Evidence map›Paper›PMID 41915744›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2026

Graph statistics theory of individualized quantitative genetics under haplotype-resolved genome assembly.

Lidan Sun, Yangyang Bian, Dengcheng Yang, Runtian Miao, Yihan Meng, Jincan Che, Ziwei Li, Zimo Li, Haoning Wang, Shuang Wu and 5 more

Abstract read
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In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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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

15 authors.

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.
Yangyang BianBeijing 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.
Runtian MiaoState 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.
Yihan MengBeijing Key Laboratory of Topological Statistics and Applications for Complex Systems, Beijing Institute of Mathematical Sciences and Applications, Beijing 101408, China.
Jincan CheBeijing Key Laboratory of Topological Statistics and Applications for Complex Systems, Beijing Institute of Mathematical Sciences and Applications, Beijing 101408, China.
Ziwei LiState 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.
Zimo LiState 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.
Haoning 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.
Shuang WuBeijing Key Laboratory of Topological Statistics and Applications for Complex Systems, Beijing Institute of Mathematical Sciences and Applications, Beijing 101408, China.
Juan MengState 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.
Yu WangBeijing Key Laboratory of Topological Statistics and Applications for Complex Systems, Beijing Institute of Mathematical Sciences and Applications, Beijing 101408, China.
Christopher GriffinApplied Research Laboratory, The Pennsylvania State University, University Park, PA 16802.ORCID 0000-0002-9962-9540
Shing-Tung YauBeijing Key Laboratory of Topological Statistics and Applications for Complex Systems, Beijing Institute of Mathematical Sciences and Applications, Beijing 101408, China.ORCID 0000-0003-3394-2187
Rongling WuBeijing Key Laboratory of Topological Statistics and Applications for Complex Systems, Beijing Institute of Mathematical Sciences and Applications, Beijing 101408, China.ORCID 0009-0000-0005-1025

Funding

Fundamental Research Fund for Central Universities QNTD202306National Natural science foundation of China 32572116Shanghai Institute of Mathematics and Interdisciplinary Sciences SIMIS-ID-2024-WN
6 · The paper itself

Abstract

Quantitative genetics is essential for genetic dissection of complex traits, yet the existing theory fails to illustrate a comprehensive landscape of genetic control mechanisms driving phenotypic variation and evolution. Here, we develop a statistical approach to assemble all genome loci into omnigenic interactome networks from diplotyped sequencing data. Such networks can not only capture dominance, epistasis, and pleiotropy and leverage these genetic concepts as bidirectional, signed, and weighted interactions among alleles and nonalleles, but also establish a framework for dissecting the genetic architecture of any single individual. While traditional approaches can only estimate coarse-grained genetic parameters at the population level, our approach can portray a fine-grained picture involving how each allele acts and interacts with every other allele for a single individual, thus facilitating its genome editing and genome engineering. By analyzing transcriptomic data of two diplotyped cultivars of a woody plant, our approach can interpret the genetic mechanisms underlying this species' cold resistance and interorgan communication. Our network-centric approach, generalized as a graph statistics theory, builds the foundation of individualized quantitative genetics, a theory that can make genetics even more transformational to precision breeding or precision medicine.

Indexed as

Genome, PlantHaplotypesModels, GeneticAllelesEpistasis, GeneticPhenotypeQuantitative Trait Locicomplex traitsdiplotypeepistasisindividualized quantitative geneticsomnigenic interactome

Identifiers

PMID41915744
PMCPMC13056068

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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.