Statistical Approaches to Testing and Estimating the Number of
Functional Variants in Complex Disease

Jurg Ott

Rockefeller University, New York ; Institute of Psychology, CAS, Beijing

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. 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. 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.

Our partition test also furnishes an estimate of the number of functional SNPs underlying disease and can be highly significant while single-locus tests are far from significant. Relations between this new test and the concept of false discovery rate (FDR) will be discussed.