TY - JOUR AU - Sobczyk, M K AU - Gaunt, T R AU - Paternoster, L T1 - MendelVar: gene prioritization at GWAS loci using phenotypic enrichment of Mendelian disease genes PY - 2021 Y1 - 2021/01/16 DO - 10.1093/bioinformatics/btaa1096 M1 - btaa1096 JO - Bioinformatics JA - Bioinformatics SN - 1367-4803 AB - Gene prioritization at human GWAS loci is challenging due to linkage-disequilibrium and long-range gene regulatory mechanisms. However, identifying the causal gene is crucial to enable identification of potential drug targets and better understanding of molecular mechanisms. Mapping GWAS traits to known phenotypically relevant Mendelian disease genes near a locus is a promising approach to gene prioritization.We present MendelVar, a comprehensive tool that integrates knowledge from four databases on Mendelian disease genes with enrichment testing for a range of associated functional annotations such as Human Phenotype Ontology, Disease Ontology and variants from ClinVar. This open web-based platform enables users to strengthen the case for causal importance of phenotypically matched candidate genes at GWAS loci. We demonstrate the use of MendelVar in post-GWAS gene annotation for type 1 diabetes, type 2 diabetes, blood lipids and atopic dermatitis.MendelVar is freely available at https://mendelvar.mrcieu.ac.ukSupplementary data are available at Bioinformatics online. Y2 - 2/17/2021 UR - https://doi.org/10.1093/bioinformatics/btaa1096 ER - TY - JOUR T1 - DECIPHER: Database of Chromosomal Imbalance and Phenotype in Humans Using Ensembl Resources A1 - Firth, Helen V A1 - Richards, Shola M A1 - Bevan, A Paul A1 - Clayton, Stephen A1 - Corpas, Manuel A1 - Rajan, Diana A1 - Vooren, Steven Van A1 - Moreau, Yves A1 - Pettett, Roger M A1 - Carter, Nigel P Y1 - 2009/// PB - The American Society of Human Genetics JF - American Journal of Human Genetics VL - 84 IS - 4 SP - 524 EP - 533 DO - 10.1016/j.ajhg.2009.03.010 UR - http://dx.doi.org/10.1016/j.ajhg.2009.03.010 N2 - Many patients suffering from developmental disorders harbor submicroscopic deletions or duplications that, by affecting the copy number of dosage-sensitive genes or disrupting normal gene expression, lead to disease. However, many aberrations are novel or extremely rare, making clinical interpretation problematic and genotype-phenotype correlations uncertain. Identification of patients sharing a genomic rearrangement and having phenotypic features in common leads to greater certainty in the pathogenic nature of the rearrangement and enables new syndromes to be defined. To facilitate the analysis of these rare events, we have developed an interactive web-based database called DECIPHER (Database of Chromosomal Imbalance and Phenotype in Humans Using Ensembl Resources) which incorporates a suite of tools designed to aid the interpretation of submicroscopic chromosomal imbalance, inversions, and translocations. DECIPHER catalogs common copy-number changes in normal populations and thus, by exclusion, enables changes that are novel and potentially pathogenic to be identified. DECIPHER enhances genetic counseling by retrieving relevant information from a variety of bioinformatics resources. Known and predicted genes within an aberration are listed in the DECIPHER patient report, and genes of recognized clinical importance are highlighted and prioritized. DECIPHER enables clinical scientists worldwide to maintain records of phenotype and chromosome rearrangement for their patients and, with informed consent, share this information with the wider clinical research community through display in the genome browser Ensembl. By sharing cases worldwide, clusters of rare cases having phenotype and structural rearrangement in common can be identified, leading to the delineation of new syndromes and furthering understanding of gene function. © 2009 The American Society of Human Genetics. ER - TY - JOUR T1 - LDlink: A web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants A1 - Machiela, Mitchell J A1 - Chanock, Stephen J Y1 - 2015/// JF - Bioinformatics VL - 31 IS - 21 SP - 3555 EP - 3557 DO - 10.1093/bioinformatics/btv402 N2 - Summary: Assessing linkage disequilibrium (LD) across ancestral populations is a powerful approach for investigating population-specific genetic structure as well as functionally mapping regions of disease susceptibility. Here, we present LDlink, a web-based collection of bioinformatic modules that query single nucleotide polymorphisms (SNPs) in population groups of interest to generate haplotype tables and interactive plots. Modules are designed with an emphasis on ease of use, query flexibility, and interactive