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eMERGE Phenome-Wide Association Study (PheWAS) identifies clinical associations and pleiotropy for stop-gain variants

Overview of attention for article published in BMC Medical Genomics, January 2016
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  • Above-average Attention Score compared to outputs of the same age (53rd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (55th percentile)

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Title
eMERGE Phenome-Wide Association Study (PheWAS) identifies clinical associations and pleiotropy for stop-gain variants
Published in
BMC Medical Genomics, January 2016
DOI 10.1186/s12920-016-0191-8
Pubmed ID
Authors

Verma, Anurag, Verma, Shefali S, Pendergrass, Sarah A, Crawford, Dana C, Crosslin, David R, Kuivaniemi, Helena, Bush, William S, Bradford, Yuki, Kullo, Iftikhar, Bielinski, Suzette J, Li, Rongling, Denny, Joshua C, Peissig, Peggy, Hebbring, Scott, De Andrade, Mariza, Ritchie, Marylyn D, Tromp, Gerard, Anurag Verma, Shefali S. Verma, Sarah A. Pendergrass, Dana C. Crawford, David R. Crosslin, Helena Kuivaniemi, William S. Bush, Yuki Bradford, Iftikhar Kullo, Suzette J. Bielinski, Rongling Li, Joshua C. Denny, Peggy Peissig, Scott Hebbring, Mariza De Andrade, Marylyn D. Ritchie, Gerard Tromp

Abstract

We explored premature stop-gain variants to test the hypothesis that variants, which are likely to have a consequence on protein structure and function, will reveal important insights with respect to the phenotypes associated with them. We performed a phenome-wide association study (PheWAS) exploring the association between a selected list of functional stop-gain genetic variants (variation resulting in truncated proteins or in nonsense-mediated decay) and an extensive group of diagnoses to identify novel associations and uncover potential pleiotropy. In this study, we selected 25 stop-gain variants: 5 stop-gain variants with previously reported phenotypic associations, and a set of 20 putative stop-gain variants identified using dbSNP. For the PheWAS, we used data from the electronic MEdical Records and GEnomics (eMERGE) Network across 9 sites with a total of 41,057 unrelated patients. We divided all these samples into two datasets by equal proportion of eMERGE site, sex, race, and genotyping platform. We calculated single effect associations between these 25 stop-gain variants and ICD-9 defined case-control diagnoses. We also performed stratified analyses for samples of European and African ancestry. Associations were adjusted for sex, site, genotyping platform and the first three principal components to account for global ancestry. We identified previously known associations, such as variants in LPL associated with hyperglyceridemia indicating that our approach was robust. We also found a total of three significant associations with p < 0.01 in both datasets, with the most significant replicating result being LPL SNP rs328 and ICD-9 code 272.1 "Disorder of Lipoid metabolism" (pdiscovery = 2.59x10-6, preplicating = 2.7x10-4). The other two significant replicated associations identified by this study are: variant rs1137617 in KCNH2 gene associated with ICD-9 code category 244 "Acquired Hypothyroidism" (pdiscovery = 5.31x103, preplicating = 1.15x10-3) and variant rs12060879 in DPT gene associated with ICD-9 code category 996 "Complications peculiar to certain specified procedures" (pdiscovery = 8.65x103, preplicating = 4.16x10-3).  In conclusion, this PheWAS revealed novel associations of stop-gained variants with interesting phenotypes (ICD-9 codes) along with pleiotropic effects.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Finland 1 2%
United States 1 2%
Unknown 57 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 36%
Professor > Associate Professor 7 12%
Researcher 7 12%
Other 5 8%
Professor 3 5%
Other 11 19%
Unknown 5 8%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 16 27%
Medicine and Dentistry 16 27%
Agricultural and Biological Sciences 7 12%
Engineering 3 5%
Psychology 2 3%
Other 7 12%
Unknown 8 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 19 August 2016.
All research outputs
#3,864,662
of 8,247,986 outputs
Outputs from BMC Medical Genomics
#219
of 435 outputs
Outputs of similar age
#116,263
of 253,918 outputs
Outputs of similar age from BMC Medical Genomics
#13
of 29 outputs
Altmetric has tracked 8,247,986 research outputs across all sources so far. This one has received more attention than most of these and is in the 52nd percentile.
So far Altmetric has tracked 435 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one is in the 48th percentile – i.e., 48% 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 253,918 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 53% of its contemporaries.
We're also able to compare this research output to 29 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 55% of its contemporaries.