Biology subjects

ClinGen Sequence Variant Interpretation Working Group,

Publications and source records attributed to ClinGen Sequence Variant Interpretation Working Group,.

3 recordsLinked to original sources

bioRxiv2024

Calibration of additional computational tools expands ClinGen recommendation options for variant classification with PP3/BP4 criteria

PurposeWe previously developed an approach to calibrate computational tools for clinical variant classification, updating recommendations for the reliable use of variant impact predictors to provide evidence strength up to Strong. A new generation of tools using distinctive approaches have since been released, and these methods must be independently calibrated for clinical application. MethodUsing our local posterior probability-based calibration and our established data set of ClinVar pathogenic and benign variants, we determined the strength of evidence provided by three new tools (AlphaMissense, ESM1b, VARITY) and calibrated scores meeting each evidence strength. Results All three tools reached the Strong level of evidence for variant pathogenicity and Moderate for benignity, though sometimes for few variants. Compared to previously recommended tools, these yielded at best only modest improvements in the tradeoffs of evidence strength and false positive predictions. ConclusionAt calibrated thresholds, three new computational predictors provided evidence for variant pathogenicity at similar strength to the four previously recommended predictors (and comparable with functional assays for some variants). This calibration broadens the scope of computational tools for application in clinical variant classification. Their new approaches offer promise for future advancement of the field.

bioinformatics↗

bioRxiv2022

Evidence-based calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for clinical use of PP3/BP4 criteria

Recommendations from the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) for interpreting sequence variants specify the use of computational predictors as Supporting level of evidence for pathogenicity or benignity using criteria PP3 and BP4, respectively. However, score intervals defined by tool developers, and ACMG/AMP recommendations that require the consensus of multiple predictors, lack quantitative support. Previously, we described a probabilistic framework that quantified the strengths of evidence (Supporting, Moderate, Strong, Very Strong) within ACMG/AMP recommendations. We have extended this framework to computational predictors and introduce a new standard that converts a tools scores to PP3 and BP4 evidence strengths. Our approach is based on estimating the local positive predictive value and can calibrate any computational tool or other continuous-scale evidence on any variant type. We estimate thresholds (score intervals) corresponding to each strength of evidence for pathogenicity and benignity for thirteen missense variant interpretation tools, using carefully assembled independent data sets. Most tools achieved Supporting evidence level for both pathogenic and benign classification using newly established thresholds. Multiple tools reached score thresholds justifying Moderate and several reached Strong evidence levels. One tool reached Very Strong evidence level for benign classification on some variants. Based on these findings, we provide recommendations for evidence-based revisions of the PP3 and BP4 ACMG/AMP criteria using individual tools and future assessment of computational methods for clinical interpretation.

bioinformatics↗

bioRxiv2018

Recommendations for Interpreting the Loss of Function PVS1 ACMG/AMP Variant Criteria

The 2015 ACMG/AMP sequence variant interpretation guideline provided a framework for classifying variants based on several benign and pathogenic evidence criteria. This guideline includes a pathogenic criterion (PVS1) for predicted loss of function variants. However, the guideline did not elaborate on the specific considerations for the different types of loss of function variants, nor did it provide decision-making pathways assimilating information about the variant type, its location within the gene, or any additional evidence for the likelihood of a true null effect. Furthermore, the ACMG/AMP guideline did not take into account the relative strengths for each evidence type and the final outcome of their combinations with respect to PVS1 strength. Finally, criteria specifying the genes for which PVS1 can be used are still missing. Here, as part of the Clinical Genomic Resource (ClinGen) Sequence Variant Interpretation (SVI) Working Groups goal of refining ACMG/AMP criteria, we provide recommendations for applying the PVS1 rule using detailed guidance addressing all the above-mentioned gaps. We evaluate the performance of the refined rule using heterogeneous types of loss of function variants (n = 56) curated by seven disease-specific groups across ten genes. Our recommendations will facilitate consistent and accurate interpretation of predicted loss of function variants.\n\nGRANT NUMBERSResearch reported in this publication was supported by the National Human Genome Research Institute (NHGRI) under award number U41HG006834. LGB was supported by the Intramural Research Program of the NHGRI grant number HG200359 09.

genomics↗