chinese population. In this study, we evaluated the genetic data of 2987 individuals from the Deciphering Disorders Involving Scoliosis and COmorbidities (DISCO) study group in accordance with the ACMG SF v3.0 list. The detected variants were evaluated using the ACMG classification guidelines, HGMD, and ClinVar database. Totally, 157 (157/2987, 5.3%) individuals had reportable variants within genes associated with cancer, cardiovascular, metabolic, and miscellaneous phenotypes. We identified 63 known pathogenic (KP) variants in 72 individuals (72/2987, 2.4%) and 96 expected pathogenic (EP) variants in 105 individuals (3.5%). Forty-five individuals carried SFs in v3.0 newly added genes, which accounted for 1.5% of our cohort. Our findings could contribute to existing knowledge of secondary findings in different ethnicities and indicate the necessity for clinicians to update the SFs gene list.
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Ji, Jianling, Ryan Schmidt, Westley Sherman, Ryan Peralta, Megan Roytman, Soheil Shams, and Gordana Raca. "Automated classification of copy number variants based on 2019 ACMG standards." Molecular Genetics and Metabolism 132 (April 2021): S287—S288. http://dx.doi.org/10.1016/s1096-7192(21)00531-x.

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Dent, C., A. Hills, J. Honeychurch, E. Watson, P. Dean, G. Woodward, M. Wadsley, et al. "Standardising genetic variant classification for FH – application of the ACMG guidelines." Atherosclerosis Supplements 28 (September 2017): e7. http://dx.doi.org/10.1016/j.atherosclerosissup.2017.08.012.

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Nykamp, Keith, Michael Anderson, Martin Powers, John Garcia, Blanca Herrera, Yuan-Yuan Ho, Yuya Kobayashi, et al. "Sherloc: a comprehensive refinement of the ACMG–AMP variant classification criteria." Genetics in Medicine 19, no. 10 (May 11, 2017): 1105–17. http://dx.doi.org/10.1038/gim.2017.37.

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Joseph, Vijai, Vignesh Ravichandran, and Kenneth Offit. "Pathogenicity of mutation analyzer (PathoMAN): A fast automation of germline genomic variant curation in clinical sequencing." Journal of Clinical Oncology 35, no. 15_suppl (May 20, 2017): 1529. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.1529.

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1529 Background: A challenge in clinical oncology is interpretation of multiplexed gene sequencing of patients at risk. The plethora of variants to be curated for pathogenicity or actionability poses a growing burden for cancer care professionals. Current guidelines by the ACMG requires the aggregation of multiple lines of genomic data evidences from diverse resources. A computational tool that automates, provide uniformity and significantly speed the interpretive process is thus necessary. Methods: The Pathogenicity of Mutation Analyzer (PathoMAN), is a tool that automates germline genomic variant curation from clinical sequencing based on ACMG guidelines. PathoMAN aggregates multiple tracks of genomic, protein and disease specific information from public sources such as ClinVar, ExAC, UniProt, 1000 genomes, dbNSFP and locus specific databases. Variant specific and gene specific annotations are used to classify variants to model the ACMG rubric. We analyzed 2500 manually curated and classified, high quality variants in 180 genes from 3 large, published studies to quantify the performance of PathoMAN; analyzing 242 pathogenic/likely pathogenic (P/LP), 1272 benign/likely benign (B/LB) and 1261 variants of uncertain significance (VUS). We report the summary of PathoMAN classifications in four categories contrasted against the manual curation. Results: PathoMan achieves an average of 75% concordance and 1.5% discordance for P/LP mutations and 60% and 0.1% for B/LB variants. PathoMAN is able to resolve 12% of reported VUS as either P/LP or B/LB. It loses resolution to classify 25% of P/LP and B/LB variants due to lack of information and due to inconsistencies in available data from public resources. Conclusions: PathoMAN provides a breakthrough in rapid classification of genetic variants by generation of robust models using a knowledgebase of diverse genetic data. It is easily accessible, web-based resource that allows the community to rapidly test a large number of variants for pathogenicity. Such bioinformatic tools are essential to reduce manual workload of a domain level experts. We propose, a new nosology for the 5 ACMG classes to facilitate better reporting to ClinVar.
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Westphal, Dominik Sebastian, Kathrin Pollmann, Christoph Marschall, Annette Wacker-Gussmann, Renate Oberhoffer-Fritz, Karl-Ludwig Laugwitz, Peter Ewert, and Cordula Maria Wolf. "It Is Not Carved in Stone—The Need for a Genetic Reevaluation of Variants in Pediatric Cardiomyopathies." Journal of Cardiovascular Development and Disease 9, no. 2 (January 25, 2022): 41. http://dx.doi.org/10.3390/jcdd9020041.

