Genetics in Medicine Open

Background: RNA sequencing (RNA-seq) is increasingly recognized as a complementary tool to DNA-based sequencing for improving the diagnostic yield in Mendelian disorders. However, how the diagnostic performance of RNA-seq varies across molecularly and phenotypically distinct patient subgroups remains poorly defined. This study aimed to evaluate and compare the diagnostic utility of RNA-seq across three stratified groups of patients with non-diagnostic exome sequencing. Methods: We performed RNA-seq on whole blood samples from 90 patients with suspected Mendelian disease in whom clinical exome or whole-exome sequencing had failed to establish a molecular diagnosis. Patients were prospectively stratified into three groups of 30: (i) patients with a candidate variant of uncertain significance (VUS) with predicted splicing impact (Group 1), (ii) patients with a specific clinical pre-diagnosis but no identified pathogenic variant (Group 2), and (iii) patients without a specific pre-diagnosis or candidate variant (Group 3). Aberrant splicing, gene expression outliers, and allele-specific expression were analyzed using multiple bioinformatic tools and compared against a GTEx-derived control cohort. Results: RNA-seq contributed to a molecular diagnosis in 29 of 88 evaluable patients (32.9%). Diagnostic yield differed substantially across groups: 82.8% (24/29) in Group 1, 6.9% (2/29) in Group 2, and 10% (3/30) in Group 3. In Group 1, RNA-seq enabled reclassification of candidate VUS through direct demonstration of aberrant splicing events. In Group 2, RNA-seq identified a somatic mosaic ACTB variant missed by exome sequencing and reclassified a previously deprioritized APPL1 VUS. In Group 3, a deep intronic pseudoexon-activating variant in IGBP1 was identified in two siblings with severe microcephaly, providing evidence for a candidate X-linked microcephaly gene, and a pathogenic RNU4-2 variant was detected in a patient with ReNU syndrome, a non-protein-coding gene not captured by standard exome sequencing. Conclusions: RNA-seq has the highest diagnostic utility when applied to evaluate candidate splice variants identified by prior DNA testing but also provides independent diagnostic value in patients without candidate variants. The systematic comparison across stratified patient groups supports the integration of RNA-seq into clinical genomic workflows and highlights the need for standardized analytic frameworks.

BackgroundThe diagnostic yield for 46,XY disorders of sex development (DSD) remains limited. Whole-genome sequencing (WGS) improves detection of both coding and non-coding variants that may be missed by routine testing. Cytochrome b5, encoded by CYB5A, is an essential co-factor for CYP17A1-mediated 17,20-lyase activity. We report on WGS on a Vietnamese family with 46,XY DSD with two siblings presenting with female external genitalia. MethodsClinical assessment and hormone profiling were conducted. WGS was conducted on peripheral blood DNA, in two affected siblings followed by variant annotation and ACMG-based classification. A minigene RNA splicing assay in HEK293 cells was used to evaluate the functional impact of the CYB5A intronic variant. ResultsThe patients hormone profile showed low testosterone and estradiol. WGS identified compound-heterozygous CYB5A variants: a paternally inherited missense variant (p.Val34Glu, likely pathogenic) and a maternally inherited deep intronic deletion (c.129+862_129+863del) for which SpliceAI predicted aberrant splicing. Minigene assays confirmed that the intronic deletion creates cryptic splice sites, resulting in pseudoexon inclusion and a premature stop codon, consistent with nonsense-mediated decay. The intronic variant meets ACMG criteria for pathogenicity. ConclusionThis family expands the spectrum of CYB5A-related DSD and demonstrates that compound-heterozygous variants, including deep intronic defects, can lead to a disruption in 17,20-lyase activity. These findings highlight the importance of WGS and functional assays for identifying clinically relevant non-coding variants in DSD.

