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Pathogenic/likely pathogenic variants in susceptibility genes that interrupt RNA splicing are a well-documented mechanism of hereditary cancer syndromes development. However, if RNA studies are not performed, most of the variants beyond the canonical GT-AG splice site are characterized as variants of uncertain significance (VUS). To decrease the VUS burden, we have bioinformatically evaluated all novel VUS detected in 732 consecutive patients tested in the routine genetic counseling process. Twelve VUS that were predicted to cause splicing defects were selected for mRNA analysis. Here, we report a functional characterization of 12 variants located beyond the first two intronic nucleotides using RNAseq in APC, ATM, FH, LZTR1, MSH6, PALB2, RAD51C, and TP53 genes. Based on the analysis of mRNA, we have successfully reclassified 50% of investigated variants. 25% of variants were downgraded to likely benign, whereas 25% were upgraded to likely pathogenic leading to improved clinical management of the patient and the family members.

Genetic variants that cause haploinsufficiency account for many autosomal dominant (AD) disorders. Gene-based diagnosis classifies variants that alter canonical splice signals as pathogenic, but due to imperfect understanding of RNA splice signals other variants that may create or eliminate splice sites are often clinically classified as variants of unknown significance (VUS). To improve recognition of pathogenic splice-altering variants in AD disorders, we used computational tools to prioritize VUS and developed a cell-based minigene splicing assay to confirm aberrant splicing. Using this two-step procedure we evaluated all rare variants in two AD cardiomyopathy genes, lamin A/C (LMNA) and myosin binding protein C (MYBPC3). We demonstrate that 13 LMNA and 35 MYBPC3 variants identified in cardiomyopathy patients alter RNA splicing, representing a 50% increase in the numbers of established damaging splice variants in these genes. Over half of these variants are annotated as VUS by clinical diagnostic laboratories. Familial analyses of one variant, a synonymous LMNA VUS, demonstrated segregation with cardiomyopathy affection status and altered cardiac LMNA splicing. Application of this strategy should improve diagnostic accuracy and variant classification in other haploinsufficient AD disorders.

Given the constantly improving cost and speed of genome sequencing, it is reasonable to expect that personal genomes will soon be known for many millions of humans. This stands in stark contrast with our limited ability to interpret the sequence variants which we find. Although it is, perhaps, easiest to interpret variants in coding regions, knowledge of functional impact is unknown for the vast majority of missense variants. While many computational approaches can predict the impact of coding variants, they are given a little weight in the current guidelines for interpreting clinical variants. Laboratory assays produce comparatively more trustworthy results, but until recently did not scale to the space of all possible mutations. The development of deep mutational scanning and other multiplexed assays of variant effect has now brought feasibility of this endeavour within view. Here, we review progress in this field over the last decade, break down the different approaches into their components, and compare methodological differences.

Panel-based genetic testing has identified increasing numbers of patients with pancreatic ductal adenocarcinoma (PDAC) who carry germ-line mutations. However, small sample sizes or number of genes evaluated limit prevalence estimates of these mutations. We estimated prevalence of mutations in PDAC patients with positive family history. We sequenced 25 cancer susceptibility genes in lymphocyte DNA from 302 PDAC patients in the Mayo Clinic Biospecimen Resource for Pancreatic Research Registry. Kindreds containing at least two first-degree relatives with PDAC met criteria for familial pancreatic cancer (FPC), while the remaining were familial, but not FPC. Thirty-six patients (12%) carried at least one deleterious mutation in one of 11 genes. Of FPC patients, 25/185 (14%) were carriers, while 11/117 (9%) non-FPC patients with family history were carriers. Deleterious mutations (n) identified in PDAC patients were BRCA2 (11), ATM (8), CDKN2A (4), CHEK2 (4), MUTYH/MYH (3 heterozygotes, not biallelic), BRCA1 (2), and 1 each in BARD1, MSH2, NBN, PALB2, and PMS2. Novel mutations were found in ATM, BARD1, and PMS2. Multiple susceptibility gene testing in PDAC patients with family history of pancreatic cancer is warranted regardless of FPC status and will inform genetic risk counseling for families.

