A complementary approach is to reduce the computation within each replicate. Lin[44] considered score statistics from regression models, showing that it is sufficient to multiply the score contributions of each subject by a normal random deviate to generate a realisation from the null distribution. Alternatively, Seaman and Müller-Mysock[45] suggest sampling directly from the multivariate distribution for all the genes. The distribution can be estimated by considering the score test from a regression model that includes all the genes as predictors. This estimation may be difficult when the number of genes exceeds the number of subjects for which the procedure may need to be applied piecewise to subsets of genes. The approach of Lin also requires the sample size to exceed the number of genes, but preliminary results suggest that it would be more robust than that of Seaman and Müller-Mysock when applied across the whole genome [44]. Both of these approaches require the analysis to be expressed as a score statistic from a regression model, which can be done in most situations but may require additional work by the user.
