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Distinct rates of VUS reclassification are observed when subclassifying VUS by evidence level.

PURPOSE: Genetic testing commonly yields a plethora of variants of uncertain significance (VUS) that can lead to ongoing uncertainty for patients and their caregivers. Although all VUS hold uncertainty, some VUS have more evidence in support of pathogenicity, whereas others have more evidence of a benign role. Sharing these nuances can help guide the investment in follow-up clinical and research investigations and may, at times, influence medical decision making despite appreciated uncertainty. METHODS: Four clinical laboratories have been subclassifying VUS to help prioritize investigation and guide reporting decisions. Each laboratory developed a distinct approach for how these subclasses are used in their laboratories and, in some cases, displayed on reports. We examined the composition of each laboratory's VUS subclasses and the likelihood variants from each subclass were reclassified toward pathogenic or benign. RESULTS: We found that variants in the lowest subclass of VUS were never reclassified as likely pathogenic or pathogenic, whereas those in the highest subclass were much more likely to be reclassified as pathogenic or likely pathogenic. CONCLUSION: Given that forthcoming professional guidance in variant classification will advise the use of VUS subclasses, the experience of our laboratories in using VUS subclasses can inform future practices.

Humans

Opportunistic screening for broad range of medically relevant secondary findings: Laboratory benefits and burdens.

PURPOSE: Exome and genome sequencing enable opportunistic screening for secondary findings (SFs). We report on exome analysis for a broad range of medically relevant SFs in the setting of the Incidental Genomics randomized clinical trial (NCT03597165). METHODS: Participants had exome sequencing and were randomized to receive only primary cancer findings (control) or cancer findings and a choice of SFs (intervention). RESULTS: Across 279 participants, there were 4441 unique variants in SF genes: 5.0% (221) were reportable pathogenic/likely pathogenic variants, and 81.4% (3615) were nonreportable variants of uncertain significance (VUS). Intervention arm participants had on average 2.6 (SD 1.66, range 0-9) pathogenic/likely pathogenic variants and 29.5 VUS (SD 13.2, range 2-74). SFs for monogenic disease risk were reported in 35.3% (49/139) of participants (American College of Medical Genetics and Genomics non-cancer subset in 1.4%) and carrier status in 89.3% (117/131). In the intervention arm, variant filtration was 7.7 times longer per case (95% CI 5.3 to 11.3, P < .0001), variant classification was 13.3 times longer (95% CI 10.6 to 16.5, P < .0001), and report preparation was 3.3 times longer (95% CI 2.6 to 4.1, P < .0001). CONCLUSION: Although the yield of reportable SFs was high, this was accompanied by many nonreportable VUS and increased efforts for exome analysis.

Humans

Personalizing CA125 Levels Using Tumor Marker Variants: A Case-Control Analysis of Diagnostic Performance for Pancreatic Cancer.

BACKGROUND: Cancer antigen 125 (CA125) is widely recognized as a useful biomarker for the surveillance of patients with ovarian and other cancers. Prior genome-wide association studies have identified variants that influence CA125 levels. We evaluated the utility of stratifying CA125 levels by such variants and evaluated diagnostic performance in control subjects and patients with pancreatic ductal adenocarcinoma (PDAC). METHODS: We measured CA125 levels in 807 control subjects and 450 patients with PDAC and genotyped 10 variants involving four genes (GAL3ST2, MSLN, D2HGDH, and MUC16). We compared CA125 levels in controls by variant and generated variant-defined CA125 cutoffs and then classified cases and controls into functional groups based on their variant profile. We used this variant classification to evaluate the diagnostic performance of CA125 in patients with PDAC. RESULTS: Six variants associated with CA125 levels were used to group controls into one of four groups. Mean CA125 levels in the highest variant group were approximately fourfold higher than in the lowest group. African Americans were more likely to have a variant group associated with low CA125 levels. After setting diagnostic cutoffs by variant group, the diagnostic sensitivity of CA125 for PDAC was 20.2% at 98% specificity (areas under the ROC curve, 0.702), not significantly different from a uniform CA125 diagnostic cutoff (areas under the ROC curve, 0.700). CONCLUSIONS: Gene variants can be used to generate personalized CA125 reference ranges. This approach did not significantly improve CA125's diagnostic performance for pancreatic cancer, but it merits evaluation in other diagnostic settings, such as detecting ovarian cancer. IMPACT: Gene variants can be used to personalize CA125 levels.

