↓ Skip to main content

Classification of genetic variants in genes associated with Lynch syndrome using a clinical history weighting algorithm

Overview of attention for article published in BMC Genomic Data, July 2016
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
3 X users

Readers on

mendeley
31 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Classification of genetic variants in genes associated with Lynch syndrome using a clinical history weighting algorithm
Published in
BMC Genomic Data, July 2016
DOI 10.1186/s12863-016-0407-0
Pubmed ID
Authors

Brian Morris, Elisha Hughes, Eric Rosenthal, Alexander Gutin, Karla R. Bowles

Abstract

Lynch syndrome is a hereditary cancer syndrome associated with high risks of colorectal and endometrial cancer that is caused by pathogenic variants in the mismatch repair genes (MLH1, MSH2, MSH6, PMS2, EPCAM). Accurate classification of variants identified in these genes as pathogenic or benign enables informed medical management decisions. Previously, we developed a clinical History Weighting Algorithm (HWA) for the classification of variants of uncertain significance (VUSs) in BRCA1 and BRCA2. The BRCA1/2 HWA is based on the premise that pathogenic variants in these genes will be identified more often in individuals with strong personal and/or family histories of breast and/or ovarian cancer, while the identification of benign variants should be independent of cancer history. Here we report the development of a similar HWA to allow for classification of VUSs in genes associated with Lynch syndrome using data collected through both syndrome-specific and pan-cancer panel testing. Upon completion of algorithm development, the HWA was tested using simulated variants constructed from 79,214 probands, as well as 379 true variants. Positive (PPV) and negative predictive values (NPV) were calculated on a per gene basis. 25,500 pathogenic and 50,500 benign simulated variants were analyzed using the HWA and the PPVs and NPVs for each gene were greater than 0.997 and 0.999, respectively. The HWA was also evaluated using 100 trials for each of the 379 true variants. PPVs of >0.998 and NPVs of >0.999 were obtained for all genes. We have developed and implemented a HWA to aid in the classification of VUSs in genes associated with Lynch syndrome. The work presented here demonstrates that this HWA is able to classify MLH1, MSH2, and MSH6 VUSs as either benign or pathogenic with high accuracy.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 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 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 31 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 19%
Student > Bachelor 4 13%
Student > Master 4 13%
Student > Ph. D. Student 3 10%
Student > Doctoral Student 2 6%
Other 6 19%
Unknown 6 19%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 10 32%
Medicine and Dentistry 7 23%
Agricultural and Biological Sciences 4 13%
Mathematics 1 3%
Decision Sciences 1 3%
Other 1 3%
Unknown 7 23%
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 02 July 2016.
All research outputs
#17,285,036
of 25,373,627 outputs
Outputs from BMC Genomic Data
#668
of 1,204 outputs
Outputs of similar age
#237,605
of 367,255 outputs
Outputs of similar age from BMC Genomic Data
#27
of 48 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,204 research outputs from this source. They receive a mean Attention Score of 4.3. This one is in the 34th percentile – i.e., 34% of its peers scored the same or lower than it.
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 367,255 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 48 others from the same source and published within six weeks on either side of this one. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.