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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A Set-Based Mixed Effect Model for Gene-Environment Interaction and Its Application to Neuroimaging Phenotypes
|
|---|---|
| Published in |
Frontiers in Neuroscience, April 2017
|
| DOI | 10.3389/fnins.2017.00191 |
| Pubmed ID | |
| Authors |
Changqing Wang, Jianping Sun, Bryan Guillaume, Tian Ge, Derrek P. Hibar, Celia M. T. Greenwood, Anqi Qiu, the Alzheimer's Disease Neuroimaging Initiative |
| Abstract |
Imaging genetics is an emerging field for the investigation of neuro-mechanisms linked to genetic variation. Although imaging genetics has recently shown great promise in understanding biological mechanisms for brain development and psychiatric disorders, studying the link between genetic variants and neuroimaging phenotypes remains statistically challenging due to the high-dimensionality of both genetic and neuroimaging data. This becomes even more challenging when studying gene-environment interaction (G×E) on neuroimaging phenotypes. In this study, we proposed a set-based mixed effect model for gene-environment interaction (MixGE) on neuroimaging phenotypes, such as structural volumes and tensor-based morphometry (TBM). MixGE incorporates both fixed and random effects of G×E to investigate homogeneous and heterogeneous contributions of multiple genetic variants and their interaction with environmental risks to phenotypes. We discuss the construction of score statistics for the terms associated with fixed and random effects of G×E to avoid direct parameter estimation in the MixGE model, which would greatly increase computational cost. We also describe how the score statistics can be combined into a single significance value to increase statistical power. We evaluated MixGE using simulated and real Alzheimer's Disease Neuroimaging Initiative (ADNI) data, and showed statistical power superior to other burden and variance component methods. We then demonstrated the use of MixGE for exploring the voxelwise effect of G×E on TBM, made feasible by the computational efficiency of MixGE. Through this, we discovered a potential interaction effect of gene ABCA7 and cardiovascular risk on local volume change of the right superior parietal cortex, which warrants further investigation. |
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 25% |
| Switzerland | 1 | 25% |
| Unknown | 2 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 33 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 18% |
| Student > Master | 5 | 15% |
| Researcher | 5 | 15% |
| Student > Bachelor | 2 | 6% |
| Professor | 2 | 6% |
| Other | 4 | 12% |
| Unknown | 9 | 27% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 4 | 12% |
| Neuroscience | 4 | 12% |
| Mathematics | 2 | 6% |
| Engineering | 2 | 6% |
| Agricultural and Biological Sciences | 1 | 3% |
| Other | 7 | 21% |
| Unknown | 13 | 39% |
Attention Score in Context
This research output has an Altmetric Attention Score of 11. 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 28 April 2017.
All research outputs
#3,275,923
of 25,382,440 outputs
Outputs from Frontiers in Neuroscience
#2,424
of 11,542 outputs
Outputs of similar age
#57,425
of 324,628 outputs
Outputs of similar age from Frontiers in Neuroscience
#39
of 197 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,542 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. This one has done well, scoring higher than 78% 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 324,628 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 82% of its contemporaries.
We're also able to compare this research output to 197 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.