Genetics in Medicine

BackgroundGenome-wide sequencing and genetic matchmaker services are propelling a new era of genotype-first ascertainment of novel genetic conditions. The degree to which reported phenotype data in discovery-focused studies address informational priorities for clinicians and families is unclear. MethodsWe identified reports published from 2017-2021 in ten genetics journals of novel Mendelian disorders ascertained genotype-first. We adjudicated the quality and detail of the phenotype data via 46 questions pertaining to six priority domains: (I) Development, cognition, and mental health; (II) Feeding and growth; (III) Medication use and treatment history; (IV) Pain, sleep, and quality of life; (V) Adulthood; and (VI) Epilepsy. For a subset of articles, all subsequent published follow-up case descriptions were identified and assessed in a similar manner. A modified Delphi approach was used to develop consensus reporting guidelines, with input from content experts across four countries. ResultsIn total, 200 of 3243 screened publications met inclusion criteria. Relevant phenotypic details across each of the six domains were rated superficial or deficient in >87% of papers. For example, less than 10% of publications provided details regarding neuropsychiatric diagnoses and "behavioural issues", or about the type/nature of feeding problems. Follow-up reports (n=95) rarely addressed the limitations of the original reports. Reporting guidelines were developed for each domain. ConclusionPhenotype information relevant to clinical management, genetic counseling, and the stated priorities of patients and families is lacking for many newly described genetic diseases. Use of the proposed guidelines could improve phenotype reporting in the genomic era.

BackgroundDiagnostic evaluation of rare genetic disorders continues to rely on multiple test modalities, despite the increasing use of short-read exome or genome sequencing as first-tier tests. Long-read genome sequencing (lrGS) has the potential to consolidate current standard-of-care (SoC) diagnostics into a single assay, but its accuracy and clinical utility in routine practice have not been established at large scale. MethodsWe studied 1000 clinical samples, including 832 index cases, representative of one year of germline diagnostic testing at two tertiary centers. Samples underwent lrGS at approximately 30x coverage, followed by variant detection and interpretation. Diagnostic outcomes were compared with those obtained through SoC testing. Using these results, we modeled the effect of implementing lrGS as a first-tier test in a cohort of 15,150 index cases who underwent diagnostic testing during a single year. ResultsThe overall concordance between SoC and lrGS testing was 96.4%. lrGS identified clinically relevant findings that improved or refined genetic diagnoses in 3.4% of cases, largely through phasing for recessive disorders and the detection of novel disease-causing variants. In 0.2% of cases, SoC testing identified variants not detected by lrGS. Modeling of a generic lrGS-first diagnostic strategy demonstrated an estimated absolute increase in diagnostic yield of 2.5% across the full annual cohort. ConclusionsLrGS demonstrated high concordance with current diagnostic approaches and provided incremental diagnostic benefits. These findings support lrGS as a feasible and effective first-tier test for rare disease diagnostics.

BackgroundThe use of in silico pathogenicity predictions as evidence when interpreting genetic variants is widely accepted as part of standard variant classification guidelines. Although numerous algorithms have been developed and evaluated for classifying missense variants, in-frame insertions/deletions (indels) have been much less well studied. MethodsWe created a dataset of 3964 small (<100bp) indels predicted to result in in-frame amino acid insertions or deletions using data from gnomAD v3.1 (minor allele frequency of 1-5%), ClinVar and the Deciphering Developmental Disorders (DDD) study. We used this dataset to evaluate the performance of nine pathogenicity predictor tools: CADD, CAPICE, FATHMM-indel, MutPred-Indel, MutationTaster2 PROVEAN, SIFT-indel, VEST-indel and VVP. ResultsOur dataset consisted of 2224 benign/likely benign and 1740 pathogenic/likely pathogenic variants from gnomAD (n=809), ClinVar (n=2882) and, DDD (n=273). We were able to generate scores across all tools for 91% of the variants, with areas under the ROC curve (AUC) of 0.81-0.96 based on the published recommended thresholds. To avoid biases caused by inclusion of our dataset in the tools training data, we also evaluated just DDD variants not present in either gnomAD or ClinVar (70 pathogenic and 81 benign). Using this subset, the AUC of all tools decreased substantially to 0.64-0.87. Overall, VEST-indel performed best, with AUCs of 0.93 (full dataset) and 0.87 (DDD subset). ConclusionsAlgorithms designed for predicting the pathogenicity of in-frame indels perform well enough to aid clinical variant classification in a similar manner to missense prediction tools.

