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In studies of families with rare disease, it is common to screen for de novo mutations, as well as recessive or dominant variants that explain the phenotype. However, the filtering strategies and software used to prioritize high-confidence variants vary from study to study. In an effort to establish recommendations for rare disease research, we explore effective guidelines for variant (SNP and INDEL) filtering and report the expected number of candidates for de novo dominant, recessive, and autosomal dominant modes of inheritance. We derived these guidelines using two large family-based cohorts that underwent whole-genome sequencing, as well as two family cohorts with whole-exome sequencing. The filters are applied to common attributes, including genotype-quality, sequencing depth, allele balance, and population allele frequency. The resulting guidelines yield ~10 candidate SNP and INDEL variants per exome, and 18 per genome for recessive and de novo dominant modes of inheritance, with substantially more candidates for autosomal dominant inheritance. For family-based, whole-genome sequencing studies, this number includes an average of three de novo, ten compound heterozygous, one autosomal recessive, four X-linked variants, and roughly 100 candidate variants following autosomal dominant inheritance. The slivar software we developed to establish and rapidly apply these filters to VCF files is available at https://github.com/brentp/slivar under an MIT license, and includes documentation and recommendations for best practices for rare disease analysis.

Background Pedigree files are ubiquitously used within bioinformatics and genetics studies to convey critical information about relatedness, sex and affected status of study samples. While the text based format of ped files is efficient for computational methods, it is not immediately intuitive to a bioinformatician or geneticist trying to understand family structures, many of which encode the affected status of individuals across multiple generations. The visualization of pedigrees into connected nodes with descriptive shapes and shading provides a far more interpretable format to recognize visual patterns and intuit family structures. Despite these advantages of a visual pedigree, it remains difficult to quickly and accurately visualize a pedigree given a pedigree text file. Results Here we describe ped_draw a command line and web tool as a simple and easy solution to pedigree visualization. Ped_draw is capable of drawing complex multi-generational pedigrees and conforms to the accepted standards for depicting pedigrees visually. The command line tool can be used as a simple one liner command, utilizing graphviz to generate an image file. The web tool, https://peddraw.github.io , allows the user to either: paste a pedigree file, type to construct a pedigree file in the text box or upload a pedigree file. Users can save the generated image file in various formats. Conclusions We believe ped_draw is a useful pedigree drawing tool that improves on current methods due to its ease of use and approachability. Ped_draw allows users with various levels of expertise to quickly and easily visualize pedigrees.

Pedigree files are ubiquitously used within bioinformatics and genetics studies to convey critical information about relatedness, sex and affected status of study samples. While the text based format of ped files is efficient for computational methods, it is not immediately intuitive to a bioinformatician or geneticist trying to understand family structures, many of which encode the affected status of individuals across multiple generations. The visualization of pedigrees into connected nodes with descriptive shapes and shading provides a far more interpretable format to recognize visual patterns and intuit family structures. Despite these advantages of a visual pedigree, it remains difficult to quickly and accurately visualize a pedigree given a pedigree text file. Here we describe ped_(d)raw a command line and web tool as a simple and easy solution to pedigree visualization. Ped_(d)raw is capable of drawing complex multi-generational pedigrees and conforms to the accepted standards for depicting pedigrees visually. The command line tool can be used as a simple one liner command, utilizing graphviz to generate an image file. The web tool, https://peddraw.github.io, allows the user to either: paste a pedigree file, type to construct a pedigree file in the text box or upload a pedigree file. Users can save the generated image file in various formats. We believe ped_(d)raw is a useful pedigree drawing tool that improves on current methods due to its ease of use and approachability. Ped_(d)raw allows users with various levels of expertise to quickly and easily visualize pedigrees.

With increasing utilization of comprehensive genomic data to guide clinical care, anticipated to become the standard of care in many clinical settings, the practice of diagnostic medicine is undergoing a notable shift. However, the move from single-gene or panel-based genetic testing to exome and genome sequencing has not been matched by the development of tools to enable diagnosticians to interpret increasingly complex or uncertain genomic findings. Here, we present gene.iobio , a real-time, intuitive and interactive web application for clinically-driven variant interrogation and prioritization. We show gene.iobio is a novel and effective approach that significantly improves upon and reimagines existing methods. In a radical departure from existing methods that present variants and genomic data in text and table formats, gene.iobio provides an interactive, intuitive and visually-driven analysis environment. We demonstrate that adoption of gene.iobio in clinical and research settings empowers clinical care providers to interact directly with patient genomic data both for establishing clinical diagnoses and informing patient care, using sophisticated genomic analyses that previously were only accessible via complex command line tools.

When ordering genetic testing or triaging candidate variants in exome and genome sequencing studies, it is critical to generate and test a comprehensive list of candidate genes that succinctly describe the complete and objective phenotypic features of disease. Significant efforts have been made to curate gene:disease associations both in academic research and commercial genetic testing laboratory settings. However, many of these valuable resources exist as islands and must be used independently, generating static, single-resource gene:disease association lists. Here we describe genepanel.iobio ( https://genepanel.iobio.io ) an easy to use, free and open-source web tool for generating disease- and phenotype-associated gene lists from multiple gene:disease association resources, including the NCBI Genetic Testing Registry (GTR), Phenolyzer, and the Human Phenotype Ontology (HPO). We demonstrate the utility of genepanel.iobio by applying it to complex, rare and undiagnosed disease cases that had reached a diagnostic conclusion. We find that genepanel.iobio is able to correctly prioritize the gene containing the diagnostic variant in roughly half of these challenging cases. Importantly, each component resource contributed diagnostic value, showing the benefits of this aggregate approach. We expect genepanel.iobio will improve the ease and diagnostic value of generating gene:disease association lists for genetic test ordering and whole genome or exome sequencing variant prioritization.

