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...
Enregistré dans:
Auteurs principaux: | Jonathan D. Mosley, John S. Witte, Emma K. Larkin, Lisa Bastarache, Christian M. Shaffer, Jason H. Karnes, C. Michael Stein, Elizabeth Phillips, Scott J. Hebbring, Murray H. Brilliant, John Mayer, Zhan Ye, Dan M. Roden, Joshua C. Denny |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Nature Portfolio
2016
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/5401bbf278ce480b9e71837f9f3eca2c |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
THE MODES OF POSTERIOR DISTRIBUTIONS FOR MIXED LINEAR MODELS
par: CARRIQUIRY,ALICIA L, et autres
Publié: (2007) -
Topologically driven linear magnetoresistance in helimagnetic FeP
par: D. J. Campbell, et autres
Publié: (2021) -
A power approximation for the Kenward and Roger Wald test in the linear mixed model.
par: Sarah M Kreidler, et autres
Publié: (2021) -
Deep-ocean mixing driven by small-scale internal tides
par: Clément Vic, et autres
Publié: (2019) -
A study paradigm integrating prospective epidemiologic cohorts and electronic health records to identify disease biomarkers
par: Jonathan D. Mosley, et autres
Publié: (2018)