Gaussian covariance graph models accounting for correlated marker effects in genome-wide prediction

2017 ◽  
Vol 134 (5) ◽  
pp. 412-421
Author(s):  
C.A. Martínez ◽  
K. Khare ◽  
S. Rahman ◽  
M.A. Elzo
Keyword(s):  
Graph Models ◽  
Genome Wide ◽  
Covariance Graph

scholarly journals Gaussian Covariance Faithful Markov Trees

2011 ◽  
Vol 2011 ◽  
pp. 1-10 ◽  
Author(s):  
Dhafer Malouche ◽  
Bala Rajaratnam
Keyword(s):  
Probability Distribution ◽  
Graphical Models ◽  
Conditional Independence ◽  
High Dimensional ◽  
Important Class ◽  
Graph Models ◽  
Selection Procedures ◽  
Gaussian Distributions ◽  
Discrete Object ◽  
Covariance Graph

Graphical models are useful for characterizing conditional and marginal independence structures in high-dimensional distributions. An important class of graphical models is covariance graph models, where the nodes of a graph represent different components of a random vector, and the absence of an edge between any pair of variables implies marginal independence. Covariance graph models also represent more complex conditional independence relationships between subsets of variables. When the covariance graph captures or reflects all the conditional independence statements present in the probability distribution, the latter is said to befaithfulto its covariance graph—though in general this is not guaranteed. Faithfulness however is crucial, for instance, in model selection procedures that proceed by testing conditional independences. Hence, an analysis of the faithfulness assumption is important in understanding the ability of the graph, a discrete object, to fully capture the salient features of the probability distribution it aims to describe. In this paper, we demonstrate that multivariate Gaussian distributions that have trees as covariance graphs are necessarily faithful.

scholarly journals Wishart distributions for decomposable covariance graph models

2011 ◽  
Vol 39 (1) ◽  
pp. 514-555 ◽  
Author(s):  
Kshitij Khare ◽  
Bala Rajaratnam
Keyword(s):  
Graph Models ◽  
Wishart Distributions ◽  
Covariance Graph

Modeling correlated marker effects in genome-wide prediction via Gaussian concentration graph models

2018 ◽  
Vol 437 ◽  
pp. 67-78 ◽  
Author(s):  
Carlos Alberto Martínez ◽  
Kshitij Khare ◽  
Syed Rahman ◽  
Mauricio A. Elzo
Keyword(s):  
Graph Models ◽  
Genome Wide

Genome-wide screen in yeast to identify modulators of mutant SOD1 toxicity

2008 ◽  
Vol 35 (S 01) ◽  
Author(s):  
D Ubmann ◽  
B Göricke ◽  
L Fichtner ◽  
I Panou ◽  
G.H Braus ◽  
...  
Keyword(s):  
Mutant Sod1 ◽  
Genome Wide

Genome wide association study on memory in schizophrenia patients and unaffected probands

2009 ◽  
Vol 42 (05) ◽  
Author(s):  
B Konte ◽  
I Giegling ◽  
AM Hartmann ◽  
H Konnerth ◽  
P Muglia ◽  
...  
Keyword(s):  
Association Study ◽  
Genome Wide Association Study ◽  
Genome Wide Association ◽  
Genome Wide
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