MutationDistiller: user-driven identification of pathogenic DNA variants

MutationDistiller is a freely available online tool for user-driven analyses of Whole Exome Sequencing data. It offers a user-friendly interface aimed at clinicians and researchers, who are not necessarily bioinformaticians. MutationDistiller combines Mutation- Taster’s pathogenicity predictions with a phenotypebased approach. Phenotypic information is not limited to symptoms included in the Human Phenotype Ontology (HPO), but may also comprise clinical diagnoses and the suspected mode of inheritance. The search can be restricted to lists of candidate genes (e.g. virtual gene panels) and by tissue-specific gene expression. The inclusion of GeneOntology (GO) and metabolic pathways facilitates the discovery of hitherto unknown disease genes. In a novel approach, we trained MutationDistiller’s HPO-based prioritization on authentic genotype–phenotype sets obtained from ClinVar and found it to match or outcompete current prioritization tools in terms of accuracy. In the output, the program provides a list of potential disease mutations ordered by the likelihood of the affected genes to cause the phenotype. MutationDistiller provides links to gene-related information from various resources. It has been extensively tested by clinicians and their suggestions have been valued in many iterative cycles of revisions. The tool, a comprehensive documentation and examples are freely available at https://www.mutationdistiller.org

A visual and curatorial approach to clinical variant prioritization and disease gene discovery in genome-wide diagnostics

Background: Genome-wide data are increasingly important in the clinical evaluation of human disease. However, the large number of variants observed in individual patients challenges the efficiency and accuracy of diagnostic review. Recent work has shown that systematic integration of clinical phenotype data with genotype information can improve diagnostic workflows and prioritization of filtered rare variants. We have developed visually interactive, analytically transparent analysis software that leverages existing disease catalogs, such as the Online Mendelian Inheritance in Man database (OMIM) and the Human Phenotype Ontology (HPO), to integrate patient phenotype and variant data into ranked diagnostic alternatives. Methods: Our tool, “OMIM Explorer” (http://www.omimexplorer.com), extends the biomedical application of semantic similarity methods beyond those reported in previous studies. The tool also provides a simple interface for translating free-text clinical notes into HPO terms, enabling clinical providers and geneticists to contribute phenotypes to the diagnostic process. The visual approach uses semantic similarity with multidimensional scaling to collapse high-dimensional phenotype and genotype data from an individual into a graphical format that contextualizes the patient within a low-dimensional disease map. The map proposes a differential diagnosis and algorithmically suggests potential alternatives for phenotype queries—in essence, generating a computationally assisted differential diagnosis informed by the individual’s personal genome. Visual interactivity allows the user to filter and update variant rankings by interacting with intermediate results. The tool also implements an adaptive approach for disease gene discovery based on patient phenotypes. Results: We retrospectively analyzed pilot cohort data from the Baylor Miraca Genetics Laboratory, demonstrating performance of the tool and workflow in the re-analysis of clinical exomes. Our tool assigned to clinically reported variants a median rank of 2, placing causal variants in the top 1 % of filtered candidates across the 47 cohort cases with reported molecular diagnoses of exome variants in OMIM Morbidmap genes. Our tool outperformed Phen-Gen, eXtasy, PhenIX, PHIVE, and hiPHIVE in the prioritization of these clinically reported variants. Conclusions: Our integrative paradigm can improve efficiency and, potentially, the quality of genomic medicine by more effectively utilizing available phenotype information, catalog data, and genomic knowledge

The Current Landscape of Genetic Testing in Cardiovascular Malformations: Opportunities and Challenges

Human cardiovascular malformations (CVMs) frequently have a genetic contribution. Through the application of novel technologies, such as next-generation sequencing, DNA sequence variants associated with CVMs are being identified at a rapid pace. While clinicians are now able to offer testing with NGS gene panels or whole exome sequencing to any patient with a CVM, the interpretation of genetic variation remains problematic. Variable phenotypic expression, reduced penetrance, inconsistent phenotyping methods, and the lack of high-throughput functional testing of variants contribute to these challenges. This article elaborates critical issues that impact the decision to broadly implement clinical molecular genetic testing in CVMs. Major benefits of testing include establishing a genetic diagnosis, facilitating cost-effective screening of family members who may have subclinical disease, predicting recurrence risk in offsprings, enabling early diagnosis and anticipatory management of CV and non-CV disease phenotypes, predicting long-term outcomes, and facilitating the development of novel therapies aimed at disease improvement or prevention. Limitations include financial cost, psychosocial cost, and ambiguity of interpretation of results. Multiplex families and patients with syndromic features are two groups where disease causation could potentially be firmly established. However, these account for the minority of the overall CVM population, and there is increasing recognition that genotypes previously associated with syndromes also exist in patients who lack non-CV findings. In all circumstances, ongoing dialog between cardiologists and clinical geneticists will be needed to accurately interpret genetic testing and improve these patients' health. This may be most effectively implemented by the creation and support of CV genetics services at centers committed to pursuing testing for patients

Variant Ranker: a web-tool to rank genomic data according to functional significance

