10.1371/journal.pgen.1007530 Ryan Sun Ryan Sun Shirley Hui Shirley Hui Gary D. Bader Gary D. Bader Xihong Lin Xihong Lin Peter Kraft Peter Kraft Powerful gene set analysis in GWAS with the Generalized Berk-Jones statistic Public Library of Science 2019 GSA Single Nucleotide Polymorphisms breast cancer pathway analysis SNP Generalized Berk-Jones statistic set-based testing settings Genome-Wide Association Studies GBJ step-down inference technique FGFR breast cancer GWAS gene summary statistics 2019-03-15 17:39:49 Dataset https://plos.figshare.com/articles/dataset/Powerful_gene_set_analysis_in_GWAS_with_the_Generalized_Berk-Jones_statistic/7853138 <div><p>A common complementary strategy in Genome-Wide Association Studies (GWAS) is to perform Gene Set Analysis (GSA), which tests for the association between one phenotype of interest and an entire set of Single Nucleotide Polymorphisms (SNPs) residing in selected genes. While there exist many tools for performing GSA, popular methods often include a number of ad-hoc steps that are difficult to justify statistically, provide complicated interpretations based on permutation inference, and demonstrate poor operating characteristics. Additionally, the lack of gold standard gene set lists can produce misleading results and create difficulties in comparing analyses even across the same phenotype. We introduce the Generalized Berk-Jones (GBJ) statistic for GSA, a permutation-free parametric framework that offers asymptotic power guarantees in certain set-based testing settings. To adjust for confounding introduced by different gene set lists, we further develop a GBJ step-down inference technique that can discriminate between gene sets driven to significance by single genes and those demonstrating group-level effects. We compare GBJ to popular alternatives through simulation and re-analysis of summary statistics from a large breast cancer GWAS, and we show how GBJ can increase power by incorporating information from multiple signals in the same gene. In addition, we illustrate how breast cancer pathway analysis can be confounded by the frequency of <i>FGFR2</i> in pathway lists. Our approach is further validated on two other datasets of summary statistics generated from GWAS of height and schizophrenia.</p></div>