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  1. Article ; Online: Genome-wide rare variant analysis for thousands of phenotypes in over 70,000 exomes from two cohorts

    Elizabeth T. Cirulli / Simon White / Robert W. Read / Gai Elhanan / William J. Metcalf / Francisco Tanudjaja / Donna M. Fath / Efren Sandoval / Magnus Isaksson / Karen A. Schlauch / Joseph J. Grzymski / James T. Lu / Nicole L. Washington

    Nature Communications, Vol 11, Iss 1, Pp 1-

    2020  Volume 10

    Abstract: Population-based association analyses of rare genetic variants with complex traits are limited by the availability of data from sufficiently large cohorts. Here, Cirulli et al. report gene-based collapsing analysis of exomes from 49,960 participants of ... ...

    Abstract Population-based association analyses of rare genetic variants with complex traits are limited by the availability of data from sufficiently large cohorts. Here, Cirulli et al. report gene-based collapsing analysis of exomes from 49,960 participants of the UK Biobank and 21,866 participants of the Healthy Nevada Project over a total of 4377 traits.
    Keywords Science ; Q
    Language English
    Publishing date 2020-01-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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  2. Article ; Online: Genome-wide rare variant analysis for thousands of phenotypes in over 70,000 exomes from two cohorts

    Elizabeth T. Cirulli / Simon White / Robert W. Read / Gai Elhanan / William J. Metcalf / Francisco Tanudjaja / Donna M. Fath / Efren Sandoval / Magnus Isaksson / Karen A. Schlauch / Joseph J. Grzymski / James T. Lu / Nicole L. Washington

    Nature Communications, Vol 11, Iss 1, Pp 1-

    2020  Volume 10

    Abstract: Population-based association analyses of rare genetic variants with complex traits are limited by the availability of data from sufficiently large cohorts. Here, Cirulli et al. report gene-based collapsing analysis of exomes from 49,960 participants of ... ...

    Abstract Population-based association analyses of rare genetic variants with complex traits are limited by the availability of data from sufficiently large cohorts. Here, Cirulli et al. report gene-based collapsing analysis of exomes from 49,960 participants of the UK Biobank and 21,866 participants of the Healthy Nevada Project over a total of 4377 traits.
    Keywords Science ; Q
    Language English
    Publishing date 2020-01-01T00:00:00Z
    Publisher Nature Publishing Group
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Linking human diseases to animal models using ontology-based phenotype annotation.

    Nicole L Washington / Melissa A Haendel / Christopher J Mungall / Michael Ashburner / Monte Westerfield / Suzanna E Lewis

    PLoS Biology, Vol 7, Iss 11, p e

    2009  Volume 1000247

    Abstract: Scientists and clinicians who study genetic alterations and disease have traditionally described phenotypes in natural language. The considerable variation in these free-text descriptions has posed a hindrance to the important task of identifying ... ...

    Abstract Scientists and clinicians who study genetic alterations and disease have traditionally described phenotypes in natural language. The considerable variation in these free-text descriptions has posed a hindrance to the important task of identifying candidate genes and models for human diseases and indicates the need for a computationally tractable method to mine data resources for mutant phenotypes. In this study, we tested the hypothesis that ontological annotation of disease phenotypes will facilitate the discovery of new genotype-phenotype relationships within and across species. To describe phenotypes using ontologies, we used an Entity-Quality (EQ) methodology, wherein the affected entity (E) and how it is affected (Q) are recorded using terms from a variety of ontologies. Using this EQ method, we annotated the phenotypes of 11 gene-linked human diseases described in Online Mendelian Inheritance in Man (OMIM). These human annotations were loaded into our Ontology-Based Database (OBD) along with other ontology-based phenotype descriptions of mutants from various model organism databases. Phenotypes recorded with this EQ method can be computationally compared based on the hierarchy of terms in the ontologies and the frequency of annotation. We utilized four similarity metrics to compare phenotypes and developed an ontology of homologous and analogous anatomical structures to compare phenotypes between species. Using these tools, we demonstrate that we can identify, through the similarity of the recorded phenotypes, other alleles of the same gene, other members of a signaling pathway, and orthologous genes and pathway members across species. We conclude that EQ-based annotation of phenotypes, in conjunction with a cross-species ontology, and a variety of similarity metrics can identify biologically meaningful similarities between genes by comparing phenotypes alone. This annotation and search method provides a novel and efficient means to identify gene candidates and animal models of human disease, which may shorten the lengthy path to identification and understanding of the genetic basis of human disease.
    Keywords Biology (General) ; QH301-705.5
    Subject code 572
    Language English
    Publishing date 2009-11-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: Using symptom-based case predictions to identify host genetic factors that contribute to COVID-19 susceptibility