BackgroundCausal variants underlying rare disorders may remain elusive even after expansive gene panels or exome sequencing (ES). Clinicians and researchers may then turn to genome sequencing (GS), though the added value of this technique and its optimal use remain poorly defined. We therefore investigated the advantages of GS within a phenotypically diverse cohort. MethodsGS was performed for 744 individuals with rare disease who were genetically undiagnosed. Analysis included review of single nucleotide, indel, structural, and mitochondrial variants. ResultsWe successfully solved 218/744 (29.3%) cases using GS, with most solves involving established disease genes (157/218, 72.0%). Of all solved cases, 148 (67.9%) had previously had non-diagnostic ES. We systematically evaluated the 218 causal variants for features requiring GS to identify and 61/218 (28.0%) met these criteria, representing 8.2% of the entire cohort. These included small structural variants (13), copy neutral inversions and complex rearrangements (8), tandem repeat expansions (6), deep intronic variants (15), and coding variants that may be more easily found using GS related to uniformity of coverage (19). ConclusionWe describe the diagnostic yield of GS in a large and diverse cohort, illustrating several types of pathogenic variation eluding ES or other techniques. Our results reveal a higher diagnostic yield of GS, supporting the utility of a genome-first approach, with consideration of GS as a secondary or tertiary test when higher-resolution structural variant analysis is needed or there is a strong clinical suspicion for a condition and prior targeted genetic testing has been negative.

PurposeIt has been hypothesized that diagnostic yield (DY) from Exome Sequencing (ES) may be lower among patients with non-European ancestries than those with European ancestry. We examined the association of DY with estimated continental genetic ancestry in a racially/ethnically diverse pediatric and prenatal clinical cohort. MethodsCases (N=845) with suspected genetic disorders underwent ES for diagnosis. Continental genetic ancestry proportions were estimated from the ES data. We compared the distribution of genetic ancestries in positive, negative, and inconclusive cases by Kolmogorov Smirnov tests and linear associations of ancestry with DY by Cochran-Armitage trend tests. ResultsWe observed no reduction in overall DY associated with any continental genetic ancestry (Africa, America, East Asia, Europe, Middle East, South Asia). However, we observed a relative increase in proportion of autosomal recessive homozygous inheritance versus other inheritance patterns associated with Middle Eastern and South Asian ancestry, due to consanguinity. ConclusionsIn this empirical study of ES for undiagnosed pediatric and prenatal genetic conditions, genetic ancestry was not associated with the likelihood of a positive diagnosis, supporting the ethical and equitable use of ES in diagnosis of previously undiagnosed but potentially Mendelian disorders across all ancestral populations.

BackgroundPediatric disorders include a range of highly genetically heterogeneous conditions that are amenable to genome-wide diagnostic approaches. Finding a molecular diagnosis is challenging but can have profound lifelong benefits. MethodsThe Deciphering Developmental Disorders (DDD) study recruited >33,500 individuals from families with severe, likely monogenic developmental disorders from 24 regional genetics services around the UK and Ireland. We collected detailed standardised phenotype data and performed whole-exome sequencing and microarray analysis to investigate novel genetic causes. We developed an augmented variant analysis and re-analysis pipeline to maximise sensitivity and specificity, and communicated candidate variants to clinical teams for validation and diagnostic interpretation. We performed multiple regression analyses to evaluate factors affecting the probability of being diagnosed. ResultsWe reported approximately one candidate variant per parent-offspring trio and 2.5 variants per singleton proband, including both sequence and structural variants. Using clinical and computational approaches to variant classification, we have achieved a diagnosis in at least 34% (4507 probands), of whom 67% have a pathogenic de novo mutation. Being recruited as a parent-offspring trio had the largest impact on the chance of being diagnosed (OR=4.70). Probands who were extremely premature (OR=0.39), had in utero exposure to antiepileptic medications (OR=0.44), or whose mothers had diabetes (OR=0.52) were less likely to be diagnosed, as were those of African ancestry (OR=0.51). ConclusionsOptimising diagnosis and discovery in highly penetrant genomic disease depends upon ongoing and novel scientific analyses, ethical recruitment and feedback policies, and collaborative clinical-research partnerships.

