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BackgroundThe success of the clinical use of sequencing based tests (from single gene to genomes) depends on the accuracy and consistency of variant interpretation. Aiming to improve the interpretation process through practice guidelines, the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) have published standards and guidelines for the interpretation of sequence variants. However, manual application of the guidelines is tedious and prone to human error. Web-based tools and software systems may not only address this problem but also document reasoning and supporting evidence, thus enabling transparency of evidence-based reasoning and resolution of discordant interpretations.ResultsIn this report, we describe the design, implementation, and initial testing of the Clinical Genome Resource (ClinGen) Pathogenicity Calculator, a configurable system and web service for the assessment of pathogenicity of Mendelian germline sequence variants. The system allows users to enter the applicable ACMG/AMP-style evidence tags for a specific allele with links to supporting data for each tag and generate guideline-based pathogenicity assessment for the allele. Through automation and comprehensive documentation of evidence codes, the system facilitates more accurate application of the ACMG/AMP guidelines, improves standardization in variant classification, and facilitates collaborative resolution of discordances. The rules of reasoning are configurable with gene-specific or disease-specific guideline variations (e.g. cardiomyopathy-specific frequency thresholds and functional assays). The software is modular, equipped with robust application program interfaces (APIs), and available under a free open source license and as a cloud-hosted web service, thus facilitating both stand-alone use and integration with existing variant curation and interpretation systems. The Pathogenicity Calculator is accessible at http://calculator.clinicalgenome.org.ConclusionsBy enabling evidence-based reasoning about the pathogenicity of genetic variants and by documenting supporting evidence, the Calculator contributes toward the creation of a knowledge commons and more accurate interpretation of sequence variants in research and clinical care.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-016-0391-z) contains supplementary material, which is available to authorized users.

Effective exchange of information about genetic variants is currently hampered by the lack of readily available globally unique variant identifiers that would enable aggregation of information from different sources. The ClinGen Allele Registry addresses this problem by providing (1) globally unique “canonical” variant identifiers (CAids) on demand, either individually or in large batches; (2) access to variant‐identifying information in a searchable Registry; (3) links to allele‐related records in many commonly used databases; and (4) services for adding links to information about registered variants in external sources. A core element of the Registry is a canonicalization service, implemented using in‐memory sequence alignment‐based index, which groups variant identifiers denoting the same nucleotide variant and assigns unique and dereferenceable CAids. More than 650 million distinct variants are currently registered, including those from gnomAD, ExAC, dbSNP, and ClinVar, including a small number of variants registered by Registry users. The Registry is accessible both via a web interface and programmatically via well‐documented Hypertext Transfer Protocol (HTTP) Representational State Transfer Application Programming Interface (REST‐APIs). For programmatic interoperability, the Registry content is accessible in the JavaScript Object Notation for Linked Data (JSON‐LD) format. We present several use cases and demonstrate how the linked information may provide raw material for reasoning about variant's pathogenicity.

Conflict resolution in genomic variant interpretation is a critical step toward improving patient care. Evaluating interpretation discrepancies in copy number variants (CNVs) typically involves assessing overlapping genomic content with focus on genes/regions that may be subject to dosage sensitivity (haploinsufficiency (HI) and/or triplosensitivity (TS)). CNVs containing dosage sensitive genes/regions are generally interpreted as "likely pathogenic" (LP) or "pathogenic" (P), and CNVs involving the same known dosage sensitive gene(s) should receive the same clinical interpretation. We compared the Clinical Genome Resource (ClinGen) Dosage Map, a publicly available resource documenting known HI and TS genes/regions, against germline, clinical CNV interpretations within the ClinVar database. We identified 251 CNVs overlapping known dosage sensitive genes/regions but not classified as LP or P; these were sent back to their original submitting laboratories for re-evaluation. Of 246 CNVs re-evaluated, an updated clinical classification was warranted in 157 cases (63.8%); no change was made to the current classification in 79 cases (32.1%); and 10 cases (4.1%) resulted in other types of updates to ClinVar records. This effort will add curated interpretation data into the public domain and allow laboratories to focus attention on more complex discrepancies.

BackgroundThe genetic etiology of autism is heterogeneous. Multiple disorders share genotypic and phenotypic traits with autism. Network based cross-disorder analysis can aid in the understanding and characterization of the molecular pathology of autism, but there are few tools that enable us to conduct cross-disorder analysis and to visualize the results.DescriptionWe have designed Autworks as a web portal to bring together gene interaction and gene-disease association data on autism to enable network construction, visualization, network comparisons with numerous other related neurological conditions and disorders. Users may examine the structure of gene interactions within a set of disorder-associated genes, compare networks of disorder/disease genes with those of other disorders/diseases, and upload their own sets for comparative analysis.ConclusionsAutworks is a web application that provides an easy-to-use resource for researchers of varied backgrounds to analyze the autism gene network structure within and between disorders.Availability: http://autworks.hms.harvard.edu/