Commonalities across computational workflows for uncovering explanatory variants in undiagnosed cases.

PURPOSE: Genomic sequencing has become an increasingly powerful and relevant tool to be leveraged for the discovery of genetic aberrations underlying rare, Mendelian conditions. Although the computational tools incorporated into diagnostic workflows for this task are continually evolving and improving, we nevertheless sought to investigate commonalities across sequencing processing workflows to reveal consensus and standard practice tools and highlight exploratory analyses where technical and theoretical method improvements would be most impactful. METHODS: We collected details regarding the computational approaches used by a genetic testing laboratory and 11 clinical research sites in the United States participating in the Undiagnosed Diseases Network via meetings with bioinformaticians, online survey forms, and analyses of internal protocols. RESULTS: We found that tools for processing genomic sequencing data can be grouped into four distinct categories. Whereas well-established practices exist for initial variant calling and quality control steps, there is substantial divergence across sites in later stages for variant prioritization and multimodal data integration, demonstrating a diversity of approaches for solving the most mysterious undiagnosed cases. CONCLUSION: The largest differences across diagnostic workflows suggest that advances in structural variant detection, noncoding variant interpretation, and integration of additional biomedical data may be especially promising for solving chronically undiagnosed cases.

ped_draw: pedigree drawing with ease.

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.

Genome sequencing reveals a deep intronic splicing ACVRL1 mutation hotspot in Hereditary Haemorrhagic Telangiectasia.

INTRODUCTION: Hereditary 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. METHODS: DNA 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. RESULTS: Eight 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. CONCLUSIONS: Despite 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.

SON-Related Zhu-Tokita-Takenouchi-Kim Syndrome With Recurrent Hemiplegic Migraine: Putative Role of PRRT2.

Background and Objectives: Zhu-Tokita-Takenouchi-Kim (ZTTK) syndrome (OMIM 617140) is a recently identified neurodevelopmental disorder caused by heterozygous loss-of-function (LoF) variants in SON. Because the SON protein functions as an RNA-splicing regulator, it has been shown that some clinical features of ZTTK syndrome can be attributed to abnormal RNA splicing. Several neurologic features have been observed in patients with ZTTK syndrome, including seizure/epilepsy and other EEG abnormalities. However, a relationship between SON LoF in ZTTK syndrome and hemiplegic migraine remains unknown. Methods: We identified a patient with a pathogenic variant in SON who shows typical clinical features of ZTTK syndrome and experienced recurrent episodes of hemiplegic migraine. To define clinical features, brain MRI and EEG during and after episodes of hemiplegic migraine were characterized. To identify molecular mechanisms for this clinical presentation, we investigated the impact of small interfering RNA (siRNA)-mediated SON knockdown on mRNA expression of the CACNA1A, ATP1A2, SCN1A, and PRRT2 genes, known to be associated with hemiplegic migraine, by quantitative RT-PCR. Pre-mRNA splicing of PRRT2 on SON knockdown was further examined by RT-PCR using primers targeting specific exons. Results: Recurrent episodes of hemiplegic migraine in our patient typically followed modest closed head injuries, and recurrent seizures occurred during the most severe of these episodes. Transient hemispheric cortical interstitial edema and asymmetric EEG slowing were identified during episodes. Our siRNA experiments revealed that SON knockdown significantly reduces PRRT2 mRNA levels in U87MG and SH-SY5Y cell lines, although a reduction in CACNA1A, ATP1A2, and SCN1A mRNA expression was not observed. We further identified that SON knockdown leads to failure in intron 2 removal from PRRT2 pre-mRNA, resulting in a premature termination codon that blocks the generation of functionally intact full-length PRRT2. Discussion: This report identifies recurrent hemiplegic migraine as a novel clinical manifestation of ZTTK syndrome, further characterizes this clinical feature, and provides evidence for downregulation of PRRT2 caused by SON LoF as a mechanism causing hemiplegic migraine. Examination of the SON gene may be indicated in individuals with recurrent hemiplegic migraine.

Clin.iobio: A Collaborative Diagnostic Workflow to Enable Team-Based Precision Genomics.

The primary goal of precision genomics is the identification of causative genetic variants in targeted or whole-genome sequencing data. The ultimate clinical hope is that these findings lead to an efficacious change in treatment for the patient. In current clinical practice, these findings are typically returned by expert analysts as static, text-based reports. Ideally, these reports summarize the quality of the data obtained, integrate known gene-phenotype associations, follow allele segregation and affected status within the sequenced samples, and weigh computational evidence of pathogenicity. These findings are used to prioritize the variant(s) most likely to cause the given patient's phenotypes. In most diagnostic settings, a team of experts contribute to these reports, including bioinformaticians, clinicians, and genetic counselors, among others. However, these experts often do not have the necessary tools to review genomic findings, test genetic hypotheses, or query specific gene and variant information. Additionally, team members often rely on different tools and methods based on their given expertise, resulting in further difficulties in communicating and discussing genomic findings. Here, we present clin.iobio-a web-based solution to collaborative genomic analysis that enables diagnostic team members to focus on their area of expertise within the diagnostic process, while allowing them to easily review and contribute to all steps of the diagnostic process. Clin.iobio integrates tools from the popular iobio genomic visualization suite into a comprehensive diagnostic workflow, encompassing (1) genomic data quality review, (2) dynamic phenotype-driven gene prioritization, (3) variant prioritization using a comprehensive set of knowledge bases and annotations, (4) and an exportable findings summary. In conclusion, clin.iobio is a comprehensive solution to team-based precision genomics, the findings of which stand to inform genomic considerations in clinical practice.

Effective variant filtering and expected candidate variant yield in studies of rare human disease.

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.

Gene.iobio: an interactive web tool for versatile, clinically-driven variant interrogation and prioritization.

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.

gene.iobio: an interactive web tool for versatile, clinically-driven variant interrogation and prioritization.

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 genomic findings. A new paradigm has emerged, where genome-based tests are often evaluated by a large multi-disciplinary collaborative team, typically including a diagnostic pathologist, a bioinformatician, a genetic counselor, and often a subspeciality clinician. This team-based approach calls for new computational tools to allow every member of the clinical care provider team, at varying levels of genetic knowledge and diagnostic expertise, to quickly and easily analyze and interpret complex genomic data. 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.

Genepanel.iobio - an easy to use web tool for generating disease- and phenotype-associated gene lists.

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.

Rapid clinical diagnostic variant investigation of genomic patient sequencing data with iobio web tools.

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