Identifying genetically driven clinical phenotypes using linear mixed models
Use of general linear mixed models (GLMMs) in genetic variance analysis can quantify the relative contribution of additive effects from genetic variation on a given trait. Here, Jonathan Mosley and colleagues apply GLMM in a phenome-wide analysis and show that genetic variations in the HLA region ar...
Guardado en:
| Autores principales: | , , , , , , , , , , , , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2016
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/5401bbf278ce480b9e71837f9f3eca2c |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| Sumario: | Use of general linear mixed models (GLMMs) in genetic variance analysis can quantify the relative contribution of additive effects from genetic variation on a given trait. Here, Jonathan Mosley and colleagues apply GLMM in a phenome-wide analysis and show that genetic variations in the HLA region are associated with 44 phenotypes, 5 phenotypes which were not previously reported in GWASes. |
|---|