Background Orofacial clefts (OFCs) and other palate abnormalities (PAs) are among the most common birth defects worldwide and are characterized by the abnormal formation of the lip and/or palate. Genetic studies have traditionally classified OFC cases as either syndromic, involving OFCs alongside other congenital anomalies, or nonsyndromic, which represent the majority of cases and occur in isolation. Emerging genomic evidence indicates that genes traditionally associated with syndromic forms of OFC can also harbor variants contributing to isolated cases, challenging the notion of a strict dichotomy between these categories and supporting their integration for gene discovery. Methods In this study, we applied multiple analytic approaches to characterize the genetic architecture of OFC and PAs by integrating genomic data from 2,497 trios with an OFC (n=2080) and PA (n=417) affected proband. We compared these findings across OFC subtypes and syndromic status with those from 5,515 control trios to identify enriched biological pathways and mechanisms and to prioritize candidate genes using variant burden testing. Results We observed a significant enrichment of de novo protein-truncating and damaging missense variants in cases compared to controls (OR = 2.17, p = 1.21x10-32), with particularly strong signals in biologically relevant gene sets involving OFC-associated, constrained, Mendelian disorder, and mouse candidate genes. Variant burden testing identified 39 OFC risk genes at FDR [≤] 0.05, which we then integrated with 593 established OFC genes to interrogate the functional underpinnings of OFC via network analysis. This analysis revealed 309 high-order interactor genes not previously associated with OFC. Notably, this OFC network clustered into ten distinct biological pathways, with nucleosome-associated genes showing significant enrichment among cases in our cohort (OR = 14.8, p = 8.1x10-4). In a final integrative step, we combined evidence across all analyses to nominate 231 candidate genes, 32 of which contained at least two deleterious de novo variants in our cohort. Conclusions These findings underscore the value of integrating diverse OFC and PA subtypes, syndromic status, and variant classes to refine the genetic architecture of these disorders, highlighting both phenotypic expansion of known disease genes and the emergence of novel gene-phenotype associations.

Background: Biallelic variants in GFM2, encoding mitochondrial elongation factor G2 (mtEFG2), a GTPase involved in the termination stage of mitochondrial translation, cause autosomal recessive combined oxidative phosphorylation deficiency. Noncoding structural variants may be missed by exome sequencing but can disrupt splicing and provide opportunities for variant-specific therapeutic rescue. We investigated the molecular mechanism underlying suspected Leigh syndrome in an infant with mitochondrial disease and evaluated whether splice-switching oligonucleotide (SSO) treatment could correct the pathogenic splicing defect. Methods: The proband underwent exome sequencing followed by short-read and long-read whole genome sequencing. RNA sequencing, reverse-transcription PCR, quantitative PCR, and cycloheximide treatment were used to characterize the effect of the identified intronic duplication on GFM2 splicing and transcript stability. Patient-derived fibroblasts were treated with SSOs targeting the aberrant splice junction. Rescue was assessed by RNA studies, western blotting, and spectrophotometric measurement of cytochrome c oxidase (COX). Results: Whole genome sequencing identified a paternally-inherited GFM2 missense variant, NM_032380.5:c.2195C>T p.(Pro732Leu), in trans to a maternally-inherited 221-nucleotide intronic duplication, NM_032380.5:c.2029-741_2029-521dup. RNA studies revealed a 87-nucleotide pseudoexon, generated by activation of a cryptic acceptor splice site within the duplicated sequence. The resulting transcript harbored a premature termination codon (PTC) and underwent nonsense-mediated decay, as confirmed by cycloheximide rescue. Together with reduced mtEFG2 protein levels on western blot, the findings supported a loss-of-function mechanism. Enzymatic analysis of affected fibroblasts showed reduced activity of the mtDNA-dependent complex IV subunit COX, with preservation of the nuclear-encoded complex II enzyme succinate dehydrogenase and the control enzyme citrate synthase, consistent with impaired mitochondrial translation. A SSO targeting the aberrant intron-pseudoexon junction nearly abolished pseudoexon inclusion, restored correctly spliced GFM2 transcript from the duplication-containing allele, increased mtEFG2 protein levels, and significantly improved COX activity. Conclusions: This study identifies a pathogenic intronic GFM2 duplication that causes mitochondrial disease through pseudoexon activation and nonsense-mediated decay. The findings demonstrate the value of integrated genome and transcriptome analysis for exome-negative mitochondrial disease and provide in-vitro proof of concept that SSOs can restore transcript processing, protein expression, and mitochondrial respiratory-chain function in patient-derived cells.