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(1) Background: In cardiomyopathies, identification of genetic variants is important for the correct diagnosis and impacts family cascade screening. A classification system was published by the American College of Medical Genetics and Genomics (ACMG) in 2015 to standardize variants’ classification. The aim of the study was to determine the rate of reclassification of previously identified variants in patients with childhood-onset cardiomyopathies. (2) Methods: Medical records of patients and their relatives were screened for clinical and genetic information at the Department of Congenital Heart Defects and Pediatric Cardiology, German Heart Center Munich. Patients without an identified genetic variant were excluded from further analyses. Previously reported variants were reevaluated by the ACMG criteria in November 2021. (3) Results: Data from 167 patients or relatives of patients with childhood-onset cardiomyopathy from 137 families were analyzed. In total, 45 different genetic variants were identified in 71 individuals. Classification changed in 29% (13/45) with the greatest shift in “variants of unknown significance” to “(likely) benign” (9/13). (4) Conclusions: In patients with childhood-onset cardiomyopathies, nearly a third of reported genetic variants change mostly to more benign classes upon reclassification. Given the impact on patient management and cascade screening, this finding underlines the importance of continuous genetic counseling and variant.
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Melidis, Damianos P., Christian Landgraf, Gunnar Schmidt, Anja Schöner-Heinisch, Sandra von Hardenberg, Anke Lesinski-Schiedat, Wolfgang Nejdl, and Bernd Auber. "GenOtoScope: Towards automating ACMG classification of variants associated with congenital hearing loss." PLOS Computational Biology 18, no. 9 (September 21, 2022): e1009785. http://dx.doi.org/10.1371/journal.pcbi.1009785.

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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.
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Nykamp, Keith, Michael Anderson, Martin Powers, John Garcia, Blanca Herrera, Yuan-Yuan Ho, Yuya Kobayashi, et al. "Correction: Sherloc: a comprehensive refinement of the ACMG–AMP variant classification criteria." Genetics in Medicine 22, no. 1 (July 26, 2019): 240. http://dx.doi.org/10.1038/s41436-019-0624-9.

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Karalidou, Vasiliki, Despoina Kalfakakou, Athanasios Papathanasiou, Florentia Fostira, and George K. Matsopoulos. "MARGINAL: An Automatic Classification of Variants in BRCA1 and BRCA2 Genes Using a Machine Learning Model." Biomolecules 12, no. 11 (October 24, 2022): 1552. http://dx.doi.org/10.3390/biom12111552.

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Implementation of next-generation sequencing (NGS) for the genetic analysis of hereditary diseases has resulted in a vast number of genetic variants identified daily, leading to inadequate variant interpretation and, consequently, a lack of useful clinical information for treatment decisions. Herein, we present MARGINAL 1.0.0, a machine learning (ML)-based software for the interpretation of rare BRCA1 and BRCA2 germline variants. MARGINAL software classifies variants into three categories, namely, (likely) pathogenic, of uncertain significance and (likely) benign, implementing the criteria established by the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG-AMP). We first annotated BRCA1 and BRCA2 variants using various sources. Then, we automatically implemented the ACMG-AMP criteria, and we finally constructed the ML model for variant classification. To maximize accuracy, we compared the performance of eight different ML algorithms in a classification scheme based on a serial combination of two classifiers. The model showed high predictive abilities with maximum accuracy of 92% and 98%, recall of 92% and 98% and specificity of 90% and 98% for the first and second classifiers, respectively. Our results indicate that using a gene and disease-specific ML automated software for clinical variant evaluation can minimize conflicting interpretations.
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Motta, Fabiana, Renan Martin, Fernanda Porto, Elizabeth Wohler, Rosane Resende, Caio Gomes, João Pesquero, and Juliana Sallum. "Pathogenicity Reclassification of RPE65 Missense Variants Related to Leber Congenital Amaurosis and Early-Onset Retinal Dystrophy." Genes 11, no. 1 (December 24, 2019): 24. http://dx.doi.org/10.3390/genes11010024.

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A challenge in molecular diagnosis and genetic counseling is the interpretation of variants of uncertain significance. Proper pathogenicity classification of new variants is important for the conclusion of molecular diagnosis and the medical management of patient treatments. The purpose of this study was to reclassify two RPE65 missense variants, c.247T>C (p.Phe83Leu) and c.560G>A (p.Gly187Glu), found in Brazilian families. To achieve this aim, we reviewed the sequencing data of a 224-gene retinopathy panel from 556 patients (513 families) with inherited retinal dystrophies. Five patients with p.Phe83Leu and seven with p.Gly187Glu were selected and their families investigated. To comprehend the pathogenicity of these variants, we evaluated them based on the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) classification guidelines. Initially, these RPE65 variants met only three pathogenic criteria: (i) absence or low frequency in the population, (ii) several missense pathogenic RPE65 variants, and (iii) 15 out of 16 lines of computational evidence supporting them as damaging, which together allowed the variants to be classified as uncertain significance. Two other pieces of evidence were accepted after further analysis of these Brazilian families: (i) p.Phe83Leu and p.Gly187Glu segregate with childhood retinal dystrophy within families, and (ii) their prevalence in Leber congenital amaurosis (LCA)/early-onset retinal dystrophy (EORD) patients can be considered higher than in other inherited retinal dystrophy patients. Therefore, these variants can now be classified as likely pathogenic according to ACMG/AMP classification guidelines.
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Rossen, Jennifer L., Brenda L. Bohnsack, Kevin X. Zhang, Alexander Ing, Andy Drackley, Valerie Castelluccio, and Hanta Ralay-Ranaivo. "Evaluation of Genetic Testing in a Cohort of Diverse Pediatric Patients in the United States with Congenital Cataracts." Genes 14, no. 3 (February 28, 2023): 608. http://dx.doi.org/10.3390/genes14030608.

