Evidence map›Paper›PMID 40067114›Full record

ArticleBriefings in bioinformatics2025

Dissecting genetic regulation of metabolic coordination.

Emily C Hector, Daiwei Zhang, Leqi Tian, Junning Feng, Xianyong Yin, Tianyi Xu, Markku Laakso, Yun Bai, Jiashun Xiao, Jian Kang and 1 more

Abstract read
In one paragraph

Article in Briefings in bioinformatics, 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

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

11 authors.

Emily C HectorDepartment of Statistics, North Carolina State University, Raleigh, NC 27695, United States.
Daiwei ZhangDepartment of Biostatistics, University of Michigan, Ann Arbor, MI 48109, United States.
Leqi TianSchool of Data Science, the Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R.China.
Junning FengSchool of Data Science, the Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R.China.
Xianyong YinDepartment of Biostatistics, University of Michigan, Ann Arbor, MI 48109, United States.
Tianyi XuSchool of Data Science, the Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R.China.
Markku LaaksoSchool of Medicine, University of Eastern Finland, FI-70211 Kuopio, Finland.
Yun BaiSchool of Medicine, the Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R.China.
Jiashun XiaoShenzhen Research Institute of Big Data, Shenzhen, Guangdong 518172, P.R.China.
Jian KangDepartment of Biostatistics, University of Michigan, Ann Arbor, MI 48109, United States.
Tianwei YuSchool of Data Science, the Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R.China.

Funding

Scalable Bayesian methods for big imaging data analysisR01DA048993 · NIDA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI JOHNSON, TIMOTHY D, KANG, JIAN · 2020 to 2024
$1.6M
Enhanced Machine Learning Tools for Complex Data Evaluation and Integration in Advancing Health OutcomesR01HL173044 · NHLBI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Baiming Zou, Fei Zou · 2025 to 2026
$1.3M
Guangdong Provincial Key Laboratory of Big Data ComputingGuangdong Talent Program 2021CX02Y145NHLBI NIH HHS R01 HL173044NIH HHS R01DA048993NSF IIS2123777Shenzhen Key Laboratory of Cross-Modal Cognitive Computing ZDSYS20230626091302006
6 · The paper itself

Abstract

Understanding genetic regulation of metabolism is critical for gaining insights into the causes of metabolic diseases. Traditional metabolome-based genome-wide association studies (mGWAS) focus on static associations between single nucleotide polymorphisms (SNPs) and metabolite levels, overlooking the changing relationships caused by genotypes within the metabolic network. Notably, some metabolites exhibit changes in correlation patterns with other metabolites under certain physiological conditions while maintaining their overall abundance level. In this manuscript, we develop Metabolic Differential-coordination GWAS (mdGWAS), an innovative framework that detects SNPs associated with the changing correlation patterns between metabolites and metabolic pathways. This approach transcends and complements conventional mean-based analyses by identifying latent regulatory factors that govern the system-level metabolic coordination. Through comprehensive simulation studies, mdGWAS demonstrated robust performance in detecting SNP-metabolite-metabolite associations. Applying mdGWAS to genotyping and mass spectrometry (MS)-based metabolomics data of the METabolic Syndrome In Men (METSIM) Study revealed novel SNPs and genes potentially involved in the regulation of the coordination between metabolic pathways.

Indexed as

Gene Expression RegulationGenome-Wide Association StudyMetabolic Networks and PathwaysMetabolomePolymorphism, Single NucleotideGenotypeHumansMaleMetabolic SyndromeMetabolomicschanging correlationgenotypingmetabolism

Identifiers

PMID40067114
PMCPMC11894804

What Socratic holds

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LicenceCC BY-NC
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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 Socratic graph.