Mutations in MAP3K7 are responsible for two distinct syndromes Cardiospondylocarpofacial (CSCF) and Frontometaphyseal dysplasia 2 (FMD2). Both are characterized by skeletal malformations, facial dysmorphisms, hearing loss, and mild intellectual disability. While cardiac defects are predominant in CSCF, keloid scar is a distinct feature in FMD2. Problem with gonadal development and disorders of sexual development (DSD) have not been previously chracterized. Here we report three syndromic cases of 46,XY DSD with CSCF or FMD2, each carrying a novel heterozygous missense variants in MAP3K7 (NM_145331.3:c.250G>A; p.V84M, NM_145331.3:c.195A>G; p.I65M, and NM_145331.3: c.574A>G; p.S192G). The DSD phenotypes include cryptorchidism, micropenis, small testis, and hypospadias. In silico tools predict all three variants are deleterious. All three MAP3K7 variants occur in the kinase domain at highly conservative positions among mammals. MAP3K7 is highly expressed in human fetal Sertoli cells. MAP3K7 knock-out in HEK293T cells led to downregulation of GATA4 and FOG2 expression by RNA-Seq. Like MAP3K1, MAP3K7 phosphorylated p38 while all three MAP3K7 variants did not alter phosphorylated p38 compared to wildtype in HEK293TMAP3K7-/- cells. Two MAP3K7 missense mutants (p.V84M and p.I65M) ectopically activate ovarian beta catenin/ Wnt signalling in TOPFLASH assays. Our data suggest that MAP3K7 contributes to male sex differentiation by increasing expression of pro-testis genes GATA4 and FOG2 in HEK293TMAP3K7-/- cells and antagonizing pro-ovarian beta-catenin signalling, and that one or more of these activities were likely affected in 3 cases of 46,XY DSD with CSCF/FMD2 during sex development.

Yield of reported results from genetic testing provides a proximal measure of clinical usefulness. While ACMG/AMP guidelines provide representations of uncertainty for individual genetic variant classification, additional factors are considered when determining whether results explain a patient's presentation. To standardize cross-consortium analysis, a working group of the Clinical Sequencing Evidence-Generating Research (CSER2) consortium iteratively identified factors used when contextualizing variant-level results to case-level interpretation (i.e., interpretation of an individual's genetic data with respect to the indication for testing). Sites independently categorized results; complex cases were discussed collaboratively, leading to revision of classification categories. Our metric incorporates factors beyond classification of reported variants. Analogous to variant-level results, "Definitive Positive" and "Probable Positive" represent certainty that results may be clinically explanatory. The category "Inconclusive" applies when results may or may not fully explain the patient presentation, with subdivision into multiple (non-exclusive) subcategories. Cases falling outside all of the other categories are considered "Negative". The overall diagnostic yield by this metric and use of categories for inconclusive results varied by CSER project, in part paralleling study design differences. This case-level categorization provides a meaningful assessment of diagnostic yield, and for inconclusive cases identifies potentially resolvable factors for case resolution.

Objectives: Diabetes affects over 500 million people globally and glycemia is inadequately managed. Metformin is the most frequently prescribed initial treatment for type 2 diabetes globally, yet glycemic response trajectories to metformin in routine real-world care and predictors of treatment response have not been well described. We aimed to identify glycemic response trajectories in adults prescribed metformin monotherapy as initial type 2 diabetes treatment and predictors of poor glycemic response to metformin. Design: Observational cohort study using latent class mixed models to identify hemoglobin A1c (HbA1c) trajectory classes, followed by random forests machine learning to predict trajectory class membership. Setting: US Veterans Affairs Healthcare System Participants: Adults treated with metformin alone for >30 days after diabetes diagnosis with a minimum of two HbA1c measurements from 90 days prior to two years after the first metformin prescription (N=140,413). Exposures: Demographic, laboratory, vital sign, and comorbidity data were included as predictors of metformin response trajectory Main Outcomes and Measures: We included all HbA1c measurements (487,604 total) for two years after metformin initiation to define metformin glycemic response trajectories. Results: We identified three HbA1c trajectories: stably low (89.7% of sample, mean HbA1c decrease from 7.2% to 6.6%), brisk response (7.1% of sample, mean HbA1c decrease from 11.4% to 7.0%), and non-response (3.1% of sample, mean HbA1c increase from 8.9% to 10.8%). Of those in the stably low and brisk response classes at 2 years, 91% maintained HbA1c at approximately 7% on metformin alone for 5 years after drug initiation. Prediction models could accurately predict brisk response (91% accuracy) but not metformin non-response (59% accuracy). Conclusions: Most individuals treated initially with metformin monotherapy have a beneficial and durable glycemic response. Predicting individuals who will not respond to metformin may be challenging but is evident within six months with recommended glycemic surveillance. The findings support current guidelines for HbA1c surveillance when initiating diabetes treatment.