Humans

Third Patient With Biallelic Variants in SMAD6 With an Overlapping Phenotype: Developmental Delays, Dysmorphic Features, and Cardiovascular Abnormalities.

SMAD6 encodes an inhibitory SMAD protein that modulates BMP and TGF-&#x3b2; signaling. Heterozygous pathogenic variants in SMAD6 have been primarily associated with aortic valve disease, radioulnar synostosis, and nonsyndromic sagittal and metopic synostosis. However, only two syndromic patients with biallelic variants have been reported in the literature. We report a 4-year-old girl with neurodevelopmental delays, dysmorphic features, complex congenital heart disease, renal asymmetry, and arterial tortuosity. Whole exome sequencing showed two homozygous SMAD6 variants of uncertain significance: c.161G>T (p.Gly54Val) and c.1A>G (p.Met1?). This is the third patient with biallelic SMAD6 variants associated with skeletal changes, more complex cardiovascular phenotype, facial dysmorphism, and novel arterial abnormalities. This suggests biallelic variants may cause a distinct and potentially more severe autosomal recessive syndrome. Functional investigation is needed to determine the molecular consequences of biallelic SMAD6 variants and to inform variant classification and mechanism. This report characterizes a potential unique genetic syndrome associated with biallelic SMAD6 variants, highlighting the importance of additional sequencing, vascular imaging, and multidisciplinary care coordination for these patients.

SMAD6

Deciphering the Role of LNX2 as a Potential Contributor to Neurodevelopmental Disorders.

BACKGROUND/OBJECTIVES: Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental condition characterized by a complex and multifactorial genetic architecture. In this study, we report a male patient, born to non-consanguineous healthy parents, presenting with ADHD and oppositional defiant disorder (ODD). METHODS: Trio-based whole-exome sequencing (WES) was performed in the proband and both parents. Variant classification was performed according to American College of Medical Genetics and Genomics (ACMG) guidelines, and the potential pathogenicity of the identified variant was further assessed through multiple in silico prediction algorithms and protein structural analyses. RESULTS: WES identified a homozygous variant in the LNX2 gene (NM_153371.4: c.1165G>A, p.Ala389Thr), classified as a variant of uncertain significance (VUS) and supported by multiple in silico predictions. LNX2 is expressed during brain development and encodes an E3 ubiquitin ligase involved in neuronal differentiation and synaptic function. The identified variant is located within the PDZ2 domain, a functionally relevant region involved in protein-protein interactions. Although the variant is reported in population databases (gnomAD ID: rs148429804), it has not been associated with any clinical phenotype, and its presence in the homozygous state has been reported only once, remaining extremely rare and lacking clinical annotation. Structural modelling predicted localized rearrangement of the hydrogen-bonding network within the PDZ2 domain without major conformational changes. Integrative transcriptomic, and single-cell analyses further supported the biological relevance of LNX2 in neurodevelopment, highlighting its preferential association with neuronal projection-cell networks, synaptic vesicle trafficking pathways, and neuron-specific regulatory programs. CONCLUSION: Although the identified LNX2 variant cannot be considered causative for the patient's phenotype and a definitive disease-gene relationship cannot be established based on a single individual, the complementary genetic, structural, and transcriptomic findings support the biological plausibility of LNX2 as a candidate gene for neurodevelopmental disorders. Additional independent patients and functional studies will be required to clarify its contribution to human disease.

Child

Clinical and genetic variant re-analysis among pediatric probands undergoing genetic testing for arrhythmia syndromes.