visualization of results. Phase 3 haplotype data from the 1000 Genomes Project are referenced for calculating pairwise metrics of LD, searching for proxies in high LD, and enumerating all observed haplotypes. LDlink is tailored for investigators interested in mapping common and uncommon disease susceptibility loci by focusing on output linking correlated alleles and highlighting putative functional variants. Availability and implementation: LDlink is a free and publically available web tool which can be accessed at http://analysistools.nci.nih.gov/LDlink/. ER - TY - JOUR T1 - GIGGLE: a search engine for large-scale integrated genome analysis A1 - Layer, Ryan M. A1 - Pedersen, Brent S. A1 - Disera, Tonya A1 - Marth, Gabor T. A1 - Gertz, Jason A1 - Quinlan, Aaron R. Y1 - 2018/// JF - Nature Methods VL - 15 IS - 2 SP - 123 EP - 126 SN - 2105674410 DO - 10.1038/nmeth.4556 N2 - GIGGLE is a genome interval search engine that enables extremely fast queries of genome features from thousands of genome annotation sets. ER - TY - JOUR T1 - Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources A1 - Köhler, Sebastian A1 - Carmody, Leigh A1 - Vasilevsky, Nicole A1 - Jacobsen, Julius O.B. A1 - Danis, Daniel A1 - Gourdine, Jean Philippe A1 - Gargano, Michael A1 - Harris, Nomi L A1 - Matentzoglu, Nicolas A1 - McMurry, Julie A. A1 - Osumi-Sutherland, David A1 - Cipriani, Valentina A1 - Balhoff, James P A1 - Conlin, Tom A1 - Blau, Hannah A1 - Baynam, Gareth A1 - Palmer, Richard A1 - Gratian, Dylan A1 - Dawkins, Hugh A1 - Segal, Michael A1 - Jansen, Anna C. A1 - Muaz, Ahmed A1 - Chang, Willie H. A1 - Bergerson, Jenna A1 - Laulederkind, Stanley J.F. A1 - Yüksel, Zafer A1 - Beltran, Sergi A1 - Freeman, Alexandra F A1 - Sergouniotis, Panagiotis I A1 - Durkin, Daniel A1 - Storm, Andrea L A1 - Hanauer, Marc A1 - Brudno, Michael A1 - Bello, Susan M A1 - Sincan, Murat A1 - Rageth, Kayli A1 - Wheeler, Matthew T A1 - Oegema, Renske A1 - Lourghi, Halima A1 - Della Rocca, Maria G. A1 - Thompson, Rachel A1 - Castellanos, Francisco A1 - Priest, James A1 - Cunningham-Rundles, Charlotte A1 - Hegde, Ayushi A1 - Lovering, Ruth C A1 - Hajek, Catherine A1 - Olry, Annie A1 - Notarangelo, Luigi A1 - Similuk, Morgan A1 - Zhang, Xingmin A A1 - Gómez-Andrés, David A1 - Lochmüller, Hanns A1 - Dollfus, Hélène A1 - Rosenzweig, Sergio A1 - Marwaha, Shruti A1 - Rath, Ana A1 - Sullivan, Kathleen A1 - Smith, Cynthia A1 - Milner, Joshua D A1 - Leroux, Dorothée A1 - Boerkoel, Cornelius F A1 - Klion, Amy A1 - Carter, Melody C A1 - Groza, Tudor A1 - Smedley, Damian A1 - Haendel, Melissa A. A1 - Mungall, Chris A1 - Robinson, Peter N Y1 - 2019/// JF - Nucleic Acids Research VL - 47 IS - D1 SP - D1018 EP - D1027 DO - 10.1093/nar/gky1105 N2 - The Human Phenotype Ontology (HPO) - a standardized vocabulary of phenotypic abnormalities associated with 7000+ diseases - is used by thousands of researchers, clinicians, informaticians and electronic health record systems around the world. Its detailed descriptions of clinical abnormalities and computable disease definitions have made HPO the de facto standard for deep phenotyping in the field of rare disease. The HPO's interoperability with other ontologies has enabled it to be used to improve diagnostic accuracy by incorporating model organism data. It also plays a key role in the popular Exomiser tool, which identifies potential disease-causing variants from whole-exome or whole-genome sequencing data. Since the HPO was first introduced in 2008, its users have become both more numerous and more diverse. To meet these emerging needs, the project has added new content, language translations, mappings and computational tooling, as well as integrations with external community data. The HPO continues to collaborate with clinical adopters to improve specific areas of the ontology and extend standardized disease descriptions. The newly redesigned HPO website (www.human-phenotype-ontology.org) simplifies browsing terms and exploring clinical features, diseases, and human genes. ER - TY - JOUR T1 - Human Disease Ontology 2018 update: Classification, content and workflow expansion A1 - Schriml, Lynn M A1 - Mitraka, Elvira A1 - Munro, James A1 - Tauber, Becky A1 - Schor, Mike A1 - Nickle, Lance A1 - Felix, Victor A1 - Jeng, Linda A1 - Bearer, Cynthia A1 - Lichenstein, Richard A1 - Bisordi, Katharine A1 - Campion, Nicole A1 - Hyman, Brooke A1 - Kurland, David A1 - Oates, Connor Patrick A1 - Kibbey, Siobhan A1 - Sreekumar, Poorna A1 - Le, Chris A1 - Giglio, Michelle A1 - Greene, Carol Y1 - 2019/// JF - Nucleic Acids Research VL - 47 IS - D1 SP - D955 EP - D962 DO - 10.1093/nar/gky1032 N2 - The Human Disease