Genome sequencing detects a wide range of clinically relevant copy-number variants and other genomic alterations.

PURPOSE: Copy-number variants (CNVs) and other non-single nucleotide variant/indel variant types contribute an important proportion of diagnoses in individuals with suspected genetic disease. This study describes the range of such variants detected by genome sequencing (GS). METHODS: For a pediatric cohort of 1032 participants undergoing clinical GS, we characterize the CNVs and other non-single nucleotide variant/indel variant types that were reported, including aneuploidies, mobile element insertions, and uniparental disomies, and we describe the bioinformatic pipeline used to detect these variants. RESULTS: Together, these genetic alterations accounted for 15.8% of reported variants. Notably, 67.9% of these were deletions, 32.9% of which overlapped a single gene, and many deletions were reported together with a second variant in the same gene in cases of recessive disease. A retrospective medical record review in a subset of this cohort revealed that up to 6 additional genetic tests were ordered in 68% (26/38) of cases, some of which failed to report the CNVs/rare variants reported on GS. CONCLUSION: GS detected a broad range of reported variant types, including CNVs ranging in size from 1 Kb to 46 Mb.

Parents' Perspectives on Secondary Genetic Ancestry Findings in Pediatric Genomic Medicine.

PURPOSE: With advances in genome sequencing technologies, large-scale genome-wide sequencing has advanced our understanding of disease risk and etiology and contributes to the rapidly expanding genomic health services in pediatric settings. Because it is possible to return ancestry estimates following clinical genomic sequencing, it is important to understand the interest in ancestry results among families who may have the option of receiving these results. METHODS: We conducted 26 semi-structured qualitative telephone interviews of parents with children/newborns with likely genetic conditions from two studies of clinical genome sequencing. Using a purposive sampling approach, we selected parents from the SouthSeq cohort, Clinical Sequencing Evidence-Generating Research (CSER Phase 2) project active in Alabama, Mississippi, and Louisiana, or an earlier Clinical Sequencing Exploratory Research (CSER Phase 1) initiative based in the same region. Our interviews focused on parental knowledge about, attitudes on, interest in, and preferences for receiving genetic ancestry results following clinical genome sequencing in the neonatal intensive care unit or in pediatric clinics. FINDINGS: Overall, parents prioritized clinical results or results that would help guide the diagnosis and treatment of their child, but they were also interested in any genetic result, including genetic ancestry, that potentially could enhance the meaning of information on disease risk, prevention and screening guidance, or family planning. While parents thought that ancestry results would help them learn about themselves and their heritage, the had concerns over the privacy, security, and accuracy of genetic ancestry information, although parents indicated that they had greater trust in ancestry findings provided as part of clinical care compared with those offered commercially. Parents also wanted ancestry results to be returned in a timely manner by knowledgeable staff, with kid-friendly materials and online tools available to aid, as needed, in the understanding of their results. IMPLICATIONS: Taken together, our results highlight that despite being in high-stress situations, such as having a newborn in the neonatal intensive care unit, parents were interested in receiving genetic ancestry results along with their clinically relevant findings.

Exome variant discrepancies due to reference-genome differences.

Despite release of the GRCh38 human reference genome more than seven years ago, GRCh37 remains more widely used by most research and clinical laboratories. To date, no study has quantified the impact of utilizing different reference assemblies for the identification of variants associated with rare and common diseases from large-scale exome-sequencing data. By calling variants on both the GRCh37 and GRCh38 references, we identified single-nucleotide variants (SNVs) and insertion-deletions (indels) in 1,572 exomes from participants with Mendelian diseases and their family members. We found that a total of 1.5% of SNVs and 2.0% of indels were discordant when different references were used. Notably, 76.6% of the discordant variants were clustered within discrete discordant reference patches (DISCREPs) comprising only 0.9% of loci targeted by exome sequencing. These DISCREPs were enriched for genomic elements including segmental duplications, fix patch sequences, and loci known to contain alternate haplotypes. We identified 206 genes significantly enriched for discordant variants, most of which were in DISCREPs and caused by multi-mapped reads on the reference assembly that lacked the variant call. Among these 206 genes, eight are implicated in known Mendelian diseases and 53 are associated with common phenotypes from genome-wide association studies. In addition, variant interpretations could also be influenced by the reference after lifting-over variant loci to another assembly. Overall, we identified genes and genomic loci affected by reference assembly choice, including genes associated with Mendelian disorders and complex human diseases that require careful evaluation in both research and clinical applications.

