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Robust identification of mosaic variants in congenital heart disease

Overview of attention for article published in Human Genetics, February 2018
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Title
Robust identification of mosaic variants in congenital heart disease
Published in
Human Genetics, February 2018
DOI 10.1007/s00439-018-1871-6
Pubmed ID
Authors

Kathryn B. Manheimer, Felix Richter, Lisa J. Edelmann, Sunita L. D’Souza, Lisong Shi, Yufeng Shen, Jason Homsy, Marko T. Boskovski, Angela C. Tai, Joshua Gorham, Christopher Yasso, Elizabeth Goldmuntz, Martina Brueckner, Richard P. Lifton, Wendy K. Chung, Christine E. Seidman, J. G. Seidman, Bruce D. Gelb

Abstract

Mosaicism due to somatic mutations can cause multiple diseases including cancer, developmental and overgrowth syndromes, neurodevelopmental disorders, autoinflammatory diseases, and atrial fibrillation. With the increased use of next generation sequencing technology, multiple tools have been developed to identify low-frequency variants, specifically from matched tumor-normal tissues in cancer studies. To investigate whether mosaic variants are implicated in congenital heart disease (CHD), we developed a pipeline using the cancer somatic variant caller MuTect to identify mosaic variants in whole-exome sequencing (WES) data from a cohort of parent/affected child trios (n = 715) and a cohort of healthy individuals (n = 416). This is a novel application of the somatic variant caller designed for cancer to WES trio data. We identified two cases with mosaic KMT2D mutations that are likely pathogenic for CHD, but conclude that, overall, mosaicism detectable in peripheral blood or saliva does not account for a significant portion of CHD etiology.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 47 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 19%
Student > Bachelor 7 15%
Student > Ph. D. Student 5 11%
Professor 4 9%
Student > Doctoral Student 4 9%
Other 11 23%
Unknown 7 15%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 15 32%
Medicine and Dentistry 12 26%
Agricultural and Biological Sciences 4 9%
Sports and Recreations 2 4%
Nursing and Health Professions 1 2%
Other 3 6%
Unknown 10 21%
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 10 February 2018.
All research outputs
#18,587,406
of 23,023,224 outputs
Outputs from Human Genetics
#2,705
of 2,959 outputs
Outputs of similar age
#328,238
of 437,841 outputs
Outputs of similar age from Human Genetics
#12
of 17 outputs
Altmetric has tracked 23,023,224 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,959 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. This one is in the 4th percentile – i.e., 4% of its peers scored the same or lower than it.
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We're also able to compare this research output to 17 others from the same source and published within six weeks on either side of this one. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.