Ontology (DO) (http://www.disease-ontology.org), database has undergone significant expansion in the past three years. The DO disease classification includes specific formal semantic rules to express meaningful disease models and has expanded from a single asserted classification to include multiple-inferred mechanistic disease classifications, thus providing novel perspectives on related diseases. Expansion of disease terms, alternative anatomy, cell type and genetic disease classifications and workflow automation highlight the updates for the DO since 2015. The enhanced breadth and depth of the DO's knowledgebase has expanded the DO's utility for exploring the multi-etiology of human disease, thus improving the capture and communication of health-related data across biomedical databases, bioinformatics tools, genomic and cancer resources and demonstrated by a 6.6× growth in DO's user community since 2015. The DO's continual integration of human disease knowledge, evidenced by the more than 200 SVN/GitHub releases/revisions, since previously reported in our DO 2015 NAR paper, includes the addition of 2650 new disease terms, a 30% increase of textual definitions, and an expanding suite of disease classification hierarchies constructed through defined logical axioms. ER - TY - ICOMM T1 - Orphanet: an online rare disease and orphan drug data base A1 - INSERM Y1 - 1999/// UR - http://www.orpha.net ER - TY - JOUR T1 - Representation of rare diseases in health information systems: The orphanet approach to serve a wide range of end users A1 - Rath, Ana A1 - Olry, Annie A1 - Dhombres, Ferdinand A1 - Brandt, Maja Miličić A1 - Urbero, Bruno A1 - Ayme, Segolene Y1 - 2012/// KW - Classification KW - Interoperability KW - Nosology KW - Ontology KW - Rare diseases KW - Relational database JF - Human Mutation VL - 33 IS - 5 SP - 803 EP - 808 DO - 10.1002/humu.22078 N2 - Rare disorders are scarcely represented in international classifications and therefore invisible in information systems. One of the major needs in health information systems and for research is to share and/or to integrate data coming from heterogeneous sources with diverse reference terminologies. ORPHANET (www.orpha.net) is a multilingual information portal on rare diseases and orphan drugs. Orphanet information system is supported by a relational database built around the concept of rare disorders. Representation of rare diseases in Orphanet encompasses levels of increasing complexity: lexical (multilingual terminology), nosological (multihierarchical classifications), relational (annotations-epidemiological data-and classes of objects-genes, manifestations, and orphan drugs-integrated in a relational database), and interoperational (semantic interoperability). Rare disorders are mapped to International Classification of Diseases (10th version), SNOMED CT, MeSH, MedDRA, and UMLS. Genes are cross-referenced with HGNC, UniProt, OMIM, and Genatlas. A suite of tools allow for extraction of massive datasets giving different views that can be used in bioinformatics to answer complex questions, intended to serve the needs of researchers and the pharmaceutical industry in developing medicinal products for rare diseases. An ontology is under development. The Orphanet nomenclature is at the crossroads of scientific data repositories and of clinical terminology standards, and is suitable to be used as a standard terminology. © 2012 Wiley Periodicals, Inc. ER - TY - JOUR T1 - OMIM.org: Leveraging knowledge across phenotype-gene relationships A1 - Amberger, Joanna S A1 - Bocchini, Carol A A1 - Scott, Alan F A1 - Hamosh, Ada Y1 - 2019/// JF - Nucleic Acids Research VL - 47 IS - D1 SP - D1038 EP - D1043 DO - 10.1093/nar/gky1151 N2 - For over 50 years Mendelian Inheritance in Man has chronicled the collective knowledge of the field of medical genetics. It initially cataloged the known X-linked, autosomal recessive and autosomal dominant inherited disorders, but grew to be the primary repository of curated information on both genes and genetic phenotypes and the relationships between them. Each phenotype and gene is given a separate entry assigned a stable, unique identifier. The entries contain structured summaries of new and important information based on expert review of the biomedical literature. OMIM.org provides interactive access to the knowledge repository, including genomic coordinate searches of the gene map, views of genetic heterogeneity of phenotypes in Phenotypic Series, and side-by-side comparisons of clinical synopses. OMIM.org also supports computational queries via a robust API. All entries have extensive targeted links