ArticleGenetic epidemiology2022
Statistical power of transcriptome-wide association studies.
Article in Genetic epidemiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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.
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Who cites it
9 citing papers in PubMed.
- Co-expression-wide association studies link genetically regulated interactions with complex traits.Nature communications · 2025Article
- Transcriptomic analysis in peripheral blood mononuclear cells from hyper and hypo weight-loss responders.Genes & nutrition · 2025Article
- Enhancing Gene Expression Predictions Using Deep Learning and Functional Annotations.Genetic epidemiology · 2025Article
- A bootstrap model comparison test for identifying genes with context-specific patterns of genetic regulation.The annals of applied statistics · 2024Article
- SUMMIT-FA: a new resource for improved transcriptome imputation using functional annotations.Human molecular genetics · 2024Article
- A BOOTSTRAP MODEL COMPARISON TEST FOR IDENTIFYING GENES WITH CONTEXT-SPECIFIC PATTERNS OF GENETIC REGULATION.bioRxiv : the preprint server for biology · 2023Article
- Transcriptome-wide association studies: recent advances in methods, applications and available databases.Communications biology · 2023Review
- MATS: a novel multi-ancestry transcriptome-wide association study to account for heterogeneity in the effects of cis-regulated gene expression on complex traits.Human molecular genetics · 2023Article
- Statistical power of transcriptome-wide association studies.Genetic epidemiology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Transcriptome-Wide Association Studies (TWASs) have become increasingly popular in identifying genes (or other endophenotypes or exposures) associated with complex traits. In TWAS, one first builds a predictive model for gene expressions using an expression quantitative trait loci (eQTL) data set in stage 1, then tests the association between the predicted gene expression and a trait based on a large, independent genome-wide association study (GWAS) data set in stage 2. However, since the sample size of the eQTL data set is usually small and the coefficient of multiple determination (i.e.,
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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 Socratic graph.