Comparative analysis of functional assay evidence use by ClinGen Variant Curation Expert Panels.

BACKGROUND: The 2015 American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) guidelines for clinical sequence variant interpretation state that "well-established" functional studies can be used as evidence in variant classification. These guidelines articulated key attributes of functional data, including that assays should reflect the biological environment and be analytically sound; however, details of how to evaluate these attributes were left to expert judgment. The Clinical Genome Resource (ClinGen) designates Variant Curation Expert Panels (VCEPs) in specific disease areas to make gene-centric specifications to the ACMG/AMP guidelines, including more specific definitions of appropriate functional assays. We set out to evaluate the existing VCEP guidelines for functional assays. METHODS: We evaluated the functional criteria (PS3/BS3) of six VCEPs (CDH1, Hearing Loss, Inherited Cardiomyopathy-MYH7, PAH, PTEN, RASopathy). We then established criteria for evaluating functional studies based on disease mechanism, general class of assay, and the characteristics of specific assay instances described in the primary literature. Using these criteria, we extensively curated assay instances cited by each VCEP in their pilot variant classification to analyze VCEP recommendations and their use in the interpretation of functional studies. RESULTS: Unsurprisingly, our analysis highlighted the breadth of VCEP-approved assays, reflecting the diversity of disease mechanisms among VCEPs. We also noted substantial variability between VCEPs in the method used to select these assays and in the approach used to specify strength modifications, as well as differences in suggested validation parameters. Importantly, we observed discrepancies between the parameters VCEPs specified as required for approved assay instances and the fulfillment of these requirements in the individual assays cited in pilot variant interpretation. CONCLUSIONS: Interpretation of the intricacies of functional assays often requires expert-level knowledge of the gene and disease, and current VCEP recommendations for functional assay evidence are a useful tool to improve the accessibility of functional data by providing a starting point for curators to identify approved functional assays and key metrics. However, our analysis suggests that further guidance is needed to standardize this process and ensure consistency in the application of functional evidence.

VarSight: prioritizing clinically reported variants with binary classification algorithms.

BACKGROUND: When applying genomic medicine to a rare disease patient, the primary goal is to identify one or more genomic variants that may explain the patient's phenotypes. Typically, this is done through annotation, filtering, and then prioritization of variants for manual curation. However, prioritization of variants in rare disease patients remains a challenging task due to the high degree of variability in phenotype presentation and molecular source of disease. Thus, methods that can identify and/or prioritize variants to be clinically reported in the presence of such variability are of critical importance. METHODS: We tested the application of classification algorithms that ingest variant annotations along with phenotype information for predicting whether a variant will ultimately be clinically reported and returned to a patient. To test the classifiers, we performed a retrospective study on variants that were clinically reported to 237 patients in the Undiagnosed Diseases Network. RESULTS: We treated the classifiers as variant prioritization systems and compared them to four variant prioritization algorithms and two single-measure controls. We showed that the trained classifiers outperformed all other tested methods with the best classifiers ranking 72% of all reported variants and 94% of reported pathogenic variants in the top 20. CONCLUSIONS: We demonstrated how freely available binary classification algorithms can be used to prioritize variants even in the presence of real-world variability. Furthermore, these classifiers outperformed all other tested methods, suggesting that they may be well suited for working with real rare disease patient datasets.

