ArticleNature genetics2024
Adjusting for genetic confounders in transcriptome-wide association studies improves discovery of risk genes of complex traits.
Article in Nature genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers, 1 of them a synthesis that pooled it.
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.
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Who cites it
71 citing papers in PubMed, 1 synthesis or guideline pooled it, 65 citations in OpenAlex.
- Multi-layered functional genomics prioritizes candidate effectors and regulatory mechanisms of ankylosing spondylitis.Frontiers in immunology · 2026Pooled it
- Co-regulation confounds direction of genetic effects in transcriptome-wide association studies.Nature genetics · 2026Article
- High false sign rates in transcriptome-wide association studies.Nature genetics · 2026Article
- GROMTools: scalable individual-level GReX imputation for mega-biobank-scale cohorts.Bioinformatics (Oxford, England) · 2026Article
- Single-cell analysis of chromatin accessibility in the human intestine identifies regulatory programs and clarifies genetic associations in Crohn's disease.Nature genetics · 2026Article
- Identifying independent causal cell types for human diseases and risk variants.Cell genomics · 2026Article
- Tissue-resident immune cells drive genetic risk in autoimmune and lung diseases.Nature immunology · 2026Article
- Post-genome-wide association study variant-to-function challenges in asthma research.The Journal of allergy and clinical immunology · 2026Review
- Data-driven RNA phenotyping captures genetically regulated dimensions of the transcriptome.American journal of human genetics · 2026Article
- A Multi-Context Regulome-Wide Association Atlas for Genetic Studies of Aging Brain Disorders.medRxiv : the preprint server for health sciences · 2026Article
- Multi-ancestry transcriptome-wide association study reveals shared and population-specific genetic effects in Alzheimer disease.American journal of human genetics · 2026Article
- A trans-omics gene-smoking interaction study of lung cancer based on consortium data.American journal of respiratory and critical care medicine · 2026Article
- Unraveling the inflammatory bridge: genomic evidence identifies TNFRSF9 as a potential biomarker linking Sjögren's syndrome to total risk non-Hodgkin lymphoma.Clinical rheumatology · 2026Article
- Identification and functional validation of EPS15L1 as a key driver of triple-negative breast cancer.NPJ breast cancer · 2026Article
- Functionally informed cis and trans proteome-wide association studies prioritize disease-critical genes.Research square · 2026Article
- mFABIO: An integrative multi-tissue TWAS fine-mapping approach to prioritize potentially causal genes and tissues underlying binary traits.PLoS genetics · 2026Article
- Design and interpretation of eQTL-GWAS colocalisation studies: Lessons from a large-scale evaluation.PLoS genetics · 2026Article
- Connecting polygenic disease risk to cell states and regulatory programs through single-cell chromatin accessibility.bioRxiv : the preprint server for biology · 2026Article
- Functionally informed cis and trans proteome-wide association studies prioritize disease-critical genes.medRxiv : the preprint server for health sciences · 2026Article
- Methods for Prioritizing Causal Genes in Molecular Studies of Human Disease: The State of the Art.Genetic epidemiology · 2026Review
11 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
Many methods have been developed to leverage expression quantitative trait loci (eQTL) data to nominate candidate genes from genome-wide association studies. These methods, including colocalization, transcriptome-wide association studies (TWAS) and Mendelian randomization-based methods; however, all suffer from a key problem-when assessing the role of a gene in a trait using its eQTLs, nearby variants and genetic components of other genes' expression may be correlated with these eQTLs and have direct effects on the trait, acting as potential confounders. Our extensive simulations showed that existing methods fail to account for these 'genetic confounders', resulting in severe inflation of false positives. Our new method, causal-TWAS (cTWAS), borrows ideas from statistical fine-mapping and allows us to adjust all genetic confounders. cTWAS showed calibrated false discovery rates in simulations, and its application on several common traits discovered new candidate genes. In conclusion, cTWAS provides a robust statistical framework for gene discovery.
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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.