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Integrating rare genetic variants into pharmacogenetic drug response predictions

Overview of attention for article published in Human Genomics, May 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#29 of 344)
  • High Attention Score compared to outputs of the same age (87th percentile)

Mentioned by

news
1 news outlet
blogs
1 blog
twitter
5 tweeters
reddit
1 Redditor

Citations

dimensions_citation
73 Dimensions

Readers on

mendeley
92 Mendeley
citeulike
1 CiteULike
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Title
Integrating rare genetic variants into pharmacogenetic drug response predictions
Published in
Human Genomics, May 2018
DOI 10.1186/s40246-018-0157-3
Pubmed ID
Authors

Magnus Ingelman-Sundberg, Souren Mkrtchian, Yitian Zhou, Volker M. Lauschke

Abstract

Variability in genes implicated in drug pharmacokinetics or drug response can modulate treatment efficacy or predispose to adverse drug reactions. Besides common genetic polymorphisms, recent sequencing projects revealed a plethora of rare genetic variants in genes encoding proteins involved in drug metabolism, transport, and response. To understand the global importance of rare pharmacogenetic gene variants, we mapped the variability in 208 pharmacogenes by analyzing exome sequencing data from 60,706 unrelated individuals and estimated the importance of rare and common genetic variants using a computational prediction framework optimized for pharmacogenetic assessments. Our analyses reveal that rare pharmacogenetic variants were strongly enriched in mutations predicted to cause functional alterations. For more than half of the pharmacogenes, rare variants account for the entire genetic variability. Each individual harbored on average a total of 40.6 putatively functional variants, rare variants accounting for 10.8% of these. Overall, the contribution of rare variants was found to be highly gene- and drug-specific. Using warfarin, simvastatin, voriconazole, olanzapine, and irinotecan as examples, we conclude that rare genetic variants likely account for a substantial part of the unexplained inter-individual differences in drug metabolism phenotypes. Combined, our data reveal high gene and drug specificity in the contributions of rare variants. We provide a proof-of-concept on how this information can be utilized to pinpoint genes for which sequencing-based genotyping can add important information to predict drug response, which provides useful information for the design of clinical trials in drug development and the personalization of pharmacological treatment.

Twitter Demographics

The data shown below were collected from the profiles of 5 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 92 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 92 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 19 21%
Student > Ph. D. Student 14 15%
Student > Master 14 15%
Other 8 9%
Student > Bachelor 7 8%
Other 14 15%
Unknown 16 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 29 32%
Pharmacology, Toxicology and Pharmaceutical Science 13 14%
Medicine and Dentistry 11 12%
Agricultural and Biological Sciences 8 9%
Chemistry 2 2%
Other 9 10%
Unknown 20 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 17. 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 11 March 2020.
All research outputs
#1,334,722
of 17,368,632 outputs
Outputs from Human Genomics
#29
of 344 outputs
Outputs of similar age
#36,232
of 288,072 outputs
Outputs of similar age from Human Genomics
#1
of 1 outputs
Altmetric has tracked 17,368,632 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 344 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.8. This one has done particularly well, scoring higher than 91% 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 288,072 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 87% of its contemporaries.
We're also able to compare this research output to 1 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