Evidence map›Paper›PMID 40328252›Full record

ArticleCell genomics2025

Trans-eQTL hotspots shape complex traits by modulating cellular states.

Kaushik Renganaath, Frank Wolfgang Albert

Abstract read
In one paragraph

Article in Cell genomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

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4 · The record

Corrections and comments

  • Update of
    2024
5 · Who and what money

Authors and funding

2 authors.

Kaushik RenganaathDepartment of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, MN 55455, USA.
Frank Wolfgang AlbertDepartment of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, MN 55455, USA. Electronic address: falbert@umn.edu.

Funding

Genomic approaches for dissecting regulatory variationR35GM124676 · NIGMS · UNIVERSITY OF MINNESOTA · PI Frank Wolfgang Albert · 2017 to 2026
$4.6M
NIGMS NIH HHS R35 GM124676
6 · The paper itself

Abstract

Regulatory genetic variation shapes gene expression, providing an important mechanism connecting DNA variation and complex traits. The causal relationships between gene expression and complex traits remain poorly understood. Here, we integrated transcriptomes and 46 genetically complex growth traits in a large cross between two strains of the yeast Saccharomyces cerevisiae. We discovered thousands of genetic correlations between gene expression and growth, suggesting potential functional connections. Local regulatory variation was a minor source of these genetic correlations. Instead, genetic correlations tended to arise from multiple independent trans-acting regulatory loci. Trans-acting hotspots that affect the expression of numerous genes accounted for particularly large fractions of genetic growth variation and of genetic correlations between gene expression and growth. Genes with genetic correlations were enriched for similar biological processes across traits but with heterogeneous direction of effect. Our results reveal how trans-acting regulatory hotspots shape complex traits by altering cellular states.

Indexed as

Quantitative Trait LociSaccharomyces cerevisiaeDNA-Binding ProteinsGene ExpressionGTPase-Activating ProteinsPhenotypeSaccharomyces cerevisiae ProteinsTranscription FactorsDNA-Binding ProteinsGTPase-Activating ProteinsIRA2 protein, S cerevisiaeMSN2 protein, S cerevisiaeSaccharomyces cerevisiae ProteinsTranscription Factorscomplex traitsexpression QTLsgene expressiongenetic variationheritabilitymediationpleiotropyQTLsquantitative geneticsyeast

Identifiers

PMID40328252
PMCPMC12143327

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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.