ObjectiveWe sought to evaluate outcomes for clinical management following a genetic diagnosis from the Deciphering Developmental Disorders (DDD) Study. DesignIndividuals in the DDD study who had a pathogenic/likely pathogenic genotype in the DECIPHER database were selected for inclusion (n=5010). Clinical notes from regional clinical genetics services notes were reviewed to assess pre-defined clinical outcomes relating to interventions, prenatal choices, and information provision. ResultsOutcomes were recorded for 4237 diagnosed probands (85% of those eligible) from all 24 recruiting centres across the UK and Ireland. Additional diagnostic or screening tests were performed in 903 (21%) probands through referral to a range of different clinical specialties, and stopped or avoided in a further 26 (0.6%). Disease-specific treatment was started in 85 (2%) probands, including seizure-control medications and dietary supplements, and contra-indicated medications were stopped/avoided as no longer necessary in a further 20 (0.5%). The option of prenatal/preimplantation genetic testing was discussed with 1204 (28%) families, despite the relatively advanced age of the parents at the time of diagnosis. Importantly, condition-specific information or literature was given to 3214 (76%) families, and 880 (21%) were involved in family support groups. In the most common condition (KBG syndrome; 79 (2%) probands), clinical interventions only partially reflected the temporal development of phenotypes, highlighting the importance of consensus management guidelines and patient support groups. ConclusionsOur results underscore the importance of achieving a clinico-molecular diagnosis to ensure timely onward referral of patients, enabling appropriate care and anticipatory surveillance, and for accessing relevant patient support groups.

BackgroundBoth promoters and untranslated regions (UTRs) have critical regulatory roles, yet variants in these regions are largely excluded from clinical genetic testing due to difficulty in interpreting pathogenicity. The extent to which these regions may harbour diagnoses for individuals with rare disease is currently unknown. MethodsWe present a framework for the identification and annotation of potentially deleterious proximal promoter and UTR variants in known dominant disease genes. We use this framework to annotate de novo variants (DNVs) in 8,040 undiagnosed individuals in the Genomics England 100,000 genomes project, which were subject to strict region-based filtering, clinical review, and validation studies where possible. In addition, we performed region and variant annotation-based burden testing in 7,862 unrelated probands against matched unaffected controls. ResultsWe prioritised eleven DNVs and identified an additional variant overlapping one of the eleven. Ten of these twelve variants (82%) are in genes that are a strong match to the individuals phenotype and six had not previously been identified. Through burden testing, we did not observe a significant enrichment of potentially deleterious promoter and/or UTR variants in individuals with rare disease collectively across any of our region or variant annotations. ConclusionsOverall, we demonstrate the value of screening promoters and UTRs to uncover additional diagnoses for previously undiagnosed individuals with rare disease and provide a framework for doing so without dramatically increasing interpretation burden.

IntroductionRapid genome sequencing (rGS) provides high diagnostic yield for critically ill infants with suspected genetic disorders, but has high upfront costs and insufficient insurance coverage. Assessing the downstream costs and health outcomes associated with rGS is important for guiding coverage decisions. This study compares 1-year healthcare costs and quality-adjusted life years (QALYs) for: 1) early rGS (within 7 days of admission) for all infants, and 2) early targeted neonatal gene sequencing (NewbornDx) for all infants, followed by later rGS (after 7 days) for undiagnosed infants. Study DesignThe Genomic Medicine for Ill Neonates and Infants (GEMINI) study was a multicenter, prospective study that enrolled 400 hospitalized infants under one year of age with suspected genetic disorders. All participants underwent both rGS and NewbornDx. Using GEMINI data and 2023 Medicare rates, we developed a decision tree to compare total costs and QALYs over a 1-year period for the two testing strategies. ResultsThe diagnostic yield and upfront testing costs were higher for rGS (49%; $12,297) than NewbornDx (27%; $2,449; p<0.05). As neither early testing nor diagnosis significantly affected QALYs, we conducted a cost-minimization analysis, focusing solely on cost differences between strategies. Over one year, early rGS was estimated to save $158,592 per patient (95% CI: $63,701-$253,292) compared to early NewbornDx with later rGS if necessary. ConclusionsEarly rGS results in substantial healthcare cost savings, highlighting the need to expand reimbursement to improve access early in a hospitalization for critically ill infants.

