ArticleAmerican journal of human genetics2023
Probabilistic integration of transcriptome-wide association studies and colocalization analysis identifies key molecular pathways of complex traits.
Article in American journal of human genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- Post-genome-wide association study variant-to-function challenges in asthma research.The Journal of allergy and clinical immunology · 2026Review
- A Multi-Context Regulome-Wide Association Atlas for Genetic Studies of Aging Brain Disorders.medRxiv : the preprint server for health sciences · 2026Article
- Host genotypes interact with microbial communities to modulate gene expression in the human intestine.medRxiv : the preprint server for health sciences · 2026Article
- Neuroimaging-derived brain endophenotypes link molecular mechanisms to Alzheimer's disease and aging.medRxiv : the preprint server for health sciences · 2025Article
- Exploring Contraindicated Medications and Corresponding Targeted Genes for Migraine Through Integrated Genetic Approaches.Brain and behavior · 2025Article
- European and African ancestry-specific plasma protein-QTL and metabolite-QTL analyses identify ancestry-specific T2D effector proteins and metabolites.Nature communications · 2025Article
- Functional characterization of eQTLs and asthma risk loci with scATAC-seq across immune cell types and contexts.American journal of human genetics · 2025Article
- Multi-INTACT: integrative analysis of the genome, transcriptome, and proteome identifies causal mechanisms of complex traits.Genome biology · 2025Article
- GWAShug: a comprehensive platform for decoding the shared genetic basis between complex traits based on summary statistics.Nucleic acids research · 2025Article
- regionalpcs improve discovery of DNA methylation associations with complex traits.Nature communications · 2025Article
- A scoping review of statistical methods to investigate colocalization between genetic associations and microRNA expression in osteoarthritis.Osteoarthritis and cartilage open · 2024Article
- Genotype × environment interactions in gene regulation and complex traits.Nature genetics · 2024Review
- Article
- Characterization of caffeine response regulatory variants in vascular endothelial cells.eLife · 2024Article
- eQTL studies: from bulk tissues to single cells.Journal of genetics and genomics = Yi chuan xue bao · 2023Review
- Combinations of genes at the 16p11.2 and 22q11.2 CNVs contribute to neurobehavioral traits.PLoS genetics · 2023Article
- eQTL Studies: from Bulk Tissues to Single Cells.ArXiv · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Integrative genetic association methods have shown great promise in post-GWAS (genome-wide association study) analyses, in which one of the most challenging tasks is identifying putative causal genes and uncovering molecular mechanisms of complex traits. Recent studies suggest that prevailing computational approaches, including transcriptome-wide association studies (TWASs) and colocalization analysis, are individually imperfect, but their joint usage can yield robust and powerful inference results. This paper presents INTACT, a computational framework to integrate probabilistic evidence from these distinct types of analyses and implicate putative causal genes. This procedure is flexible and can work with a wide range of existing integrative analysis approaches. It has the unique ability to quantify the uncertainty of implicated genes, enabling rigorous control of false-positive discoveries. Taking advantage of this highly desirable feature, we further propose an efficient algorithm, INTACT-GSE, for gene set enrichment analysis based on the integrated probabilistic evidence. We examine the proposed computational methods and illustrate their improved performance over the existing approaches through simulation studies. We apply the proposed methods to analyze the multi-tissue eQTL data from the GTEx project and eight large-scale complex- and molecular-trait GWAS datasets from multiple consortia and the UK Biobank. Overall, we find that the proposed methods markedly improve the existing putative gene implication methods and are particularly advantageous in evaluating and identifying key gene sets and biological pathways underlying complex traits.
Indexed as
Identifiers
What OpenQuestion holds
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.