ArticlePLoS genetics2020
Integrating comprehensive functional annotations to boost power and accuracy in gene-based association analysis.
Article in PLoS genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Integrating bulk and single-cell RNA sequencing with GWAS reveals regulatory networks underpinning complex traits in beef cattle.Journal of animal science and biotechnology · 2026Article
- regionalpcs improve discovery of DNA methylation associations with complex traits.Nature communications · 2025Article
- Protein phosphatase 1 catalytic subunit gamma is a causative gene for meat lightness and redness.PLoS genetics · 2024Article
- The goldmine of GWAS summary statistics: a systematic review of methods and tools.BioData mining · 2024Article
- Combining Hyperspectral Techniques and Genome-Wide Association Studies to Predict Peanut Seed Vigor and Explore Associated Genetic Loci.International journal of molecular sciences · 2024Article
- Article
- FAVOR-GPT: a generative natural language interface to whole genome variant functional annotations.Bioinformatics advances · 2024Article
- cLD: Rare-variant linkage disequilibrium between genomic regions identifies novel genomic interactions.PLoS genetics · 2023Article
- FAVOR: functional annotation of variants online resource and annotator for variation across the human genome.Nucleic acids research · 2023Article
- mBAT-combo: A more powerful test to detect gene-trait associations from GWAS data.American journal of human genetics · 2023Article
- CLIN_SKAT: an R package to conduct association analysis using functionally relevant variants.BMC bioinformatics · 2022Article
- sumSTAAR: A flexible framework for gene-based association studies using GWAS summary statistics.PLoS computational biology · 2022Article
- Association analyses of rare variants identify two genes associated with refractive error.PloS one · 2022Article
- SEAGLE: A Scalable Exact Algorithm for Large-Scale Set-Based Gene-Environment Interaction Tests in Biobank Data.Frontiers in genetics · 2021Article
- Identifying Susceptibility Loci for Cutaneous Squamous Cell Carcinoma Using a Fast Sequence Kernel Association Test.Frontiers in genetics · 2021Article
- PTWAS: investigating tissue-relevant causal molecular mechanisms of complex traits using probabilistic TWAS analysis.Genome biology · 2020Article
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Authors and funding
5 authors.
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Abstract
Gene-based association tests aggregate genotypes across multiple variants for each gene, providing an interpretable gene-level analysis framework for genome-wide association studies (GWAS). Early gene-based test applications often focused on rare coding variants; a more recent wave of gene-based methods, e.g. TWAS, use eQTLs to interrogate regulatory associations. Regulatory variants are expected to be particularly valuable for gene-based analysis, since most GWAS associations to date are non-coding. However, identifying causal genes from regulatory associations remains challenging and contentious. Here, we present a statistical framework and computational tool to integrate heterogeneous annotations with GWAS summary statistics for gene-based analysis, applied with comprehensive coding and tissue-specific regulatory annotations. We compare power and accuracy identifying causal genes across single-annotation, omnibus, and annotation-agnostic gene-based tests in simulation studies and an analysis of 128 traits from the UK Biobank, and find that incorporating heterogeneous annotations in gene-based association analysis increases power and performance identifying causal genes.
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