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Bayesian structural equation models for inferring relationships between phenotypes: a review of methodology, identifiability, and applications
Author(s) -
Wu XiaoLin,
Heringstad Bjørg,
Gianola Daniel
Publication year - 2010
Publication title -
journal of animal breeding and genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.689
H-Index - 51
eISSN - 1439-0388
pISSN - 0931-2668
DOI - 10.1111/j.1439-0388.2009.00835.x
Subject(s) - identifiability , structural equation modeling , bayesian probability , computer science , linear model , statistical model , econometrics , computational biology , biology , mathematics , machine learning , artificial intelligence
Structural equation models provide a general statistical modelling technique for estimating and testing relationships among variables. Such relationships are often not revealed by standard linear models, but are of importance for understanding mechanisms underlying e.g., production‐related diseases, such as mastitis. This paper gives a review of Bayesian structural equation models concerning methodology and identifiability, focused on animal breeding and genetics modelling. Applications of this type of methods in animal breeding are also reviewed critically, with discussion on advantages and disadvantages of these approaches.