The analysis of large exploratory studies creates new problems for methodology and interpretation. Primarily, there is the multiple testing problem, whereby the chance of an exceptional result increases with the number of tests performed, even when there is no true association. To alleviate this problem, two broad strategies have emerged: first, to devise more sensitive tests, so that the penalty for multiple testing is less severe; and, secondly, to propose different measures of experimental error for which the interpretation of multiple testing is less serious. Furthermore, genome-wide analysis creates problems of computational and cost efficiencies on account of the large volume of data to be generated and analysed.