BACKGROUND: Despite increases in genetic testing, longitudinal data regarding changes in diagnostic yield and variant reclassification for inherited arrhythmia syndromes are limited. OBJECTIVE: Determine longitudinal changes in diagnostic yield and variant classification. METHODS: Single-center retrospective study of probands <18 years undergoing genetic testing for suspected inherited cardiac conditions associated with arrhythmias, 2007 to 2018. Variants were classified as diagnostic (pathogenic/likely pathogenic), non-diagnostic (benign/likely benign [B/LB]), or variants of uncertain significance (VUS). Variant reclassification was performed in October 2023 using VarSome and American College of Medical Genetics criteria. We evaluated results by era (early 2007-2013 vs. later 2014-2018, coinciding with Sanger and next-generation sequencing, respectively) and by likelihood of disease based on clinical evaluation. RESULTS: Of 306 probands, initial testing was 23.2% diagnostic, 55.6% non-diagnostic (33.7% no variant, 21.9% B/LB), and 21.2% VUS. When comparing eras, diagnostic yield decreased (34.1%-15.3%), VUS increased (9.3%-29.9%), and non-diagnostic remained similar (55% to 57%). Variants for 22.7% (46/203) of probands with &#x2265;1 variant changed: 9.9% of diagnostic variants (7/71) downgraded to VUS or non-diagnostic, and 60.0% of VUS changed (23.1% upgraded, 36.9% downgraded). B/LB variants did not change. Probands with higher disease likelihood had 6-times the odds of diagnostic results compared to lower disease likelihood, regardless of era (odds ratio 6.3, 95% confidence interval 3.2-12.4, P < .0001). CONCLUSION: Variant reclassification led to changes in 23% of probands, both downgrading and upgrading status, even among probands initially thought to be pathogenic. When comparing later to earlier eras, VUS variants increased while diagnostic yield decreased. Findings support the need for variant re-interpretation and periodic reclassification over time.

Humans

What Should a Clinical Cardiologist Know About Cardiogenetics?

Inherited cardiovascular diseases are becoming increasingly prominent in clinical practice, significantly impacting diagnosis, risk assessment, and family screening strategies. Progress in genetic testing has broadened access to cardiogenetic evaluations, while also presenting new challenges in interpreting variants and incorporating findings into clinical care. This narrative review explores 20 essential questions that clinical cardiologists may face when dealing with suspected or confirmed inherited cardiac conditions. Organized as a practical, question-driven guide, it outlines when to consider a genetic cause, how to choose and interpret genetic tests, and how to manage patients regardless of their genetic test results. The review emphasizes variant classification based on American College of Medical Genetics and Genomics criteria, the importance of clinical context in interpreting uncertain results, and the principles behind family cascade screening. Particular attention is given to the management of relatives who carry a genetic variant but show no symptoms, and to the current limitations of genetic testing technologies (eg, performance). Ethical considerations, including the appropriate timing of testing in children minors, are also discussed. By connecting genetic insights with clinical cardiology, this review aims to support practical, informed decision making and promote effective collaboration with cardiogenetic specialists.

Humans

The impact of tokenizer selection in genomic language models.

MOTIVATION: Genomic language models have recently emerged as a new method to decode, interpret, and generate genetic sequences. Existing genomic language models have utilized various tokenization methods, including character tokenization, overlapping and nonoverlapping k-mer tokenization, and byte-pair encoding, a method widely used in natural language models. Genomic sequences differ from natural language because of their low character variability, complex and overlapping features, and inconsistent directionality. These features make subword tokenization in genomic language models significantly different from both traditional language models and protein language models. RESULTS: This study explores the impact of tokenization in genomic language models by evaluating their downstream performance on 44 classification fine-tuning tasks. We also perform a direct comparison of byte pair encoding and character tokenization in Mamba, a state-space model. Our results indicate that character tokenization outperforms subword tokenization methods on tasks that rely on nucleotide-level resolution, such as splice site prediction and promoter detection. While byte-pair tokenization had stronger performance on the SARS-CoV-2 variant classification task, we observed limited statistically significant differences between tokenization methods on the remaining downstream tasks. AVAILABILITY AND IMPLEMENTATION: Detailed results of all benchmarking experiments are available in https://github.com/leannmlindsey/DNAtokenization. Training datasets and pretrained models are available at https://huggingface.co/datasets/leannmlindsey. Datasets and processing scripts are available at doi: 10.5281/zenodo.16287401 and doi: 10.5281/zenodo.16287130.

