Evidence map›Paper›PMID 31765992›Full record

ArticleiScience2019

A Drive to Driven Model of Mapping Intraspecific Interaction Networks.

Libo Jiang, Jian Xu, Mengmeng Sang, Yan Zhang, Meixia Ye, Hanyuan Zhang, Biyin Wu, Youxiu Zhu, Peng Xu, Ruyu Tai and 7 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Disentangling leaf-microbiome interactions inFrontiers in plant science · 2022
    Article
  2. Review
  3. Article
  4. Article
  5. Review
  6. 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

17 authors.

Libo JiangBeijing Advanced Innovation Center for Tree Breeding by Molecular Design, Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China.
Jian XuKey Laboratory of Aquatic Genomics, Ministry of Agriculture, CAFS Key Laboratory of Aquatic Genomics and Beijing Key Laboratory of Fishery Biotechnology, Chinese Academy of Fishery Sciences, Beijing 100141, China.
Mengmeng SangBeijing Advanced Innovation Center for Tree Breeding by Molecular Design, Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China.
Yan ZhangKey Laboratory of Aquatic Genomics, Ministry of Agriculture, CAFS Key Laboratory of Aquatic Genomics and Beijing Key Laboratory of Fishery Biotechnology, Chinese Academy of Fishery Sciences, Beijing 100141, China.
Meixia YeBeijing Advanced Innovation Center for Tree Breeding by Molecular Design, Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China.
Hanyuan ZhangKey Laboratory of Aquatic Genomics, Ministry of Agriculture, CAFS Key Laboratory of Aquatic Genomics and Beijing Key Laboratory of Fishery Biotechnology, Chinese Academy of Fishery Sciences, Beijing 100141, China.
Biyin WuKey Laboratory of Aquatic Genomics, Ministry of Agriculture, CAFS Key Laboratory of Aquatic Genomics and Beijing Key Laboratory of Fishery Biotechnology, Chinese Academy of Fishery Sciences, Beijing 100141, China.
Youxiu ZhuKey Laboratory of Aquatic Genomics, Ministry of Agriculture, CAFS Key Laboratory of Aquatic Genomics and Beijing Key Laboratory of Fishery Biotechnology, Chinese Academy of Fishery Sciences, Beijing 100141, China.
Peng XuKey Laboratory of Aquatic Genomics, Ministry of Agriculture, CAFS Key Laboratory of Aquatic Genomics and Beijing Key Laboratory of Fishery Biotechnology, Chinese Academy of Fishery Sciences, Beijing 100141, China; Fujian Collaborative Innovation Center for Exploitation and Utilization of Marine Biological Resources, Xiamen University, Xiamen, Fujian 361102, China.
Ruyu TaiKey Laboratory of Aquatic Genomics, Ministry of Agriculture, CAFS Key Laboratory of Aquatic Genomics and Beijing Key Laboratory of Fishery Biotechnology, Chinese Academy of Fishery Sciences, Beijing 100141, China.
Zixia ZhaoKey Laboratory of Aquatic Genomics, Ministry of Agriculture, CAFS Key Laboratory of Aquatic Genomics and Beijing Key Laboratory of Fishery Biotechnology, Chinese Academy of Fishery Sciences, Beijing 100141, China.
Yanliang JiangKey Laboratory of Aquatic Genomics, Ministry of Agriculture, CAFS Key Laboratory of Aquatic Genomics and Beijing Key Laboratory of Fishery Biotechnology, Chinese Academy of Fishery Sciences, Beijing 100141, China.
Chuanju DongKey Laboratory of Aquatic Genomics, Ministry of Agriculture, CAFS Key Laboratory of Aquatic Genomics and Beijing Key Laboratory of Fishery Biotechnology, Chinese Academy of Fishery Sciences, Beijing 100141, China; College of Fishery, Henan Normal University, Xinxiang, Henan 453007, China.
Lidan SunBeijing Advanced Innovation Center for Tree Breeding by Molecular Design, Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China.
Christopher H GriffinApplied Research Laboratory, The Pennsylvania State University, University Park, PA 16802, USA.
Claudia GragnoliDivision of Endocrinology, Diabetes, and Metabolic Disease, Translational Medicine, Department of Medicine, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA 19106, USA; Molecular Biology Laboratory, Bios Biotech Multi Diagnostic Health Center, Rome 00197, Italy.
Rongling WuBeijing Advanced Innovation Center for Tree Breeding by Molecular Design, Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China; Center for Statistical Genetics, The Pennsylvania State University, Hershey, PA 17033, USA. Electronic address: rwu@bjfu.edu.cn.

Funding

Genetic Neuro-Endocrine Role in Depression and Type 2 DiabetesR01HD086911 · NICHD · THOMAS JEFFERSON UNIVERSITY · PI GRAGNOLI, CLAUDIA · 2016 to 2020
$1.5M
NICHD NIH HHS R01 HD086911
6 · The paper itself

Abstract

Community ecology theory suggests that an individual's phenotype is determined by the phenotypes of its coexisting members to the extent at which this process can shape community evolution. Here, we develop a mapping theory to identify interaction quantitative trait loci (QTL) governing inter-individual dependence. We mathematically formulate the decision-making strategy of interacting individuals. We integrate these mathematical descriptors into a statistical procedure, enabling the joint characterization of how QTL drive the strengths of ecological interactions and how the genetic architecture of QTL is driven by ecological networks. In three fish full-sib mapping experiments, we identify a set of genome-wide QTL that control a range of societal behaviors, including mutualism, altruism, aggression, and antagonism, and find that these intraspecific interactions increase the genetic variation of body mass by about 50%. We showcase how the interaction QTL can be used as editors to reconstruct and engineer new social networks for ecological communities.

Indexed as

Biological SciencesEvolutionary EcologyMathematical Biosciences

Identifiers

PMID31765992
PMCPMC6883333

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.