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A method to reduce ancestry related germline false positives in tumor only somatic variant calling

Overview of attention for article published in BMC Medical Genomics, October 2017
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  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#11 of 1,309)
  • High Attention Score compared to outputs of the same age (95th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

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7 news outlets
blogs
1 blog
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14 X users

Citations

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31 Dimensions

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45 Mendeley
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Title
A method to reduce ancestry related germline false positives in tumor only somatic variant calling
Published in
BMC Medical Genomics, October 2017
DOI 10.1186/s12920-017-0296-8
Pubmed ID
Authors

Rebecca F. Halperin, John D. Carpten, Zarko Manojlovic, Jessica Aldrich, Jonathan Keats, Sara Byron, Winnie S. Liang, Megan Russell, Daniel Enriquez, Ana Claasen, Irene Cherni, Baffour Awuah, Joseph Oppong, Max S. Wicha, Lisa A. Newman, Evelyn Jaigge, Seungchan Kim, David W. Craig

Abstract

Significant clinical and research applications are driving large scale adoption of individualized tumor sequencing in cancer in order to identify tumors-specific mutations. When a matched germline sample is available, somatic mutations may be identified using comparative callers. However, matched germline samples are frequently not available such as with archival tissues, which makes it difficult to distinguish somatic from germline variants. While population databases may be used to filter out known germline variants, recent studies have shown private germline variants result in an inflated false positive rate in unmatched tumor samples, and the number germline false positives in an individual may be related to ancestry. First, we examined the relationship between the germline false positives and ancestry. Then we developed and implemented a tumor only caller (LumosVar) that leverages differences in allelic frequency between somatic and germline variants in impure tumors. We used simulated data to systematically examine how copy number alterations, tumor purity, and sequencing depth should affect the sensitivity of our caller. Finally, we evaluated the caller on real data. We find the germline false-positive rate is significantly higher for individuals of non-European Ancestry largely due to the limited diversity in public polymorphism databases and due to population-specific characteristics such as admixture or recent expansions. Our Bayesian tumor only caller (LumosVar) is able to greatly reduce false positives from private germline variants, and our sensitivity is similar to predictions based on simulated data. Taken together, our results suggest that studies of individuals of non-European ancestry would most benefit from our approach. However, high sensitivity requires sufficiently impure tumors and adequate sequencing depth. Even in impure tumors, there are copy number alterations that result in germline and somatic variants having similar allele frequencies, limiting the sensitivity of the approach. We believe our approach could greatly improve the analysis of archival samples in a research setting where the normal is not available.

X Demographics

X Demographics

The data shown below were collected from the profiles of 14 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 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 45 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 20%
Researcher 7 16%
Student > Bachelor 2 4%
Professor 2 4%
Student > Doctoral Student 2 4%
Other 8 18%
Unknown 15 33%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 6 13%
Agricultural and Biological Sciences 5 11%
Pharmacology, Toxicology and Pharmaceutical Science 4 9%
Medicine and Dentistry 4 9%
Computer Science 2 4%
Other 7 16%
Unknown 17 38%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 62. 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 22 June 2020.
All research outputs
#647,727
of 24,294,745 outputs
Outputs from BMC Medical Genomics
#11
of 1,309 outputs
Outputs of similar age
#14,136
of 331,245 outputs
Outputs of similar age from BMC Medical Genomics
#1
of 9 outputs
Altmetric has tracked 24,294,745 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,309 research outputs from this source. They receive a mean Attention Score of 4.6. This one has done particularly well, scoring higher than 99% 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 331,245 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them