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A comparison of association methods correcting for population stratification in case-control studies
Chengqing Wu1, Andrew DeWan, Josephine Hoh
1Department of Epidemiology and Public Health, Yale University, New Haven, CT 06510, USA.
Annals of Human Genetics
|February 2, 2011
Summary
Population stratification in genetic studies can yield false disease associations. Principal component-based logistic regression (PCA-L) and LAPSTRUCT effectively correct for population structure, outperforming other methods in case-control association studies.
Area of Science:
- Population Genetics
- Statistical Genomics
- Bioinformatics
Background:
- Population stratification poses a significant challenge in case-control studies, potentially leading to spurious disease-marker associations or reduced statistical power.
- Accurate control for population structure is crucial for reliable genetic association findings.
Purpose of the Study:
- To evaluate and compare the performance of six distinct methods designed to correct for population stratification in case-control association studies.
- To assess the effectiveness of these methods across various population structure models, including admixture scenarios for unrelated samples.
Main Methods:
- Comparative analysis of six population stratification correction methods: genomic control (GC), EIGENSTRAT, principal component-based logistic regression (PCA-L), LAPSTRUCT, ROADTRIPS, and EMMAX.
- Inclusion of the uncorrected Armitage test as a baseline for comparison.
- Extensive simulation studies incorporating diverse population structure models for unrelated individuals.
Main Results:
- PCA-L and LAPSTRUCT demonstrated robust performance across all simulated scenarios.
- GC, ROADTRIPS, and EMMAX showed limitations in correcting stratification at single nucleotide polymorphisms (SNPs) with high differentiation between ancestral populations.
- EIGENSTRAT, PCA-L, and LAPSTRUCT were comparable and generally superior to GC and ROADTRIPS.
- EMMAX exhibited the highest power under the tested population structure settings for unrelated individuals.
- The uncorrected Armitage test displayed inflated Type I error due to the lack of stratification adjustment.
Conclusions:
- PCA-L and LAPSTRUCT are recommended for effectively correcting population stratification in case-control genetic association studies.
- EIGENSTRAT, PCA-L, and LAPSTRUCT offer reliable performance, outperforming GC and ROADTRIPS in most situations.
- Careful consideration of the chosen method is essential to avoid misleading genetic association results.
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