Exome and genome sequencing have greatly improved the diagnosis of rare genetic disorders but remain limited in their ability to identify and classify non-coding variants, including intronic variants, cryptic splice-site alterations, and disruptions in regulatory regions. RNA sequencing (RNA-seq) has emerged as a powerful tool to bridge this gap by providing functional insights into genomic variants that disrupt splicing or gene expression, thereby aiding in variant interpretation and classification. We retrospectively reviewed 30 cases from the Utah Penelope Program and the Undiagnosed Diseases Network over a three-year period, in which RNA-seq was performed on whole blood and/or fibroblasts following either negative DNA sequencing or the identification of candidate variants requiring functional assessment. In these cases, RNA-seq identified exon skipping, cryptic splice-site activation, and intron retention, leading to transcript disruption. Additionally, positional enrichment analysis clarified X-inactivation patterns and dosage effects, confirming the pathogenicity of copy number variants. By detecting these transcript-level alterations, RNA-seq provided functional evidence supporting the reclassification of multiple variants of uncertain significance, contributing to diagnostic resolution in selected cases. This study underscores the clinical utility of RNA-seq in detecting splicing and regulatory defects that DNA sequencing and predictive tools alone cannot resolve. Integrating RNA-seq into clinical workflows can support variant classification, aid in diagnostic resolution for selected cases, and provide mechanistic insights into genetic disorders, contributing to patient care and genetic counseling.

IntroductionHereditary haemorrhagic telangiectasia (HHT) is a genetically heterogeneous disorder caused by mutations in the genes ENG, ACVRL1, and SMAD4. Yet the genetic cause remains unknown for some families even after exhaustive exome analysis. We hypothesised that non-coding regions of the known HHT genes may harbour variants that disrupt splicing in these cases.MethodsDNA from 35 individuals with clinical findings of HHT and 2 healthy controls from 13 families underwent whole genome sequencing. Additionally, 87 unrelated cases suspected to have HHT were evaluated using a custom designed next-generation sequencing panel to capture the coding and non-coding regions of ENG, ACVRL1 and SMAD4. Individuals from both groups had tested negative previously for a mutation in the coding region of known HHT genes. Samples were sequenced on a HiSeq2500 instrument and data were analysed to identify novel and rare variants.ResultsEight cases had a novel non-coding ACVRL1 variant that disrupted splicing. One family had an ACVRL1intron 9:chromosome 3 translocation, the first reported case of a translocation causing HHT. The other seven cases had a variant located within a ~300 bp CT-rich ‘hotspot’ region of ACVRL1intron 9 that disrupted splicing.ConclusionsDespite the difficulty of interpreting deep intronic variants, our study highlights the importance of non-coding regions in the disease mechanism of HHT, particularly the CT-rich hotspot region of ACVRL1intron 9. The addition of this region to HHT molecular diagnostic testing algorithms will improve clinical sensitivity.

Computational analysis of genome or exome sequences may improve inherited disease diagnosis, but is costly and time-consuming. We describe the use of , a web-based tool suite for intuitive, real-time genome diagnostic analyses. We used to identify the disease-causing variant in a patient with early infantile epileptic encephalopathy with prior nondiagnostic genetic testing. tools can be used by clinicians to rapidly identify disease-causing variants from genomic patient sequencing data.

ObjectivesTo test the hypothesis that ROSAH (retinal dystrophy, optic nerve oedema, splenomegaly, anhidrosis and headache) syndrome, caused by dominant mutation in ALPK1, is an autoinflammatory disease.MethodsThis cohort study systematically evaluated 27 patients with ROSAH syndrome for inflammatory features and investigated the effect of ALPK1 mutations on immune signalling. Clinical, immunologic and radiographical examinations were performed, and 10 patients were empirically initiated on anticytokine therapy and monitored. Exome sequencing was used to identify a new pathogenic variant. Cytokine profiling, transcriptomics, immunoblotting and knock-in mice were used to assess the impact of ALPK1 mutations on protein function and immune signalling.ResultsThe majority of the cohort carried the p.Thr237Met mutation but we also identified a new ROSAH-associated mutation, p.Tyr254Cys.Nearly all patients exhibited at least one feature consistent with inflammation including recurrent fever, headaches with meningeal enhancement and premature basal ganglia/brainstem mineralisation on MRI, deforming arthritis and AA amyloidosis. However, there was significant phenotypic variation, even within families and some adults lacked functional visual deficits. While anti-TNF and anti-IL-1 therapies suppressed systemic inflammation and improved quality of life, anti-IL-6 (tocilizumab) was the only anticytokine therapy that improved intraocular inflammation (two of two patients).Patients’ primary samples and in vitro assays with mutated ALPK1 constructs showed immune activation with increased NF-κB signalling, STAT1 phosphorylation and interferon gene expression signature. Knock-in mice with the Alpk1 T237M mutation exhibited subclinical inflammation.Clinical features not conventionally attributed to inflammation were also common in the cohort and included short dental roots, enamel defects and decreased salivary flow.ConclusionROSAH syndrome is an autoinflammatory disease caused by gain-of-function mutations in ALPK1 and some features of disease are amenable to immunomodulatory therapy.