Case for genome sequencing in infants and children with rare, undiagnosed or genetic diseases.

Up to 350 million people worldwide suffer from a rare disease, and while the individual diseases are rare, in aggregate they represent a substantial challenge to global health systems. The majority of rare disorders are genetic in origin, with children under the age of five disproportionately affected. As these conditions are difficult to identify clinically, genetic and genomic testing have become the backbone of diagnostic testing in this population. In the last 10 years, next-generation sequencing technologies have enabled testing of multiple disease genes simultaneously, ranging from targeted gene panels to exome sequencing (ES) and genome sequencing (GS). GS is quickly becoming a practical first-tier test, as cost decreases and performance improves. A growing number of studies demonstrate that GS can detect an unparalleled range of pathogenic abnormalities in a single laboratory workflow. GS has the potential to deliver unbiased, rapid and accurate molecular diagnoses to patients across diverse clinical indications and complex presentations. In this paper, we discuss clinical indications for testing and historical testing paradigms. Evidence supporting GS as a diagnostic tool is supported by superior genomic coverage, types of pathogenic variants detected, simpler laboratory workflow enabling shorter turnaround times, diagnostic and reanalysis yield, and impact on healthcare.

Evaluating Clinical Genome Sequence Analysis by Watson for Genomics.

Background: Oncologists increasingly rely on clinical genome sequencing to pursue effective, molecularly targeted therapies. This study assesses the validity and utility of the artificial intelligence Watson for Genomics (WfG) for analyzing clinical sequencing results. Methods: This study identified patients with solid tumors who participated in in-house genome sequencing projects at a single cancer specialty hospital between April 2013 and October 2016. Targeted genome sequencing results of these patients' tumors, previously analyzed by multidisciplinary specialists at the hospital, were reanalyzed by WfG. This study measures the concordance between the two evaluations. Results: In 198 patients, in-house genome sequencing detected 785 gene mutations, 40 amplifications, and 22 fusions after eliminating single nucleotide polymorphisms. Breast cancer (n = 40) was the most frequent diagnosis in this analysis, followed by gastric cancer (n = 31), and lung cancer (n = 30). Frequently detected single nucleotide variants were found in TP53 (n = 107), BRCA2 (n = 24), and NOTCH2 (n = 23). MYC (n = 10) was the most frequently detected gene amplification, followed by ERBB2 (n = 9) and CCND1 (n = 6). Concordant pathogenic classifications (i.e., pathogenic, benign, or variant of unknown significance) between in-house specialists and WfG included 705 mutations (89.8%; 95% CI, 87.5%-91.8%), 39 amplifications (97.5%; 95% CI, 86.8-99.9%), and 17 fusions (77.3%; 95% CI, 54.6-92.2%). After about 12 months, reanalysis using a more recent version of WfG demonstrated a better concordance rate of 94.5% (95% CI, 92.7-96.0%) for gene mutations. Across the 249 gene alterations determined to be pathogenic by both methods, including mutations, amplifications, and fusions, WfG covered 84.6% (88 of 104) of all targeted therapies that experts proposed and offered an additional 225 therapeutic options. Conclusions: WfG was able to scour large volumes of data from scientific studies and databases to analyze in-house clinical genome sequencing results and demonstrated the potential for application to clinical practice; however, we must train WfG in clinical trial settings.

ClinGen Pathogenicity Calculator: a configurable system for assessing pathogenicity of genetic variants.

BACKGROUND: The 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. RESULTS: In 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 . CONCLUSIONS: By 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.

Genetic Testing for Rare Cancer: The Wider Issues.

