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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    • "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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    ABSTRACT: Protein posttranslational modifications (PTMs) play key roles in a variety of protein activities and cellular processes. Different PTMs show distinct impacts on protein functions, and normal protein activities are consequences of all kinds of PTMs working together. With the development of high throughput technologies such as tandem mass spectrometry (MS/MS) and next generation sequencing, more and more nonsynonymous single-nucleotide variations (nsSNVs) that cause variation of amino acids have been identified, some of which result in the damage of PTMs. The damaged PTMs could be the reason of the development of some human diseases. In this study, we elucidated the proteome wide relationship of eight damaged PTMs to human inherited diseases and cancers. Some human inherited diseases or cancers may be the consequences of the interactions of damaged PTMs, rather than the result of single damaged PTM site.
    Computational and Mathematical Methods in Medicine 10/2015; 2015(8):1-12. DOI:10.1155/2015/124630 · 0.77 Impact Factor
    • "Database Disease Genes Records URL Reference OMIM Over 7000 14 972 23 034 [18] "
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    ABSTRACT: With the continuing development and improvement of genome-wide techniques, a great number of candidate genes are discovered. How to identify the most likely disease genes among a large number of candidates becomes a fundamental challenge in human health. A common view is that genes related to a specific or similar disease tend to reside in the same neighbourhood of biomolecular networks. Recently, based on such observations, many methods have been developed to tackle this challenge. In this review, we firstly introduce the concept of disease genes, their properties, and available data for identifying them. Then we review the recent computational approaches for prioritizing candidate disease genes based on Protein-Protein Interaction (PPI) networks and investigate their advantages and disadvantages. Furthermore, some pieces of existing software and network resources are summarized. Finally, we discuss key issues in prioritizing candidate disease genes and point out some future research directions.
    Tsinghua Science & Technology 10/2015; 20(5):500-512.
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    • "Over a hundred thousand genetic variants have been identified across a large number of Mendelian disorders (Amberger et al., 2011), complex traits (Hindorff et al., 2009), and cancer types (Chin et al., 2011). However, many fundamental questions regarding genotype-phenotype relationships remain unresolved (Vidal et al., 2011). "
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    ABSTRACT: How disease-associated mutations impair protein activities in the context of biological networks remains mostly undetermined. Although a few renowned alleles are well characterized, functional information is missing for over 100,000 disease-associated variants. Here we functionally profile several thousand missense mutations across a spectrum of Mendelian disorders using various interaction assays. The majority of disease-associated alleles exhibit wild-type chaperone binding profiles, suggesting they preserve protein folding or stability. While common variants from healthy individuals rarely affect interactions, two-thirds of disease-associated alleles perturb protein-protein interactions, with half corresponding to "edgetic" alleles affecting only a subset of interactions while leaving most other interactions unperturbed. With transcription factors, many alleles that leave protein-protein interactions intact affect DNA binding. Different mutations in the same gene leading to different interaction profiles often result in distinct disease phenotypes. Thus disease-associated alleles that perturb distinct protein activities rather than grossly affecting folding and stability are relatively widespread. Copyright © 2015 Elsevier Inc. All rights reserved.
    Cell 04/2015; 161(3):647-660. DOI:10.1016/j.cell.2015.04.013 · 32.24 Impact Factor
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