title: Choice of population structure informative principal components for adjustment in a case-control study.
abstract: BACKGROUND
  There are many ways to perform adjustment for population structure. It remains unclear what the optimal approach is and whether the optimal approach varies by the type of samples and substructure present. The simplest and most straightforward approach is to adjust for the continuous principal components (PCs) that capture ancestry. Through simulation, we explored the issue of which ancestry informative PCs should be adjusted for in an association model to control for the confounding nature of population structure while maintaining maximum power. A thorough examination of selecting PCs for adjustment in a case-control study across the possible structure scenarios that could occur in a genome-wide association study has not been previously reported.
  
  
  RESULTS
  We found that when the SNP and phenotype frequencies do not vary over the sub-populations, all methods of selection provided similar power and appropriate Type I error for association. When the SNP is not structured and the phenotype has large structure, then selection methods that do not select PCs for inclusion as covariates generally provide the most power. When there is a structured SNP and a non-structured phenotype, selection methods that include PCs in the model have greater power. When both the SNP and the phenotype are structured, all methods of selection have similar power.
  
  
  CONCLUSIONS
  Standard practice is to include a fixed number of PCs in genome-wide association studies. Based on our findings, we conclude that if power is not a concern, then selecting the same set of top PCs for adjustment for all SNPs in logistic regression is a strategy that achieves appropriate Type I error. However, standard practice is not optimal in all scenarios and to optimize power for structured SNPs in the presence of unstructured phenotypes, PCs that are associated with the tested SNP should be included in the logistic model.
authors.affiliation: Department of Biostatistics, Boston University School of Public Health, 801 Massachusetts Avenue, Boston MA 02118, USA.
authors.forename: Gina M
authors.initials: GM
authors.lastname: Peloso
authors.forename: Kathryn L
authors.initials: KL
authors.lastname: Lunetta
mesh_terms: D001827:Body Height; D016022:Case-Control Studies; D003198:Computer Simulation; D005787:Gene Frequency; D005828:Genetics, Population / Q000379:methods*; D055106:Genome-Wide Association Study; D006801:Humans; D016015:Logistic Models; D015233:Models, Statistical; D016017:Odds Ratio; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D025341:Principal Component Analysis / Q000379:methods*