GWAS and microarray analyses both allow unbiased identification of candidate genes and pathways associated with cancer development. These two approaches each have advantages and drawbacks. By combining data from multiple expression studies, analyses of gene expressions have the statistical power to detect even small differences in gene expression between normal and tumor tissues. On the other hand, because genes in the human genome are involved in multiple interactions, modulation of the expression of a single gene may cause a “ripple effect” on multiple downstream targets, making it difficult to separate causal and induced changes in gene expression. This is unlikely to be an issue in GWASs. GWASs, however, are often statistically underpowered to detect SNPs with small effect size.
When we compared candidate genes for prostate cancer identified by GWAS with those identified by microarray, we noted a significant positive correlation between the GWAS and microarray –log(P)s. The correlation was small, with the Pearson rank correlation coefficient being only 0.04, but positive correlation between two ranks is expected to be driven by a relatively small number of causal genes. Not all causal genes will be detected by GWAS. Even if the gene is mechanistically linked to prostate tumorigenesis, it can be detected by GWAS only if it carries genetic variants that modulate its function. On the other hand, genes identified by microarray analysis are expected to be a mix of causal genes and the genes that are differentially expressed because of the ripple effect of the causal genes. This suggests that only a fraction of the genes significant in both analyses are causal genes.
We found that the top GWAS and differentially expressed candidates were enriched in cell adhesion genes. If we consider all known cell adhesion genes in the genome, only 74 genes or 10% of them were among the top differentially expressed genes. If the cell adhesion pathway is associated with prostate tumorigenesis, one can expect that other cell adhesion genes—those that did not make it to the top 1,649 genes—also will tend to be significantly positively associated. We found that the average GWAS-derived P value for the cell adhesion genes that failed to reach the top 1649 was lower than the average value for the GWAS genes (t test
0.001). A similar result was obtained for the P values derived from the analysis of the gene expression: the absolute Z score was higher among cell adhesion genes (excluding those among the top 1649 genes) than was the average Z score (t test
0.07 on the two-tailed test and P
0.03 on the one-tailed test). This suggests that cell adhesion function as a whole is associated with prostate tumorigenesis.
Both GWAS and microarray genes form functional clusters related to different aspects of cell adhesion, including cell adhesion itself, cell junction, extracellular matrix glycoproteins, fibronectin, actin cytoskeleton, and cell motility. Several other clusters also show a mechanistic association with cell adhesion. For example, cadherin uptake from the cell surface by endocytosis regulates the level of the free cadherins on the cell surface and therefore cell adhesion 
. Also, zinc finger proteins with the LIM domain are important for focal adhesion and cell adhesion to fibronectin 
. The modulation of the cell adhesion function seems not to be limited to any specific adhesion type but includes cadherins, integrins, and selectins as well as adhesion molecules associated with tight junctions.
The results of a number of studies suggested the involvement of the cell adhesion system in prostate cancer development. Cadherins play a role in regulating tumor cell proliferation through cyclins and cyclin-dependent kinases 
. Integrins are involved in different aspects of prostate tumorigenesis, including cell proliferation, cell motility, and apoptosis 
. Modulation of cell adhesion can play an important role in epithelial-to-mesenchymal transition that is believed to be a key step in malignant transformation 
. Also the results of a number of studies suggestd an involvement of cell adhesion in angiogenesis 
GWAS-identified genes are considered to be cancer susceptibility genes that are mainly associated with tumor initiation. We believe, however, that genes identified by GWAS are also likely to include genes important for tumor progression. Indeed, the detection of tumor is usually symptomatic: the tumor needs to reach a certain size to be detected. This suggests that genes involved in tumor progression will be among GWAS-detected candidate genes. Therefore, GWAS and gene expression analysis may target essentially the same set of genes, providing the theoretical basis for the joint analysis of GWAS and microarray data.
In summary, our analysis found a considerable overlap between prostate cancer genes identified by GWAS and those identified by global profiling of the gene expression. We identified cell adhesion as a biological function associated with prostate tumorigenesis. The results of this study also suggest that combining GWAS and microarray data might be a more effective approach than using just the analysis of the individual datasets, and can help to refine the identification of candidate genes and/or functions involved in tumor development.