Purpose Patients carrying Germline Pathogenic Variants (GPVs) in multiple cancer susceptibility genes (CSGs) can be described within the context of Multi-locus Inherited Neoplasia Allele Syndrome (MINAS). The role of each GPV is typically interpreted based on clinical phenotypes. Here, we used tumor sequencing, particularly mutational signatures, to investigate the contribution of GPVs in MUTYH and PALB2 to colorectal polyposis and breast cancer in a single patient at a molecular level. Methods We analyzed tumor sequencing data, including mutational signatures and genomic scars, of a breast tumor and a colorectal polyp from a patient with biallelic GPVs in MUTYH and a heterozygous GPV in PALB2. Results The colorectal polyp showed a dominant contribution of MUTYH-associated Base Excision Repair deficiency (BERd) mutational signatures, with no evidence of Homologous Recombination Repair Deficiency (HRD). In contrast, the breast tumor showed both MUTYH-driven BERd and HRD-associated signatures, including SBS3, ID6 and an elevated HRD score, despite the absence of a detectable second hit in PALB2. These findings suggest a differential contribution from the CSGs, with MUTYH contributing to both lesions and PALB2 contributing specifically to the breast tumor. The observed pattern does not align with the additive or synergistic models described in MINAS. Conclusions Our study provides evidence that mutational signatures can elucidate the contribution of multiple CSGs to tumorigenesis within a single patient. These findings extend current interpretations of MINAS beyond additive or synergistic phenotypes, which may help to better understand tumor etiology, with potential clinical implications, including eligibility for targeted therapies.

Purpose. Periodic reanalysis of genome sequencing data can yield additional diagnoses as knowledge evolves, yet manual reanalysis is labour-intensive. We compared automated and manual reanalysis approaches in rare disease genomics. Methods. We reanalyzed 377 rare disease cases: 158 with pathogenic or likely pathogenic (P/LP) findings, 49 with variants of uncertain significance (VUS) findings, and 170 had no findings. Manual reanalysis used standard diagnostic workflow for all cases without prior P/LP diagnoses (219 cases). An automated pipeline using Talos was benchmarked on the 158 P/LP cases before application to the 219-case reanalysis cohort. The mean reanalysis interval was 660 days. Results. Manual reanalysis identified three additional P/LP cases and two newly classified as VUS, increasing P/LP cases from 158 (41.9%) to 161 (42.7%). Talos recovered all three P/LP findings but only identified one of the two new VUS findings. Benchmarking showed 80.0% singleton concordance and 75.2% (82.8% proband-only) trio concordance, with approximately three variants per case. Conclusion. Reanalysis at 1.8 years yields modest but clinically meaning- ful gain. Automated reanalysis closely approximates manual performance while reducing hands-on effort, supporting scalable reanalysis in routine genomic care. Keywords: rare disease genomics, genome sequencing, automated reanalysis, variant prioritization, Talos, diagnostic yield

