Causal graphical models in systems genetics: a unified framework for joint inference of causal network and genetic architecture for correlated phenotypes

DOI10.1214/09-AOAS288zbMath1189.62172arXiv1010.1402WikidataQ34473049 ScholiaQ34473049MaRDI QIDQ977638

Elias Chaibub Neto, Mark P. Keller, Brian S. Yandell, Alan D. Attie

Publication date: 23 June 2010

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1010.1402

zbMATH Keywords

Markov chain Monte CarloQTL mappingcausal graphical modelshomogeneous conditional Gaussian regression modelsjoint inference of phenotype network and genetic architecturesystems genetics

Mathematics Subject Classification ID

Related Items (4)

Learning directed acyclic graphs by determination of candidate causes for discrete variables ⋮ Sparse Estimation of Conditional Graphical Models With Application to Gene Networks ⋮ A sparse conditional Gaussian graphical model for analysis of genetical genomics data ⋮ Causal graphical models in systems genetics: a unified framework for joint inference of causal network and genetic architecture for correlated phenotypes

Uses Software

• TETRAD
• R/qtl

Cites Work

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• The geometry of proper scoring rules
• Probabilistic modelling in bioinformatics and medical informatics.
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• Causation, prediction, and search
• Bayesian model averaging: A tutorial. (with comments and a rejoinder).
• Learning Bayesian networks: The combination of knowledge and statistical data
• Model Selection and Accounting for Model Uncertainty in Graphical Models Using Occam's Window
• The Method of Path Coefficients
• Bayesian Graphical Models for Discrete Data

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