    Irene V. van Blokland / Pauline Lanting / Anil P. S. Ori / Judith M. Vonk / Robert C. A. Warmerdam / Johanna C. Herkert / Floranne Boulogne / Annique Claringbould / Esteban A. Lopera-Maya / Meike Bartels / Jouke-Jan Hottenga / Andrea Ganna / Juha Karjalainen / Lifelines COVID-19 cohort study / The COVID-19 Host Genetics Initiative / Caroline Hayward / Chloe Fawns-Ritchie / Archie Campbell / David Porteous /
    Elizabeth T. Cirulli / Kelly M. Schiabor Barrett / Stephen Riffle / Alexandre Bolze / Simon White / Francisco Tanudjaja / Xueqing Wang / Jimmy M. Ramirez / Yan Wei Lim / James T. Lu / Nicole L. Washington / Eco J. C. de Geus / Patrick Deelen / H. Marike Boezen / Lude H. Franke

    PLoS ONE, Vol 16, Iss

    2021  Volume 8

    Abstract: Epidemiological and genetic studies on COVID-19 are currently hindered by inconsistent and limited testing policies to confirm SARS-CoV-2 infection. Recently, it was shown that it is possible to predict COVID-19 cases using cross-sectional self-reported ... ...

    Abstract Epidemiological and genetic studies on COVID-19 are currently hindered by inconsistent and limited testing policies to confirm SARS-CoV-2 infection. Recently, it was shown that it is possible to predict COVID-19 cases using cross-sectional self-reported disease-related symptoms. Here, we demonstrate that this COVID-19 prediction model has reasonable and consistent performance across multiple independent cohorts and that our attempt to improve upon this model did not result in improved predictions. Using the existing COVID-19 prediction model, we then conducted a GWAS on the predicted phenotype using a total of 1,865 predicted cases and 29,174 controls. While we did not find any common, large-effect variants that reached genome-wide significance, we do observe suggestive genetic associations at two SNPs (rs11844522, p = 1.9x10-7; rs5798227, p = 2.2x10-7). Explorative analyses furthermore suggest that genetic variants associated with other viral infectious diseases do not overlap with COVID-19 susceptibility and that severity of COVID-19 may have a different genetic architecture compared to COVID-19 susceptibility. This study represents a first effort that uses a symptom-based predicted phenotype as a proxy for COVID-19 in our pursuit of understanding the genetic susceptibility of the disease. We conclude that the inclusion of symptom-based predicted cases could be a useful strategy in a scenario of limited testing, either during the current COVID-19 pandemic or any future viral outbreak.
    Keywords Medicine ; R ; Science ; Q
    Subject code 006
    Language English
    Publishing date 2021-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

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  5. Article ; Online: Using symptom-based case predictions to identify host genetic factors that contribute to COVID-19 susceptibility.

    Irene V van Blokland / Pauline Lanting / Anil P S Ori / Judith M Vonk / Robert C A Warmerdam / Johanna C Herkert / Floranne Boulogne / Annique Claringbould / Esteban A Lopera-Maya / Meike Bartels / Jouke-Jan Hottenga / Andrea Ganna / Juha Karjalainen / Lifelines COVID-19 cohort study / COVID-19 Host Genetics Initiative / Caroline Hayward / Chloe Fawns-Ritchie / Archie Campbell / David Porteous /
    Elizabeth T Cirulli / Kelly M Schiabor Barrett / Stephen Riffle / Alexandre Bolze / Simon White / Francisco Tanudjaja / Xueqing Wang / Jimmy M Ramirez / Yan Wei Lim / James T Lu / Nicole L Washington / Eco J C de Geus / Patrick Deelen / H Marike Boezen / Lude H Franke

    PLoS ONE, Vol 16, Iss 8, p e

    2021  Volume 0255402

    Abstract: Epidemiological and genetic studies on COVID-19 are currently hindered by inconsistent and limited testing policies to confirm SARS-CoV-2 infection. Recently, it was shown that it is possible to predict COVID-19 cases using cross-sectional self-reported ... ...