ObjectivesIt is not known which clinical features optimally differentiate type 1 and 2 diabetes at diagnosis. We aimed to determine which clinical features differentiate adult-onset type 1 and 2 diabetes at diagnosis and develop classification models combining these features with and without islet-autoantibodies. DesignA prospective cohort study with prediction model development and validation. SettingUK primary and secondary care. Participants1800 adults ([≥]18 years) diagnosed with diabetes in the previous 12 months, excluding known secondary or monogenic diabetes. Main outcome measuresType 1 and 2 diabetes defined by a combination of insulin treatment and endogenous insulin production (measured using C-peptide) assessed [≥]three years after diabetes diagnosis. ResultsEleven clinical features and routinely measured biomarkers discriminated type 1 from type 2 diabetes independently of diagnosis age and BMI. Lower age-at-diagnosis, BMI and waist-hip ratio, unintentional weight-loss, and higher presentation HbA1c or glucose were the most discriminative features, with other features only weakly discriminative. Models integrating clinical features with and without islet-autoantibodies, developed in those age 18-50 years at diabetes diagnosis, had high performance in internal validation (clinical features only: AUCROC (95% CI) 0.94 (0.93, 0.96), clinical features and islet-autoantibodies: AUCROC 0.97 (0.96, 0.98)), and maintained high discrimination in older adults (age >50 at diagnosis; clinical features only: AUCROC 0.93 (0.90, 0.96), clinical features and islet-autoantibodies: AUCROC 0.97 (0.94, 0.99)). Simplifying the models to a point-based score (the StartRight Score) resulted in similar performance. These models had higher performance than current clinical guidance. In UK primary care data models were strongly predictive of outcomes associated with type 1 diabetes, including in those initially treated as type 2 diabetes. ConclusionsLower age-at-diagnosis, BMI, and wait-hip ratio, unintentional weight-loss and high presentation glycaemia are the most discriminative features for diagnosis of type 1 diabetes in adults. Models combining routine clinical features, with or without islet-autoantibodies, have high accuracy and could assist clinical classification and prioritisation of classification biomarker testing. Study registrationhttps://clinicaltrials.gov/study/NCT03737799 Summary boxesO_ST_ABSSection 1: What is already known on this topicC_ST_ABSO_LIMost type 1 diabetes occurs in adults, but differentiating it from type 2 diabetes, which is much more common, is challenging, and misclassification is common. C_LIO_LIAge-at-diagnosis and BMI are currently the only clinical features robustly shown to distinguish between type 1 and type 2 diabetes at diagnosis; many other features included in textbooks and guidelines have little supporting evidence. C_LIO_LIGuideline bodies, including the UK National Institute for Health and Care Excellence (NICE), have identified a need for evidence on what features discriminate type 1 and 2 diabetes in adults, and how these features can be combined to improve diagnosis. C_LI Section 2: What this study addsO_LIThis is the first study to prospectively assess the utility of clinical features for diabetes subtype at diagnosis. C_LIO_LIThe five most discriminative routine clinical features for distinguishing type 1 from type 2 diabetes at diagnosis are age-at-diagnosis, BMI, waist-hip ratio, pre-diagnosis unintentional weight-loss, and presentation glycaemia (HbA1c or glucose). C_LIO_LIMany features included in current guidelines were only very weakly discriminative of subtype, and no single clinical feature was able to adequately differentiate between type 1 and type 2 diabetes alone. C_LIO_LIA clinical prediction model combining ten routinely available clinical features, with or without islet-autoantibodies, as both a prototype calculator and a points-based score (the StartRight Score), had high accuracy in differentiating type 1 from type 2 diabetes and outperforms current clinical guidance and islet-autoantibody assessment alone. C_LI

Aims CGM devices report glucose only within fixed limits (typically 40-400 mg/dL; 2.2-22.2 mmol/L), truncating extreme values to a boundary ("capping"). We characterised prevalence, duration, and consequences of capping in type 1 diabetes trial data. Materials and Methods We analysed 46,990,617 CGM readings from 948 participants across four publicly available clinical trial datasets (Dexcom G4 Platinum or G6 sensors). Capping prevalence, run duration, and associations with age, HbA1c and sex were characterised across all datasets. In the 77 participants of the Replace-BG trial CGM-plus-blood glucose monitor (BGM) arm, CGM-derived metrics were compared with contemporaneous BGM measurements across 1,162 non-overlapping 14-day windows. Results Between 93.5% and 100% of participants had at least one capped reading, and capped values comprised 0.47-0.98% of all readings. In the three datasets for which duration could be calculated, over 70% of upper-cap runs exceeded 15 minutes and over one third exceeded 60 minutes. Upper-limit capping was inversely associated with age (Spearman {rho} -0.20 to -0.47, p[&le;]0.002) in three of the datasets, and positively associated with baseline HbA1c ({rho} 0.39-0.62, p<0.001) in all four datasets. A within-participant analysis showed that capping burden did not predict CGM-BGM divergence in any summary metric (all p>0.2), and a systematic CGM-BGM offset in mean glucose and time in range (TIR) reflected the physiological lag between blood and interstitial fluid rather than capping artefact. Conclusions Sensor limit capping is near-universal in type 1 diabetes, produces sustained periods of right-censored glucose data disproportionately affecting younger patients, and does not substantially distort standard summary metrics at the population level. Clinicians and trialists should be aware that CGM data can confirm extreme glucose events but cannot quantify their severity.

