59 research outputs found

    Victor McKusick and his short course.

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    The Short Course in Human and Mammalian Genetics and Genomics (aka the Short Course or the Bar Harbor course ) is one of Victor McKusick\u27s landmark contributions to medical genetics. Conceived in 1959 as a way to increase the contribution of genetic advances to medicine, it has directly affected more than 7000 students and 600 participating faculty from around the world. Now, more than 10 years after his death, it continues to be a vibrant disseminator of genetics, and genomics knowledge for medicine, a catalytic agent for ongoing research and a source of collegiality in our field. What an extraordinary gift

    Evolutionary Signatures amongst Disease Genes Permit Novel Methods for Gene Prioritization and Construction of Informative Gene-Based Networks

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    Genes involved in the same function tend to have similar evolutionary histories, in that their rates of evolution covary over time. This coevolutionary signature, termed Evolutionary Rate Covariation (ERC), is calculated using only gene sequences from a set of closely related species and has demonstrated potential as a computational tool for inferring functional relationships between genes. To further define applications of ERC, we first established that roughly 55% of genetic diseases posses an ERC signature between their contributing genes. At a false discovery rate of 5% we report 40 such diseases including cancers, developmental disorders and mitochondrial diseases. Given these coevolutionary signatures between disease genes, we then assessed ERC's ability to prioritize known disease genes out of a list of unrelated candidates. We found that in the presence of an ERC signature, the true disease gene is effectively prioritized to the top 6% of candidates on average. We then apply this strategy to a melanoma-associated region on chromosome 1 and identify MCL1 as a potential causative gene. Furthermore, to gain global insight into disease mechanisms, we used ERC to predict molecular connections between 310 nominally distinct diseases. The resulting “disease map” network associates several diseases with related pathogenic mechanisms and unveils many novel relationships between clinically distinct diseases, such as between Hirschsprung's disease and melanoma. Taken together, these results demonstrate the utility of molecular evolution as a gene discovery platform and show that evolutionary signatures can be used to build informative gene-based networks

    Saturation of the Human Phenome

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    The phenome is the complete set of phenotypes resulting from genetic variation in populations of an organism. Saturation of a phenome implies the identification and phenotypic description of mutations in all genes in an organism, potentially constrained to those encoding proteins. The human genome is believed to contain 20-25,000 protein coding genes, but only a small fraction of these have documented mutant phenotypes, thus the human phenome is far from complete. In model organisms, genetic saturation entails the identification of multiple mutant alleles of a gene or locus, allowing a consistent description of mutational phenotypes for that gene. Saturation of several model organisms has been attempted, usually by targeting annotated coding genes with insertional transposons (Drosophila melanogaster, Mus musculus) or by sequence directed deletion (Saccharomyces cerevisiae) or using libraries of antisense oligonucleotide probes injected directly into animals (Caenorhabditis elegans, Danio rerio). This paper reviews the general state of the human phenome, and discusses theoretical and practical considerations toward a saturation analysis in humans. Throughout, emphasis is placed on high penetrance genetic variation, of the kind typically asociated with monogenic versus complex traits

    Random walks on mutual microRNA-target gene interaction network improve the prediction of disease-associated microRNAs

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    Background: MicroRNAs (miRNAs) have been shown to play an important role in pathological initiation, progression and maintenance. Because identification in the laboratory of disease-related miRNAs is not straightforward, numerous network-based methods have been developed to predict novel miRNAs in silico. Homogeneous networks (in which every node is a miRNA) based on the targets shared between miRNAs have been widely used to predict their role in disease phenotypes. Although such homogeneous networks can predict potential disease-associated miRNAs, they do not consider the roles of the target genes of the miRNAs. Here, we introduce a novel method based on a heterogeneous network that not only considers miRNAs but also the corresponding target genes in the network model. Results: Instead of constructing homogeneous miRNA networks, we built heterogeneous miRNA networks consisting of both miRNAs and their target genes, using databases of known miRNA-target gene interactions. In addition, as recent studies demonstrated reciprocal regulatory relations between miRNAs and their target genes, we considered these heterogeneous miRNA networks to be undirected, assuming mutual miRNA-target interactions. Next, we introduced a novel method (RWRMTN) operating on these mutual heterogeneous miRNA networks to rank candidate disease-related miRNAs using a random walk with restart (RWR) based algorithm. Using both known disease-associated miRNAs and their target genes as seed nodes, the method can identify additional miRNAs involved in the disease phenotype. Experiments indicated that RWRMTN outperformed two existing state-of-the-art methods: RWRMDA, a network-based method that also uses a RWR on homogeneous (rather than heterogeneous) miRNA networks, and RLSMDA, a machine learning-based method. Interestingly, we could relate this performance gain to the emergence of "disease modules" in the heterogeneous miRNA networks used as input for the algorithm. Moreover, we could demonstrate that RWRMTN is stable, performing well when using both experimentally validated and predicted miRNA-target gene interaction data for network construction. Finally, using RWRMTN, we identified 76 novel miRNAs associated with 23 disease phenotypes which were present in a recent database of known disease-miRNA associations. Conclusions: Summarizing, using random walks on mutual miRNA-target networks improves the prediction of novel disease-associated miRNAs because of the existence of "disease modules" in these networks

