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Integrating Evolutionary Game Theory into Mechanistic Genotype–Phenotype Mapping

Overview of attention for article published in Trends in Genetics, March 2016
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4 X users
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65 Mendeley
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Article details
Title
Integrating Evolutionary Game Theory into Mechanistic Genotype–Phenotype Mapping
Published in
Trends in Genetics, March 2016
DOI 10.1016/j.tig.2016.02.004
Pubmed ID
Authors
Abstract

Natural selection has shaped the evolution of organisms toward optimizing their structural and functional design. However, how this universal principle can enhance genotype-phenotype mapping of quantitative traits has remained unexplored. Here we show that the integration of this principle and functional mapping through evolutionary game theory gains new insight into the genetic architecture of complex traits. By viewing phenotype formation as an evolutionary system, we formulate mathematical equations to model the ecological mechanisms that drive the interaction and coordination of its constituent components toward population dynamics and stability. Functional mapping provides a procedure for estimating the genetic parameters that specify the dynamic relationship of competition and cooperation and predicting how genes mediate the evolution of this relationship during trait formation.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
United States 1 2%
Taiwan 1 2%
Chile 1 2%
Unknown 62 95%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 16 25%
Researcher 10 15%
Student > Master 7 11%
Student > Bachelor 6 9%
Professor 5 8%
Other 10 15%
Unknown 11 17%
Readers by discipline
Readers by discipline Count As %
Agricultural and Biological Sciences 24 37%
Biochemistry, Genetics and Molecular Biology 14 22%
Nursing and Health Professions 2 3%
Neuroscience 2 3%
Environmental Science 1 2%
Other 5 8%
Unknown 17 26%
Attention Score in Context

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 01 June 2016.
All research outputs
#22,067,008
of 33,503,639 outputs
Outputs from Trends in Genetics
#2,431
of 2,819 outputs
Outputs of similar age
#197,202
of 335,168 outputs
Outputs of similar age from Trends in Genetics
#14
of 19 outputs
Altmetric has tracked 33,503,639 research outputs across all sources so far. This one is in the 33rd percentile – i.e., 33% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,819 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.5. This one is in the 13th percentile – i.e., 13% 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 335,168 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 19 others from the same source and published within six weeks on either side of this one. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.