Evidence map›Paper›PMID 34927094›Full record

ArticleSTAR protocols2021

FunGraph: A statistical protocol to reconstruct omnigenic multilayer interactome networks for complex traits.

Ang Dong, Li Feng, Dengcheng Yang, Shuang Wu, Jinshuai Zhao, Jing Wang, Rongling Wu

Abstract read
In one paragraph

Article in STAR protocols, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers 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
  2. 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

7 authors.

Ang DongCenter for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China.
Li FengCenter for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China.
Dengcheng YangCenter for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China.
Shuang WuCenter for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China.
Jinshuai ZhaoCenter for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China.
Jing WangCenter for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China.
Rongling WuCenter for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We describe a statistical protocol of how to reconstruct and dissect functional omnigenic multilayer interactome networks that mediate complex dynamic traits in a genome-wide association study (GWAS). This protocol, named FunGraph, can analyze how each locus affects phenotypic variation through its own direct effect and a complete set of indirect effects due to regulation by other loci co-existing in large-scale networks. FunGraph is applicable to any GWAS aimed to characterize the genetic architecture of dynamic phenotypic traits. For complete details on the use and execution of this protocol, please refer to Wang et al. (2021).

Indexed as

Databases, GeneticModels, StatisticalComputational BiologyGenome-Wide Association StudyMultifactorial InheritancePhenotypeSoftwareBioinformaticsComputer sciencesGeneticsSystems biology

Identifiers

PMID34927094
PMCPMC8649398

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.