ObjectiveTo identify clinical and demographic predictors of genetic testing (GT) completion and diagnostic yield among patients with genetic eye disorders (GED) at a large U.S. tertiary academic center. DesignRetrospective cohort study. ParticipantsPatients with clinically diagnosed GEDs evaluated at the Wilmer Eye Institutes Genetic Eye Disease (GEDi) Center between 2002 and 2025. MethodsDemographic, clinical, and GT data were extracted. Bivariate analyses and multivariable logistic regression identified factors associated with GT completion and molecular diagnosis. Subgroup analyses examined racial disparities between Black, non-Hispanic White, and Other race participants. Main Outcome MeasuresProportion of patients completing GT, molecular diagnostic yield, and clinical/demographic predictors of each. ResultsOf 1809 participants (median age at presentation 44 years, symptom onset 28 years; median follow-up 5.2 years), 72% (1296) completed genetic testing, with a molecular diagnosis achieved in 63%, inconclusive results in 21%, and no diagnosis in 16%. GT completion was more likely among younger participants with earlier symptom onset, longer follow-up, and worse visual acuity (VA). Molecular diagnosis was more likely in participants with earlier symptom onset, worse VA, male sex, and syndromic or X-linked phenotypes. Black and Other race participants had significantly lower odds of completing GT (Black: OR [95% CI] 0.48 [0.37-0.63]; Other: 0.52 [0.35-0.78]) and receiving a molecular diagnosis (Black: 0.39 [0.28-0.54]; Other: 0.62 [0.38-0.99]), and consistently exhibited worse VA at both baseline and follow-up. Notably, Black and Other race participants presented at significantly younger ages than White participants, and disparities in GT completion and visual outcomes persisted despite equivalent or shorter time from presentation to GT, suggesting barriers arise independently of delays in care engagement. Among solved cases, 132 causative genes were identified; ABCA4, USH2A, PRPH2, RHO, and BEST1 accounted for 45% of molecular diagnoses. ConclusionsThis is the largest single-center GED genetic testing cohort reported in the U.S. and reveals significant disparities in GT completion and yield by race, age, sex, and disease-level factors. Our findings underscore the need to expand early access to GT, diversify genomic databases, and address systemic barriers to ensure equity in GED diagnosis, clinical trial access, and delivery of emerging therapies.

PurposeSouthSeq, a translational research study to perform genome sequencing (GS) for infants with symptoms suggestive of a genetic disorder, was conducted in NICUs in the Southeastern US. Recruitment targeted racial/ethnic minorities and rural, medically underserved areas that are historically under-represented in genomic medicine research. MethodsGS and analysis were performed for 367 newborns to detect disease-causal genetic variation concurrent with standard of care evaluation and testing. ResultsDefinitive diagnostic (DD) or likely diagnostic (LD) genetic findings were identified in 30% of newborns and 14% harbored an uncertain result. Only 39% of DD/LD findings were identified via concurrent standard of care suggesting that GS testing is better for obtaining early genetic diagnosis. We also identified phenotypes that correlate with the likelihood of receiving a DD/LD finding, such as craniofacial, ophthalmologic, auditory, skin, and hair abnormalities. We did not observe any differences in diagnostic rates between racial/ethnic groups. ConclusionWe describe one of the largest to-date GS cohorts of ill newborns, enriched for African American and rural patients. Our results demonstrate the utility of GS as it provides early in life detection of clinically relevant genetic variation not identified via current standard clinical testing, particularly for newborns exhibiting certain phenotypic features.

