  Methods to effectively detect multi-locus genetic association are becoming
increasingly relevant in the genetic dissection of complex trait in humans.
Current approaches typically consider a limited number of hypotheses, most of
which are related to the effect of a single locus or of a relatively small
number of neighboring loci on a chromosomal region. We have developed a novel
method that is specifically designed to detect genetic association involving
multiple disease-susceptibility loci, possibly on different chromosomes. Our
approach relies on the efficient discovery of patterns comprising spatially
unrestricted polymorphic markers and on the use of appropriate test statistics
to evaluate pattern-trait association. Power calculations using multi-locus
disease models demonstrate significant gain of power by using this method in
detecting multi-locus genetic association when compared to a standard single
marker analysis method. When analyzing a Schizophrenia dataset, we confirmed a
previously identified gene-gene interaction. In addition, a less conspicuous
association involving different markers on the same two genes was also
identified, implicating genetic heterogeneity.
