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Comparison of population-based association study methods correcting for population stratification.

Zhang F, Wang Y, Deng HW - PLoS ONE (2008)

Bottom Line: We found that the performance of PCA was very stable under various scenarios.GC appeared to be strongly conservative in significantly stratified populations.Our study intends to provide a practical guideline for researchers to select proper study methods and make appropriate inference of the results in population-based association studies.

View Article: PubMed Central - PubMed

Affiliation: Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China.

ABSTRACT
Population stratification can cause spurious associations in population-based association studies. Several statistical methods have been proposed to reduce the impact of population stratification on population-based association studies. We simulated a set of stratified populations based on the real haplotype data from the HapMap ENCODE project, and compared the relative power, type I error rates, accuracy and positive prediction value of four prevailing population-based association study methods: traditional case-control tests, structured association (SA), genomic control (GC) and principal components analysis (PCA) under various population stratification levels. Additionally, we evaluated the effects of sample sizes and frequencies of disease susceptible allele on the performance of the four analytical methods in the presence of population stratification. We found that the performance of PCA was very stable under various scenarios. Our comparison results suggest that SA and PCA have comparable performance, if sufficient ancestral informative markers are used in SA analysis. GC appeared to be strongly conservative in significantly stratified populations. It may be better to apply GC in the stratified populations with low stratification level. Our study intends to provide a practical guideline for researchers to select proper study methods and make appropriate inference of the results in population-based association studies.

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Performance of the four analytical methods in stratified populations with stratification levels varying from 0.3−0.3 to 0.5−0.1 (sample size = 1200, frequency of disease susceptible allele = 0.20±0.02 and number of AIMs = 40).
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pone-0003392-g001: Performance of the four analytical methods in stratified populations with stratification levels varying from 0.3−0.3 to 0.5−0.1 (sample size = 1200, frequency of disease susceptible allele = 0.20±0.02 and number of AIMs = 40).

Mentions: The comparison results of the four association study methods under different scenarios are summarized in figures 1∼4. It is obvious that the performance of all analytical methods is affected by various parameters investigated here. The effects of each parameter on the performance of the four analytical methods are detailed in the following:


Comparison of population-based association study methods correcting for population stratification.

Zhang F, Wang Y, Deng HW - PLoS ONE (2008)

Performance of the four analytical methods in stratified populations with stratification levels varying from 0.3−0.3 to 0.5−0.1 (sample size = 1200, frequency of disease susceptible allele = 0.20±0.02 and number of AIMs = 40).
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Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2562035&req=5

pone-0003392-g001: Performance of the four analytical methods in stratified populations with stratification levels varying from 0.3−0.3 to 0.5−0.1 (sample size = 1200, frequency of disease susceptible allele = 0.20±0.02 and number of AIMs = 40).
Mentions: The comparison results of the four association study methods under different scenarios are summarized in figures 1∼4. It is obvious that the performance of all analytical methods is affected by various parameters investigated here. The effects of each parameter on the performance of the four analytical methods are detailed in the following:

Bottom Line: We found that the performance of PCA was very stable under various scenarios.GC appeared to be strongly conservative in significantly stratified populations.Our study intends to provide a practical guideline for researchers to select proper study methods and make appropriate inference of the results in population-based association studies.

View Article: PubMed Central - PubMed

Affiliation: Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China.

ABSTRACT
Population stratification can cause spurious associations in population-based association studies. Several statistical methods have been proposed to reduce the impact of population stratification on population-based association studies. We simulated a set of stratified populations based on the real haplotype data from the HapMap ENCODE project, and compared the relative power, type I error rates, accuracy and positive prediction value of four prevailing population-based association study methods: traditional case-control tests, structured association (SA), genomic control (GC) and principal components analysis (PCA) under various population stratification levels. Additionally, we evaluated the effects of sample sizes and frequencies of disease susceptible allele on the performance of the four analytical methods in the presence of population stratification. We found that the performance of PCA was very stable under various scenarios. Our comparison results suggest that SA and PCA have comparable performance, if sufficient ancestral informative markers are used in SA analysis. GC appeared to be strongly conservative in significantly stratified populations. It may be better to apply GC in the stratified populations with low stratification level. Our study intends to provide a practical guideline for researchers to select proper study methods and make appropriate inference of the results in population-based association studies.

Show MeSH