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The aim of this study was to evaluate the diagnostic yield from prior genetic testing in a 20-year cohort of pediatric patients with congenital cataracts. A retrospective review of patients with congenital cataracts who underwent genetic testing was completed from 2003–2022. The diagnostic yield of the test was determined by variant classification and inheritance pattern. Variants from initial testing underwent reclassification in accordance with ACMG-AMP (American College of Medical Genetics and Genomics—American Association of Molecular Pathology) 2015 or 2020 ACMG CNV guidelines. A total of 95 variants were identified in 52 patients with congenital cataracts (42 bilateral, 10 unilateral); 42 % were White, 37% were Hispanic, 8% were Black, and 6% were Asian. The majority of patients (92%) did not have a family history of congenital cataracts but did have systemic illnesses (77%). Whole exome sequencing and targeted congenital cataract panels showed diagnostic yields of 46.2% and 37.5%, respectively. Microarray had the lowest yield at 11%. Compared to the initial classification, 16% (15 of 92 variants) had discrepant reclassifications. More testing is needed, and an increased focus is warranted in the field of ocular genetics on congenital cataracts, particularly in those with systemic illnesses and no family history, to advance our knowledge of this potentially blinding condition.
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Cornelis, Stéphanie S., Miriam Bauwens, Lonneke Haer-Wigman, Marieke De Bruyne, Madhulatha Pantrangi, Elfride De Baere, Robert B. Hufnagel, Claire-Marie Dhaenens, and Frans P. M. Cremers. "Compendium of Clinical Variant Classification for 2,246 Unique ABCA4 Variants to Clarify Variant Pathogenicity in Stargardt Disease Using a Modified ACMG/AMP Framework." Human Mutation 2023 (December 26, 2023): 1–12. http://dx.doi.org/10.1155/2023/6815504.

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Biallelic variants in ABCA4 cause Stargardt disease (STGD1), the most frequent heritable macular disease. Determination of the pathogenicity of variants in ABCA4 proves to be difficult due to (1) the high number of benign and pathogenic variants in the gene; (2) the presence of many rare ABCA4 variants; (3) the presence of complex alleles for which phasing data are absent; (4) the extensive variable expressivity of this disease and (5) reduced penetrance of hypomorphic variants. Therefore, the classification of many variants in ABCA4 is currently of uncertain significance. Here, we complemented the ABCA4 Leiden Open Variation Database (LOVD) with data from ~11,000 probands with ABCA4-associated inherited retinal diseases from literature up to the end of 2020. We carefully adapted the ACMG/AMP classifications to ABCA4 incorporating ClinGen recommendations and assigned these classifications to all 2,246 unique variants from the ABCA4 LOVD to increase the knowledge of pathogenicity. In total, 1,248 variants were categorized with a likely pathogenic or pathogenic classification, whereas 194 variants were categorized with a likely benign or benign classification. This uniform and improved structured reclassification, incorporating the largest dataset of ABCA4-associated retinopathy cases so far, will improve both the diagnosis as well as genetic counselling for individuals with ABCA4-associated retinopathy.
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Davieson, Connor D., Katie E. Joyce, Lakshya Sharma, and Claire L. Shovlin. "DNA variant classification–reconsidering “allele rarity” and “phenotype” criteria in ACMG/AMP guidelines." European Journal of Medical Genetics 64, no. 10 (October 2021): 104312. http://dx.doi.org/10.1016/j.ejmg.2021.104312.

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Brandt, Tracy, Laura M. Sack, Dolores Arjona, Duanjun Tan, Hui Mei, Hong Cui, Hua Gao, et al. "Adapting ACMG/AMP sequence variant classification guidelines for single-gene copy number variants." Genetics in Medicine 22, no. 2 (September 19, 2019): 336–44. http://dx.doi.org/10.1038/s41436-019-0655-2.

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Tavtigian, Sean V., Steven M. Harrison, Kenneth M. Boucher, and Leslie G. Biesecker. "Fitting a naturally scaled point system to the ACMG/AMP variant classification guidelines." Human Mutation 41, no. 10 (August 30, 2020): 1734–37. http://dx.doi.org/10.1002/humu.24088.

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Vargas‐Parra, Gardenia, Jesús Valle, Paula Rofes, Mireia Gausachs, Agostina Stradella, José M. Moreno‐Cabrera, Angela Velasco, et al. "Comprehensive analysis and ACMG‐based classification of CHEK2 variants in hereditary cancer patients." Human Mutation 41, no. 12 (October 14, 2020): 2128–42. http://dx.doi.org/10.1002/humu.24110.

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Nieto-Patlán, Alejandro, Lindsay Worley, William Hankey, Kyle Hilliard, Justine Siew, Lijun Wang, Bertrand Boisson, et al. "177 ClinGen Framework for PIK3CD Variant Classification: Use of Adapted ACMG/AMP Guidelines." Clinical Immunology 262 (May 2024): 110119. http://dx.doi.org/10.1016/j.clim.2024.110119.