Congenital heart defects (CHD) are a common congenital anomaly and a leading cause of neonatal mortality. Even in ostensibly isolated cases, genetic testing can reveal monogenic causes of isolated CHD or identify syndromic conditions before additional features become clinically apparent. A timely and accurate genetic diagnosis can inform medical management and surveillance, reduce the need for unnecessary investigations, and offer families valuable information about prognosis, recurrence risk, and anticipatory guidance. In September of 2023, Primary Childrens Hospital introduced a universal genetic testing protocol that implemented whole genome sequencing for all neonates admitted to the cardiac intensive care unit (CICU) undergoing cardiac surgery before 30 days of life, with the goal of increasing the number of patients who receive a timely genetic diagnosis and improving clinical care. This is a retrospective chart review of patients who underwent whole genome sequencing (WGS) under the new universal genetic testing protocol at Primary Childrens Hospital from its initiation in September 2023 to February 2026. Over the study period, 217 neonates with CHD participated in the universal WGS protocol. Of these patients, 23 (10.6%) received a genetic diagnosis that was causative of their CHD, of which 11 patients (48%) had no major extracardiac features at the time testing was ordered. Twenty patients were diagnosed with a syndromic condition, and three patients were diagnosed with a non-syndromic condition. All of these patients received additional referrals to specialists following their new diagnosis, and six families used results to inform decisions regarding continuation of care. An additional 19 patients (8.8%) received WGS results that were clinically relevant but non-diagnostic for their CHD, including partial diagnoses, secondary findings, and carrier status. In total, 19.4% of patients (n=42) had clinically relevant variants identified on their WGS.

The NeuroDev study, conducted in Kenya and South Africa, is a large-scale clinical, genetic, and epidemiologic characterization of neurodevelopmental disorders (NDDs) on the African continent. NeuroDev assessments capture birth, demographic, and developmental history; cognitive and behavioral outcomes; and physical health variables. DNA samples are collected for exome sequencing and clinical genetic analysis. This paper presents novel data from 521 children with NDDs, 739 of those childrens parents, and 255 unrelated, typically-developing children. The analyses offer unique genetic and phenotypic characterizations of NDDs in two African countries and underscore the importance of including underrepresented populations in NDD research. Ultimately, 107 children with NDDs from the NeuroDev cohort (22.1%) had likely pathogenic or pathogenic variants in established NDD genes. High rates of genetic diagnosis were associated with high rates of environmental risk factors for NDDs. All data, materials, and measures generated from this study are publicly available through the US National Institute of Mental Health.

PurposeCarrier screening for hereditary conditions is challenged by genes with complex genomic architecture, where short-read sequencing can fail to detect clinically relevant variants. This study evaluated a unified, amplification-based nanopore sequencing workflow across multiple laboratories for comprehensive analysis of such loci. MethodsA modular long-read sequencing assay was evaluated across five laboratories using targeted PCR enrichment, Oxford Nanopore sequencing, and automated variant analysis. The workflow interrogated genes associated with spinal muscular atrophy, thalassemia, cystic fibrosis, fragile X syndrome, congenital adrenal hyperplasia, Gaucher disease, and hemophilia A. Performance was assessed against orthogonal methods for single nucleotide variants (SNVs), indels, copy-number variants, repeat expansions, and structural rearrangements. ResultsAcross 882 unique samples (1,266 tests), overall agreement with comparator methods exceeded 96% for variant-level detection and 97% for genotype status classification. Long-read sequencing enabled phasing of paralogous loci, integrated sizing and interruption analysis for FMR1 repeats, and simultaneous detection of SNVs and structural variants in globin loci and CYP21A2-TNXB region, reducing reliance on multiple workflows. ConclusionThis multisite evaluation suggests that targeted long-read sequencing can consolidate complex variant detection into a single workflow, improving analytical completeness and operational efficiency for carrier screening.

