Evidence map›Paper›PMID 42918898›Full record

ArticleNature genetics2026

High false sign rates in transcriptome-wide association studies.

Peter A Gerlach, Nikhil Milind, Jeffrey P Spence, Jonathan K Pritchard

Abstract read
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In one paragraph

Article in Nature genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Peter A Gerlach *Department of Genetics, Stanford University, Stanford, CA, USA. pgerlach@stanford.edu.ORCID http://orcid.org/0009-0000-7108-0026
Nikhil Milind *Department of Genetics, Stanford University, Stanford, CA, USA. nmilind@stanford.edu.ORCID http://orcid.org/0000-0002-7975-247X
Jeffrey P SpenceInstitute for Human Genetics, University of California, San Francisco, CA, USA. jeff.spence@ucsf.edu.ORCID http://orcid.org/0000-0002-3199-1447
Jonathan K PritchardDepartment of Genetics, Stanford University, Stanford, CA, USA. pritch@stanford.edu.ORCID http://orcid.org/0000-0002-8828-5236

Funding

Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.) R01HG014005Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.) U01HG012069
6 · The paper itself

Abstract

Transcriptome-wide association studies (TWAS) are widely used to identify genes involved in complex traits and to infer the direction of gene effects on traits. However, despite their popularity, it remains unclear how accurately TWAS recover the true direction of a gene's effect on a trait. Here we estimate the false sign rate of TWAS for plasma proteins, leveraging the expectation that increased gene expression should generally increase protein expression. We then extend this framework to complex traits, where loss-of-function burden tests provide the expected direction of effect. In both analyses, we observe high discordance with expectations, with TWAS showing a false sign rate of 23% for plasma proteins and 33% for complex traits. We propose that much of the sign discordance arises when expression quantitative trait loci in non-trait-relevant tissues tag haplotypes associated with traits in genome-wide association studies via distinct, tightly linked regulatory variants, yielding spurious TWAS associations with the correct genes but with unreliable direction of effect. These findings show that TWAS-based direction-of-effect estimates should be interpreted with caution and raise concerns about the reliability of TWAS more broadly.

Identifiers

PMID42918898

What OpenQuestion holds

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Registered trials

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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.