PurposeThe Clinical Genome Resource (ClinGen) CDH1 Variant Curation Expert Panel (VCEP) developed specifications for CDH1 variant curation with a goal to resolve variants of uncertain significance (VUS) and with ClinVar conflicting interpretations for effective medical care. In addition, the CDH1 VCEP continues to update these specifications in keeping with evolving clinical practice and variant interpretation guidelines. MethodsCDH1 variant classification specifications were modified based on updated genetic testing clinical criteria, new recommendations from ClinGen, and expert knowledge from ongoing CDH1 variant curations. Trained biocurators curated 273 variants using updated CDH1 interpretation guidelines and incorporated published and unpublished data provided by diagnostic laboratories. All variants were reviewed by the ClinGen VCEP and classifications submitted to ClinVar. ResultsUpdated CDH1-specific variant interpretation guidelines include eleven major modifications since the initial specifications from 2018. Using the refined guidelines, 97% (36/37) of variants with ClinVar conflicting interpretations were resolved into benign, likely benign, likely pathogenic, or pathogenic, and 35% (15/43) of VUS were resolved into benign or likely benign. Overall, 88% (239/273) of curated variants had non-VUS classifications. ConclusionThe development and evolution of CDH1-specific criteria by the expert panel results in decreased uncertain and conflicting interpretations of variants in this clinically actionable gene.

BackgroundPediatric neuromuscular diseases are genetically and clinically heterogeneous. A substantial proportion remain without a definitive genetic diagnosis despite available clinical molecular testing. RNA-sequencing (RNA-seq) can be used to complement genome or exome sequencing to elucidate or to identify the functional impact of variants of uncertain significance, but when manually analyzed is limited to candidate DNA variants or phenotype-driven gene lists. Open-source computational tools have been developed to systematically and unbiasedly analyze RNA-seq data for aberrant splicing, expression, or allelic imbalance. However, best use practice of these tools is yet to be established. MethodsTo assess the performance of selected tools, we collected RNA-seq from 97 previously diagnosed samples to establish a truth set for benchmarking. Pathogenic variants were categorized as: true positives with confirmed aberrant RNA events and true negatives with no transcriptomic effect. We assessed performance of eight commonly used tools for splicing, expression and allelic imbalance analysis. We then applied the optimal strategy to 74 undiagnosed RNA-seq samples to identify new candidate diagnoses. ResultsAcross 68 diagnosed probands with aberrant RNA events, tools correctly identified 28 diagnoses. Splicing analysis tools provided most of the findings, but allelic imbalance tools uniquely identified 4, underscoring their value. Conversely, the false positive rate was highest for the splice tools and lowest for expression analysis. Application of tools led to identification of candidate variants for only 9 out of 74 undiagnosed patients. ConclusionsInclusion of RNA-seq tools can expedite variant prioritization, characterization and interpretation in the diagnostic pipeline but remain complementary to manual analysis of loci where candidate variants were identified by DNA sequencing.

PurposeVariants in SLC6A1 result in a rare neurodevelopmental disorder characterized by a variable clinical presentation of symptoms including developmental delay, epilepsy, motor dysfunction, and autism spectrum disorder. SLC6A1 haploinsufficiency has been confirmed as the predominant pathway of SLC6A1-related neurodevelopmental disorders (NDDs), however, the molecular mechanism underlying the variable clinical presentation remains unclear. MethodsHere, through work of the Undiagnosed Diseases Network, we identify an undiagnosed individual with an inherited p.(A334S) variant of uncertain significance. To resolve this case and better understand the variable expressivity with SLC6A1, we assess the phenotypes of the proband with a cohort of cases diagnosed with SLC6A1-related NDDs. We then create an allelic series in the Drosophila melanogaster to functionally characterize case variants. ResultsWe identify significant clinical overlap between the unsolved case and confirmed cases of SLC6A1-related NDDs and find a mild to severe clinical presentation associated with missense variants. We confirm phenotypes in flies expressing SLC6A1 variants consistent with a partial loss-of-function mechanism. ConclusionWe conclude that the p.(A334S) variant is a hypomorphic allele and begin to elucidate the underlying variability in SLC6A1-related NDDs. These insights will inform clinical diagnosis, prognosis, treatment and inform therapeutic design for those living with SLC6A1- related NDDs.

