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Attention Score in Context
| Title |
A re-formulation of generalized linear mixed models to fit family data in genetic association studies
|
|---|---|
| Published in |
Frontiers in Genetics, March 2015
|
| DOI | 10.3389/fgene.2015.00120 |
| Pubmed ID | |
| Authors |
Tao Wang, Peng He, Kwang Woo Ahn, Xujing Wang, Soumitra Ghosh, Purushottam Laud |
| Abstract |
The generalized linear mixed model (GLMM) is a useful tool for modeling genetic correlation among family data in genetic association studies. However, when dealing with families of varied sizes and diverse genetic relatedness, the GLMM has a special correlation structure which often makes it difficult to be specified using standard statistical software. In this study, we propose a Cholesky decomposition based re-formulation of the GLMM so that the re-formulated GLMM can be specified conveniently via "proc nlmixed" and "proc glimmix" in SAS, or OpenBUGS via R package BRugs. Performances of these procedures in fitting the re-formulated GLMM are examined through simulation studies. We also apply this re-formulated GLMM to analyze a real data set from Type 1 Diabetes Genetics Consortium (T1DGC). |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Switzerland | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 3% |
| Unknown | 30 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 9 | 29% |
| Student > Master | 4 | 13% |
| Other | 3 | 10% |
| Student > Bachelor | 2 | 6% |
| Professor | 2 | 6% |
| Other | 6 | 19% |
| Unknown | 5 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 9 | 29% |
| Biochemistry, Genetics and Molecular Biology | 6 | 19% |
| Mathematics | 2 | 6% |
| Unspecified | 1 | 3% |
| Environmental Science | 1 | 3% |
| Other | 5 | 16% |
| Unknown | 7 | 23% |
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 April 2015.
All research outputs
#14,220,809
of 22,797,621 outputs
Outputs from Frontiers in Genetics
#3,921
of 11,761 outputs
Outputs of similar age
#139,765
of 264,714 outputs
Outputs of similar age from Frontiers in Genetics
#102
of 141 outputs
Altmetric has tracked 22,797,621 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,761 research outputs from this source. They receive a mean Attention Score of 3.7. This one has gotten more attention than average, scoring higher than 62% 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 264,714 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 141 others from the same source and published within six weeks on either side of this one. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.