OBJECTIVE: To investigate how many novel pathogenic (P) and likely pathogenic (LP) nonprotein-truncating or noncanonical splicing variants would be classified as variants of unknown significance (VUS) if they were detected in fetuses without abnormalities.
METHODS: The study included 156 patients with neurodevelopmental disorders diagnosed through postnatal exome sequencing. Causative P/LP nonprotein-truncating and noncanonical splicing variants were retrospectively reclassified in cases without specific prenatal manifestations, disregarding postnatal symptoms.
RESULTS: Of the 156 patients, 72 had a nontruncating or noncanonical splicing variant. Six patients were excluded for having more than one possible causative variant. Twelve patients had prenatal malformations known to be associated with the diagnosed disorder; therefore, variant interpretation remained unchanged. In 33 of the 54 remaining cases, the variant had been previously reported as P/LP. Reclassification of the other 21 LP/P variants revealed that 16 would have been classified as VUS if detected prenatally.
CONCLUSIONS: In our cohort, ∼24% (16/66) of causative nonprotein-truncating/noncanonical splicing variants would have been classified as VUS if sequencing had been conducted during pregnancy. The potential for false-negative results, stemming from limitations in the phenotypic information available prenatally, should be discussed with prospective parents. The criteria for classifying and reporting variants in the prenatal setting may require adjustment.

Variants that alter gene splicing are estimated to comprise up to a third of all disease-causing variants, yet they are hard to predict from DNA sequencing data alone. To overcome this, many groups are incorporating RNA-based analyses, which are resource intensive, particularly for diagnostic laboratories. There are thousands of functionally validated variants that induce mis-splicing; however, this information is not consolidated, and they are under-represented in ClinVar, which presents a barrier to variant interpretation and can result in duplication of validation efforts. To address this issue, we developed SpliceVarDB, an online database consolidating over 50,000 variants assayed for their effects on splicing in over 8,000 human genes. We evaluated over 500 published data sources and established a spliceogenicity scale to standardize, harmonize, and consolidate variant validation data generated by a range of experimental protocols. According to the strength of their supporting evidence, variants were classified as \"splice-altering\" (∼25%), \"not splice-altering\" (∼25%), and \"low-frequency splice-altering\" (∼50%), which correspond to weak or indeterminate evidence of spliceogenicity. Importantly, 55% of the splice-altering variants in SpliceVarDB are outside the canonical splice sites (5.6% are deep intronic). These variants can support the variant curation diagnostic pathway and can be used to provide the high-quality data necessary to develop more accurate in silico splicing predictors. The variants are accessible through an online platform, SpliceVarDB, with additional features for visualization, variant information, in silico predictions, and validation metrics. SpliceVarDB is a very large collection of splice-altering variants and is available at https://splicevardb.org.

The identification of the genetic causes of inherited disorders from next-generation sequencing (NGS) data remains a complicated process, in particular due to challenges in interpretation of the vast amount of generated data and hundreds of candidate variants identified. Inconsistencies in variant classification, where genetic centers classify the same variant differently, can hinder accurate diagnoses for rare diseases. Publicly available databases that collect data on human genetic variations and their association with diseases provide ample opportunities to discover conflicts in variant interpretation worldwide. In this study, we explored patterns of variant classification discrepancies using data from ClinVar, a public archive of variant interpretations. We found that 5.7% of variants have conflicting interpretations (COIs) reported, and the vast majority of interpretation conflicts arise for variants of uncertain significance (VUS). As many as 78% of clinically relevant genes harbor variants with COIs, and genes with high COI rates tended to have more exons and longer transcripts, with a greater proportion of genes linked to several distinct conditions. The enrichment analysis of COI-enriched genes revealed that the products of these genes are involved in cardiac disorders, muscle development, and function. To improve diagnoses, we believe that specific variant interpretation rules could be developed for such genes. Additionally, our findings underscore the need for the publication of variant pathogenicity evidence and the importance of considering every variant as VUS unless proven otherwise.

BACKGROUND: Variant interpretation is essential for identifying patients\' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb).
RESULTS: The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past three decades, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 190 VIPs, resulting in a total of 407 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods.
CONCLUSIONS: VIPdb version 2 summarizes 407 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications. VIPdb is available at  https://genomeinterpretation.org/vipdb.

In silico variant effect predictions are available for nearly all missense variants but played a minimal role in clinical variant classification because they were deemed to provide only supporting evidence. Recently, the ClinGen Sequence Variant Interpretation (SVI) Working Group updated recommendations for variant effect prediction use. By analyzing control pathogenic and benign variants across all genes, they were able to compute evidence strength for predictor score intervals with some intervals generating moderate, strong, or even very strong evidence. However, this genome-wide approach could obscure heterogeneous predictor performance in different genes. We quantified the gene-by-gene performance of two top predictors, REVEL and BayesDel, by analyzing control variants in each predictor score interval in 3,668 disease-relevant genes. Approximately 10% of intervals had sufficient control variants for analysis, and ∼70% of these intervals exceeded the maximum number of incorrect predictions implied by the SVI recommendations. These trending discordant intervals arose owing to the divergence of the gene-specific distribution of predictions from the genome-wide distribution, suggesting that gene-specific calibration is needed in many cases. Approximately 22% of ClinVar missense variants of uncertain significance in genes we analyzed (REVEL = 100,629, BayesDel = 71,928) had predictions in trending discordant intervals. Thus, genome-wide calibrations could result in many variants receiving inappropriate evidence strength. To facilitate a review of the SVI\'s calibrations, we developed a web application enabling visualization of gene-specific predictions and trending concordant and discordant intervals.