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Lee, Jee-Soo, Sohee Oh, Sue Kyung Park, Min-Hyuk Lee, Jong Won Lee, Sung-Won Kim, Byung Ho Son, et al. "Reclassification of BRCA1 and BRCA2 variants of uncertain significance: a multifactorial analysis of multicentre prospective cohort." Journal of Medical Genetics 55, no. 12 (November 10, 2018): 794–802. http://dx.doi.org/10.1136/jmedgenet-2018-105565.

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BackgroundBRCA1 and BRCA2 (BRCA1/2) variants classified ambiguously as variants of uncertain significance (VUS) are a major challenge for clinical genetic testing in breast cancer; their relevance to the cancer risk is unclear and the association with the response to specific BRCA1/2-targeted agents is uncertain. To minimise the proportion of VUS in BRCA1/2, we performed the multifactorial likelihood analysis and validated this method using an independent cohort of patients with breast cancer.MethodsWe used a data set of 2115 patients with breast cancer from the nationwide multicentre prospective Korean Hereditary Breast Cancer study. In total, 83 BRCA1/2 VUSs (BRCA1, n=26; BRCA2, n=57) were analysed. The multifactorial probability was estimated by combining the prior probability with the overall likelihood ratio derived from co-occurrence of each VUS with pathogenic variants, personal and family history, and tumour characteristics. The classification was compared with the interpretation according to the American College of Medical Genetics and Genomics–Association for Molecular Pathology (ACMG/AMP) guidelines. An external validation was conducted using independent data set of 810 patients.ResultsWe were able to redefine 38 VUSs (BRCA1, n=10; BRCA2, n=28). The revised classification was highly correlated with the ACMG/AMP guideline-based interpretation (BRCA1, p for trend=0.015; BRCA2, p=0.001). Our approach reduced the proportion of VUS from 19% (154/810) to 8.9% (72/810) in the retrospective validation data set.ConclusionThe classification in this study would minimise the ‘uncertainty’ in clinical interpretation, and this validated multifactorial model can be used for the reliable annotation of BRCA1/2 VUSs.
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Vatsyayan, Aastha, Mukesh Kumar, Bhaskar Jyoti Saikia, Vinod Scaria, and Binukumar B. K. "WilsonGenAI a deep learning approach to classify pathogenic variants in Wilson Disease." PLOS ONE 19, no. 5 (May 17, 2024): e0303787. http://dx.doi.org/10.1371/journal.pone.0303787.

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Background Advances in Next Generation Sequencing have made rapid variant discovery and detection widely accessible. To facilitate a better understanding of the nature of these variants, American College of Medical Genetics and Genomics and the Association of Molecular Pathologists (ACMG-AMP) have issued a set of guidelines for variant classification. However, given the vast number of variants associated with any disorder, it is impossible to manually apply these guidelines to all known variants. Machine learning methodologies offer a rapid way to classify large numbers of variants, as well as variants of uncertain significance as either pathogenic or benign. Here we classify ATP7B genetic variants by employing ML and AI algorithms trained on our well-annotated WilsonGen dataset. Methods We have trained and validated two algorithms: TabNet and XGBoost on a high-confidence dataset of manually annotated, ACMG & AMP classified variants of the ATP7B gene associated with Wilson’s Disease. Results Using an independent validation dataset of ACMG & AMP classified variants, as well as a patient set of functionally validated variants, we showed how both algorithms perform and can be used to classify large numbers of variants in clinical as well as research settings. Conclusion We have created a ready to deploy tool, that can classify variants linked with Wilson’s disease as pathogenic or benign, which can be utilized by both clinicians and researchers to better understand the disease through the nature of genetic variants associated with it.
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Ruffo, Paola, Benedetta Perrone, and Francesca Luisa Conforti. "SOD-1 Variants in Amyotrophic Lateral Sclerosis: Systematic Re-Evaluation According to ACMG-AMP Guidelines." Genes 13, no. 3 (March 18, 2022): 537. http://dx.doi.org/10.3390/genes13030537.

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Amyotrophic lateral sclerosis (ALS) is the most common type of motor neuron disease whose causes are unclear. The first ALS gene associated with the autosomal dominant form of the disease was SOD1. This gene has a high rate of rare variants, and an appropriate classification is essential for a correct ALS diagnosis. In this study, we re-evaluated the classification of all previously reported SOD1 variants (n = 202) from ALSoD, project MinE, and in-house databases by applying the ACMG-AMP criteria to ALS. New bioinformatics analysis, frequency rating, and a thorough search for functional studies were performed. We also proposed adjusting criteria strength describing how to apply them to SOD1 variants. Most of the previously reported variants have been reclassified as likely pathogenic and pathogenic based on the modified weight of the PS3 criterion, highlighting how in vivo or in vitro functional studies are determining their interpretation and classification. Furthermore, this study reveals the concordance and discordance of annotations between open databases, indicating the need for expert review to adapt the study of variants to a specific disease. Indeed, in complex diseases, such as ALS, the oligogenic inheritance, the presence of genes that act as risk factors and the reduced penetration must be considered. Overall, the diagnosis of ALS remains clinical, and improving variant classification could support genetic data as diagnostic criteria.
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Godley, Lucy, Xi Luo, Justyne Ross, Sarah Jackson, Anupriya Agarwal, Panagiotis Baliakas, Alison A. Bertuch, et al. "Myeloid Malignancy Variant Curation Expert Panel: An ASH-Sponsored Clingen Expert Panel to Optimize and Validate Acmg/AMP Variant Interpretation Guidelines for Genes Associated with Inherited Myeloid Neoplasms." Blood 132, Supplement 1 (November 29, 2018): 5849. http://dx.doi.org/10.1182/blood-2018-99-118979.

