Effects of Population Structure on Genetic Association Studies
Author Information
Author(s): Xu Hongyan, Shete Sanjay
Primary Institution: University of Texas M. D. Anderson Cancer Center
Hypothesis
How can the degree of population structure be quantified, and how does the population structure affect association studies?
Conclusion
Population structure inflates p-values in genetic association studies, especially for very small p-values, and genomic control can correct for this effect if the appropriate number of markers is used.
Supporting Evidence
- Population structure can inflate p-values in genetic association studies.
- Genomic control can effectively correct for population structure when the appropriate number of markers is used.
- The study used real data from the Collaborative Study on the Genetics of Alcoholism (COGA).
- The mean of FST in the Affymetrix dataset was 0.085, indicating substantial population structure.
Takeaway
When studying genetics, if the groups of people being compared are too different, it can make it look like there are more differences than there really are. We found a way to fix this problem by using the right number of markers.
Methodology
The study used SNP data from the Collaborative Study on the Genetics of Alcoholism (COGA) to assess the effects of population structure on association studies.
Potential Biases
Potential for confounding and spurious results due to population structure.
Limitations
The study's findings may not be generalizable beyond the specific datasets used, and the exact reasons for varying the number of loci in genomic controls were not fully explored.
Participant Demographics
Among the 304 unrelated individuals, 265 were White, 30 were Black, and 9 were others.
Digital Object Identifier (DOI)
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