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Clinical Genetic Testing for the Cardiomyopathies and Arrhythmias: A Systematic Framework for Establishing Clinical Validity and Addressing Genotypic and Phenotypic Heterogeneity

Overview of attention for article published in Frontiers in Cardiovascular Medicine, June 2016
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Title
Clinical Genetic Testing for the Cardiomyopathies and Arrhythmias: A Systematic Framework for Establishing Clinical Validity and Addressing Genotypic and Phenotypic Heterogeneity
Published in
Frontiers in Cardiovascular Medicine, June 2016
DOI 10.3389/fcvm.2016.00020
Pubmed ID
Authors

John Garcia, Jackie Tahiliani, Nicole Marie Johnson, Sienna Aguilar, Daniel Beltran, Amy Daly, Emily Decker, Eden Haverfield, Blanca Herrera, Laura Murillo, Keith Nykamp, Scott Topper

Abstract

Advances in DNA sequencing have made large, diagnostic gene panels affordable and efficient. Broad adoption of such panels has begun to deliver on the promises of personalized medicine, but has also brought new challenges such as the presence of unexpected results, or results of uncertain clinical significance. Genetic analysis of inherited cardiac conditions is particularly challenging due to the extensive genetic heterogeneity underlying cardiac phenotypes, and the overlapping, variable, and incompletely penetrant nature of their clinical presentations. The design of effective diagnostic tests and the effective use of the results depend on a clear understanding of the relationship between each gene and each considered condition. To address these issues, we developed simple, systematic approaches to three fundamental challenges: (1) evaluating the strength of the evidence suggesting that a particular condition is caused by pathogenic variants in a particular gene, (2) evaluating whether unusual genotype/phenotype observations represent a plausible expansion of clinical phenotype associated with a gene, and (3) establishing a molecular diagnostic strategy to capture overlapping clinical presentations. These approaches focus on the systematic evaluation of the pathogenicity of variants identified in clinically affected individuals, and the natural history of disease in those individuals. Here, we applied these approaches to the evaluation of more than 100 genes reported to be associated with inherited cardiomyopathies and arrhythmias including hypertrophic cardiomyopathy, dilated cardiomyopathy, arrhythmogenic right ventricular dysplasia or cardiomyopathy, long QT syndrome, short QT syndrome, Brugada, and catecholaminergic polymorphic ventricular tachycardia, and to a set of related syndromes such as Noonan Syndrome and Fabry disease. These approaches provide a framework for delivering meaningful and accurate genetic test results to individuals with hereditary cardiac conditions.

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Mendeley readers

Mendeley readers

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Geographical breakdown

Country Count As %
Unknown 40 100%

Demographic breakdown

Readers by professional status Count As %
Other 7 18%
Student > Ph. D. Student 6 15%
Researcher 6 15%
Student > Master 5 13%
Student > Doctoral Student 2 5%
Other 6 15%
Unknown 8 20%
Readers by discipline Count As %
Medicine and Dentistry 14 35%
Biochemistry, Genetics and Molecular Biology 12 30%
Nursing and Health Professions 2 5%
Agricultural and Biological Sciences 2 5%
Neuroscience 2 5%
Other 2 5%
Unknown 6 15%
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 15 September 2016.
All research outputs
#15,377,977
of 22,877,793 outputs
Outputs from Frontiers in Cardiovascular Medicine
#2,568
of 6,779 outputs
Outputs of similar age
#223,005
of 352,119 outputs
Outputs of similar age from Frontiers in Cardiovascular Medicine
#9
of 13 outputs
Altmetric has tracked 22,877,793 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,779 research outputs from this source. They receive a mean Attention Score of 4.1. This one has gotten more attention than average, scoring higher than 58% 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 352,119 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 13 others from the same source and published within six weeks on either side of this one. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.