to other genomic resources and additional references. Updates to OMIM can be found on the update list or followed through the MIMmatch service. Updated user guides and tutorials are available on the website. As of September 2018, OMIM had over 24,600 entries, and the OMIM Morbid Map Scorecard had 6,259 molecularized phenotypes connected to 3,961 genes. ER - TY - JOUR T1 - INRICH: Interval-based enrichment analysis for genome-wide association studies A1 - Lee, Phil H A1 - O'dushlaine, Colm A1 - Thomas, Brett A1 - Purcell, Shaun M Y1 - 2012/// JF - Bioinformatics VL - 28 IS - 13 SP - 1797 EP - 1799 DO - 10.1093/bioinformatics/bts191 N2 - Here we present INRICH (INterval enRICHment analysis), a pathway-based genome-wide association analysis tool that tests for enriched association signals of predefined gene-sets across independent genomic intervals. INRICH has wide applicability, fast running time and, most importantly, robustness to potential genomic biases and confounding factors. Such factors, including varying gene size and single-nucleotide polymorphism density, linkage disequilibrium within and between genes and overlapping genes with similar annotations, are often not accounted for by existing gene-set enrichment methods. By using a genomic permutation procedure, we generate experiment-wide empirical significance values, corrected for the total number of sets tested, implicitly taking overlap of sets into account. By simulation we confirm a properly controlled type I error rate and reasonable power of INRICH under diverse parameter settings. As a proof of principle, we describe the application of INRICH on the NHGRI GWAS catalog. © The Author 2012. Published by Oxford University Press. All rights reserved. ER - TY - JOUR T1 - ClinVar: improvements to accessing data A1 - Landrum, Melissa J A1 - Chitipiralla, Shanmuga A1 - Brown, Garth R A1 - Chen, Chao A1 - Gu, Baoshan A1 - Hart, Jennifer A1 - Hoffman, Douglas A1 - Jang, Wonhee A1 - Kaur, Kuljeet A1 - Liu, Chunlei A1 - Lyoshin, Vitaly A1 - Maddipatla, Zenith A1 - Maiti, Rama A1 - Mitchell, Joseph A1 - O’Leary, Nuala A1 - Riley, George R A1 - Shi, Wenyao A1 - Zhou, George A1 - Schneider, Valerie A1 - Maglott, Donna A1 - Holmes, J Bradley A1 - Kattman, Brandi L Y1 - 2019/// JF - Nucleic Acids Research SP - gkz972 EP - gkz972 DO - 10.1093/nar/gkz972 UR - https://doi.org/10.1093/nar/gkz972 N1 - gkz972 N2 - ClinVar is a freely available, public archive of human genetic variants and interpretations of their relationships to diseases and other conditions, maintained at the National Institutes of Health (NIH). Submitted interpretations of variants are aggregated and made available on the ClinVar website (https://www.ncbi.nlm.nih.gov/clinvar/), and as downloadable files via FTP and through programmatic tools such as NCBI’s E-utilities. The default view on the ClinVar website, the Variation page, was recently redesigned. The new layout includes several new sections that make it easier to find submitted data as well as summary data such as all diseases and citations reported for the variant. The new design also better represents more complex data such as haplotypes and genotypes, as well as variants that are in ClinVar as part of a haplotype or genotype but have no interpretation for the single variant. ClinVar's variant-centric XML had its production release in April 2019. The ClinVar website and E-utilities both have been updated to support the VCV (variation in ClinVar) accession numbers found in the variant-centric XML file. ClinVar's search engine has been fine-tuned for improved retrieval of search results. ER - TY - JOUR T1 - PanelApp crowdsources expert knowledge to establish consensus diagnostic gene panels A1 - Martin, Antonio Rueda A1 - Williams, Eleanor A1 - Foulger, Rebecca E A1 - Leigh, Sarah A1 - Daugherty, Louise C A1 - Niblock, Olivia A1 - Leong, Ivone U S A1 - Smith, Katherine R A1 - Gerasimenko, Oleg A1 - Haraldsdottir, Eik A1 - Thomas, Ellen A1 - Scott, Richard H A1 - Baple, Emma A1 - Tucci, Arianna A1 - Brittain, Helen A1 - de Burca, Anna A1 - Ibañez, Kristina A1 - Kasperaviciute, Dalia A1 - Smedley, Damian A1 - Caulfield, Mark A1 - Rendon, Augusto A1 - McDonagh, Ellen M Y1 - 2019/// JF - Nature Genetics VL - 51 IS - 11 SP - 1560 EP - 1565 DO - 10.1038/s41588-019-0528-2 UR - https://doi.org/10.1038/s41588-019-0528-2 N2 - A fundamental problem in rare-disease diagnostics is the lack of consensus as to which genes have sufficient evidence to attribute causation. To address this issue, we have created PanelApp (https://panelapp.genomicsengland.co.uk), a publicly available knowledge base of curated virtual gene panels. ER -