ArticleGenome research2012
Integrating genetic and gene expression evidence into genome-wide association analysis of gene sets.
Article in Genome research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 63 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
63 citing papers in PubMed, 104 citations in OpenAlex.
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- Systems biology and single-cell transcriptome analysis identify potential therapeutic targets and impaired neurogenesis in human cortical development related to autism spectrum disorder.Molecular diversity · 2026Article
- Revolutionizing multi-omics analysis with artificial intelligence and data processing.Quantitative biology (Beijing, China) · 2025Review
- Weighted gene co-expression network analysis - based selection of hub genes related to phenolic and volatile compounds and seed coat color in sorghum.BMC plant biology · 2025Article
- The molecular subtypes of autoimmune diseases.Computational and structural biotechnology journal · 2024Review
- bootGSEA: a bootstrap and rank aggregation pipeline for multi-study and multi-omics enrichment analyses.Frontiers in bioinformatics · 2024Article
- Mega-scale Bayesian regression methods for genome-wide prediction and association studies with thousands of traits.Genetics · 2023Article
- An Expectation-Maximization Algorithm for Combining a Sample of Partially Overlapping Covariance Matrices.Axioms · 2023Article
- Transcriptome analysis reveals key regulatory genes for root growth related to potassium utilization efficiency in rapeseed (Frontiers in plant science · 2023Article
- Article
- Cooperation between melanoma cell states promotes metastasis through heterotypic cluster formation.Developmental cell · 2021Article
- Developmental chromatin programs determine oncogenic competence in melanoma.Science (New York, N.Y.) · 2021Article
- Network Modeling in Biology: Statistical Methods for Gene and Brain Networks.Statistical science : a review journal of the Institute of Mathematical Statistics · 2021Article
- A Novel Computational Framework to Predict Disease-Related Copy Number Variations by Integrating Multiple Data Sources.Frontiers in genetics · 2021Article
- A general framework for integrative analysis of incomplete multiomics data.Genetic epidemiology · 2020Article
- Regulation of the error-prone DNA polymerase Polκ by oncogenic signaling and its contribution to drug resistance.Science signaling · 2020Article
- The Evolving Field of Genetic Epidemiology: From Familial Aggregation to Genomic Sequencing.American journal of epidemiology · 2019Review
- iFunMed: Integrative functional mediation analysis of GWAS and eQTL studies.Genetic epidemiology · 2019Article
- Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective.Metabolites · 2019Review
3 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
Abstract
Single variant or single gene analyses generally account for only a small proportion of the phenotypic variation in complex traits. Alternatively, gene set or pathway association analyses are playing an increasingly important role in uncovering genetic architectures of complex traits through the identification of systematic genetic interactions. Two dominant paradigms for gene set analyses are association analyses based on SNP genotypes and those based on gene expression profiles. However, gene-disease association can manifest in many ways, such as alterations of gene expression, genotype, and copy number; thus, an integrative approach combining multiple forms of evidence can more accurately and comprehensively capture pathway associations. We have developed a single statistical framework, Gene Set Association Analysis (GSAA), that simultaneously measures genome-wide patterns of genetic variation and gene expression variation to identify sets of genes enriched for differential expression and/or trait-associated genetic markers. Simulation studies illustrate that joint analyses of genomic data increase the power to detect real associations when compared with gene set methods that use only one genomic data type. The analysis of two human diseases, glioblastoma and Crohn's disease, detected abnormalities in previously identified disease-associated pathways, such as pathways related to PI3K signaling, DNA damage response, and the activation of NFKB. In addition, GSAA predicted novel pathway associations, for example, differential genetic and expression characteristics in genes from the ABC transporter family in glioblastoma and from the HLA system in Crohn's disease. These demonstrate that GSAA can help uncover biological pathways underlying human diseases and complex traits.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.