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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

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Published on: June 21, 2018

Participant identification in genetic association studies: improved methods and practical implications.

Nicholas Masca¹, Paul R Burton, Nuala A Sheehan

¹Department of Health Sciences, University of Leicester, Leicester, UK.

International Journal of Epidemiology

|December 14, 2011

Summary

This summary is machine-generated.

Determining individual participation in genomic studies is possible but depends on factors like ancestry and sample size. Researchers propose a generalized estimating equation approach for robust analysis, recommending protected access for sensitive summary statistics.

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Published on: August 21, 2016

Area of Science:

Genomic privacy
Statistical genetics
Bioinformatics

Background:

A prior method for identifying individuals in genomic mixtures raised concerns about public data sharing.
Genome-wide association studies (GWAS) require data sharing for scientific advancement, but participant confidentiality is paramount.
Clarifying theoretical underpinnings and evaluating performance are crucial for safe web dissemination of summary genomic data.

Purpose of the Study:

To evaluate the feasibility of identifying study participants from aggregate statistics.
To develop a robust statistical method for analyzing genomic mixture data.
To inform guidelines for publishing summary statistics from genetic studies.

Main Methods:

Proposed a generalized estimating equation (GEE) approach for analyzing genomic mixture data.
Maintained the simplicity of linear regression while enhancing robustness to variance/covariance approximations.
Accommodated linkage disequilibrium in the statistical model.

Main Results:

Confirmed that identifying a 'candidate' individual's participation is theoretically possible using aggregate statistics.
Demonstrated critical dependence of identification methods on participant ancestry, case/control ratios, and SNP count.
Highlighted the limitations of single-criterion publication guidelines.

Conclusions:

Simple publication guidelines are insufficient for protecting participant privacy.
Advocated against open web posting of 'directed' summary statistics.
Recommended protected access mechanisms, such as internet-based checks, for sensitive genomic data.