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Abstract Clinical Genome Resource (ClinGen) is an NIH/NHGRI-funded effort dedicated to building an authoritative central resource that defines the clinical relevance of genes and variants for use in precision medicine and research. ClinGen has developed both gene and variant expert panels to adapt the American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) guidelines for consistent and accurate variant classification of specific genes and diseases. Here, we describe a new effort initiated in 2018 and supported by the American Society of Hematology (ASH) in collaboration with ClinGen to develop expert panels. This effort was motivated by the increasing use of genomics in clinical hematology and the lack of resources containing expert interpretation of germline variation. This panel, named the ClinGen Myeloid Malignancy Variant Curation Expert Panel is focused on the curation and annotation of variants in genes associated with familial/inherited risk for myeloid malignancies. Our team consists of expert clinicians, clinical laboratory diagnosticians, and researchers interested in developing and implementing standardized protocols for sequence variant specific annotations of genes in inherited myeloid malignancies. The optimization of the ACMG/AMP guidelines encompasses disease-/gene-informed specifications or strength adjustments of existing rules, including defining gene-specific population frequency cutoffs, and specifying recommendations for the use of computational/predictive data, as supported by published functional and clinical data in addition to guidance on ACMG/AMP variant interpretation provided by the ClinGen effort. Our initial focus has been to organize sub-groups of teams to develop approaches for evaluating ACMG/AMP codes to interpret germline variants of the RUNX1 gene. Once the curation of RUNX1 variants is underway, we will extend our focus to include CEBPA, DDX41, ETV6, and GATA2. These efforts will be bolstered by encouraging submission of existing variant interpretations to ClinVar or other public variant databases by the Hematology community. In summary, the ClinGen Myeloid Malignancy Variant Curation Expert Panel aims to develop recommendations to optimize ACMG/AMP criteria for standardization of variant interpretation in myeloid leukemia genes and make expert-reviewed and interpreted variants available to the hematology community through ClinVar and the ClinGen website (www.clinicalgenome.org) to support patient care and research. Disclosures DiNardo: Karyopharm: Honoraria; Agios: Consultancy; Medimmune: Honoraria; Celgene: Honoraria; Bayer: Honoraria; Abbvie: Honoraria. Nichols:Incyte: Research Funding; Alpine Immune Sciences: Research Funding. Plon:Baylor Genetics: Membership on an entity's Board of Directors or advisory committees.
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Ross, Justyne E., Bing M. Zhang, Kristy Lee, Shruthi Mohan, Brian R. Branchford, Paul Bray, Stefanie N. Dugan, et al. "Specifications of the variant curation guidelines for ITGA2B/ITGB3: ClinGen Platelet Disorder Variant Curation Panel." Blood Advances 5, no. 2 (January 20, 2021): 414–31. http://dx.doi.org/10.1182/bloodadvances.2020003712.

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Abstract Accurate and consistent sequence variant interpretation is critical to the correct diagnosis and appropriate clinical management and counseling of patients with inherited genetic disorders. To minimize discrepancies in variant curation and classification among different clinical laboratories, the American College of Medical Genetics and Genomics (ACMG), along with the Association for Molecular Pathology (AMP), published standards and guidelines for the interpretation of sequence variants in 2015. Because the rules are not universally applicable to different genes or disorders, the Clinical Genome Resource (ClinGen) Platelet Disorder Expert Panel (PD-EP) has been tasked to make ACMG/AMP rule specifications for inherited platelet disorders. ITGA2B and ITGB3, the genes underlying autosomal recessive Glanzmann thrombasthenia (GT), were selected as the pilot genes for specification. Eight types of evidence covering clinical phenotype, functional data, and computational/population data were evaluated in the context of GT by the ClinGen PD-EP. The preliminary specifications were validated with 70 pilot ITGA2B/ITGB3 variants and further refined. In the final adapted criteria, gene- or disease-based specifications were made to 16 rules, including 7 with adjustable strength; no modification was made to 5 rules; and 7 rules were deemed not applicable to GT. Employing the GT-specific ACMG/AMP criteria to the pilot variants resulted in a reduction of variants classified with unknown significance from 29% to 20%. The overall concordance with the initial expert assertions was 71%. These adapted criteria will serve as guidelines for GT-related variant interpretation to increase specificity and consistency across laboratories and allow for better clinical integration of genetic knowledge into patient care.
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Schmidt-Edelkraut, Udo, Elena Ioana Braicu, Sajo Kaduthanam, Salvador Santiago-Mozos, Markus Hartenfeller, Ram Narang, Martin Stein, et al. "Confident BRCA1/2 variant classification: using ACMG and public data for systematic molecular profiling." Annals of Oncology 29 (October 2018): vii71. http://dx.doi.org/10.1093/annonc/mdy375.025.