BACKGROUND: In this article, we delineate a loosely selected cohort comprising patients with a history of early-onset breast cancer and/or a familial occurrence of cancer. The aim of this study was to gain insights into the presence of breast cancer-related gene variants in a population from a micro-region in southern Brazil, specifically the Metropolitan Region of Curitiba. This area exhibits a highly genetically mixed population, mirroring the general characteristics of the Brazilian people.
METHODS: Comprehensive next-generation sequencing (NGS) multigene panel testing was conducted on 12 patients from the region, utilizing three different library preparation methods.
RESULTS: Two pathogenic variants and one candidate pathogenic variant were identified: BRCA2 c.8878C>T, p.Gln2960Ter; CHEK2 c.1100del, p.Thr367Metfs15, and BRCA2 c.3482dup, p.Asp1161Glufs3.
CONCLUSIONS: BRCA2 c.3482dup, a novel candidate pathogenic variant, previously unpublished, is reported. The prevalence of pathogenic variants in this small cohort is similar to that described in the literature. All different library preparation methods were equally proficient in enabling the detection of these variants.

Accurate interpretation of sequence variants in inherited retinal dystrophy (IRD) is vital given the significant genetic heterogeneity observed in this disorder. To achieve consistent and accurate diagnoses, establishment of standardized guidelines for variant interpretation is essential. The American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) guidelines for variant interpretation serve as the global \"cross-disease\" standard for classifying variants in Mendelian hereditary disorders. These guidelines propose a systematic approach for categorizing variants into 5 classes based on various types of evidence, such as population data, computational data, functional data, and segregation data. However, for clinical genetic diagnosis and to ensure standardized diagnosis and treatment criteria, additional specifications based on features associated with each disorder are necessary. In this context, we present a comprehensive framework outlining the newly specified ACMG/AMP rules tailored explicitly to IRD in the Japanese population on behalf of the Research Group on Rare and Intractable Diseases (Ministry of Health, Labour and Welfare of Japan). These guidelines consider disease frequencies, allele frequencies, and both the phenotypic and the genotypic characteristics unique to IRD in the Japanese population. Adjustments and modifications have been incorporated to reflect the specific requirements of the population. By incorporating these IRD-specific factors and refining the existing ACMG/AMP guidelines, we aim to enhance the accuracy and consistency of variant interpretation in IRD cases, particularly in the Japanese population. These guidelines serve as a valuable resource for ophthalmologists and clinical geneticists involved in the diagnosis and treatment of IRD, providing them with a standardized framework to assess and classify genetic variants.

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OBJECTIVE: To investigate the number of rare missense variants observed in human genome sequences by ACMG/AMP PP3/BP4 evidence strength, following the calibrated PP3/BP4 computational recommendations.
METHODS: Missense variants from the genome sequences of 300 probands from the Rare Genomes Project with suspected rare disease were analyzed using computational prediction tools able to reach PP3_Strong and BP4_Moderate evidence strengths (BayesDel, MutPred2, REVEL, and VEST4). The numbers of variants at each evidence strength were analyzed across disease-associated genes and genome-wide.
RESULTS: From a median of 75.5 rare (≤1% allele frequency) missense variants in disease-associated genes per proband, a median of one reached PP3_Strong, 3-5 PP3_Moderate, and 3-5 PP3_Supporting. Most were allocated BP4 evidence (median 41-49 per proband) or were indeterminate (median 17.5-19 per proband). Extending the analysis to all protein-coding genes genome-wide, the number of PP3_Strong variants increased approximately 2.6-fold compared to disease-associated genes, with a median per proband of 1-3 PP3_Strong, 8-16 PP3_Moderate, and 10-17 PP3_Supporting.
CONCLUSIONS: A small number of variants per proband reached PP3_Strong and PP3_Moderate in 3,424 disease-associated genes, and though not the intended use of the recommendations, also genome-wide. Use of PP3/BP4 evidence as recommended from calibrated computational prediction tools in the clinical diagnostic laboratory is unlikely to inappropriately contribute to the classification of an excessive number of variants as Pathogenic or Likely Pathogenic by ACMG/AMP rules.

UNASSIGNED: Variant interpretation is essential for identifying patients\' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb).
UNASSIGNED: The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past 25 years, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 186 VIPs, resulting in a total of 403 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods.
UNASSIGNED: VIPdb version 2 summarizes 403 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications.
UNASSIGNED: VIPdb version 2 is available at https://genomeinterpretation.org/vipdb.