Neurofibromatosis type 1 (NF1) is a common autosomal dominant genetic tumor predisposition syndrome.1 NF1 patients display remarkable phenotypic variability, even within families carrying the same NF1 mutation.2 With few exceptions, the identification of specific genotype-phenotype correlations has remained elusive.3-6 We utilized RNA-seq data and direct DNA sequencing to determine HLA genotypes for individuals with NF1-associated high-grade glioma (HGG, n=25), low-grade glioma (LGG, n=79), and malignant peripheral nerve sheath tumors (MPNST, n=105). Odds ratios (OR), binomial p-values and false discovery values were calculated by comparing observed carrier frequencies against expected frequencies derived from ethnicity-matched population data. We find that specific HLA class I and II alleles are associated with different NF1 tumor types. For example, HLA-B*40:02 is significantly associated with NF1-MPNST (OR=3.71, p=0.001, Q=0.02), increasing the lifetime risks for MPNST from 10% to about 29%. The relative cancer risk for an individual in the general population carrying a risk allele can be high, however, that individuals absolute risk for cancer typically remains very low. In contrast, individuals that carry a risk allele and are also burdened with a tumor predisposition syndrome will have a substantially higher absolute risk to develop a tumor, simply because they start at a higher baseline susceptibility for tumors. The identification of HLA-risk alleles for NF1 tumor development is therefore important, as it will allow for a risk-adapted screening or more aggressive treatment of individuals with a specific HLA haplotype. If confirmed, this study will thus improve clinical care and potential outcomes of individuals with NF1.

Purpose Multiplexed assays of variant effect (MAVEs) are transforming clinical variant interpretation. However, many genes are associated with more than one disease, making it unclear whether functional data generated in one disease context may be directly applicable to another. For example, germline BAP1 missense variants are associated with both BAP1 tumor predisposition syndrome (BAP1-TPDS) and Kury-Isidor syndrome (KURIS), a rare neurodevelopmental disorder. Here, we demonstrate how phenotype-specific calibration of BAP1 MAVE data enables disease-specific variant classification. Methods Saturation genome editing (SGE) data for BAP1 were recalibrated using either BAP1-TPDS- or KURIS-associated missense variants as pathogenic controls. Functional evidence strength was quantified using the Odds of Pathogenicity (OddsPath) framework and mapped to ACMG/AMP PS3/BS3 criteria. Recalibrated functional evidence was integrated with standard clinical criteria for variant classification. A workshop was developed to teach phenotype-specific MAVE recalibration to clinicians and variant curators and evaluated for educational impact. Results Phenotype-specific recalibration using BAP1-TPDS and KURIS controls yielded OddsPath values consistent with PS3_Strong evidence in both contexts. Application of KURIS-specific recalibration enabled the diagnosis of KURIS in an individual with a previously uncertain BAP1 missense variant. The educational workshop enabled quantitatively improved understanding in applying functional evidence. Conclusion Phenotype-specific recalibration enables appropriately calibrated reuse of MAVE datasets across distinct disease contexts, increasing the clinical utility of MAVE datasets and the interpretability of variants in pleiotropic genes. This framework expands the diagnostic utility of existing functional datasets without requiring new experimental assays.

Deleterious CDKN2A germline variants account for ~40% of familial melanoma cases, while rare variants in CDK4, BAP1, and telomere-maintenance genes collectively attribute ~10% of familial risk. We sought to identify new high-penetrance susceptibility variants by sequencing 305 melanoma cases from 89 multi-case families negative for known predisposition gene variants. In one family, cutaneous melanoma co-segregated with a rare variant in DMRTA1 (p.Glu383Gln), located less than 480 kb upstream of CDKN2A on chromosome 9. Whole-genome sequencing then revealed an intergenic 234kb deletion that co-segregated with melanoma in 18 out of 21 cases across four generations. Further investigations revealed a further 10 families carrying this deletion, co-segregating with melanoma. The deleted region was predicted to encompass regulatory sequences and to interact with the CDKN2A promoter region. Tiled CRISPR inhibition of the predicted enhancer region confirmed interactions between the distant upstream deletion with CDKN2A resulting in decreased p16 transcript mRNA expression. Deletion carriers exhibited nearcomplete loss of p16 mRNA expression from the affected chromosome. This distant noncoding deletion is one of the most common founder variants predisposing to melanoma and reveals a new mechanism controlling p16 expression. Routine screening for this deletion in individuals with perceived high risk of melanoma is warranted.