    Abstract Epidemiological and genetic studies on COVID-19 are currently hindered by inconsistent and limited testing policies to confirm SARS-CoV-2 infection. Recently, it was shown that it is possible to predict COVID-19 cases using cross-sectional self-reported disease-related symptoms. Here, we demonstrate that this COVID-19 prediction model has reasonable and consistent performance across multiple independent cohorts and that our attempt to improve upon this model did not result in improved predictions. Using the existing COVID-19 prediction model, we then conducted a GWAS on the predicted phenotype using a total of 1,865 predicted cases and 29,174 controls. While we did not find any common, large-effect variants that reached genome-wide significance, we do observe suggestive genetic associations at two SNPs (rs11844522, p = 1.9x10-7; rs5798227, p = 2.2x10-7). Explorative analyses furthermore suggest that genetic variants associated with other viral infectious diseases do not overlap with COVID-19 susceptibility and that severity of COVID-19 may have a different genetic architecture compared to COVID-19 susceptibility. This study represents a first effort that uses a symptom-based predicted phenotype as a proxy for COVID-19 in our pursuit of understanding the genetic susceptibility of the disease. We conclude that the inclusion of symptom-based predicted cases could be a useful strategy in a scenario of limited testing, either during the current COVID-19 pandemic or any future viral outbreak.
    Keywords Medicine ; R ; Science ; Q
    Subject code 006
    Language English
    Publishing date 2021-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

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  6. Article: Deletions of chromosomal regulatory boundaries are associated with congenital disease

    Ibn-Salem, Jonas / Christopher J Mungall / Claus-Eric Ott / Damian Smedley / Ho-Ryun Chung / Malte Spielmann / Matthew E Hurles / Melissa Haendel / Michael I Love / Ni Huang / Nicole L Washington / Paul N Schofield / Peter N Robinson / Sebastian Bauer / Sebastian Köhler / Stefan Mundlos / Suzanna E Lewis

    Genome biology. 2014 Sept., v. 15, no. 9

    2014  

    Abstract: BACKGROUND: Recent data from genome-wide chromosome conformation capture analysis indicate that the human genome is divided into conserved megabase-sized self-interacting regions called topological domains. These topological domains form the regulatory ... ...

    Abstract BACKGROUND: Recent data from genome-wide chromosome conformation capture analysis indicate that the human genome is divided into conserved megabase-sized self-interacting regions called topological domains. These topological domains form the regulatory backbone of the genome and are separated by regulatory boundary elements or barriers. Copy-number variations can potentially alter the topological domain architecture by deleting or duplicating the barriers and thereby allowing enhancers from neighboring domains to ectopically activate genes causing misexpression and disease, a mutational mechanism that has recently been termed enhancer adoption. RESULTS: We use the Human Phenotype Ontology database to relate the phenotypes of 922 deletion cases recorded in the DECIPHER database to monogenic diseases associated with genes in or adjacent to the deletions. We identify combinations of tissue-specific enhancers and genes adjacent to the deletion and associated with phenotypes in the corresponding tissue, whereby the phenotype matched that observed in the deletion. We compare this computationally with a gene-dosage pathomechanism that attempts to explain the deletion phenotype based on haploinsufficiency of genes located within the deletions. Up to 11.8% of the deletions could be best explained by enhancer adoption or a combination of enhancer adoption and gene-dosage effects. CONCLUSIONS: Our results suggest that enhancer adoption caused by deletions of regulatory boundaries may contribute to a substantial minority of copy-number variation phenotypes and should thus be taken into account in their medical interpretation.
    Keywords chromosomes ; databases ; gene dosage ; genes ; humans ; phenotype ; topology
    Language English
    Dates of publication 2014-09
    Size p. 423.
    Publishing place Springer-Verlag
    Document type Article
    ZDB-ID 2040529-7
    ISSN 1474-760X ; 1465-6914 ; 1465-6906
    ISSN (online) 1474-760X ; 1465-6914
    ISSN 1465-6906
    DOI 10.1186/s13059-014-0423-1
    Database NAL-Catalogue (AGRICOLA)

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