Kivonat:
Despite the rapid evolution of measurement technologies in
biomedicine and genetics, most of the recent studies aiming to
explore the genetic background of multifactorial diseases were
only moderately successful. One of the causes of this phenomenon
is that the bottleneck of genetic research is no longer the
measurement process related to various laboratory technologies,
but rather the analysis and interpretation of results. The
commonly applied univariate methods are inadequate for exploring
complex dependency patterns of multifactorial diseases which
includes nearly all common diseases, such as depression,
hypertension, and asthma. A comprehensive investigation requires
multivariate modeling methods that enable the analysis of
interactions between factors, and allow a more detailed
interpretation of studies measuring complex phenotype
descriptors. In this paper we discuss various aspects of
multivariate modeling through a case study analyzing the effect
of the single nucleotide polymorphism rs6295 in the HTR1A gene
on depression and impulsivity. We overview basic concepts
related to multivariate modeling and compare the properties of
two investigated modeling techniques: Structural Equation
Modeling and Bayesian network based learning algorithms. The
resulting models demonstrate the advantages of the Bayesian
approach in terms of model properties and effect size as it
allows coherent handling of the weakly significant effect of
rs6295. Results also confirm the mediating role of impulsivity
between the SNP rs6295 of HTR1A and depression.