    Genetics of the epilepsies in childhood

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    Ovaj pregledni članak, namijenjen široj medicinskoj javnosti, ima dva cilja: prvi je informirati o primjeni najnovijih teorijskih dostignuća medicinske genetike na području epilepsija, a drugi je cilj procjena rizika obolijevanja članova obitelji bolesnika od epilepsije. U okviru prvog cilja opisano je nekoliko primjera novootkrivenih gena čije su mutacije odgovorne za nastanak nekih neuroloških bolesti praćenih simptomatskom epilepsijom: tu pripadaju neke dominantno nasljedne malformacije, neurokutani sindromi i recesivno nasljedne metaboličke bolesti. Za dalje razumijevanje genetike epilepsija puno je, međutim, važnije identificiranje, mapiranje i kloniranje gena te otkrivanje genskih produkata triju idiopatskih epilepsija, što je ostvareno posljednjih desetak godina. To su: autosomno dominantno nasljedna noćna frontalna epilepsija, dominantno nasljedne benigne neonatalne konvulzije i progresivna mioklonička epilepsija Unverricht Lundborg. Drugi cilj ovoga pregleda usmjerenje na potrebe svakodnevnog rada s bolesnicima koji boluju od epilepsije i njihovih obitelji: iznijeti kvantitativno izražene teorijske i empirijske rizike kojima su izložena braća, sestre i potomci bolesnika u usporedbi s rizicima u općoj populaciji. Takvi podaci, iznijeti u obliku tablica i grafikona, temelj su za argumentirano genetsko infomiranje i savjetovanje bolesnika epileptičara.This review article, written for the broader medical community, has two aims. The first one is to inform about the implementation of the latest theoretical advances in medical genetics as applied to the field of the epilepsies. The second aim is to present data for the genetic counselor in the epileptic clinic about the risk of developing epilepsy in family members of epileptic patients. Recently discovered genes are described, the mutations of which are responsible for the occurence of some neurologic diseases which are the cause of symptomatic epilepsy. Included here are some dominantly inherited cerebral malformations, neurocutaneous syndromes and many recessively inherited metabolic diseases. However, for a deeper insight into the pathogenesis of the epilepsies much more important are the identification, mapping, cloning of genes and defining of the corresponding gene products achieved during the last ten years for three idiopathic epilepsies: the dominantly inherited nocturnal frontal epilepsy, the dominantly inherited benign neonatal convulsions and the progressive myoclonic epilepsy of Unverricht and Lundborg. The second part of this article is intended for the everyday work of the practitioner in the epilepsy clinic in genetic counselling of epileptic patients and their families. Theoretical and empyrical risks of developing epilepsy are provided for siblings and descendants of the epileptic patients. Such data, shown in the form of tables and graphs, are indispensable for scientifically based genetic informing and counseling

    Doctor of Philosophy

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    dissertationThe objective of this work is to examine the efficacy of natural language processing (NLP) in summarizing bibliographic text for multiple purposes. Researchers have noted the accelerating growth of bibliographic databases. Information seekers using traditional information retrieval techniques when searching large bibliographic databases are often overwhelmed by excessive, irrelevant data. Scientists have applied natural language processing technologies to improve retrieval. Text summarization, a natural language processing approach, simplifies bibliographic data while filtering it to address a user's need. Traditional text summarization can necessitate the use of multiple software applications to accommodate diverse processing refinements known as "points-of-view." A new, statistical approach to text summarization can transform this process. Combo, a statistical algorithm comprised of three individual metrics, determines which elements within input data are relevant to a user's specified information need, thus enabling a single software application to summarize text for many points-of-view. In this dissertation, I describe this algorithm, and the research process used in developing and testing it. Four studies comprised the research process. The goal of the first study was to create a conventional schema accommodating a genetic disease etiology point-of-view, and an evaluative reference standard. This was accomplished through simulating the task of secondary genetic database curation. The second study addressed the development iv and initial evaluation of the algorithm, comparing its performance to the conventional schema using the previously established reference standard, again within the task of secondary genetic database curation. The third and fourth studies evaluated the algorithm's performance in accommodating additional points-of-view in a simulated clinical decision support task. The third study explored prevention, while the fourth evaluated performance for prevention and drug treatment, comparing results to a conventional treatment schema's output. Both summarization methods identified data that were salient to their tasks. The conventional genetic disease etiology and treatment schemas located salient information for database curation and decision support, respectively. The Combo algorithm located salient genetic disease etiology, treatment, and prevention data, for the associated tasks. Dynamic text summarization could potentially serve additional purposes, such as consumer health information delivery, systematic review creation, and primary research. This technology may benefit many user groups
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