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Brandt, Tracy, Laura M. Sack, Dolores Arjona, Duanjun Tan, Hui Mei, Hong Cui, Hua Gao, et al. "Correction: Adapting ACMG/AMP sequence variant classification guidelines for single-gene copy-number variants." Genetics in Medicine 22, no. 3 (December 17, 2019): 670–71. http://dx.doi.org/10.1038/s41436-019-0725-5.

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Dickson, Alexa, Meagan Corliss, Jonathan Heusel, Ellen Ziegemeier, Jorge Llibre-Guerra, Alison Goate, Carlos Cruchaga, et al. "P430: Application of ACMG/AMP variant classification guidelines to Alzheimer’s disease-associated genetic variation." Genetics in Medicine Open 1, no. 1 (2023): 100477. http://dx.doi.org/10.1016/j.gimo.2023.100477.

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Walker, Romy, Khalid Mahmood, Julia Como, Mark Clendenning, Jihoon E. Joo, Peter Georgeson, Sharelle Joseland, et al. "DNA Mismatch Repair Gene Variant Classification: Evaluating the Utility of Somatic Mutations and Mismatch Repair Deficient Colonic Crypts and Endometrial Glands." Cancers 15, no. 20 (October 10, 2023): 4925. http://dx.doi.org/10.3390/cancers15204925.

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Germline pathogenic variants in the DNA mismatch repair (MMR) genes (Lynch syndrome) predispose to colorectal (CRC) and endometrial (EC) cancer. Lynch syndrome specific tumor features were evaluated for their ability to support the ACMG/InSiGHT framework in classifying variants of uncertain clinical significance (VUS) in the MMR genes. Twenty-eight CRC or EC tumors from 25 VUS carriers (6xMLH1, 9xMSH2, 6xMSH6, 4xPMS2), underwent targeted tumor sequencing for the presence of microsatellite instability/MMR-deficiency (MSI-H/dMMR) status and identification of a somatic MMR mutation (second hit). Immunohistochemical testing for the presence of dMMR crypts/glands in normal tissue was also performed. The ACMG/InSiGHT framework reclassified 7/25 (28%) VUS to likely pathogenic (LP), three (12%) to benign/likely benign, and 15 (60%) VUS remained unchanged. For the seven re-classified LP variants comprising nine tumors, tumor sequencing confirmed MSI-H/dMMR (8/9, 88.9%) and a second hit (7/9, 77.8%). Of these LP reclassified variants where normal tissue was available, the presence of a dMMR crypt/gland was found in 2/4 (50%). Furthermore, a dMMR endometrial gland in a carrier of an MSH2 exon 1-6 duplication provides further support for an upgrade of this VUS to LP. Our study confirmed that identifying these Lynch syndrome features can improve MMR variant classification, enabling optimal clinical care.
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Kirkland, Nathan, Marzia Pasquali, Rong Mao, Elena Coupal, and Kianoush Sadre-Bazzaz. "Classification of variants in ACADVL following the 2015 ACMG variant classification guidelines and correlation with clinical and biochemical data." Molecular Genetics and Metabolism 132 (April 2021): S31. http://dx.doi.org/10.1016/s1096-7192(21)00130-x.

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Hopkins, Jasmin J., Matthew N. Wakeling, Matthew B. Johnson, Sarah E. Flanagan, and Thomas W. Laver. "REVEL Is Better at Predicting Pathogenicity of Loss-of-Function than Gain-of-Function Variants." Human Mutation 2023 (December 4, 2023): 1–6. http://dx.doi.org/10.1155/2023/8857940.

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In silico predictive tools can help determine the pathogenicity of variants. The 2015 American College of Medical Genetics and Genomics (ACMG) guidelines recommended that scores from these tools can be used as supporting evidence of pathogenicity. A subsequent publication by the ClinGen Sequence Variant Interpretation Working Group suggested that high scores from some tools were sufficiently predictive to be used as moderate or strong evidence of pathogenicity. REVEL is a widely used metapredictor that uses the scores of 13 individual in silico tools to calculate the pathogenicity of missense variants. Its ability to predict missense pathogenicity has been assessed extensively; however, no study has previously tested whether its performance is affected by whether the missense variant acts via a loss-of-function (LoF) or gain-of-function (GoF) mechanism. We used a highly curated dataset of 66 confirmed LoF and 65 confirmed GoF variants to evaluate whether this affected the performance of REVEL. 98% of LoF and 100% of GoF variants met the author-recommended REVEL threshold of 0.5 for pathogenicity, while 89% of LoF and 88% of GoF variants exceeded the 0.75 threshold. However, while 55% of LoF variants met the threshold recommended for a REVEL score to count as strong evidence of pathogenicity from the ACMG guidelines (0.932), only 35% of GoF variants met this threshold ( P = 0.0352 ). GoF variants are therefore less likely to receive the highest REVEL scores which would enable the REVEL score to be used as strong evidence of pathogenicity. This has implications for classification with the ACMG guidelines as GoF variants are less likely to meet the criteria for pathogenicity.
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Froyen, Guy, Marie Le Mercier, Els Lierman, Karl Vandepoele, Friedel Nollet, Elke Boone, Joni Van der Meulen, et al. "Standardization of Somatic Variant Classifications in Solid and Haematological Tumours by a Two-Level Approach of Biological and Clinical Classes: An Initiative of the Belgian ComPerMed Expert Panel." Cancers 11, no. 12 (December 16, 2019): 2030. http://dx.doi.org/10.3390/cancers11122030.

