Evidence map›Paper›PMID 41821682›Full record

ArticleHorticulture research2026

An omnigenic interactome model to chart the genetic architecture of individual plants.

Changjian Fa, Guijia Wang, Wenqi Pan, Yu Wang, Jincan Che, Ang Dong, Dengcheng Yang, Rongling Wu, Shing-Tung Yau, Lidan Sun

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Horticulture research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Graph statistics theory of individualized quantitative genetics under haplotype-resolved genome assembly.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
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

10 authors.

Changjian FaCenter for Computational Biology, School of Grassland Science, Beijing Forestry University, Beijing 100083, China.
Guijia WangState Key Laboratory of Efficient Production of Forest Resources, Beijing Key Laboratory of Ornamental Plants Germplasm Innovation and Molecular Breeding, National Engineering Research Center for Floriculture, School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China.
Wenqi PanBeijing Key Laboratory of Topological Statistics and Applications for Complex Systems, Beijing Institute of Mathematical Sciences and Applications, Beijing 101408, China.
Yu WangBeijing Key Laboratory of Topological Statistics and Applications for Complex Systems, Beijing Institute of Mathematical Sciences and Applications, Beijing 101408, China.
Jincan CheCenter for Computational Biology, School of Grassland Science, Beijing Forestry University, Beijing 100083, China.ORCID https://orcid.org/0000-0003-4158-9926
Ang DongBeijing Key Laboratory of Topological Statistics and Applications for Complex Systems, Beijing Institute of Mathematical Sciences and Applications, Beijing 101408, China.
Dengcheng YangBeijing Key Laboratory of Topological Statistics and Applications for Complex Systems, Beijing Institute of Mathematical Sciences and Applications, Beijing 101408, China.
Rongling WuProgram in Applied Statistics, Shanghai Institute for Mathematics and Interdisciplinary Sciences, Shanghai 200433, China.ORCID https://orcid.org/0009-0000-0005-1025
Shing-Tung YauBeijing Key Laboratory of Topological Statistics and Applications for Complex Systems, Beijing Institute of Mathematical Sciences and Applications, Beijing 101408, China.
Lidan SunState Key Laboratory of Efficient Production of Forest Resources, Beijing Key Laboratory of Ornamental Plants Germplasm Innovation and Molecular Breeding, National Engineering Research Center for Floriculture, School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Complex traits are controlled by many unknown genes, making it difficult to elucidate a global picture of the genotype-phenotype map. Here, we develop a statistical mechanics model to contextualize all possible genes into informative, dynamic, omnidirectional, and personalized idopNetworks. This model, derived from the combination of functional mapping and evolutionary game theory, can visualize and trace how genes act and interact with each other to shape the genetic architecture of complex traits. The model can estimate changes in the genotypic value of one gene due to the influence of other genes, specifically on individual subjects, surpassing traditional quantitative genetic studies that can only capture the marginal effect of a gene at the population level. We reconstruct growth idopNetworks from a genome-wide mapping data in a woody plant, mei, identifying unique genetic interaction architecture that distinguishes between fast-growing trees and slow-growing trees. We perform computer simulation to validate the statistical power of the model. IdopNetworks can disentangle the genetic control mechanisms of complex traits and provide guidance on how to alter phenotypic values of specific individuals by promoting or inhibiting the expression of interactive genes.

Identifiers

PMID41821682
PMCPMC12977963

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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.