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Genomic approaches for the elucidation of genes and gene networks underlying cardiovascular traits

Overview of attention for article published in Biophysical Reviews, June 2018
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Title
Genomic approaches for the elucidation of genes and gene networks underlying cardiovascular traits
Published in
Biophysical Reviews, June 2018
DOI 10.1007/s12551-018-0435-2
Pubmed ID
Authors

M. E. Adriaens, C. R. Bezzina

Abstract

Genome-wide association studies have shed light on the association between natural genetic variation and cardiovascular traits. However, linking a cardiovascular trait associated locus to a candidate gene or set of candidate genes for prioritization for follow-up mechanistic studies is all but straightforward. Genomic technologies based on next-generation sequencing technology nowadays offer multiple opportunities to dissect gene regulatory networks underlying genetic cardiovascular trait associations, thereby aiding in the identification of candidate genes at unprecedented scale. RNA sequencing in particular becomes a powerful tool when combined with genotyping to identify loci that modulate transcript abundance, known as expression quantitative trait loci (eQTL), or loci modulating transcript splicing known as splicing quantitative trait loci (sQTL). Additionally, the allele-specific resolution of RNA-sequencing technology enables estimation of allelic imbalance, a state where the two alleles of a gene are expressed at a ratio differing from the expected 1:1 ratio. When multiple high-throughput approaches are combined with deep phenotyping in a single study, a comprehensive elucidation of the relationship between genotype and phenotype comes into view, an approach known as systems genetics. In this review, we cover key applications of systems genetics in the broad cardiovascular field.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 19 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 37%
Researcher 2 11%
Student > Doctoral Student 1 5%
Student > Bachelor 1 5%
Student > Master 1 5%
Other 3 16%
Unknown 4 21%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 11 58%
Agricultural and Biological Sciences 1 5%
Chemistry 1 5%
Medicine and Dentistry 1 5%
Unknown 5 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 24 June 2018.
All research outputs
#18,640,437
of 23,092,602 outputs
Outputs from Biophysical Reviews
#496
of 799 outputs
Outputs of similar age
#253,545
of 328,678 outputs
Outputs of similar age from Biophysical Reviews
#5
of 8 outputs
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So far Altmetric has tracked 799 research outputs from this source. They receive a mean Attention Score of 2.7. This one is in the 12th percentile – i.e., 12% of its peers scored the same or lower than it.
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