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Related Experiment Video

Updated: Jan 27, 2026

Identification of Coding and Non-coding RNA Classes Expressed in Swine Whole Blood

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Published on: November 28, 2018

Coalitional Game Theory Facilitates Identification of Non-Coding Variants Associated With Autism.

Min Woo Sun¹, Anika Gupta¹, Maya Varma¹

¹Departments of Pediatrics (Division of Systems Medicine), Psychiatry (by courtesy), and Biomedical Data Science, Stanford University, Stanford, CA, USA.

Biomedical Informatics Insights

|March 20, 2019

Summary

Coalitional game theory identified 30 non-coding genetic positions contributing to autism spectrum disorder (ASD) risk. This method reveals how genetic variants cooperate to influence complex neurodevelopmental disorders like ASD.

Keywords:

autism spectrum disorder coalitional game theory non-coding genome

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Area of Science:

Genetics
Computational Biology
Neurodevelopmental Disorders

Background:

Autism spectrum disorder (ASD) has a significant genetic component, with numerous variants contributing to its complex presentation.
The combinatorial interactions of low-penetrance genetic variants, both additive and epistatic, remain largely undefined in ASD.
Non-coding genomic regions are implicated in ASD, but their cooperative roles are understudied.

Purpose of the Study:

To extend coalitional game theory (CGT) for analyzing cooperativity among non-coding genomic regions in ASD.
To identify specific non-coding variants and regions that significantly contribute to ASD genetic architecture.
To explore the role of human accelerated regions (HARs) in ASD etiology.

Main Methods:

Applied CGT to alteration data from 4595 fully sequenced genomes (756 multiplex families) for ASD cases and controls.
Encoded genomes into binary matrices focusing on three non-coding region types implicated in ASD.
Utilized the Shapley value metric to quantify individual variant contributions to ASD risk.

Main Results:

Identified 30 non-coding positions with significantly elevated Shapley values, indicating substantial contributions to ASD genetic coordination.
A subset of these mutated non-coding regions, located within HARs, are linked to biological pathways and behavioral outcomes relevant to autism.
Demonstrated the utility of CGT in uncovering influential non-coding genetic players.

Conclusions:

CGT is effective in identifying significant non-coding genetic contributors to ASD from large genomic datasets.
SNPs within HARs are important players in the genetic underpinnings of ASD.
This approach enhances understanding of the complex genetic architecture of neurodevelopmental disorders.