High-volume "-omics" technologies and the future of molecular epidemiology.

A s David Hunter puts it so aptly in his commentary,' Pandora's box has been opened: Agenomics, transcriptomics, metabolomics, proteomics, interactomics, methylom ics .... It seems like every day a new genome-wide technology is introduced. Is this to become "a diagnostic dream and an analytical nightmare?"2 What is the real value added that can be expected for molecular epidemiology? Much of the use of "-omics" technologies to date has been exploratory in nature.3 Analyses are typically performed in 2 stages using a training sample to search for patterns in a high-dimensional data followed by a validation sample to test the predictions of the model. A wide range of data-mining tools have been developed for this purpose, including hierarchical clustering, classification and regression trees, self-organizing maps, random forests, multifactor dimension reduction, and neural nets, to name a few (see Hoh and Ott4 for a partial review). I would like to explore a somewhat different paradigm: using these "-omics" technologies to inform hypothesis-directed pathway-based approaches to molecular epi demiology and to help direct genome-wide exploratory analyses into more promising directions. In either case, the basic idea entails using "-omics" data to inform the estimation of the parameters of a model for the epidemiologic data at hand.5 For example, in a candidate gene association study of a highly polymorphic gene like ATM, one might include in silico measures of evolutionary conservation and predicted effects on protein conformation or in vitro functional assays of the effects of each polymorphism6 as prior covariates in a hierarchical model for the relative risk of each variant.7'8 On a broader scale, one might use readily available genomic annotation data to prioritize the signals from an initial genome-wide association scan so as to improve the selection of markers to carry forward for testing in the later stages of a multistage study.9"10 If the multiple comparisons problem in a genome-wide association scan is not daunting enough, consider a study of the genetics of gene expression involving perhaps tens of thousands of expressed genes examined in relation to hundreds of thousands of single nucleotide polymorphism (SNPs),1" or all possible gene-gene interactions,12 or all possible haplo 13 type associations m a genome-wide SNP scan. Here, the opportunity to exploit bioinfor matics resources to focus the search has enormous potential that has so far not been tapped. In other circumstances, rather than relying on external bioinformatics databases, an investigator might want to apply some of these "-omics" tools directly to the samples from an epidemiologic case-control or cohort study to better characterize intermediate path ways. 14 Here, one might think of the "-omics" data as providing the "missing link" among exposures, genes, and disease. The cost of these assays, difficulties in getting subject participation, or the need for special tissue preparations may preclude obtaining such data on all subjects in a large-scale study. Furthermore, the problem of reverse causation in a case-control design (the biomarker being affected by the disease or its treatment rather than the other way around) could make any simple case-control comparison of the biomarker meaningless. This suggests the need for some form of multistage sampling15 combined with modeling of the latent disease process.8 For example, one could use data