Complex diseases often display geographic distribution patterns.
Therefore, the integration of genetic and environmental factors using
geographic information systems (GIS) and specific statistical analyses
that consider the spatial dimension of data greatly assist in the research
of their gene-environment interactions (GxE). The objectives of the
present work were to assess the application of a geostatistical
interpolation technique (kriging) in the study of complex diseases with
a distinct heterogeneous geographic distribution and to test its
performance as an alternative to conventional genetic imputation
methods. Using multiple sclerosis as a case study, kriging demonstrated
to be a flexible and valuable tool for integrating information from
various sources and at a different spatial resolution into a model that
easily allowed to visualize its heterogeneous geographic distribution in
Europe and to explore the intertwined interactions between several
known genetic and environmental risk factors. Even though the
performance of kriging did not surpass the results obtained with current
imputation techniques, this pilot study revealed a worse performance of
the latter for rare variants in chromosomal regions with a low density
of markers
