1,721,191 research outputs found
Genome-wide association meta-analysis of 30,000 samples identifies seven novel loci for quantitative ECG traits
No full text<p><strong>Introduction</strong></p>
<p>These are the <em>Summary Level-data</em> as presented in:</p>
<p>"Genome-wide association meta-analysis of 30,000 samples identifies seven novel loci for quantitative ECG traits". Eur J Hum Genet. 2019 Jan 24. doi: 10.1038/s41431-018-0295-z.<em> [Epub ahead of print]</em></p>
<p>If you use these data please cite the corresponding manuscript, which can be downloaded here: <a href="http://em.rdcu.be/wf/click?upn=lMZy1lernSJ7apc5DgYM8eFz0euOx0-2B13Abimi4Sb0A-3D_2NNavOiAD9A7CPFnsa04dGla3sU002fLfkDtL-2FhGlad0GuoM-2B3OlDb0C5GiEhwIvtH7ba4KKF45ipTOFodx6CqvVvoP2GQ992sPGoV9ZPWIe04tUd8-2BGWey0In0TXPII5zK-2Bfp8Wk9TpEqEcSd-2BEmywqZc8o5TW4xGPXZqmchfUH8chy3P4SEtpzHXMG1LwsIYrKfwegqTXG85RAJPr-2B21Tk9SobtpvFs0frMkJ4ekKsl33ryoZfFPk1byjQunJYn4-2BB0iqMgGs6cXv0AOgAxg-3D-3D">https://rdcu.be/bh8mu</a>. When you have any questions or comments regarding this study or these files, please contact me via:</p>
<p>Jessica van Setten, PhD | <em>Department of Cardiology, University Medical Center Utrecht, Utrecht University</em> | j.vansetten [at] umcutrecht [dot] nl</p>
<p> </p>
<p><strong>Files and description</strong></p>
<p>There are four files available:</p>
<ol>
<li>RR_summary_Sept2018.txt.gz - gzipped file containing all the (unfiltered) meta-analysis results for RR interval</li>
<li>PR_summary_Sept2018.txt.gz - gzipped file containing all the (unfiltered) meta-analysis results for PR interval</li>
<li>QT_summary_Sept2018.txt.gz - gzipped file containing all the (unfiltered) meta-analysis results for QT interval</li>
<li>QRS_summary_Sept2018.txt.gz - gzipped file containing all the (unfiltered) meta-analysis results for QRS duration</li>
</ol>
<p>All these files have the same lay-out and are gzipped. The reference used for meta-analysis of GWAS was Genome of the Netherlands v4. </p>
<ul>
<li><em>SNP</em> - variantID (rsID), please note that few hundred variants do not have an rsID, but are NA instead. These can still be identified by chromosome and position.</li>
<li><em>CHR</em> - chromosome numbers [1-22 and X].</li>
<li><em>POS</em> - base pair position, hg19 / build37.</li>
<li><em>CODED_ALLELE</em> - coded allele, <em>i.e.</em> the effect allele, as represented (and harmonized) across cohorts. Note that this is not necessarily the minor allele.</li>
<li><em>NON_CODED_ALLELE</em> - the other allele, <em>i.e.</em> the non-effect allele.</li>
<li><em>CODED_ALLELE_FREQ</em> - coded allele frequency, <em>i.e.</em> the effect allele frequency. Note that this is not necessarily the minor allele frequency.</li>
<li><em>BETA</em> - beta from the fixed-effects model.</li>
<li><em>SE </em>- standard error from the fixed-effects model.</li>
<li><em>P </em>- P-value from the fixed-effects model.</li>
<li><em>NEAREST_GENE</em> - the gene closest to the respective variant.</li>
</ul>
<p> </p>
PR interval genome-wide association meta-analysis identifies 50 loci associated with atrial and atrioventricular electrical activity
No full text<p><strong>Introduction</strong></p>
<p>These are the <em>Summary Level-data</em> as presented in:</p>
<p>"PR interval genome-wide association meta-analysis identifies 50 loci associated with atrial and atrioventricular electrical activity". <em>Nature Communications </em>volume 9, Article number: 2904 (2018) doi: 10.1038/s41467-018-04766-9 </p>
<p>If you use these data please cite the corresponding manuscript, which can be downloaded here: https://www.nature.com/articles/s41467-018-04766-9. When you have any questions or comments regarding this study or these files, please contact me via:</p>
<p>Jessica van Setten, PhD | <em>Department of Cardiology, University Medical Center Utrecht, Utrecht University</em> | j.vansetten [at] umcutrecht [dot] nl</p>
<p> </p>
<p><strong>Files and description</strong></p>
<p>The file PR_interval_July2018_summary_results.txt is gzipped and contains the PR interval GWAS meta-analysis summary results of 92,000 European samples. The imputation reference panel used for most studies was HapMap2 (please note: hg18, build36). </p>
<ul>
<li><em>SNP</em> - variantID (rsID).</li>
<li><em>CHR</em> - chromosome numbers [1-22 and X].</li>
<li><em>POS</em> - base pair position, hg18 / build36.</li>
<li><em>CODED_ALLELE</em> - coded allele, <em>i.e.</em> the effect allele, as represented (and harmonized) across cohorts. Note that this is not necessarily the minor allele.</li>
<li><em>NON_CODED_ALLELE</em> - the other allele, <em>i.e.</em> the non-effect allele.</li>
<li><em>CODED_ALLELE_FREQ</em> - coded allele frequency, <em>i.e.</em> the effect allele frequency. Note that this is not necessarily the minor allele frequency.</li>
<li><em>BETA_FIXED</em> - beta from the fixed-effects model.</li>
<li><em>SE_FIXED </em>- standard error from the fixed-effects model.</li>
<li><em>P_FIXED </em>- P-value from the fixed-effects model.</li>
</ul>
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Integrating omics for an improved understanding of cardiac diseases
No full textAtrial fibrillation (AF) and heart failure (HF) are diseases affecting approximately 60 million people worldwide each. AF patients suffer from an irregular heartbeat and HF patients experience a loss of pump function. Pharmacological treatments of AF, HF, and related heart muscle diseases (cardiomyopathies) focus predominantly on symptom relief and a more favourable prognosis, while prevention and treatment of underlying causes should be targeted. We searched for genes, proteins, and metabolites related to AF, HF, and cardiomyopathies, because perturbations in genes and proteins contribute to cardiac diseases and dysfunctional energy metabolism fails to meet the high energy demands of the heart to provide the body with blood, which weakens the heart. Indeed, we identified new and confirmed known associations. The findings not only deepen our understanding of cardiac diseases but also provide actionable leads for drug development and personalised therapeutics by pinpointing which drugs target these genes and proteins. Additionally, we evaluated pathogenic genetic variants in the general population, observing many healthy participants carry these variants associated with the inherited cardiomyopathies. Clustering HF patients using proteins, resulted in three subgroups with different rates of progression, which help identify patients in need of timely intervention. These studies show that the integration of data on genes, proteins, and metabolites reflects a promising trend in cardiovascular research, paving the way for more targeted therapeutics. As technology advances and datasets expand, knowledge on pathogenicity can be reevaluated and future studies will elucidate even more mechanisms contributing to cardiac disease, ultimately leading to improved clinical care
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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