Identifying useful predictors from genome-wide SNP association studies

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Disease-genetic marker association studies, using genome-wide SNP genotyping technologies with up to a million markers, are becoming commonplace. Often, the stated goal of such studies is to develop a "genetic predictor" to identify those in the population who are at higher risk of developing disease. However, the statistical analyses of these studies have tended to comprise simple tests for association between disease and each marker in turn. Although any single disease-associated marker will have some predictive utility, this one-at-a-time focus is too simplistic for several reasons. Firstly, if a particular marker variant is rare in the population, it will have limited predictive capability, even if the associated risk or odds ratio is quite large. Secondly, when distinct genes or markers are highly correlated, the predictive capability of the combination cannot be calculated by examining the effects of each separately. Thirdly, the impact of some genes or markers may be through interactions with environmental exposures or interactions with other genes. Such possibilities also need to be considered, despite the concomitant increase in the number of statistical tests.