Graphical chain models for the analysis of complex genetic diseases: an application to hypertension

A crucial task in modern genetic medicine is the understanding of
complex genetic diseases. The main complicating features are that a
combination of genetic and environmental risk factors is involved,
and the phenotype of interest may be complex. Traditional
statistical techniques based on lod-scores fail when the disease is
no longer monogenic and the underlying disease transmission model
is not defined. Different kinds of association tests have been
proved to be an appropriate and powerful statistical tool to detect
a candidate gene for a complex disorder. However, statistical
techniques able to investigate direct and indirect influences among
phenotypes, genotypes and environmental risk factors, are required
to analyse the association structure of complex diseases. In this
paper we propose graphical models as a natural tool to analyse the
multifactorial structure of complex genetic diseases. An
application of this model to primary hypertension data set is
illustrated.