Identification of a potential genetic susceptibility to cancer and confirmation of a pathogenic gene mutation raises a number of challenging issues for the patient with cancer, their relatives and the health professionals caring for them. The specific risks and management issues associated with rare cancer types have been addressed in the earlier chapters. This chapter considers the wider issues involved in genetic counselling and genetic testing for a genetic susceptibility to cancer for patients, families and health professionals. The first part of the chapter will present the issues raised by the current practice in genetic counselling and genetic testing for cancer susceptibility. The second part of the chapter will address some of the issues raised by the advances in genetic testing technology and the future opportunities provided by personalised medicine and targeted cancer therapy. Facilitating these developments requires closer integration of genomics into mainstream cancer care, challenging the existing paradigm of genetic medicine, adding additional layers of complexity to the risk assessment and management of cancer and presenting wider issues for patients, families, health professionals and clinical services.

HPMV: human protein mutation viewer - relating sequence mutations to protein sequence architecture and function changes.

Next-generation sequencing advances are rapidly expanding the number of human mutations to be analyzed for causative roles in genetic disorders. Our Human Protein Mutation Viewer (HPMV) is intended to explore the biomolecular mechanistic significance of non-synonymous human mutations in protein-coding genomic regions. The tool helps to assess whether protein mutations affect the occurrence of sequence-architectural features (globular domains, targeting signals, post-translational modification sites, etc.). As input, HPMV accepts protein mutations - as UniProt accessions with mutations (e.g. HGVS nomenclature), genome coordinates, or FASTA sequences. As output, HPMV provides an interactive cartoon showing the mutations in relation to elements of the sequence architecture. A large variety of protein sequence architectural features were selected for their particular relevance to mutation interpretation. Clicking a sequence feature in the cartoon expands a tree view of additional information including multiple sequence alignments of conserved domains and a simple 3D viewer mapping the mutation to known PDB structures, if available. The cartoon is also correlated with a multiple sequence alignment of similar sequences from other organisms. In cases where a mutation is likely to have a straightforward interpretation (e.g. a point mutation disrupting a well-understood targeting signal), this interpretation is suggested. The interactive cartoon can be downloaded as standalone viewer in Java jar format to be saved and viewed later with only a standard Java runtime environment. The HPMV website is: http://hpmv.bii.a-star.edu.sg/ .

Preemptive genotyping for personalized medicine: design of the right drug, right dose, right time-using genomic data to individualize treatment protocol.

OBJECTIVE: To report the design and implementation of the Right Drug, Right Dose, Right Time-Using Genomic Data to Individualize Treatment protocol that was developed to test the concept that prescribers can deliver genome-guided therapy at the point of care by using preemptive pharmacogenomics (PGx) data and clinical decision support (CDS) integrated into the electronic medical record (EMR). PATIENTS AND METHODS: We used a multivariate prediction model to identify patients with a high risk of initiating statin therapy within 3 years. The model was used to target a study cohort most likely to benefit from preemptive PGx testing among the Mayo Clinic Biobank participants, with a recruitment goal of 1000 patients. We used a Cox proportional hazards model with variables selected through the Lasso shrinkage method. An operational CDS model was adapted to implement PGx rules within the EMR. RESULTS: The prediction model included age, sex, race, and 6 chronic diseases categorized by the Clinical Classifications Software for International Classification of Diseases, Ninth Revision codes (dyslipidemia, diabetes, peripheral atherosclerosis, disease of the blood-forming organs, coronary atherosclerosis and other heart diseases, and hypertension). Of the 2000 Biobank participants invited, 1013 (51%) provided blood samples, 256 (13%) declined participation, 555 (28%) did not respond, and 176 (9%) consented but did not provide a blood sample within the recruitment window (October 4, 2012, through March 20, 2013). Preemptive PGx testing included CYP2D6 genotyping and targeted sequencing of 84 PGx genes. Synchronous real-time CDS was integrated into the EMR and flagged potential patient-specific drug-gene interactions and provided therapeutic guidance. CONCLUSION: This translational project provides an opportunity to begin to evaluate the impact of preemptive sequencing and EMR-driven genome-guided therapy. These interventions will improve understanding and implementation of genomic data in clinical practice.