ArticlePLoS genetics2019
Powerful gene set analysis in GWAS with the Generalized Berk-Jones statistic.
Article in PLoS genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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Who cites it
47 citing papers in PubMed.
- Integrative TWAS and multi-omics analyses prioritize HSPE1 as a candidate risk gene for bipolar disorder with immune cell-specific regulatory evidence.European archives of psychiatry and clinical neuroscience · 2026Article
- Integrative Multi-omics Analysis for Prioritization of Candidate Genes in Polycystic Ovary Syndrome.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026Article
- Integrative statistical genetics prioritizes candidate ASCVD susceptibility genes across tissues.Molecular medicine (Cambridge, Mass.) · 2026Article
- Unveiling novel susceptibility genes for sarcoidosis by a cross-tissue transcriptome-wide association study.Sarcoidosis, vasculitis, and diffuse lung diseases : official journal of WASOG · 2026Article
- Single-nucleus analysis reveals human-specific oligodendrocyte polarization and conserved neuronal responses after severe traumatic brain injury.Nature communications · 2026Article
- Gene set analysis for time-to-event outcome: comparison of a new approach based on the generalized Berk-Jones statistic with existing methods in presence of intra gene-set correlation.Briefings in bioinformatics · 2026Article
- An integrative association analysis for complex diseases in underrepresented groups by leveraging the trans-ethnic genetic similarity.Briefings in bioinformatics · 2026Article
- Integrative multi-layer genetic analysis identifies novel susceptibility genes for urolithiasis.Urolithiasis · 2026Article
- Unveiling novel susceptibility genes for kidney stone disease by a cross-tissue transcriptome-wide association study.World journal of urology · 2026Article
- Genetic insights and mechanistic parallels in gestational diabetes mellitus and type 2 diabetes.Nature communications · 2026Article
- Genetic Variation and Ultrafiltration with Peritoneal Dialysis: A Genome-Wide Association Study.Journal of the American Society of Nephrology : JASN · 2026Article
- Integrative genomic profiling identifies MLPH as a candidate gene in prostate cancer.Frontiers in medicine · 2026Article
- From GWAS to TWAS: multi-omics dissection of sepsis uncovers consistent impact ofArchives of medical science : AMS · 2026Article
- A Cross-Tissue Transcriptome-Wide Association Study Identified Susceptibility Genes for Intervertebral Disc Degeneration.JOR spine · 2025Article
- A cross-tissue transcriptome-wide association study identifies novel susceptibility genes for atrial fibrillation.Journal of arrhythmia · 2025Article
- Cross-tissue transcriptome-wide association study reveals novel psoriasis susceptibility genes.Journal of translational autoimmunity · 2025Article
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- A cross-tissue transcriptome-wide association study identifies new susceptibility genes for benign prostatic hyperplasia.Scientific reports · 2025Article
- Integrative multi-omics analysis identifies TGFA as a novel glioma susceptibility gene and therapeutic target.Frontiers in neurology · 2025Article
- A splicing-based multitissue association study of joint transcriptomes identified susceptibility genes for osteoarthritis.Frontiers in immunology · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
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 FGFR2 in pathway lists. Our approach is further validated on two other datasets of summary statistics generated from GWAS of height and schizophrenia.
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