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Studying the effects of groups of Single Nucleotide Polymorphisms (SNPs), asin a gene, genetic pathway, or network, can provide novel insight into complexdiseases, above that which can be gleaned from studying SNPs individually.Common challenges in set-based genetic association testing include weak effectsizes, correlation between SNPs in a SNP-set, and scarcity of signals, withsingle-SNP effects often ranging from extremely sparse to moderately sparse innumber. Motivated by these challenges, we propose the Generalized Berk-Jones(GBJ) test for the association between a SNP-set and outcome. The GBJ extendsthe Berk-Jones (BJ) statistic by accounting for correlation among SNPs, and itprovides advantages over the Generalized Higher Criticism (GHC) test whensignals in a SNP-set are moderately sparse. We also provide an analytic p-valuecalculation procedure for SNP-sets of any finite size. Using this p-valuecalculation, we illustrate that the rejection region for GBJ can be describedas a compromise of those for BJ and GHC. We develop an omnibus statistic aswell, and we show that this omnibus test is robust to the degree of signalsparsity. An additional advantage of our method is the ability to conductinference using individual SNP summary statistics from a genome-wideassociation study. We evaluate the finite sample performance of the GBJ thoughsimulation studies and application to gene-level association analysis of breastcancer risk.

Set-Based Tests for Genetic Association Using the Generalized Berk-Jones Statistic