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March 2010 Causal graphical models in systems genetics: A unified framework for joint inference of causal network and genetic architecture for correlated phenotypes
Elias Chaibub Neto, Mark P. Keller, Alan D. Attie, Brian S. Yandell
Ann. Appl. Stat. 4(1): 320-339 (March 2010). DOI: 10.1214/09-AOAS288

Abstract

Causal inference approaches in systems genetics exploit quantitative trait loci (QTL) genotypes to infer causal relationships among phenotypes. The genetic architecture of each phenotype may be complex, and poorly estimated genetic architectures may compromise the inference of causal relationships among phenotypes. Existing methods assume QTLs are known or inferred without regard to the phenotype network structure. In this paper we develop a QTL-driven phenotype network method (QTLnet) to jointly infer a causal phenotype network and associated genetic architecture for sets of correlated phenotypes. Randomization of alleles during meiosis and the unidirectional influence of genotype on phenotype allow the inference of QTLs causal to phenotypes. Causal relationships among phenotypes can be inferred using these QTL nodes, enabling us to distinguish among phenotype networks that would otherwise be distribution equivalent. We jointly model phenotypes and QTLs using homogeneous conditional Gaussian regression models, and we derive a graphical criterion for distribution equivalence. We validate the QTLnet approach in a simulation study. Finally, we illustrate with simulated data and a real example how QTLnet can be used to infer both direct and indirect effects of QTLs and phenotypes that co-map to a genomic region.

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Elias Chaibub Neto. Mark P. Keller. Alan D. Attie. Brian S. Yandell. "Causal graphical models in systems genetics: A unified framework for joint inference of causal network and genetic architecture for correlated phenotypes." Ann. Appl. Stat. 4 (1) 320 - 339, March 2010. https://doi.org/10.1214/09-AOAS288

Information

Published: March 2010
First available in Project Euclid: 11 May 2010

zbMATH: 1189.62172
MathSciNet: MR2758174
Digital Object Identifier: 10.1214/09-AOAS288

Rights: Copyright © 2010 Institute of Mathematical Statistics

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Vol.4 • No. 1 • March 2010
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