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Genomic control, a new approach to genetic-based association studies.

B Devlin1, K Roeder, L Wasserman

  • 1Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA. devlinbj@msx.upmc.edu

Theoretical Population Biology
|February 22, 2002
PubMed
Summary

Genomic control (GC) and Structured Association (SA) are novel methods for identifying disease-associated genes in complex human diseases. These approaches address population stratification, a common issue in genetic association studies.

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Area of Science:

  • Genetics
  • Population Genetics
  • Statistical Genetics

Background:

  • Discovering genes for complex human diseases requires new methods beyond those for simple genetic diseases.
  • Large population samples and genotyping are powerful for genetic association studies but can be confounded by population heterogeneity.
  • Population substructure can lead to spurious gene-disease associations, necessitating robust statistical correction methods.

Purpose of the Study:

  • To introduce and detail genomic control (GC) and Structured Association (SA) as methods to overcome population stratification in genetic association studies.
  • To explain how GC and SA utilize genome-wide polymorphisms to correct for spurious associations arising from population heterogeneity.
  • To extend GC methodology to quantitative trait, case-control, haplotype, and multiallelic marker studies.

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Main Methods:

  • Genomic Control (GC): Estimates and corrects for overdispersion in association statistics caused by population substructure using genome-wide polymorphisms.
  • Structured Association (SA): Assumes a heterogeneous population comprises homogeneous subpopulations and probabilistically assigns individuals to these latent groups.
  • Extensions of GC: Application to quantitative traits, case-control studies, and analysis involving haplotypes and multiallelic markers.

Main Results:

  • GC effectively corrects for population stratification, providing reliable gene-disease association results comparable to family-based studies.
  • SA offers an alternative approach by modeling population structure to control for confounding.
  • The described extensions enhance the applicability of GC across various genetic study designs.

Conclusions:

  • Genomic control and Structured Association are crucial advancements for accurate gene discovery in complex human diseases.
  • These methods enable the use of large, convenient population-based samples while mitigating the risks of population stratification.
  • The developed extensions broaden the utility of GC for diverse genetic research scenarios, facilitating more efficient genetic liability discovery.