Natural Language Processing

Shared inheritance reveals landscape of somatic and germline cancer risk in TP53.

Pathogenic variants in TP53, the key tumor suppressor gene underlying Li-Fraumeni syndrome (LFS), are among the best-established causes of inherited cancer predisposition. However, large-scale sequencing has revealed that many apparently pathogenic TP53 variants detected in blood are the result of somatic clonal expansions, complicating risk interpretation. Using blood-derived whole-exome data from 469,391 UK Biobank participants, we combined the variant allele fraction (VAF) with haplotype-sharing analysis to distinguish germline and somatic TP53 variants. Germline variants were concentrated at sites linked to partial loss of p53 function and lower disease penetrance, whereas classic LFS alleles appeared to be predominantly somatically acquired. Classic LFS alleles at high VAF conferred markedly increased risk of hematological malignancy but not solid tumors, indicating an important contribution from large TP53-mutant clonal expansions. The prevalence of somatic clonal expansion also correlated with missense variant pathogenicity, suggesting that somatic activity provides an informative in vivo proxy for functional impact. These results provide new insights into TP53-associated cancer risk at the population level, demonstrate that somatic rather than germline risk predominates in middle-aged healthy adults, and provide a scalable framework for variant classification in large-scale population genomics.

Humans

Pan-cancer analysis of biallelic inactivation in tumor suppressor genes identifies KEAP1 zygosity as a predictive biomarker in lung cancer.

The canonical model of tumor suppressor gene (TSG)-mediated oncogenesis posits that loss of both alleles is necessary for inactivation. Here, through allele-specific analysis of sequencing data from 48,179 cancer patients, we define the prevalence, selective pressure for, and functional consequences of biallelic inactivation across TSGs. TSGs largely assort into distinct classes associated with either pan-cancer (Class 1) or lineage-specific (Class 2) patterns of selection for biallelic loss, although some TSGs are predominantly monoallelically inactivated (Class 3/4). We demonstrate that selection for biallelic inactivation can be utilized to identify driver genes in non-canonical contexts, including among variants of unknown significance (VUSs) of several TSGs such as KEAP1. Genomic, functional, and clinical data collectively indicate that KEAP1 VUSs phenocopy established KEAP1 oncogenic alleles and that zygosity, rather than variant classification, is predictive of therapeutic response. TSG zygosity is therefore a fundamental determinant of disease etiology and therapeutic sensitivity.

Kelch-Like ECH-Associated Protein 1

Strategies for mosaic variant calling in brain disorders.

The human brain is a genomic mosaic, where postzygotic mutations arising from embryogenesis to senescence drive diverse neurodevelopmental and neurodegenerative diseases. Because of numerous sequencing artifacts at ultralow variant allele frequencies (VAFs), detecting these variants remains a significant analytical challenge. This review focuses on single-nucleotide variants and small indels, summarizing current strategies for aligning sampling methods, including bulk, laser capture microdissection, and single-cell genomics, with the expected clonal architecture of the brain. It emphasizes that mosaic detection sensitivity is fundamentally constrained by sequencing depth, since even the most advanced algorithms cannot identify variants not physically represented in the sequencing library. The review further recommends the selection of variant calling algorithms based on validated VAF detection performance, matching tools like MuTect2 and MosaicForecast to their optimal performance ranges. Furthermore, we discuss how multitissue sampling, as emphasized by the SMaHT project, addresses the matched-control dilemma and supports accurate variant classification via cross-tissue VAF gradients. Integrating these established pipelines with multiomics modalities, including transcriptomic and epigenetic data, could advance the field toward a functional understanding of how the somatic genome impacts human brain health and disease.

Humans

Increased yield of genetic diagnoses in inherited heart diseases using expanded genome and RNA-splicing analyses.

