↓ Skip to main content

Integrating rare genetic variants into pharmacogenetic drug response predictions

Overview of attention for article published in Human Genomics, May 2018
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#43 of 470)
  • High Attention Score compared to outputs of the same age (87th percentile)
  • High Attention Score compared to outputs of the same age and source (81st percentile)

Mentioned by

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

Citations

dimensions_citation
132 Dimensions

Readers on

mendeley
141 Mendeley
citeulike
1 CiteULike
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
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 141 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 141 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 26 18%
Student > Ph. D. Student 24 17%
Student > Master 19 13%
Student > Bachelor 16 11%
Student > Postgraduate 9 6%
Other 22 16%
Unknown 25 18%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 45 32%
Pharmacology, Toxicology and Pharmaceutical Science 21 15%
Medicine and Dentistry 15 11%
Agricultural and Biological Sciences 9 6%
Neuroscience 3 2%
Other 16 11%
Unknown 32 23%

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
#1,844,856
of 23,072,295 outputs
Outputs from Human Genomics
#43
of 470 outputs
Outputs of similar age
#41,689
of 330,746 outputs
Outputs of similar age from Human Genomics
#3
of 16 outputs
Altmetric has tracked 23,072,295 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 470 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.9. This one has done particularly well, scoring higher than 90% 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 330,746 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 16 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.