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Related Experiment Video

Updated: May 26, 2026

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Published on: January 9, 2020

A brain region-specific predictive gene map for autism derived by profiling a reference gene set.

Ajay Kumar¹, Catherine Croft Swanwick, Nicole Johnson

¹MindSpec, McLean, Virginia, United States of America.

|December 17, 2011

Summary

Researchers identified novel autism spectrum disorder (ASD) candidate genes using a bioinformatics approach. This predictive map highlights 442 new genes for future research into ASD

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Paradigms for Behavioral Assessment in Drosophila Model of Autism Spectrum Disorder

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Paradigms for Behavioral Assessment in Drosophila Model of Autism Spectrum Disorder

Paradigms for Behavioral Assessment in Drosophila Model of Autism Spectrum Disorder

Published on: September 6, 2024

Area of Science:

Genetics and Bioinformatics
Neuroscience
Computational Biology

Background:

The genetic basis of autism spectrum disorders (ASD) is complex and not fully understood.
Structural variations in chromosomes are increasingly linked to ASD etiology.
Identifying specific candidate genes is crucial for understanding ASD.

Purpose of the Study:

To develop a predictive map of novel candidate genes associated with autism spectrum disorders (ASD).
To leverage bioinformatics to analyze functional and expression profiles of known ASD genes.
To identify new genetic targets for future ASD research.

Main Methods:

Integrated bioinformatics approach using a reference set of 84 known ASD genes (AutRef84).
Functional profiling via Gene Ontology (GO) enrichment analysis (neurogenesis, cell adhesion, ion channels).
Expression profiling using DAVID analysis in relevant brain regions.
Genome-wide screening using the derived AutRef84 profile.

Main Results:

A composite functional and expression profile for ASD candidate genes was established.
The profile identified 460 potential ASD candidate genes, including 18 previously known ones.
442 novel putative ASD risk genes were discovered.
The ASD gene profile demonstrated disease specificity compared to control and other disease gene sets.

Conclusions:

A predictive gene map for ASD was successfully generated using a novel bioinformatics strategy.
The study identified a significant number of novel candidate genes for autism spectrum disorders.
These novel genes represent high-priority targets for future etiological and therapeutic research in ASD.