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In most diagnostic laboratories, targeted next-generation sequencing (NGS) is currently the default assay for the detection of somatic variants in solid as well as haematological tumours. Independent of the method, the final outcome is a list of variants that differ from the human genome reference sequence of which some may relate to the establishment of the tumour in the patient. A critical point towards a uniform patient management is the assignment of the biological contribution of each variant to the malignancy and its subsequent clinical impact in a specific malignancy. These so-called biological and clinical classifications of somatic variants are currently not standardized and are vastly dependent on the subjective analysis of each laboratory. This subjectivity can thus result in a different classification and subsequent clinical interpretation of the same variant. Therefore, the ComPerMed panel of Belgian experts in cancer diagnostics set up a working group with the goal to harmonize the biological classification and clinical interpretation of somatic variants detected by NGS. This effort resulted in the establishment of a uniform, two-level classification workflow system that should enable high consistency in diagnosis, prognosis, treatment and follow-up of cancer patients. Variants are first classified into a tumour-independent biological five class system and subsequently in a four tier ACMG clinical classification. Here, we describe the ComPerMed workflow in detail including examples for each step of the pipeline. Moreover, this workflow can be implemented in variant classification software tools enabling automatic reporting of NGS data, independent of panel, method or analysis software.
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Lopez-Perolio, Irene, Raphaël Leman, Raquel Behar, Vanessa Lattimore, John F. Pearson, Laurent Castéra, Alexandra Martins, et al. "Alternative splicing and ACMG-AMP-2015-based classification of PALB2 genetic variants: an ENIGMA report." Journal of Medical Genetics 56, no. 7 (March 19, 2019): 453–60. http://dx.doi.org/10.1136/jmedgenet-2018-105834.

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BackgroundPALB2 monoallelic loss-of-function germ-line variants confer a breast cancer risk comparable to the average BRCA2 pathogenic variant. Recommendations for risk reduction strategies in carriers are similar. Elaborating robust criteria to identify loss-of-function variants in PALB2—without incurring overprediction—is thus of paramount clinical relevance. Towards this aim, we have performed a comprehensive characterisation of alternative splicing in PALB2, analysing its relevance for the classification of truncating and splice site variants according to the 2015 American College of Medical Genetics and Genomics-Association for Molecular Pathology guidelines.MethodsAlternative splicing was characterised in RNAs extracted from blood, breast and fimbriae/ovary-related human specimens (n=112). RNAseq, RT-PCR/CE and CloneSeq experiments were performed by five contributing laboratories. Centralised revision/curation was performed to assure high-quality annotations. Additional splicing analyses were performed in PALB2 c.212–1G>A, c.1684+1G>A, c.2748+2T>G, c.3113+5G>A, c.3350+1G>A, c.3350+4A>C and c.3350+5G>A carriers. The impact of the findings on PVS1 status was evaluated for truncating and splice site variant.ResultsWe identified 88 naturally occurring alternative splicing events (81 newly described), including 4 in-frame events predicted relevant to evaluate PVS1 status of splice site variants. We did not identify tissue-specific alternate gene transcripts in breast or ovarian-related samples, supporting the clinical relevance of blood-based splicing studies.ConclusionsPVS1 is not necessarily warranted for splice site variants targeting four PALB2 acceptor sites (exons 2, 5, 7 and 10). As a result, rare variants at these splice sites cannot be assumed pathogenic/likely pathogenic without further evidences. Our study puts a warning in up to five PALB2 genetic variants that are currently reported as pathogenic/likely pathogenic in ClinVar.
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Abad Baucells, Clàudia, Ria Schönauer, and Jan Halbritter. "The genetics of cystinuria – an update and critical reevaluation." Current Opinion in Nephrology & Hypertension 33, no. 2 (November 6, 2023): 231–37. http://dx.doi.org/10.1097/mnh.0000000000000949.

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Purpose of review We aimed to critically evaluate how the establishment of genotype-based treatment for cystinuria has been hampered due to the large number of variants of unknown significance (VUS) within the disease causing genes as well as challenges in accessing a large enough sample size for systematic analysis of endpoint parameters that truly reflect disease severity. This review further discusses how to overcome these hurdles with the establishment of a cystinuria-specific refinement of the current American College of Medical Genetics and Genomics (ACMG)-criteria of variant interpretation. Recent findings Novel tools such as AlphaMissense combined with the establishment of a refined ACMG criterion will play a significant role in classifying VUS within the responsible disease genes SLC3A1 (rBAT) and SLC7A9 (BAT1). This will also be essential in elucidating the role of promising candidate genes, such as SLC7A13 (AGT1), which have been derived from murine model systems and still need further research to determine if they are involved in human cystinuria. Summary Cystinuria was one of the first disorders to receive a gene-based classification, nonetheless, the clinically actionable implications of genetic diagnostics is still minor. This is due to poorly characterized genotype-phenotype correlations which results in a lack of individualized (genotype-) based management and metaphylaxis.
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Amendola, Laura M., Kathleen Muenzen, Leslie G. Biesecker, Kevin M. Bowling, Greg M. Cooper, Michael O. Dorschner, Catherine Driscoll, et al. "Variant Classification Concordance using the ACMG-AMP Variant Interpretation Guidelines across Nine Genomic Implementation Research Studies." American Journal of Human Genetics 107, no. 5 (November 2020): 932–41. http://dx.doi.org/10.1016/j.ajhg.2020.09.011.