Accurate haplotype phasing is critical for interpreting human genetic variation. Long-read whole-genome sequencing has emerged as a powerful approach for read-based phasing, particularly where parental DNA is absent, yet the determinants of phasing accuracy remain incompletely defined. Here, we evaluate haplotype phasing performance across sequencing technology, reference genome, read length, and coverage depth using Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio) data from two Genome in a Bottle reference samples (HG002 and HG005). In clinically relevant genes, alignment to the T2T-CHM13 (T2T) reference genome improves phasing performance relative to GRCh38, reducing mean gene-level phasing error rates by 3-9-fold. T2T alignment increases phase set NG50 and yields 1.5-2-fold more phased variant pairs. At similar read N50 values, ONT has a higher phasing error rate than PacBio in certain genes. Downsampling demonstrates that phasing error rates plateau at [~]20x coverage. Longer ONT read lengths reduce phasing error rates and extend phase set contiguity. Haplotype-resolved assemblies produce substantially higher phasing error rates than alignment-based phasing, demonstrating the advantage of an alignment-based approach. To enable per-variant-pair confidence assessment, we introduce PhaseQuality, a technology-specific stratification method that assigns confidence tiers to phased variants based solely on sequencing data. PhaseQuality accurately assigns 82-99% of known phasing errors to lower-confidence tiers, reducing error rates among high-confidence pairs to <0.5%. Together, these results demonstrate the primary technical determinants of long-read haplotype phasing accuracy and provide practical benchmarks for optimizing reference genome selection, coverage targets, and read length for long-read sequencing studies.

Interpretation of rare-disease genomes remains constrained by variant-centric analytical frameworks that insufficiently capture the cumulative impact of multiple variants within a gene. GenePy provides an individual-level, gene-based burden metric that integrates variant consequence, allele frequency, and zygosity into a unified quantitative score, enabling a transition from discrete variant annotation to aggregated gene-level interpretation. In the context of Genomics England, this formulation supports a panel-agnostic, genotype-to-phenotype diagnostic strategy for unresolved monogenic disorders by prioritising genes with elevated mutational burden per individual. Here, we present a fully automated, containerised GenePy workflow deployed through Nextflow and integrated within the Genomics England (GEL) Research Environment via the Lifebit CloudOS platform. This implementation provides scalable, secure, and governance-compliant computation of gene-level burden scores across population-scale cohorts. The workflow harmonises variant annotation, quality control, and chunked data aggregation within modular, reproducible processes designed for high-throughput execution on cloud-native infrastructure. By enabling robust, portable, and auditable gene-level scoring across large rare-disease sequencing datasets, this framework enhances analytical resolution and supports downstream statistical prioritisation, integrative phenotype matching, and hypothesis generation within genotype-to-phenotype diagnostic workflows.

Background: Citrin deficiency, caused by biallelic pathogenic variants in SLC25A13, must be identified early to prevent serious complications such as hyperammonemia and liver failure. However, clinical diagnosis is often delayed due to its nonspecific presentation and limited sensitivity of amino acid-based newborn screening methods. Although genome-based evaluations are being investigated to address these issues, concerns about their cost, turnaround time, variant interpretation ability, and data handling highlight the need for a more practical yet reliable alternative. We investigated the feasibility of applying proteomic approach on dried blood spots (DBS), which are routinely used in newborn screening. Methods: We performed untargeted liquid chromatography-tandem mass spectrometry to analyze the proteome of DBS using a previously developed "non-targeted analysis of non-specifically DBS-absorbed proteins" (NANDA) workflow. SLC25A13 protein abundance was quantified in individuals with biallelic loss-of-function mutations, compound loss-of-function/missense mutations, and heterozygous carriers; this was also evaluated in healthy and diseased controls representing relevant differential diagnoses. To leverage proteomic information, we derived a multivariate proteomic signature using feature selection and evaluated its performance with leave-one-out cross-validation. Biological relevance was assessed by enrichment analysis, and complementary transcriptomics was performed using RNA sequencing. Results: A total of 7,474 proteins, including SLC25A13, were consistently detected in DBS. SLC25A13 was undetectable in individuals with biallelic loss-of-function mutations. However, individuals with compound loss-of-function/missense genotypes showed reduced but measurable SLC25A13 levels, comparable to those observed in heterozygous carriers. In contrast, a compact 15-protein signature accurately identified individuals with compound loss-of-function/missense genotypes (AUC, 0.99; sensitivity, 1.00; specificity, 0.95). The signature was enriched for Ca2+-response, and transcriptomics showed downregulation of genes related to multimodal ion channels in affected individuals compared to controls. Conclusions: DBS-based proteomic profiling may assist in the diagnosis of citrin deficiency through SLC25A13-quantification and a biologically plausible multivariate signature. More broadly, this strategy offers a promising new diagnostic layer for protein disorders, providing a proteomic readout in a clinically practical DBS format with potential utility for future diagnostic and screening applications.