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Related Experiment Videos

Unbiased methods for population-based association studies.

B Devlin1, K Roeder, S A Bacanu

  • 1Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, USA. devlinbj@msx.upmc.edu

Genetic Epidemiology
|January 5, 2002
PubMed
Summary
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Population substructure can cause false disease-gene links in large genetic studies. Genomic control (GC) and structured association (SA) methods correct for this, improving the reliability of genetic association studies.

Area of Science:

  • Genetics
  • Biostatistics
  • Population Genetics

Background:

  • Large-scale genetic studies utilize population-based samples and genotyping to identify disease-gene associations.
  • Population substructure is a significant confounder, potentially inducing spurious associations between genes and diseases.

Purpose of the Study:

  • To review and compare two statistical methods, genomic control (GC) and structured association (SA), for correcting population stratification in genetic association studies.
  • To evaluate the strengths and weaknesses of GC and SA in mitigating spurious findings due to population substructure.

Main Methods:

  • Genomic Control (GC): Estimates and corrects for "overdispersion" in association statistics caused by population substructure by analyzing genome-wide polymorphisms.
  • Structured Association (SA): A latent class method that models subpopulations within a heterogeneous sample, estimating individual ancestry probabilities using multiple polymorphisms.

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Main Results:

  • Both GC and SA effectively address concerns related to population substructure in genetic association analyses.
  • GC offers robustness, simplicity, and broad applicability, including DNA pooling.
  • SA provides greater statistical power in specific scenarios like admixed populations or when associations vary across subpopulations, despite increased complexity.

Conclusions:

  • GC and SA are valuable tools for correcting population stratification, enhancing the accuracy of disease-gene association studies.
  • The choice between GC and SA depends on the specific study design, population characteristics, and desired statistical power.
  • Both methods have limitations that warrant careful consideration in their application.