Title: Set-Based Tests for Genetic Association Using the Generalized Berk-Jones Statistic,
Abstract: Studying the effects of groups of Single Nucleotide Polymorphisms (SNPs), as
in a gene, genetic pathway, or network, can provide novel insight into complex
diseases, above that which can be gleaned from studying SNPs individually.
Common challenges in set-based genetic association testing include weak effect
sizes, correlation between SNPs in a SNP-set, and scarcity of signals, with
single-SNP effects often ranging from extremely sparse to moderately sparse in
number. Motivated by these challenges, we propose the Generalized Berk-Jones
(GBJ) test for the association between a SNP-set and outcome. The GBJ extends
the Berk-Jones (BJ) statistic by accounting for correlation among SNPs, and it
provides advantages over the Generalized Higher Criticism (GHC) test when
signals in a SNP-set are moderately sparse. We also provide an analytic p-value
calculation procedure for SNP-sets of any finite size. Using this p-value
calculation, we illustrate that the rejection region for GBJ can be described
as a compromise of those for BJ and GHC. We develop an omnibus statistic as
well, and we show that this omnibus test is robust to the degree of signal
sparsity. An additional advantage of our method is the ability to conduct
inference using individual SNP summary statistics from a genome-wide
association study. We evaluate the finite sample performance of the GBJ though
simulation studies and application to gene-level association analysis of breast
cancer risk.