BACKGROUND: The increasing volume and complexity of high-throughput genomic data make analysis and prioritization of variants difficult for researchers with limited bioinformatics skills. Variant Ranker allows researchers to rank identified variants and determine the most confident variants for experimental validation. RESULTS: We describe Variant Ranker, a user-friendly simple web-based tool for ranking, filtering and annotation of coding and non-coding variants. Variant Ranker facilitates the identification of causal variants based on novelty, effect and annotation information. The algorithm implements and aggregates multiple prediction algorithm scores, conservation scores, allelic frequencies, clinical information and additional open-source annotations using accessible databases via ANNOVAR. The available information for a variant is transformed into user-specified weights, which are in turn encoded into the ranking algorithm. Through its different modules, users can (i) rank a list of variants (ii) perform genotype filtering for case-control samples (iii) filter large amounts of high-throughput data based on user custom filter requirements and apply different models of inheritance (iv) perform downstream functional enrichment analysis through network visualization. Using networks, users can identify clusters of genes that belong to multiple ontology categories (like pathways, gene ontology, disease categories) and therefore expedite scientific discoveries. We demonstrate the utility of Variant Ranker to identify causal genes using real and synthetic datasets. Our results indicate that Variant Ranker exhibits excellent performance by correctly identifying and ranking the candidate genes CONCLUSIONS: Variant Ranker is a freely available web server on http://paschou-lab.mbg.duth.gr/Software.html . This tool will enable users to prioritise potentially causal variants and is applicable to a wide range of sequencing data

Doctor of Philosophy

dissertationSuccessful molecular diagnosis using an exome sequence hinges on accurate association of damaging variants to the patient's phenotype. Unfortunately, many clinical scenarios (e.g., single affected or small nuclear families) have little power to confidently identify damaging alleles using sequence data alone. Today's diagnostic tools are simply underpowered for accurate diagnosis in these situations, limiting successful diagnoses. In response, clinical genetics relies on candidate-gene and variant lists to limit the search space. Despite their practical utility, these lists suffer from inherent and significant limitations. The impact of false negatives on diagnostic accuracy is considerable because candidate-genes and variants lists are assembled ad hoc, choosing alleles based upon expert knowledge. Alleles not in the list are not considered-ending hope for novel discoveries. Rational alternatives to ad hoc assemblages of candidate lists are thus badly needed. In response, I created Phevor, the Phenotype Driven Variant Ontological Re-ranking tool. Phevor works by combining knowledge resident in biomedical ontologies, like the human phenotype and gene ontologies, with the outputs of variant-interpretation tools such as SIFT, GERP+, Annovar and VAAST. Phevor can then accurately to prioritize candidates identified by third-party variant-interpretation tools in light of knowledge found in the ontologies, effectively bypassing the need for candidate-gene and variant lists. Phevor differs from tools such as Phenomizer and Exomiser, as it does not postulate a set of fixed associations between genes and phenotypes. Rather, Phevor dynamically integrates knowledge resident in multiple bio-ontologies into the prioritization process. This enables Phevor to improve diagnostic accuracy for established diseases and previously undescribed or atypical phenotypes. Inserting known disease-alleles into otherwise healthy exomes benchmarked Phevor. Using the phenotype of the known disease, and the variant interpretation tool VAAST (Variant Annotation, Analysis and Search Tool), Phevor can rank 100% of the known alleles in the top 10 and 80% as the top candidate. Phevor is currently part of the pipeline used to diagnose cases as part the Utah Genome Project. Successful diagnoses of several phenotypes have proven Phevor to be a reliable diagnostic tool that can improve the analysis of any disease-gene search

Resources and tools for rare disease variant interpretation

: Collectively, rare genetic disorders affect a substantial portion of the world's population. In most cases, those affected face difficulties in receiving a clinical diagnosis and genetic characterization. The understanding of the molecular mechanisms of these diseases and the development of therapeutic treatments for patients are also challenging. However, the application of recent advancements in genome sequencing/analysis technologies and computer-aided tools for predicting phenotype-genotype associations can bring significant benefits to this field. In this review, we highlight the most relevant online resources and computational tools for genome interpretation that can enhance the diagnosis, clinical management, and development of treatments for rare disorders. Our focus is on resources for interpreting single nucleotide variants. Additionally, we present use cases for interpreting genetic variants in clinical settings and review the limitations of these results and prediction tools. Finally, we have compiled a curated set of core resources and tools for analyzing rare disease genomes. Such resources and tools can be utilized to develop standardized protocols that will enhance the accuracy and effectiveness of rare disease diagnosis

Bioinformatics for personal genome interpretation

none4An international consortium released the first draft sequence of the human genome 10 years ago. Although the analysis of this data has suggested the genetic underpinnings of many diseases, we have not yet been able to fully quantify the relationship between genotype and phenotype. Thus, a major current effort of the scientific community focuses on evaluating individual predispositions to specific phenotypic traits given their genetic backgrounds. Many resources aim to identify and annotate the specific genes responsible for the observed phenotypes. Some of these use intra-species genetic variability as a means for better understanding this relationship. In addition, several online resources are now dedicated to collecting single nucleotide variants and other types of variants, and annotating their functional effects and associations with phenotypic traits. This information has enabled researchers to develop bioinformatics tools to analyze the rapidly increasing amount of newly extracted variation data and to predict the effect of uncharacterized variants. In this work, we review the most important developments in the field-the databases and bioinformatics tools that will be of utmost importance in our concerted effort to interpret the human variome. © The Author 2012. Published by Oxford University Press.openCapriotti, Emidio; Nehrt, Nathan L.; Kann, Maricel G.; Bromberg, YanaCapriotti, Emidio; Nehrt, Nathan L.; Kann, Maricel G.; Bromberg, Yan