PURPOSE: The Australian Genomics Cardiovascular Disorders Flagship investigated genome sequencing as a first-line genetic test in 600 individuals with cardiomyopathy, primary arrhythmia syndromes, or congenital heart disease. Analysis of disease-specific virtual gene panels achieved a genetic diagnosis in 38% of participants. We sought to increase genetic diagnosis yields by analyzing lesser-evidenced disease genes, the mitochondrial genome, and by functional analysis of predicted splice-altering variants. METHODS: Genome sequences of 520 participants with cardiomyopathy or primary arrhythmia syndromes were reanalyzed in 572 cardiac genes and the mitochondrial genome. Participants with congenital heart disease were excluded. Variants predicted in silico to disrupt splicing were assessed with blood RNA and minigenes. RESULTS: A new genetic diagnosis was achieved in 4% (19/520) of participants, including deep intronic and mitochondrial genome variants. Ten participants had diagnostic variants in lesser evidenced disease genes; 9 had splicing variant pathogenicity functionally validated. Eleven participants had a newly identified variant of uncertain significance with high suspicion of pathogenicity, warranting clinical review. Our data supported the gene-disease association of 1 new cardiomyopathy gene, TBX20. CONCLUSION: Identifying new gene-disease relationships, maintaining contemporary gene panels, and integrating functional studies to refine splicing variant classifications increase genetic diagnoses for cardiomyopathies and primary arrhythmia syndromes.

Humans

Survey of diagnostic laboratories highlights need for improved standards in somatic genomic testing and reporting.

There is a growing international need to support somatic genomic testing, standardised variant curation and improved patient access to molecular profiling for somatic conditions, including cancer. We conducted a survey of scope, curation, reporting and sharing practices of diagnostic laboratories performing somatic testing in Australia and New Zealand. Laboratories with accreditation (n&#x2009;=&#x2009;41) were invited in 2023 to complete a semi-structured, 25-question interview. Responses were received for 27 laboratories (66% response rate) offering solid tumour, haematological malignancy and non-cancer services. Only 36% of laboratories offered tests capturing the full breadth of variants, from single-nucleotide variants to gene fusions. Knowledge sharing was rare, with only one laboratory submitting variant classifications to a public knowledge base. Most laboratories (96%) conducted somatic testing in oncology. Of cancer laboratories, 35% offered testing considered capable of comprehensive genomic profiling (CGP). Almost half of cancer laboratories had already adopted the 2022 ClinGen/CGC/VICC oncogenicity guidelines, and 84% were using AMP/ASCO/CAP 2017 clinical significance guidelines. Only 47% of mixed discipline&#xa0;cancer laboratories reported biomarkers such as tumour mutational burden, with wide variation in reporting of matched therapy options. Our study has generated a unique overview of somatic laboratory practices in the region, and areas for global standardisation in somatic molecular testing and reporting. We also provide a model for practice and guideline uptake assessment, for application by other country-wide networks. This is particularly relevant in anticipation of CGP mainstreaming, with the increasing complexity of sequencing interpretation for laboratories and clinicians.

Humans

CAKR: commutative algebra k-mer representations for genomics.

Despite the availability of various sequence analysis models, comparative genomic analysis remains a challenge in genomics, genetics, and phylogenetics. Commutative algebra, a fundamental tool in algebraic geometry and number theory, has rarely been used in data and biological sciences. In this study, we introduce commutative algebra k-mer representations as a nonlinear algebraic framework for analyzing genomic sequences. This representation bridges commutative algebra, algebraic topology, combinatorics, and machine learning to establish a mathematical framework for comparative genomic analysis. We evaluate its effectiveness on three tasks including genetic variant classification, phylogenetic tree reconstruction, and viral classification, typically requiring alignment-based, alignment-free, and machine-learning approaches, respectively. In this work, we show that commutative algebra k-mer representations outperform five state-of-the-art sequence analysis methods across twelve primary datasets, with two additional supplementary fragment-placement benchmarks, especially in viral classification, and maintain relatively stable predictive accuracy as dataset size increases, underscoring scalability and robustness.

Genomics

Genome-wide variation analysis of two Salvia hispanica L. genotypes and implication for associations with metabolic and adaptive traits.

