@article{10.1093/bioinformatics/btaa1096,
    author = {Sobczyk, M K and Gaunt, T R and Paternoster, L},
    title = "{MendelVar: gene prioritization at GWAS loci using phenotypic enrichment of Mendelian disease genes}",
    journal = {Bioinformatics},
    year = {2021},
    month = {01},
    abstract = "{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.}",
    issn = {1367-4803},
    doi = {10.1093/bioinformatics/btaa1096},
    url = {https://doi.org/10.1093/bioinformatics/btaa1096},
    note = {btaa1096},
    eprint = {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btaa1096/35920697/btaa1096.pdf},
}
@article{Firth2009,
abstract = {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. {\textcopyright} 2009 The American Society of Human Genetics.},
author = {Firth, Helen V and Richards, Shola M and Bevan, A Paul and Clayton, Stephen and Corpas, Manuel and Rajan, Diana and Vooren, Steven Van and Moreau, Yves and Pettett, Roger M and Carter, Nigel P},
doi = {10.1016/j.ajhg.2009.03.010},
issn = {00029297},
journal = {American Journal of Human Genetics},
number = {4},
pages = {524--533},
pmid = {19344873},
publisher = {The American Society of Human Genetics},
title = {{DECIPHER: Database of Chromosomal Imbalance and Phenotype in Humans Using Ensembl Resources}},
url = {http://dx.doi.org/10.1016/j.ajhg.2009.03.010},
volume = {84},
year = {2009}
}
@article{Machiela2015,
abstract = {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/.},
author = {Machiela, Mitchell J and Chanock, Stephen J},
doi = {10.1093/bioinformatics/btv402},
issn = {14602059},
journal = {Bioinformatics},
number = {21},
pages = {3555--3557},
pmid = {26139635},
title = {{LDlink: A web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants}},
volume = {31},
year = {2015}
}
@article{Layer2018,
abstract = {GIGGLE is a genome interval search engine that enables extremely fast queries of genome features from thousands of genome annotation sets.},
archivePrefix = {arXiv},
arxivId = {1305.3665},
author = {Layer, Ryan M. and Pedersen, Brent S. and Disera, Tonya and Marth, Gabor T. and Gertz, Jason and Quinlan, Aaron R.},
doi = {10.1038/nmeth.4556},
eprint = {1305.3665},
isbn = {2105674410},
issn = {15487105},
journal = {Nature Methods},
number = {2},
pages = {123--126},
pmid = {18285525},
title = {{GIGGLE: a search engine for large-scale integrated genome analysis}},
volume = {15},
year = {2018}
}
@article{Kohler2019,
abstract = {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.},
author = {K{\"{o}}hler, Sebastian and Carmody, Leigh and Vasilevsky, Nicole and Jacobsen, Julius O.B. and Danis, Daniel and Gourdine, Jean Philippe and Gargano, Michael and Harris, Nomi L and Matentzoglu, Nicolas and McMurry, Julie A. and Osumi-Sutherland, David and Cipriani, Valentina and Balhoff, James P and Conlin, Tom and Blau, Hannah and Baynam, Gareth and Palmer, Richard and Gratian, Dylan and Dawkins, Hugh and Segal, Michael and Jansen, Anna C. and Muaz, Ahmed and Chang, Willie H. and Bergerson, Jenna and Laulederkind, Stanley J.F. and Y{\"{u}}ksel, Zafer and Beltran, Sergi and Freeman, Alexandra F and Sergouniotis, Panagiotis I and Durkin, Daniel and Storm, Andrea L and Hanauer, Marc and Brudno, Michael and Bello, Susan M and Sincan, Murat and Rageth, Kayli and Wheeler, Matthew T and Oegema, Renske and Lourghi, Halima and {Della Rocca}, Maria G. and Thompson, Rachel and Castellanos, Francisco and Priest, James and Cunningham-Rundles, Charlotte and Hegde, Ayushi and Lovering, Ruth C and Hajek, Catherine and Olry, Annie and Notarangelo, Luigi and Similuk, Morgan and Zhang, Xingmin A and G{\'{o}}mez-Andr{\'{e}}s, David and Lochm{\"{u}}ller, Hanns and Dollfus, H{\'{e}}l{\`{e}}ne and Rosenzweig, Sergio and Marwaha, Shruti and Rath, Ana and Sullivan, Kathleen and Smith, Cynthia and Milner, Joshua D and Leroux, Doroth{\'{e}}e and Boerkoel, Cornelius F and Klion, Amy and Carter, Melody C and Groza, Tudor and Smedley, Damian and Haendel, Melissa A. and Mungall, Chris and Robinson, Peter N},
doi = {10.1093/nar/gky1105},
issn = {13624962},
journal = {Nucleic Acids Research},
number = {D1},
pages = {D1018--D1027},
pmid = {30476213},
