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Pathogenic variant burden in the ExAC database: an empirical approach to evaluating population data for clinical variant interpretation

Overview of attention for article published in Genome Medicine, February 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 (85th percentile)
  • Average Attention Score compared to outputs of the same age and source

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22 X users

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Title
Pathogenic variant burden in the ExAC database: an empirical approach to evaluating population data for clinical variant interpretation
Published in
Genome Medicine, February 2017
DOI 10.1186/s13073-017-0403-7
Pubmed ID
Authors

Yuya Kobayashi, Shan Yang, Keith Nykamp, John Garcia, Stephen E. Lincoln, Scott E. Topper

Abstract

The frequency of a variant in the general population is a key criterion used in the clinical interpretation of sequence variants. With certain exceptions, such as founder mutations, the rarity of a variant is a prerequisite for pathogenicity. However, defining the threshold at which a variant should be considered "too common" is challenging and therefore diagnostic laboratories have typically set conservative allele frequency thresholds. Recent publications of large population sequencing data, such as the Exome Aggregation Consortium (ExAC) database, provide an opportunity to characterize with accuracy and precision the frequency distributions of very rare disease-causing alleles. Allele frequencies of pathogenic variants in ClinVar, as well as variants expected to be pathogenic through the nonsense-mediated decay (NMD) pathway, were analyzed to study the burden of pathogenic variants in 79 genes of clinical importance. Of 1364 BRCA1 and BRCA2 variants that are well characterized as pathogenic or that are expected to lead to NMD, 1350 variants had an allele frequency of less than 0.0025%. The remaining 14 variants were previously published founder mutations. Importantly, we observed no difference in the distributions of pathogenic variants expected to be lead to NMD compared to those that are not. Therefore, we expanded the analysis to examine the distributions of NMD expected variants in 77 additional genes. These 77 genes were selected to represent a broad set of clinical areas, modes of inheritance, and penetrance. Among these variants, most (97.3%) had an allele frequency of less than 0.01%. Furthermore, pathogenic variants with allele frequencies greater than 0.01% were well characterized in publications and included many founder mutations. The observations made in this study suggest that, with certain caveats, a very low allele frequency threshold can be adopted to more accurately interpret sequence variants.

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The data shown below were collected from the profiles of 22 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 261 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 1%
Hong Kong 1 <1%
France 1 <1%
Unknown 256 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 49 19%
Researcher 49 19%
Student > Master 37 14%
Other 26 10%
Student > Bachelor 19 7%
Other 36 14%
Unknown 45 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 104 40%
Agricultural and Biological Sciences 46 18%
Medicine and Dentistry 30 11%
Computer Science 6 2%
Neuroscience 4 2%
Other 15 6%
Unknown 56 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 17 April 2018.
All research outputs
#3,125,637
of 25,446,666 outputs
Outputs from Genome Medicine
#703
of 1,589 outputs
Outputs of similar age
#60,977
of 425,188 outputs
Outputs of similar age from Genome Medicine
#17
of 27 outputs
Altmetric has tracked 25,446,666 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,589 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.7. This one has gotten more attention than average, scoring higher than 55% 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 425,188 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 85% of its contemporaries.
We're also able to compare this research output to 27 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.