ArticleNature communications2026
Multi-ancestry gene expression models amplify transcriptome-wide association study discovery and validation.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Multi-ancestry gene expression models amplify transcriptome-wide association study discovery and validation.Nature communications · 2026Article
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.
Funding
Abstract
Our understanding of the influence of ancestral background on genetically determined expression remains limited, especially when gene expression models are applied to studies from different or multiple populations. We perform transcriptome-wide association studies of 6 psychiatric conditions, leveraging gene expression models trained in cohorts with different proportions of African, European, and Indigenous American genetic ancestries. For comparison, we repeat each transcriptome-wide association study using a model trained in individuals of predominantly European ancestry. We identify 1416 statistically significant gene-level associations (false discovery rate adjusted p < 0.05) across the 6 diagnoses, of which 62% are uniquely detected by the admixed gene models. Notably, we observe high correlation (
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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.