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Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics$
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Christine Sinoquet and Raphaël Mourad

Print publication date: 2014

Print ISBN-13: 9780198709022

Published to Oxford Scholarship Online: December 2014

DOI: 10.1093/acprof:oso/9780198709022.001.0001

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Structural Equation Models for Studying Causal Phenotype Networks in Quantitative Genetics

Structural Equation Models for Studying Causal Phenotype Networks in Quantitative Genetics

Chapter:
(p.196) Chapter 8 Structural Equation Models for Studying Causal Phenotype Networks in Quantitative Genetics
Source:
Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics
Author(s):

Guilherme J. M. Rosa

Bruno D. Valente

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780198709022.003.0008

Phenotypic traits may exert causal effects between them. For example, high yield in agricultural species may increase the liability to certain diseases and, conversely, the incidence of a disease may affect yield negatively. Likewise, the transcriptome may be a function of the reproductive status or developmental stage in plants and animals, which may depend on other physiological variables as well. Knowledge of phenotype networks describing such interrelationships can be used to predict the behavior of complex systems, e.g., biological pathways underlying complex traits such as diseases, growth, and reproduction. This chapter reviews the application of structural equation models and related techniques to study causal relationships among phenotypic traits in quantitative genetics. It is discussed how genetic factors can confound the search for causal associations, as well as how pedigree and genomic information can be used to control for such confounding effects and to aid causal inference.

Keywords:   structural equation models, quantitative genetics, causal phenotype

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