The promise of gene expression studies using microarray technology has inspired much new hope for finding complex diseases genes. It has become clear that complex diseases result from collective actions of many genetic and nongenetic factors. Therefore, genetic dissection of complex diseases should be carried out in a global context. The technology of gene expression microarray analysis (GEMA) can provide such global information on transcription activities of essentially all genes simultaneously. It is hoped that this promising technology can be applied to samples drawn from large-scale, well-defined genetic epidemiological studies and help us untangle the web of pathways leading to complex diseases. However, extremely noisy GEMA data pose serious challenges in terms of the statistical methodologies needed. Extensive work is needed in order to respond to the challenges before one can fully utilize the potential power provided by GEMA. We begin in this paper by identifying several statistical problems related to the application of GEMA to genetic epidemiological analysis, and consider study designs that might benefit from this promising new technology. While it is still too early to tell how much of the enormous potential of GEMA will be realized ultimately, its success will probably depend most critically on the ability of statistical genetics to rise to the challenge of mining information from a sea of noise.