Evidence map›Paper›PMID 32787845›Full record

ArticleBMC bioinformatics2020

Game theoretic centrality: a novel approach to prioritize disease candidate genes by combining biological networks with the Shapley value.

Min Woo Sun, Stefano Moretti, Kelley M Paskov, Nate T Stockham, Maya Varma, Brianna S Chrisman, Peter Y Washington, Jae-Yoon Jung, Dennis P Wall

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.0field-weighted citation impact, top 26% of its field
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

7 citing papers in PubMed, 18 citations in OpenAlex.

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  7. Journal of biosciences · 2022
    Review
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

9 authors at 3 institutions in 2 countries.

Min Woo SunDepartment of Biomedical Data Science, Stanford University, Stanford, USA.
Stefano MorettiLAMSADE, CNRS, Université Paris-Dauphine, Université PSL, Paris, France.
Kelley M PaskovDepartment of Biomedical Data Science, Stanford University, Stanford, USA.
Nate T StockhamDepartment of Neuroscience, Stanford University, Stanford, USA.
Maya VarmaDepartment of Computer Science, Stanford University, Stanford, USA.
Brianna S ChrismanDepartment of Bioengineering, Stanford University, Stanford, USA.
Peter Y WashingtonDepartment of Bioengineering, Stanford University, Stanford, USA.
Jae-Yoon JungDepartment of Biomedical Data Science, Stanford University, Stanford, USA.
Dennis P WallDepartment of Biomedical Data Science, Stanford University, Stanford, USA. dpwall@stanford.edu.ORCID http://orcid.org/0000-0002-7889-9146
Stanford University · USCentre National de la Recherche Scientifique · FRStanford Medicine · US

Funding

POSTDOCTORAL TRAINING IN MEDICAL INFORMATION SCIENCEST15LM007033 · NLM · STANFORD UNIVERSITY · PI SYLVIA KATINA PLEVRITIS · 1985 to 2026
$25.5M
Biomedical Data Science Graduate Training at StanfordT32LM012409 · NLM · STANFORD UNIVERSITY · PI PLEVRITIS, SYLVIA KATINA · 2016 to 2020
$1.5M
NLM NIH HHS T15 LM007033NLM NIH HHS T32 LM012409
6 · The paper itself

Abstract

backgroundComplex human health conditions with etiological heterogeneity like Autism Spectrum Disorder (ASD) often pose a challenge for traditional genome-wide association study approaches in defining a clear genotype to phenotype model. Coalitional game theory (CGT) is an exciting method that can consider the combinatorial effect of groups of variants working in concert to produce a phenotype. CGT has been applied to associate likely-gene-disrupting variants encoded from whole genome sequence data to ASD; however, this previous approach cannot take into account for prior biological knowledge. Here we extend CGT to incorporate a priori knowledge from biological networks through a game theoretic centrality measure based on Shapley value to rank genes by their relevance-the individual gene's synergistic influence in a gene-to-gene interaction network. Game theoretic centrality extends the notion of Shapley value to the evaluation of a gene's contribution to the overall connectivity of its corresponding node in a biological network.

resultsWe implemented and applied game theoretic centrality to rank genes on whole genomes from 756 multiplex autism families. Top ranking genes with the highest game theoretic centrality in both the weighted and unweighted approaches were enriched for pathways previously associated with autism, including pathways of the immune system. Four of the selected genes HLA-A, HLA-B, HLA-G, and HLA-DRB1-have also been implicated in ASD and further support the link between ASD and the human leukocyte antigen complex.

conclusionsGame theoretic centrality can prioritize influential, disease-associated genes within biological networks, and assist in the decoding of polygenic associations to complex disorders like autism.

Indexed as

AlgorithmsGame TheoryGene Regulatory NetworksGenetic Association StudiesAutism Spectrum DisorderGenome-Wide Association StudyHumansProtein Interaction MappingReproducibility of ResultsAutism spectrum disorderBiological networkCoalitional game theoryGame theoretic centralityShapley value

Identifiers

PMID32787845
PMCPMC7430867
OpenAlexW3048480439

What OpenQuestion holds

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LicenceCC BY
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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 OpenQuestion graph.