  It has been shown that a random-effects framework can be used to test the
association between a gene's expression level and the number of DNA copies of a
set of genes. This gene-set modelling framework was later applied to find
associations between mRNA expression and microRNA expression, by defining the
gene sets using target prediction information.
  Here, we extend the model introduced by Menezes et al (2009) to consider the
effect of not just copy number, but also of other molecular profiles such as
methylation changes and loss-of-heterozigosity (LOH), on gene expression
levels. We will consider again sets of measurements, to improve robustness of
results and increase the power to find associations. Our approach can be used
genome-wide to find associations, yields a test to help separate true
associations from noise and can include confounders.
  We apply our method to colon and to breast cancer samples, for which
genome-wide copy number, methylation and gene expression profiles are
available. Our findings include interesting gene expression-regulating
mechanisms, which may involve only one of copy number or methylation, or both
for the same samples. We even are able to find effects due to different
molecular mechanisms in different samples.
  Our method can equally well be applied to cases where other types of
molecular (high-dimensional) data are collected, such as LOH, SNP genotype and
microRNA expression data. Computationally efficient, it represents a flexible
and powerful tool to study associations between high-dimensional datasets. The
method is freely available via the SIM BioConductor package.
