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Mendeley readers
Attention Score in Context
| Title |
Family-based association analysis: a fast and efficient method of multivariate association analysis with multiple variants
|
|---|---|
| Published in |
BMC Bioinformatics, February 2015
|
| DOI | 10.1186/s12859-015-0484-5 |
| Pubmed ID | |
| Authors |
Sungho Won, Wonji Kim, Sungyoung Lee, Young Lee, Joohon Sung, Taesung Park |
| Abstract |
Many disease phenotypes are outcomes of the complicated interplay between multiple genes, and multiple phenotypes are affected by a single or multiple genotypes. Therefore, joint analysis of multiple phenotypes and multiple markers has been considered as an efficient strategy for genome-wide association analysis, and in this work we propose an omnibus family-based association test for the joint analysis of multiple genotypes and multiple phenotypes. The proposed test can be applied for both quantitative and dichotomous phenotypes, and it is robust under the presence of population substructure, as long as large-scale genomic data is available. Using simulated data, we showed that our method is statistically more efficient than the existing methods, and the practical relevance is illustrated by application of the approach to obesity-related phenotypes. The proposed method may be more statistically efficient than the existing methods. The application was developed in C++ and is available at the following URL: http://healthstat.snu.ac.kr/software/mfqls/ . |
X Demographics
The data shown below were collected from the profiles of 3 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 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Colombia | 1 | 4% |
| Unknown | 24 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 6 | 24% |
| Student > Ph. D. Student | 6 | 24% |
| Researcher | 5 | 20% |
| Student > Doctoral Student | 3 | 12% |
| Professor > Associate Professor | 2 | 8% |
| Other | 3 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 8 | 32% |
| Biochemistry, Genetics and Molecular Biology | 5 | 20% |
| Medicine and Dentistry | 5 | 20% |
| Mathematics | 1 | 4% |
| Computer Science | 1 | 4% |
| Other | 3 | 12% |
| Unknown | 2 | 8% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 13 March 2015.
All research outputs
#16,247,214
of 23,940,793 outputs
Outputs from BMC Bioinformatics
#5,509
of 7,489 outputs
Outputs of similar age
#237,653
of 392,305 outputs
Outputs of similar age from BMC Bioinformatics
#93
of 133 outputs
Altmetric has tracked 23,940,793 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,489 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 18th percentile – i.e., 18% of its peers scored the same or lower than it.
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We're also able to compare this research output to 133 others from the same source and published within six weeks on either side of this one. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.