Introduction: BEACONS-NBS (Building Evidence and Collaboration for GenOmics in Nationwide Newborn Screening) is the first research study to integrate whole genome sequencing into newborn screening (NBS) across multiple U.S. states and territorial public health laboratory programs (PHLPs). We developed a list of conditions for screening. Methods: We designed inclusion criteria and assembled an initial condition list from published resources. The list was revised by clinical experts, molecular geneticists, genetic counselors, PHLPs, rare disease advocacy organizations, the BEACONS-NBS Community Advisory Board, and project leadership from the National Institutes of Health. For each condition, we provided a rationale for early detection, diagnostic signs or biomarkers, and treatments or surveillance strategies. Results: The BEACONS-NBS condition list includes 777 conditions associated with 743 genes, one copy number variant, and two aneuploidies and is larger than those used in other genomic NBS research studies in the U.S. and United Kingdom. Most conditions are inborn errors of immunity (37.2%), inherited metabolic disorders (18.7%), or endocrine conditions (18.1%). Nearly all conditions (93.3%) can be confirmed using a non-genetic test. Discussion: BEACONS-NBS has established a condition list for implementation across multiple state and territorial PHLPs, enabling the prospective evaluation of feasibility of population-wide genomic NBS.

BackgroundWhole-genome sequencing (WGS) projects for rare disease diagnosis typically yield a diagnostic rate of approximately 25-40%, dependent particularly on patient selection and the extent of prior genetic testing. The Scottish Genomes Partnership (SGP) is a collaborative research programme involving four Scottish Regional Genetics Centres, four Scottish Medical Schools, and Genomics Englands 100,000 Genomes Project. It aims to facilitate genome sequencing and diagnosis for patients in the Scottish NHS with suspected rare Mendelian diseases. Within SGP, short-read sequencing (SRS) achieved a diagnostic rate of 23% in affected families. MethodsTo increase the diagnostic yield, we applied Oxford Nanopore Technologies (ONT) long-read sequencing (LRS) to a cohort of 24 SGP families (74 individuals) who remained undiagnosed after SRS. We also re-analysed previously generated SRS data to identify pathogenic structural variants (SVs). We benchmarked several existing software tools for SV detection using LRS and defined key requirements for sample processing and DNA quality. Custom SV prioritisation and bioinformatics pipelines were developed to integrate SV discovery with genotype-phenotype analysis. ResultsBenchmarking showed that minimap2 + cuteSV was optimal for single-sample SV discovery, while minimap2 + Sniffles2 performed best for family-based analysis. SV calling across the cohort yielded 60,022 filtered SVs spanning autosomes and sex chromosomes. Each family had between 23,024 and 25,009 SVs genome-wide (median: 23,814). A total of 392 SVs genome-wide and 8 within a disease-gene panel were prioritised across autosomal dominant/de novo, recessive, compound heterozygous, and X-linked modes, with counts varying between families. In three exemplar families, pathogenic or likely pathogenic de novo SVs were identified in both LRS and SRS data: one at the DLX5/6 locus, one in AUTS2, and one in FN1. We provide genome-wide de novo SVs and compound heterozygous (SV + SNV) variants, and deposit raw and processed sequencing data for all families in the Genomics England Research Environment to support future gene discovery. ConclusionsThis study demonstrates that in-depth SV analysis can increase molecular diagnostic rates in rare disease patients with presumed monogenic aetiology. Pathogenic or likely pathogenic de novo SVs were identified in three families, resolving the diagnostic odyssey for at least two of the 24 families.

BackgroundsDespite rapid advances in genomic sequencing, most rare genetic variants remain insufficiently characterized for clinical use, limiting the potential of personalized medicine. When classifying whether a variant is pathogenic, clinical labs adhere to diagnostic guidelines that comprehensively evaluate many forms of evidence including case data, computational predictions, and functional screening. While a substantial amount of clinical evidence has been developed for many of these variants, the majority cannot be definitively classified as pathogenic or benign, and thus persist as Variants of Uncertain Significance (VUS). MethodsWe processed over 2.4 million plaintext variant summaries from ClinVar, employing sentence-level classification to remove content that does not contain evidence and removing uninformative or highly similar summaries. We then trained ClinVar-BERT to discern clinical evidence within these summaries by fine-tuning a BioBERT-based model with labeled records. ResultsWe validated ClinVar-BERT model predictions for variant summaries that are classified as uncertain (VUS) using orthogonal functional screening data. ClinVar-BERT significantly separated estimates of functional impact in clinically actionable genes, including BRCA1 (p = 1.90x10-20), TP53 (p = 1.14x10-47), and PTEN (p = 3.82 x 10-7) and achieved an AUROC of 0.927 when classifying whether variants result in loss of function or have uncertain effects. ConclusionThese findings suggest that ClinVar-BERT is capable of discerning evidence from diagnostic reports and can be useful for prioritizing variants for re-assessment by diagnostic laboratories and expert curation panels.