BACKGROUND: Advances in next-generation sequencing have accelerated genome-wide exploration of genetic diversity in underutilized oilseed crops. Salvia hispanica L. (chia), a high-nutrient pseudocereal rich in omega-3 fatty acids, is increasingly valued for its health benefits and commercial potential, yet it remains poorly characterized at the genomic level. Understanding the scale and nature of genomic variation is essential for improving complex traits such as oil yield, stress tolerance, and seed quality. METHODS: Two contrasting chia genotypes, Black-chia (CACH-B) and White- chia (CACH-W), were resequenced using the Bio-Resequencing Toolkit (BRT) pipeline. High-coverage sequencing, with a mapping rate exceeding 99% and an average depth of approximately 28&#xd7;, facilitated the detection and annotation of single-nucleotide polymorphisms (SNPs), insertions and deletions (InDels), copy-number variations (CNVs), and structural variants (SVs). The functional classification of variant impacts enabled the identification of genes potentially linked to metabolic and adaptive traits. RESULTS: A total of 1.97 million SNPs, 401,493 InDels, 836 CNVs, and 15,288 SVs were identified across the chia genome. Notably, approximately 53% of exonic SNPs were non-synonymous (dN/dS&#xa0;&#x2248;&#xa0;1.28), predominantly affecting lipid metabolism, transcriptional regulation, and stress response pathways, potentially altering key agronomic traits. In addition, CNV hotspots were concentrated in chromosomes 3 and 6, overlapping MYB, WRKY, and bZIP transcription factor loci, may potentially be involved in stress tolerance and yield. Furthermore, structural rearrangements, including inversions and duplications within the FAD2, FAD3, and CYP450 gene clusters, were potentially associated with seed pigmentation and omega-3 biosynthesis, pointing to their potential breeding relevance. Observed heterozygosity (H&#x2092;&#xa0;&#x2248;&#xa0;0.71) and nucleotide diversity (&#x3c0;&#xa0;&#x2248;&#xa0;7&#xa0;&#xd7;&#xa0;10-3) indicated moderate to high allelic richness. In addition, the low FST value (0.038) indicates substantial genomic similarity between the two genotypes. CONCLUSION: This study presents the first comprehensive map integrating SNPs, CNVs, and SVs in S. hispanica L. The results reveal a structurally dynamic genome characterized by substantial sequence and structural variation, providing valuable insights into genomic diversity and potential adaptive mechanisms in chia. The coexistence of high SNP diversity and abundant structural variation underpins chia's nutritional specialization and environmental resilience. These results deliver a foundational genomic resource for marker-assisted breeding, genome-wide association studies, and the development of climate-resilient chia cultivars.

Copy-number variation, structural variation

Reinforcement learning-based dynamic ensemble for missense variant effect prediction and tiered prioritization of VUS.

BACKGROUND: Accurate classification of missense variants remains a challenging task despite major advances in genomics. Numerous computational models have been developed to assist in variant classification, but often require repeated integration and benchmarking efforts. Ensemble methods have been proposed to overcome the limitations of single predictors, but mostly rely on fixed, predefined weights that constrain their ability to capture interactions among predictive signals. METHODS: We present GenixRL, a dynamic ensemble framework that reformulates model fusion as a reinforcement learning optimization problem. GenixRL uses a Q-learning agent to learn a policy that dynamically weights the probabilistic outputs of complementary predictors, including BayesDel (addAF and noAF), ClinPred, and MetaRNN. Replacing static weighting with policy learning allows GenixRL to adaptively identify optimal weightings and substantially improve classification accuracy. RESULTS: In benchmark evaluation against 25 state-of-the-art predictors, GenixRL achieved an AUROC of 0.9644 on an independent ClinVar dataset. On saturation genome editing assays for BRCA1 and BRCA2, GenixRL achieved the best performance and ranked highest on 14 of 17 clinically significant genes in a zero-shot evaluation. Applied to uncertain and conflicting ClinVar variants, GenixRL enabled tiered, evidence-based prioritization of hundreds of thousands of variants as likely pathogenic or pathogenic with high confidence, supported by orthogonal population evidence from gnomAD. CONCLUSION: GenixRL advances pathogenicity prediction for missense variants and provides an adaptive ensemble that sorts variants of uncertain significance into tiered candidates for expert curation and functional validation.

Mutation, Missense

RNA splicing evidence enables robust classification of BRCA1 exon 18 variants: Results from the ENIGMA consortium.