Parkinson’s disease (PD) is a common and incurable neurodegenerative disorder that arises from the loss of dopaminergic neurons in the substantia nigra and is mainly characterized by progressive loss of motor function. Monogenic familial PD is associated with highly penetrant variants in specific genes, notably the PRKN gene, where homozygous or compound heterozygous loss-of-function variants predominate. PRKN encodes Parkin, an E3 ubiquitin-protein ligase important for protein ubiquitination and mitophagy of damaged mitochondria. Accordingly, Parkin plays a central role in mitochondrial quality control but is itself also subject to a strict protein quality control system that rapidly eliminates certain disease-linked Parkin variants. Here, we summarize the cellular and molecular functions of Parkin, highlighting the various mechanisms by which PRKN gene variants result in loss-of-function. We emphasize the importance of high-throughput assays and computational tools for the clinical classification of PRKN gene variants and how detailed insights into the pathogenic mechanisms of PRKN gene variants may impact the development of personalized therapeutics.

Increasing numbers of genes are being implicated in Mendelian disorders and incorporated into clinical test panels. However, lack of evidence supporting the gene-disease relationship can hinder interpretation. We explored the utility of testing 51 additional genes for hypertrophic cardiomyopathy (HCM), one of the most commonly tested Mendelian disorders. Using genome sequencing data from 240 sarcomere gene negative HCM cases and 6229 controls, we undertook case-control and individual variant analyses to assess 51 genes that have been proposed for HCM testing. We found no evidence to suggest that rare variants in these genes are prevalent causes of HCM. One variant, in a single case, was categorized as likely to be pathogenic. Over 99% of variants were classified as a variant of uncertain significance (VUS) and 54% of cases had one or more VUS. For almost all genes, the gene-disease relationship could not be validated and lack of evidence precluded variant interpretation. Thus, the incremental diagnostic yield of extending testing was negligible, and would, we propose, be outweighed by problems that arise with a high rate of uninterpretable findings. These findings highlight the need for rigorous, evidence-based selection of genes for clinical test panels.

Background: The genomic era has transformed not only the tools of medicine but the very logic by which we understand health and disease. Whole Exome Sequencing (WES), Clinical Exome Sequencing (CES), and Whole Genome Sequencing (WGS) have catalyzed a shift from Mendelian simplicity to polygenic complexity, from genetic determinism to probabilistic interpretation. This epistemological evolution calls into question long-standing notions of causality, certainty, and identity in clinical genomics. Yet, as the promise of precision medicine grows, so too do the tensions it generates: fragmented data, interpretative opacity, and the ethical puzzles of Variants of Uncertain Significance (VUSs) and unsolicited secondary findings. Results: Despite technological refinement, the diagnostic yield of Next-Generation Sequencing (NGS) remains inconsistent, hindered by the inherent intricacy of gene–environment interactions and constrained by rigid classificatory systems like OMIM and HPO. VUSs (neither definitively benign nor pathogenic) occupy a liminal space that resists closure, burdening both patients and clinicians with uncertainty. Meanwhile, secondary findings, though potentially life-altering, challenge the boundaries of consent, privacy, and responsibility. In both adult and pediatric contexts, genomic knowledge reshapes notions of autonomy, risk, and even personhood. Conclusions: Genomic medicine has to develop into a flexible, morally sensitive paradigm that neither celebrates certainty nor ignores ambiguity. Open infrastructures, dynamic variant reclassification, and a renewed focus on interdisciplinary and humanistic approaches are essential. Only by embracing the uncertainty intrinsic to our biology can precision medicine fulfill its promise, not as a deterministic science, but as a nuanced dialogue between genes, environments, and lived experience.

