Challenging false discovery rate: a partition test based on p values in human case-control association studies.

BACKGROUND/AIMS We consider the situation that multiple genetic variants are underlying a heritable trait and assume that each contributes to the trait only to a small degree. The aim is to develop a statistical test for disease association of these multiple variants. METHODS We expect that p values resulting from a genome-wide case-control association analysis will fall into two classes: those reflecting true association and those occurring randomly in the interval from 0 to 1. We develop a partition test to find the set of smallest p values deviating most from the number of p values expected under randomness. RESULTS Power calculations demonstrate the superiority of our partition test over conventional SNP-by-SNP analyses. Applications of the partition test to six published datasets show that our test is particularly suitable when multiple SNPs appear to contribute to a trait, and furnished more significant results than a well-known procedure to estimate the false discovery rate. CONCLUSIONS Our partition test also furnishes an estimate of the number of functional SNPs underlying disease and can be highly significant, while single-locus tests may be far from significant.