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Attention Score in Context
| Title |
Penalized multivariate linear mixed model for longitudinal genome-wide association studies
|
|---|---|
| Published in |
BMC Proceedings, June 2014
|
| DOI | 10.1186/1753-6561-8-s1-s73 |
| Pubmed ID | |
| Authors |
Jin Liu, Jian Huang, Shuangge Ma |
| Abstract |
We consider analysis of Genetic Analysis Workshop 18 data, which involves multiple longitudinal traits and dense genome-wide single-nucleotide polymorphism (SNP) markers. We use a multivariate linear mixed model to account for the covariance of random effects and multivariate residuals. We divide the SNPs into groups according to the genes they belong to and score them using weighted sum statistics. We propose a penalized approach for genetic variant selection at the gene level. The overall modeling and penalized selection method is referred to as the penalized multivariate linear mixed model. Cross-validation is used for tuning parameter selection. A resampling approach is adopted to evaluate the relative stability of the identified genes. Application to the Genetic Analysis Workshop 18 data shows that the proposed approach can effectively select markers associated with phenotypes at gene level. |
X Demographics
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 15 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 5 | 33% |
| Researcher | 3 | 20% |
| Student > Master | 3 | 20% |
| Lecturer | 2 | 13% |
| Student > Doctoral Student | 1 | 7% |
| Other | 0 | 0% |
| Unknown | 1 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Mathematics | 5 | 33% |
| Agricultural and Biological Sciences | 4 | 27% |
| Medicine and Dentistry | 2 | 13% |
| Computer Science | 1 | 7% |
| Biochemistry, Genetics and Molecular Biology | 1 | 7% |
| Other | 0 | 0% |
| Unknown | 2 | 13% |
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 07 January 2015.
All research outputs
#18,388,295
of 22,776,824 outputs
Outputs from BMC Proceedings
#265
of 374 outputs
Outputs of similar age
#163,828
of 228,210 outputs
Outputs of similar age from BMC Proceedings
#9
of 21 outputs
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