Purpose Guidelines for variant interpretation incorporate variant hotspots in critical functional domains as evidence for pathogenicity (e.g., PM1 and PP2), but do not use “coldspots,” that is, regions without essential functions that tolerate variation, as evidence a variant is benign. To improve variant classification we evaluated BRCA1 and BRCA2 missense variants reported in ClinVar to identify regions where pathogenic missenses are extremely infrequent, defined as coldspots. Methods We used Bayesian approaches to model variant classification in these regions. Results BRCA1 exon 11 (~60% of the coding sequence), and BRCA2 exons 10 and 11 (~65% of the coding sequence), are coldspots. Of 89 pathogenic (P) or likely pathogenic (LP) missense variants in BRCA1 , none are in exon 11 (odds <0.01, 95% confidence interval [CI] 0.0–0.01). Of 34 P or LP missense variants in BRCA2 , none are in exons 10–11 (odds <0.01, 95% CI 0.0–0.01). More than half of reported missense variants of uncertain significance (VUS) in BRCA1 and BRCA2 are in coldspots (3115/5301 = 58.8%). Reclassifying these 3115 VUS as likely benign would substantially improve variant classification. Conclusion In BRCA1 and BRCA2 coldspots, missense variants are very unlikely to be pathogenic. Classification schemes that incorporate coldspots can reduce the number of VUS and mitigate risks from reporting benign variation as VUS.

To improve methods for predicting the impact of missense variants of uncertain significance (VUS) in BRCA1 and BRCA2 on protein function. Functional data for 248 BRCA1 and 207 BRCA2 variants from assays with established high sensitivity and specificity for damaging variants were used to recalibrate 40 in silico algorithms predicting the impact of variants on protein activity. Additional random forest (RF) and naïve voting method (NVM) metapredictors for both BRCA1 and BRCA2 were developed to increase predictive accuracy. Optimized thresholds for in silico prediction models significantly improved the accuracy of predicted functional effects for BRCA1 and BRCA2 variants. In addition, new BRCA1-RF and BRCA2-RF metapredictors showed area under the curve (AUC) values of 0.92 (95% confidence interval [CI]: 0.88–0.96) and 0.90 (95% CI: 0.84–0.95), respectively. Similarly, the BRCA1-NVM and BRCA2-NVM models had AUCs of 0.93 and 0.90. The RF and NVM models were used to predict the pathogenicity of all possible missense variants in BRCA1 and BRCA2. The recalibrated algorithms and new metapredictors significantly improved upon current models for predicting the impact of variants in cancer risk–associated domains of BRCA1 and BRCA2. Prediction of the functional impact of all possible variants in BRCA1 and BRCA2 provides important information about the clinical relevance of variants in these genes.

Precision medicine starts with a precision diagnosis. Yet up to 80% of cases of monogenic diabetes, a form of diabetes characterized by mutations in a single gene, are either overlooked or misdiagnosed. A genetic test for monogenic diabetes does not always lead to a precise diagnosis, as novel variants are often classified as variants of unknown significance. Variant interpretation requires collation of a framework of evidence, including population, computational, and segregation data, and can be assisted by functional analysis. The inclusion of functional data can be challenging, depending on the number of benign and pathogenic variants available for benchmarking assays. Glucokinase is the rate-limiting step for glucose metabolism in the pancreatic beta-cell and governs the threshold for glucose-stimulated insulin release. Loss-of-function alleles in the glucokinase (GCK) gene are a cause of stable fasting hyperglycemia from birth and/or diabetes. In this study, we functionally characterized 25 variants identified during diagnostic testing or in exome sequencing studies. We assessed their kinetic characteristics, stability, and interaction with pharmacological and physiological regulators. We integrated our functional data with existing data from the ClinGen Monogenic Diabetes Variant Curation Expert Review panel using a gene-specific framework to assist variant classification. We show how functional evidence can aid variant classification, thus enabling diagnostic certainty.