An alternative approach, which we take here, is to exploit the fact that the null distributions of the MOCS and SOCS statistics depend solely on the pairwise correlation matrix of the contributing genotypes. In the absence of the original genotypes, this correlation matrix can still be estimated from ethnicity-matched, publicly available genotypic data, as has been proposed by us and others for conditional multi-SNP analysis of GWAS results[16,17]. This approach has been implemented in the Versatile Gene-based Association Study (VEGAS) software and yields results close to those from phenotype label permutation[11]. However, while VEGAS is faster than estimation via phenotype label permutation, it still relies on a Monte Carlo method for estimating the p-values. This limits its efficiency for highly significant gene scores.
