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Genomic prediction of celiac disease targeting HLA-positive individuals

Overview of attention for article published in Genome Medicine, July 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (90th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (62nd percentile)

Mentioned by

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29 tweeters

Citations

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12 Dimensions

Readers on

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39 Mendeley
Title
Genomic prediction of celiac disease targeting HLA-positive individuals
Published in
Genome Medicine, July 2015
DOI 10.1186/s13073-015-0196-5
Pubmed ID
Authors

Gad Abraham, Alexia Rohmer, Jason A. Tye-Din, Michael Inouye

Abstract

Genomic prediction aims to leverage genome-wide genetic data towards better disease diagnostics and risk scores. We have previously published a genomic risk score (GRS) for celiac disease (CD), a common and highly heritable autoimmune disease, which differentiates between CD cases and population-based controls at a clinically-relevant predictive level, improving upon other gene-based approaches. HLA risk haplotypes, particularly HLA-DQ2.5, are necessary but not sufficient for CD, with at least one HLA risk haplotype present in up to half of most Caucasian populations. Here, we assess a genomic prediction strategy that specifically targets this common genetic susceptibility subtype, utilizing a supervised learning procedure for CD that leverages known HLA-DQ2.5 risk. Using L1/L2-regularized support-vector machines trained on large European case-control datasets, we constructed novel CD GRSs specific to individuals with HLA-DQ2.5 risk haplotypes (GRS-DQ2.5) and compared them with the predictive power of the existing CD GRS (GRS14) as well as two haplotype-based approaches, externally validating the results in a North American case-control study. Consistent with previous observations, both the existing GRS14 and the GRS-DQ2.5 had better predictive performance than the HLA haplotype approaches. GRS-DQ2.5 models, based on directly genotyped or imputed markers, achieved similar levels of predictive performance (AUC = 0.718-0.73), which were substantially higher than those obtained from the DQ2.5 zygosity alone (AUC = 0.558), the HLA risk haplotype method (AUC = 0.634), or the generic GRS14 (AUC = 0.679). In a screening model of at-risk individuals, the GRS-DQ2.5 lowered the number of unnecessary follow-up tests for CD across most sensitivity levels. Relative to a baseline implicating all DQ2.5-positive individuals for follow-up, the GRS-DQ2.5 resulted in a net saving of 2.2 unnecessary follow-up tests for each justified test while still capturing 90 % of DQ2.5-positive CD cases. Genomic risk scores for CD that target genetically at-risk sub-groups improve predictive performance beyond traditional approaches and may represent a useful strategy for prioritizing individuals at increased risk of disease, thus potentially reducing unnecessary follow-up diagnostic tests.

Twitter Demographics

The data shown below were collected from the profiles of 29 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 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Denmark 1 3%
Unknown 38 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 21%
Researcher 6 15%
Student > Bachelor 5 13%
Student > Doctoral Student 5 13%
Other 4 10%
Other 11 28%
Readers by discipline Count As %
Agricultural and Biological Sciences 12 31%
Medicine and Dentistry 9 23%
Biochemistry, Genetics and Molecular Biology 7 18%
Unspecified 4 10%
Computer Science 3 8%
Other 4 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 16. 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 March 2019.
All research outputs
#979,434
of 13,477,663 outputs
Outputs from Genome Medicine
#251
of 957 outputs
Outputs of similar age
#21,284
of 230,149 outputs
Outputs of similar age from Genome Medicine
#3
of 8 outputs
Altmetric has tracked 13,477,663 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 957 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 23.6. This one has gotten more attention than average, scoring higher than 73% 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 230,149 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 90% of its contemporaries.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 5 of them.