title = {{Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources}},
volume = {47},
year = {2019}
}
@article{Schriml2019,
abstract = {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.},
author = {Schriml, Lynn M and Mitraka, Elvira and Munro, James and Tauber, Becky and Schor, Mike and Nickle, Lance and Felix, Victor and Jeng, Linda and Bearer, Cynthia and Lichenstein, Richard and Bisordi, Katharine and Campion, Nicole and Hyman, Brooke and Kurland, David and Oates, Connor Patrick and Kibbey, Siobhan and Sreekumar, Poorna and Le, Chris and Giglio, Michelle and Greene, Carol},
doi = {10.1093/nar/gky1032},
issn = {13624962},
journal = {Nucleic Acids Research},
number = {D1},
pages = {D955--D962},
title = {{Human Disease Ontology 2018 update: Classification, content and workflow expansion}},
volume = {47},
year = {2019}
}
@misc{INSERM1999,
author = {INSERM},
title = {{Orphanet: an online rare disease and orphan drug data base}},
url = {http://www.orpha.net.},
year = {1999}
}
@article{Rath2012,
abstract = {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. {\textcopyright} 2012 Wiley Periodicals, Inc.},
author = {Rath, Ana and Olry, Annie and Dhombres, Ferdinand and Brandt, Maja Mili{\v{c}}i{\'{c}} and Urbero, Bruno and Ayme, Segolene},
doi = {10.1002/humu.22078},
issn = {10597794},
journal = {Human Mutation},
keywords = {Classification,Interoperability,Nosology,Ontology,Rare diseases,Relational database},
number = {5},
pages = {803--808},
title = {{Representation of rare diseases in health information systems: The orphanet approach to serve a wide range of end users}},
volume = {33},
year = {2012}
}
@article{Amberger2019,
abstract = {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.},
author = {Amberger, Joanna S and Bocchini, Carol A and Scott, Alan F and Hamosh, Ada},
doi = {10.1093/nar/gky1151},
issn = {13624962},
journal = {Nucleic Acids Research},
number = {D1},
pages = {D1038--D1043},
title = {{OMIM.org: Leveraging knowledge across phenotype-gene relationships}},
volume = {47},
year = {2019}
}
@article{Lee2012,
abstract = {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. {\textcopyright} The Author 2012. Published by Oxford University Press. All rights reserved.},
author = {Lee, Phil H and O'dushlaine, Colm and Thomas, Brett and Purcell, Shaun M},
doi = {10.1093/bioinformatics/bts191},
issn = {13674803},
journal = {Bioinformatics},
number = {13},
pages = {1797--1799},
title = {{INRICH: Interval-based enrichment analysis for genome-wide association studies}},
volume = {28},
year = {2012}
}
@article{10.1093/nar/gkz972,
abstract = {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.},
annote = {gkz972},
author = {Landrum, Melissa J and Chitipiralla, Shanmuga and Brown, Garth R and Chen, Chao and Gu, Baoshan and Hart, Jennifer and Hoffman, Douglas and Jang, Wonhee and Kaur, Kuljeet and Liu, Chunlei and Lyoshin, Vitaly and Maddipatla, Zenith and Maiti, Rama and Mitchell, Joseph and O'Leary, Nuala and Riley, George R and Shi, Wenyao and Zhou, George and Schneider, Valerie and Maglott, Donna and Holmes, J Bradley and Kattman, Brandi L},
doi = {10.1093/nar/gkz972},
issn = {0305-1048},
journal = {Nucleic Acids Research},
pages = {gkz972},
title = {{ClinVar: improvements to accessing data}},
url = {https://doi.org/10.1093/nar/gkz972},
year = {2019}
}
@article{Martin2019,
abstract = {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.},
author = {Martin, Antonio Rueda and Williams, Eleanor and Foulger, Rebecca E and Leigh, Sarah and Daugherty, Louise C and Niblock, Olivia and Leong, Ivone U S and Smith, Katherine R and Gerasimenko, Oleg and Haraldsdottir, Eik and Thomas, Ellen and Scott, Richard H and Baple, Emma and Tucci, Arianna and Brittain, Helen and de Burca, Anna and Iba{\~{n}}ez, Kristina and Kasperaviciute, Dalia and Smedley, Damian and Caulfield, Mark and Rendon, Augusto and McDonagh, Ellen M},
doi = {10.1038/s41588-019-0528-2},
issn = {1546-1718},
journal = {Nature Genetics},
number = {11},
pages = {1560--1565},
title = {{PanelApp crowdsources expert knowledge to establish consensus diagnostic gene panels}},
url = {https://doi.org/10.1038/s41588-019-0528-2},
volume = {51},
year = {2019}
}