Evidence map›Paper›PMID 40791462›Full record

ArticlebioRxiv : the preprint server for biology2025

When does accounting for gene-environment interactions improve complex trait prediction? A case study with

Fabio Morgante, Francesco Tiezzi

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. 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

5 · Who and what money

Authors and funding

2 authors.

Fabio MorganteCenter for Human Genetics, Clemson University, Greenwood, SC, USA.ORCID 0000-0003-3285-1988
Francesco TiezziCenter for Human Genetics, Clemson University, Greenwood, SC, USA.ORCID 0000-0002-4358-9236

Funding

Statistical Methods for Gene Regulatory Analysis From Single Cell Genomics DataP20GM139769 · NIGMS · CLEMSON UNIVERSITY · PI ANHOLT, ROBERT R. H, ARNO, GAVIN · 2021 to 2025
$10.8M
Understanding and using gene-by-context interactions in human complex trait geneticsR35GM146868 · NIGMS · CLEMSON UNIVERSITY · PI Fabio Morgante · 2022 to 2026
$1.9M
NIGMS NIH HHS P20 GM139769NIGMS NIH HHS R35 GM146868
6 · The paper itself

Abstract

Gene-environment interactions (G×E) have been shown to explain a non-negligible proportion of variance for a plethora of complex traits in different species, including livestock, plants, and humans. While several studies have shown that including G×E can improve prediction accuracy in agricultural species, no increase in accuracy has been observed in human studies. In this work, we sought to investigate the reasons for the contradictory results between agricultural species and humans. Model organisms are useful for studying G×E, since environments can be controlled and genotypes can be replicated across environments. Thus, we used data from a previous study in

Identifiers

PMID40791462
PMCPMC12338705

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.