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Challenges and opportunities in genome-wide environmental interaction (GWEI) studies

Overview of attention for article published in Human Genetics, July 2012
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  • Good Attention Score compared to outputs of the same age (65th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (56th percentile)

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Title
Challenges and opportunities in genome-wide environmental interaction (GWEI) studies
Published in
Human Genetics, July 2012
DOI 10.1007/s00439-012-1192-0
Pubmed ID
Authors

Hugues Aschard, Sharon Lutz, Bärbel Maus, Eric J. Duell, Tasha E. Fingerlin, Nilanjan Chatterjee, Peter Kraft, Kristel Van Steen

Abstract

The interest in performing gene-environment interaction studies has seen a significant increase with the increase of advanced molecular genetics techniques. Practically, it became possible to investigate the role of environmental factors in disease risk and hence to investigate their role as genetic effect modifiers. The understanding that genetics is important in the uptake and metabolism of toxic substances is an example of how genetic profiles can modify important environmental risk factors to disease. Several rationales exist to set up gene-environment interaction studies and the technical challenges related to these studies-when the number of environmental or genetic risk factors is relatively small-has been described before. In the post-genomic era, it is now possible to study thousands of genes and their interaction with the environment. This brings along a whole range of new challenges and opportunities. Despite a continuing effort in developing efficient methods and optimal bioinformatics infrastructures to deal with the available wealth of data, the challenge remains how to best present and analyze genome-wide environmental interaction (GWEI) studies involving multiple genetic and environmental factors. Since GWEIs are performed at the intersection of statistical genetics, bioinformatics and epidemiology, usually similar problems need to be dealt with as for genome-wide association gene-gene interaction studies. However, additional complexities need to be considered which are typical for large-scale epidemiological studies, but are also related to "joining" two heterogeneous types of data in explaining complex disease trait variation or for prediction purposes.

X Demographics

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The data shown below were collected from the profile of 1 X user 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 135 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 2%
Germany 2 1%
Netherlands 1 <1%
Italy 1 <1%
Singapore 1 <1%
United Kingdom 1 <1%
Japan 1 <1%
Spain 1 <1%
Unknown 124 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 44 33%
Researcher 28 21%
Student > Master 14 10%
Student > Bachelor 9 7%
Student > Doctoral Student 7 5%
Other 15 11%
Unknown 18 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 32 24%
Medicine and Dentistry 22 16%
Biochemistry, Genetics and Molecular Biology 15 11%
Psychology 10 7%
Computer Science 9 7%
Other 24 18%
Unknown 23 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 2015.
All research outputs
#8,577,479
of 25,837,817 outputs
Outputs from Human Genetics
#1,013
of 3,002 outputs
Outputs of similar age
#59,207
of 181,172 outputs
Outputs of similar age from Human Genetics
#9
of 23 outputs
Altmetric has tracked 25,837,817 research outputs across all sources so far. This one has received more attention than most of these and is in the 66th percentile.
So far Altmetric has tracked 3,002 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.1. This one has gotten more attention than average, scoring higher than 65% 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 181,172 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 65% of its contemporaries.
We're also able to compare this research output to 23 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 56% of its contemporaries.