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Updated: Jun 7, 2026

A Strategy to Identify de Novo Mutations in Common Disorders such as Autism and Schizophrenia

A Strategy to Identify de Novo Mutations in Common Disorders such as Autism and Schizophrenia

Published on: June 15, 2011

Using global unique identifiers to link autism collections.

Stephen B Johnson¹, Glen Whitney, Matthew McAuliffe

¹Department of Biomedical Informatics, Columbia University, New York, New York 10032, USA. sbj2@columbia.edu

Journal of the American Medical Informatics Association : JAMIA

|October 22, 2010

Summary

This summary is machine-generated.

A new web service generates global unique identifiers to link research data and specimens, successfully tested on simulated and real autism study participants. This method enhances data linkage and research reproducibility.

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A Strategy to Identify de Novo Mutations in Common Disorders such as Autism and Schizophrenia

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Published on: June 15, 2011

Eye Tracking Young Children with Autism

Eye Tracking Young Children with Autism

Published on: March 27, 2012

Area of Science:

Biomedical Informatics
Data Management
Research Infrastructure

Background:

Linking disparate research data collections is crucial for comprehensive analysis.
Existing methods for data linkage often lack standardization and scalability.
Autism research generates large, complex datasets requiring robust identification systems.

Purpose of the Study:

To propose and evaluate a centralized web service for generating global unique identifiers (GUIDs).
To enable secure and efficient linking of research data and biological specimens.
To enhance data discoverability and facilitate research reproducibility in autism studies.

Main Methods:

A web service was developed collaboratively by the Simons Foundation Autism Research Initiative and the National Database for Autism Research.
Investigators input participant information into a client application, which sends encrypted data to a server for GUID generation.
System performance was evaluated through a large-volume simulation (one million individuals) and a field test (2000 families, >8000 participants).

Main Results:

Large-volume simulation demonstrated no instances of false identity or false data splits.
Field testing successfully generated identifiers for 96% of children and 77% of parents in an autism study cohort.
An average of 80% of hash codes per subject were generated successfully, exceeding the requirement for data matching.

Conclusions:

The developed system effectively balances individual distinctiveness, data accuracy, and confidentiality.
Successful implementation requires careful navigation of institutional review board approvals, participant consent, and site compliance.
Global unique identifiers offer significant potential for linking research data, augmenting data availability, and supporting research replication.