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48

Lyra, Paulo, Lucas Dalcolmo, Michael Parsons, Thales Nepomuceno, Samuel Brito, Nam Phuong N. Nguyen, Geise de Oliveira, et al. "Abstract 7325: Integration of functional data to classify BRCA1/2 missense variants: An ENIGMA project." Cancer Research 84, no. 6_Supplement (March 22, 2024): 7325. http://dx.doi.org/10.1158/1538-7445.am2024-7325.

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Abstract The success of precision cancer prevention and treatment hinges on accurate discrimination between benign and pathogenic germline alleles in cancer susceptibility genes. Variants of uncertain clinical significance (VUS) present a challenge for cancer risk assessment and functional data will be essential to resolve many VUS. To address this, we developed a cloud-based environment to collate, curate, integrate and analyze all published functional data related to BRCA1 and BRCA2 missense VUS. Then, functional evidence criteria for pathogenicity were determined for each assay using the American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) classification guidelines and ClinGen criteria. Our approach involved retrieving published articles reporting one or more functional assays of BRCA1/2 missense variants reporting variant impact on various biochemical and cell biological functions. Functional results were recorded using original authors' thresholds and categories, then converted to ordinal variables to harmonize: [0 = no functional impact], [1 = intermediate impact], and [2 = functional impact]. For BRCA1, we updated our previous published integration (Genet Med. 2021;23:306-315) with 14 additional publications, collating results from 53 instances of functional assays reporting functional data on 3,219 unique missense variants. For BRCA2, 28 publications were identified reporting data from 141 instances of functional assays on 4,759 unique missense variants. Utilizing a panel of 542 and 396 known pathogenic and benign reference variants for BRCA1 and BRCA2, respectively, we determined the sensitivity, specificity, and ACMG/AMP odds of pathogenicity for each instance of the assay. The remaining variants were assigned a relevant functional assay code weight for each assay instance. Our study successfully derived ACMG/AMP evidence strength from functional data for 3,040 BRCA1 (94% of 3,219) and 2,704 BRCA2 (57% of 4,759) missense variants. This comprehensive work underscores the utility of functional data in informing classification of BRCA1 and BRCA2 missense VUS. Citation Format: Paulo Lyra, Lucas Dalcolmo, Michael Parsons, Thales Nepomuceno, Samuel Brito, Nam Phuong N. Nguyen, Geise de Oliveira, Joao Paulo da Siva, Laura Caleca, Tanisha Taneja, Chunling Hu, Marcy Richardson, Maria Rossing, Aditi Hazra, Alexandra Martins, Sandrine Caputo, Gael A. Millot, Joanne NgeowYuen Yie, Marcelo A. Carvalho, Melissa Cline, Paolo Radice, Rachael Carlsen, Romy Mesman, Valentina Zampiga, Rehan Villani, Vijay Josef, Shyam Sharan, Kyriaki Michailidou, Amanda B. Spurdle, Fergus Couch, Maaike P.G. Vreeswijk, Alvaro N. Monteiro. Integration of functional data to classify BRCA1/2 missense variants: An ENIGMA project [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7325.
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Bhasin, Meghna Ahuja, Alexej Knaus, Pietro Incardona, Alexander Schmid, Manuel Holtgrewe, Miriam Elbracht, Peter M. Krawitz, and Tzung-Chien Hsieh. "Enhancing Variant Prioritization in VarFish through On-Premise Computational Facial Analysis." Genes 15, no. 3 (March 17, 2024): 370. http://dx.doi.org/10.3390/genes15030370.

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Genomic variant prioritization is crucial for identifying disease-associated genetic variations. Integrating facial and clinical feature analyses into this process enhances performance. This study demonstrates the integration of facial analysis (GestaltMatcher) and Human Phenotype Ontology analysis (CADA) within VarFish, an open-source variant analysis framework. Challenges related to non-open-source components were addressed by providing an open-source version of GestaltMatcher, facilitating on-premise facial analysis to address data privacy concerns. Performance evaluation on 163 patients recruited from a German multi-center study of rare diseases showed PEDIA’s superior accuracy in variant prioritization compared to individual scores. This study highlights the importance of further benchmarking and future integration of advanced facial analysis approaches aligned with ACMG guidelines to enhance variant classification.
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Osundiji, Mayowa, Jessie Cameron, Rory Olson, Bukola Olarewaju, and Andreas Schulze. "P046: ACMG/AMP variant classification framework in arginase 1 deficiency: Implications for birth prevalence estimates and diagnostics." Genetics in Medicine Open 2 (2024): 100923. http://dx.doi.org/10.1016/j.gimo.2024.100923.

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