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Natural genetic variation of the cardiac transcriptome in non-diseased donors and patients with dilated cardiomyopathy

Overview of attention for article published in Genome Biology, September 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (96th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

news
8 news outlets
blogs
1 blog
twitter
26 X users

Citations

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73 Dimensions

Readers on

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127 Mendeley
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Title
Natural genetic variation of the cardiac transcriptome in non-diseased donors and patients with dilated cardiomyopathy
Published in
Genome Biology, September 2017
DOI 10.1186/s13059-017-1286-z
Pubmed ID
Authors

Matthias Heinig, Michiel E. Adriaens, Sebastian Schafer, Hanneke W. M. van Deutekom, Elisabeth M. Lodder, James S. Ware, Valentin Schneider, Leanne E. Felkin, Esther E. Creemers, Benjamin Meder, Hugo A. Katus, Frank Rühle, Monika Stoll, François Cambien, Eric Villard, Philippe Charron, Andras Varro, Nanette H. Bishopric, Alfred L. George, Cristobal dos Remedios, Aida Moreno-Moral, Francesco Pesce, Anja Bauerfeind, Franz Rüschendorf, Carola Rintisch, Enrico Petretto, Paul J. Barton, Stuart A. Cook, Yigal M. Pinto, Connie R. Bezzina, Norbert Hubner

Abstract

Genetic variation is an important determinant of RNA transcription and splicing, which in turn contributes to variation in human traits, including cardiovascular diseases. Here we report the first in-depth survey of heart transcriptome variation using RNA-sequencing in 97 patients with dilated cardiomyopathy and 108 non-diseased controls. We reveal extensive differences of gene expression and splicing between dilated cardiomyopathy patients and controls, affecting known as well as novel dilated cardiomyopathy genes. Moreover, we show a widespread effect of genetic variation on the regulation of transcription, isoform usage, and allele-specific expression. Systematic annotation of genome-wide association SNPs identifies 60 functional candidate genes for heart phenotypes, representing 20% of all published heart genome-wide association loci. Focusing on the dilated cardiomyopathy phenotype we found that eQTL variants are also enriched for dilated cardiomyopathy genome-wide association signals in two independent cohorts. RNA transcription, splicing, and allele-specific expression are each important determinants of the dilated cardiomyopathy phenotype and are controlled by genetic factors. Our results represent a powerful resource for the field of cardiovascular genetics.

X Demographics

X Demographics

The data shown below were collected from the profiles of 26 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 127 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 127 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 31 24%
Student > Ph. D. Student 20 16%
Student > Master 11 9%
Student > Doctoral Student 8 6%
Student > Bachelor 6 5%
Other 13 10%
Unknown 38 30%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 35 28%
Medicine and Dentistry 21 17%
Agricultural and Biological Sciences 14 11%
Computer Science 6 5%
Psychology 3 2%
Other 8 6%
Unknown 40 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 78. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 30 November 2017.
All research outputs
#551,799
of 25,481,734 outputs
Outputs from Genome Biology
#324
of 4,480 outputs
Outputs of similar age
#11,539
of 323,675 outputs
Outputs of similar age from Genome Biology
#6
of 57 outputs
Altmetric has tracked 25,481,734 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,480 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one has done particularly well, scoring higher than 92% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 323,675 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 96% of its contemporaries.
We're also able to compare this research output to 57 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.