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Case-control association studies in mixed populations: correcting using genomic control.

Dvora Shmulewitz1, Junying Zhang, David A Greenberg

  • 1Division of Statistical Genetics, Department of Biostatistics, Columbia University, New York, NY, USA.

Human Heredity
|April 7, 2005
PubMed
Summary

Genomic control (GC) methods for correcting population stratification in genetic studies can lead to reduced power to detect true associations or fail to eliminate spurious ones. Its effectiveness varies, making application challenging.

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Area of Science:

  • Genetics
  • Population Genetics
  • Statistical Genetics

Background:

  • Case-control association studies in admixed populations risk spurious disease-marker associations due to differing subpopulation disease prevalence and marker frequencies.
  • Genomic control (GC) is a method to correct for such spurious associations caused by population stratification.

Purpose of the Study:

  • To evaluate the effectiveness of genomic control (GC) methods in correcting for population stratification in genetic association studies.
  • To determine how well GC performs under varying conditions of subpopulation prevalence and marker frequency differences.

Main Methods:

  • Simulated admixed populations with distinct disease and marker frequencies but no true marker-disease association.
  • Generated case-control datasets and calculated chi-squared statistics for marker-disease association.

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  • Applied two GC procedures: correction using the mean chi-squared value and the median chi-squared value (divided by 0.456).

    • Main Results:

    • GC corrections became conservative (false positive rate <5%) as subpopulation prevalence and marker frequency differences increased.
    • The mean correction method maintained false positive rates near 5% with average allele frequency differences <0.26, but became conservative with few markers showing large differences.
    • The median correction was generally conservative, but became anticonservative when markers with substantial frequency differences were included.

    Conclusions:

    • Genomic control can lead to a loss of statistical power (conservative) or fail to eliminate spurious associations (anticonservative).
    • The mean correction factor can be useful for population stratification but its applicability is context-dependent and difficult to ascertain.
    • The performance of GC is sensitive to the degree of population stratification and marker frequency differences.