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Association studies for quantitative traits in structured populations.

Silviu-Alin Bacanu1, Bernie Devlin, Kathryn Roeder

  • 1Department of Psychiatry, University of Pittsburgh School of Medicine, 3811 O'Hara Street, Pittsburgh, PA 15213, USA. roeder@stat.cmu.edu

Genetic Epidemiology
|January 5, 2002
PubMed
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Genomic control (GC) now extends to quantitative traits and multiple genes, effectively managing spurious associations from population structure. This method demonstrates good power for genetic studies, even with complex models.

Area of Science:

  • Genetics
  • Statistical genetics
  • Population genetics

Background:

  • Genetic polymorphisms are crucial for understanding common diseases.
  • Genomic control (GC) was developed to address spurious associations in population-based genetic studies.
  • Existing GC methods are limited to binary traits and single-locus models.

Purpose of the Study:

  • To generalize genomic control (GC) for quantitative traits (QT) and multilocus models.
  • To evaluate the effectiveness of GC in controlling spurious associations due to population substructure in QT analyses.
  • To assess the statistical power of GC for QT association tests.

Main Methods:

  • Statistical analysis and simulations were employed.
  • Generalized GC to accommodate quantitative traits and multilocus genetic models.

Related Experiment Videos

  • Simulated data to test GC performance under various population structures and genetic models.

    • Main Results:

    • GC successfully controls spurious associations for quantitative traits, including gene-gene interactions, in the presence of population substructure.
    • GC demonstrates good statistical power for detecting causal quantitative trait loci with 2.5-5% heritability, using both random and selected samples.
    • More complex genetic models result in smaller GC corrections, suggesting increased power when models are accurate.

    Conclusions:

    • The generalized GC method is effective for quantitative trait association studies with population structure.
    • GC provides a robust framework for genetic association studies involving complex traits and multiple genes.
    • Accurate a priori specification of genetic models enhances the power of GC analysis.