The Evidence-based Network for the Interpretation of Germline Mutant Alleles (ENIGMA) research consortium conducted a comprehensive study to characterize spliceogenic variants in BRCA1 exon 18. The absence of systematic RNA-based assessment for these variants has led to inconsistent interpretation, limiting accurate classification and management of individuals and their families. The splicing profile of 166 variants was assessed using minigene assays; 32 were additionally analyzed in blood-derived RNA from 51 individuals and 18 in mouse embryonic stem cell (mESC)-based assays to evaluate homology-directed repair (HDR) capacity. mRNA assessment by RT-PCR in blood samples and minigene assays showed a significant positive correlation, with splicing analysis in mESCs displaying highly concordant results. The mESC-based HDR assay showed that the in-frame exon 18 skipping (&#x394;18) transcript encodes a non-functional protein lacking rescue activity. Linear regression analysis using mESC splicing and functional data indicated that &#x2265;59% of full-length (FL) levels and <34% of &#x394;18 were associated with benign HDR activity. These thresholds differ from those recommended by the ClinGen ENIGMA BRCA1 and BRCA2 Variant Curation Expert Panel American College of Medical Genetics and Genomics (ACMG)/Association for Molecular Pathology (AMP) specifications for applying BP7_strong(RNA): >30% functional transcripts or <70% non-functional transcripts. Incorporation of RNA splicing evidence into variant interpretation increased pathogenic (28.6%-31.7%) and benign (3.7%-24.4%) classifications while reducing likely pathogenic (19.5%-17.7%), uncertain (18.9%-8.5%), and likely benign (29.3%-17.7%) categories. Experimental mRNA profiling impacted the interpretation of 34% of variants and resolved uncertainty in approximately 10% of cases. Exon 18 skipping was less tolerated, indicating that the degree of splice perturbation required to impair BRCA1 function may depend on the nature of the resulting non-functional transcript.

Humans

Systematic functional evaluation of CNGA1 missense variants associated with retinitis pigmentosa.

BACKGROUND: Missense variants are frequently classified as variants of uncertain significance (VUS) according to the guidelines of the American College of Medical Genetics and Genomics and the Association of Molecular Pathology (ACMG/AMP). Consequently, disease relevance remains elusive, impeding molecular genetic diagnostics, patients` and family genetic counseling, and identification of patients eligible for clinical trials. Functional studies are critical for resolving the clinical significance of VUS. CNGA1 encodes the main subunit of the rod cyclic nucleotide-gated (CNG) channel, a vital component of the phototransduction cascade. Variants in CNGA1 are a rare cause of autosomal recessive retinitis pigmentosa and a phase I/II gene augmentation trial (NCT06291935) is currently ongoing highlighting the necessity to differentiate benign from pathogenic variants. METHODS: CNGA1 missense variants compiled from retinal disease patient cohorts, public databases and literature were functionally investigated using a medium-throughput aequorin-based assay and in vitro minigene splice assays for predicted exonic spliceogenic variants. Functional data were correlated with the in silico prediction of five variant effect predictors (VEPs) and applied to support or revise variants' ACMG/AMP classification. RESULTS: Data mining revealed 86 missense CNGA1 variants - including three novel - most of them lacking functional data; 65.1% of the variants were initially classified as VUS. The aequorin-based assay showed that 72.1% of tested variants significantly impaired CNG channel function and were classified as functionally abnormal, while 23.3% were functionally normal and 5% remained functionally uncertain. Correlation of the functional data with in silico predictions identified AlphaMissense and CPT-1 to be the most suitable tools for assessing CNGA1 missense variants. Using in vitro minigene splice assays, two putative missense variants were shown to induce missplicing. Based on the functional findings, 62.1% of the variants initially classified as VUS were re-categorized as likely pathogenic or likely benign. Furthermore, 93.3% of the variants initially classified as likely pathogenic showed an effect on CNGA1 channel function, confirming their disease relevance and supporting their reclassification as pathogenic. CONCLUSION: This study represents the first comprehensive functional assessment of disease-associated CNGA1 missense variants, thus significantly advancing the understanding of their disease relevance and improving molecular genetic diagnostics in patients.

Humans