A new face and new challenges for Online Mendelian Inheritance in Man (OMIM(R))

McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland 21287, USA.
Human Mutation (Impact Factor: 5.14). 05/2011; 32(5):564-7. DOI: 10.1002/humu.21466
Source: PubMed


OMIM's task of cataloging the association between human phenotypes and their causative genes (the Morbid Map of the Genome) and classifying and naming newly recognized disorders is growing rapidly. Establishing the relationship between genotype and phenotype has become increasingly complex. New technologies such as genome-wide association studies (GWAS) and array comparative genomic hybridization (aCGH) define "risk alleles" that are inherently prone to substantial interpretation and modification. In addition, whole exome and genome sequencing are expected to result in many reports of new mendelian disorders and their causative genes. In preparation for the onslaught of new information, we have launched a new Website to allow a more comprehensive and structured view of the contents of OMIM and to improve interconnectivity with complementary clinical and basic science genetics resources. This article focuses on the content of OMIM, the process and intent of disease classification and nosology, and anticipated improvements in our new Website (

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    • "In the diagnostic context of human clinical phenotype analysis, semantic similarity calculations quantitatively compare patient phenotype term sets to sets defined by a catalog of known diseases or syndromes. We used as the substrate for our calculations the HPO mapping of the OMIM catalog, which provides descriptions of thousands of known genetic diseases and the corresponding genes in which causative variants have been observed [20, 30,333435. A variety of semantic scoring methods have been developed . "
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    ABSTRACT: 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. Our tool, “OMIM Explorer” (, 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. 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. 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.
    Preview · Article · Dec 2016 · Genome Medicine
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    • "Apart from the disadvantages and the benefits discussed above for each different approach, the main drawback shared by the above-mentioned GP methods is that they completely neglect the class imbalance problem characterizing GP: there are much fewer causative genes (the positive instances) than noncausative ones (the negative instances). For instance, around 40% (10/09/15 update) of known genetic diseases in the OMIM (Online Mendelian Inheritance in Man) database have still fewer or almost none established gene-disease asso- ciations [11] . Computational methodologies usually suffer from a drastic performance deterioration in case of imbalance classes, since algorithms tend more to focus on the classification of major class samples while ignoring or misclassifying minority class samples [12] . "
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    • "Annotations of nsSNVs (deleterious or neutral ) were based on the information from the databases mentioned above and on Online Mendelian Inheritance in Man (OMIM; [26] for reference. Moreover, we identified the elaborate annotated information of nsSNV-related diseases from SwissVar [9] and the explicit matching of nsSNVs with PTM sites was performed. "
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