PurposePhenotype-based ascertainment of probands in studies of Mendelian disorders may exclude individuals with mild phenotypes or that lack health care access. We explore this premise in All of Us Research Program participants with pathogenic variation causal for three Mendelian conditions: autosomal dominant polycystic kidney disease (ADPKD), Marfan syndrome, and neurofibromatosis type 1 (NF1). MethodsWe identified All of Us Research Program participants with putatively pathogenic variation in NF1, FBN1, PKD1, and PKD2. Concept terms were extracted from electronic health records to assess participant diagnosis and phenotype. Variant annotation and participant surveys were evaluated to identify biological and social factors differentiating diagnosed and undiagnosed individuals. ResultsLarge proportions of individuals with pathogenic variation in NF1, FBN1, or PKD1/PKD2 lack the associated diagnosis of NF1 (47%), Marfan syndrome (58%), or ADPKD (51%), respectively. Pathogenic variants in diagnosed individuals have greater inferred deleteriousness for NF1 and ADPKD, and undiagnosed individuals had less severe phenotypes compared to diagnosed individuals for all three conditions. ConclusionA genotype-first ascertainment of individuals in genomic research allows for a more comprehensive assessment of Mendelian disease and removes biases that confound our understanding of the penetrance and presentation of these conditions.

BackgroundRare diseases affect over 300 million people worldwide, with more than half of these cases presenting in childhood. Despite advances in genomic technologies, significant diagnostic delays remain, particularly in underrepresented populations. Although whole-genome sequencing is expected to be more effective than whole-exome sequencing, there are currently no direct patient-level comparisons from Albania. MethodsWe conducted a prospective, head-to-head comparison of whole-genome sequencing and whole-exome sequencing in 72 pediatric patients from Albania with suspected rare genetic disorders. Both technologies were applied in parallel using ISO-accredited pipelines. Variants were classified according to the ACMG guidelines and assessed for clinical relevance. Primary and secondary findings, as well as dual diagnoses, were recorded and compared. ResultsA molecular diagnosis was achieved in 72.2% of patients. Whole genome sequencing yielded diagnostic or secondary findings in 68.1% of cases, while whole exome sequencing identified primary diagnoses in 30.6% of cases. WGS made a primary diagnosis in 37.5% of cases, resolving complex or blended phenotypes and detecting variant types that were missed by WES, including deep intronic, regulatory and structural variants. Furthermore, WGS identified medically actionable secondary findings in 15.3% of patients, providing direct information for clinical management. Overall, WGS outperformed WES across variant classes and inheritance modes. ConclusionWGS demonstrated clear diagnostic superiority over WES in this pediatric cohort, particularly in identifying complex phenotypes and structural variants. These results suggest that WGS should be considered a primary diagnostic tool and highlight the importance of ensuring equitable access to genomic technologies for underrepresented populations.

PurposeThe majority of missense variants in clinical genetic tests are classified as variants of uncertain significance. Broadening the evidence of the PS1 and PM5 criteria has the potential to increase conclusive variant interpretation. MethodsWe hypothesized that incorporation of pathogenic missense variants in conserved residues across paralogous genes can increase the number of variants where ACMG PS1/PM5 criteria can be applied. We mapped over 2.5 million pathogenic and general population variants from ClinVar, HGMD, and gnomAD databases onto 9,990 genes and aligned these by gene families. Subsequently, we developed a novel framework to extend PS1/PM5 by incorporating pathogenic paralogous variants annotations (para-PS1/PM5). ResultsWe demonstrate that para-PS1/PM5 criteria increase the number of classifiable amino acids 3.6-fold compared to PS1 and PM5. Across all gene families with at least two disease-associated genes, the calculated likelihood ratios suggest moderate evidence for pathogenicity. Moreover, for 36 genes, the extended para-PS1/PM5 criteria reach strong evidence level. ConclusionWe show that single pathogenic paralogous variants incorporation at paralogous protein positions increases the applicability of the PS1 and PM5 criteria, likely leading to a reduction of variants of uncertain significance across many monogenic disorders. Future iterations of the ACMG guidelines may consider para-PS1 and para-PM5.