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Identification of novel candidate disease genes from de novo exonic copy number variants

Overview of attention for article published in Genome Medicine, September 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (57th percentile)

Mentioned by

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26 tweeters
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1 Facebook page

Citations

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18 Dimensions

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43 Mendeley
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Title
Identification of novel candidate disease genes from de novo exonic copy number variants
Published in
Genome Medicine, September 2017
DOI 10.1186/s13073-017-0472-7
Pubmed ID
Authors

Tomasz Gambin, Bo Yuan, Weimin Bi, Pengfei Liu, Jill A. Rosenfeld, Zeynep Coban-Akdemir, Amber N. Pursley, Sandesh C. S. Nagamani, Ronit Marom, Sailaja Golla, Lauren Dengle, Heather G. Petrie, Reuben Matalon, Lisa Emrick, Monica B. Proud, Diane Treadwell-Deering, Hsiao-Tuan Chao, Hannele Koillinen, Chester Brown, Nora Urraca, Roya Mostafavi, Saunder Bernes, Elizabeth R. Roeder, Kimberly M. Nugent, Patricia I. Bader, Gary Bellus, Michael Cummings, Hope Northrup, Myla Ashfaq, Rachel Westman, Robert Wildin, Anita E. Beck, LaDonna Immken, Lindsay Elton, Shaun Varghese, Edward Buchanan, Laurence Faivre, Mathilde Lefebvre, Christian P. Schaaf, Magdalena Walkiewicz, Yaping Yang, Sung-Hae L. Kang, Seema R. Lalani, Carlos A. Bacino, Arthur L. Beaudet, Amy M. Breman, Janice L. Smith, Sau Wai Cheung, James R. Lupski, Ankita Patel, Chad A. Shaw, Paweł Stankiewicz

Abstract

Exon-targeted microarrays can detect small (<1000 bp) intragenic copy number variants (CNVs), including those that affect only a single exon. This genome-wide high-sensitivity approach increases the molecular diagnosis for conditions with known disease-associated genes, enables better genotype-phenotype correlations, and facilitates variant allele detection allowing novel disease gene discovery. We retrospectively analyzed data from 63,127 patients referred for clinical chromosomal microarray analysis (CMA) at Baylor Genetics laboratories, including 46,755 individuals tested using exon-targeted arrays, from 2007 to 2017. Small CNVs harboring a single gene or two to five non-disease-associated genes were identified; the genes involved were evaluated for a potential disease association. In this clinical population, among rare CNVs involving any single gene reported in 7200 patients (11%), we identified 145 de novo autosomal CNVs (117 losses and 28 intragenic gains), 257 X-linked deletion CNVs in males, and 1049 inherited autosomal CNVs (878 losses and 171 intragenic gains); 111 known disease genes were potentially disrupted by de novo autosomal or X-linked (in males) single-gene CNVs. Ninety-one genes, either recently proposed as candidate disease genes or not yet associated with diseases, were disrupted by 147 single-gene CNVs, including 37 de novo deletions and ten de novo intragenic duplications on autosomes and 100 X-linked CNVs in males. Clinical features in individuals with de novo or X-linked CNVs encompassing at most five genes (224 bp to 1.6 Mb in size) were compared to those in individuals with larger-sized deletions (up to 5 Mb in size) in the internal CMA database or loss-of-function single nucleotide variants (SNVs) detected by clinical or research whole-exome sequencing (WES). This enabled the identification of recently published genes (BPTF, NONO, PSMD12, TANGO2, and TRIP12), novel candidate disease genes (ARGLU1 and STK3), and further confirmation of disease association for two recently proposed disease genes (MEIS2 and PTCHD1). Notably, exon-targeted CMA detected several pathogenic single-exon CNVs missed by clinical WES analyses. Together, these data document the efficacy of exon-targeted CMA for detection of genic and exonic CNVs, complementing and extending WES in clinical diagnostics, and the potential for discovery of novel disease genes by genome-wide assay.

Twitter Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 43 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 23%
Student > Ph. D. Student 9 21%
Other 5 12%
Student > Doctoral Student 5 12%
Unspecified 4 9%
Other 10 23%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 14 33%
Medicine and Dentistry 9 21%
Unspecified 8 19%
Agricultural and Biological Sciences 7 16%
Computer Science 3 7%
Other 2 5%

Attention Score in Context

This research output has an Altmetric Attention Score of 14. 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 11 October 2017.
All research outputs
#935,409
of 12,275,303 outputs
Outputs from Genome Medicine
#270
of 895 outputs
Outputs of similar age
#36,189
of 271,365 outputs
Outputs of similar age from Genome Medicine
#8
of 19 outputs
Altmetric has tracked 12,275,303 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 895 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 22.7. This one has gotten more attention than average, scoring higher than 69% 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 271,365 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 86% of its contemporaries.
We're also able to compare this research output to 19 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.