Properties of structured association approaches to detecting population stratification.
OBJECTIVE
To examine the properties of the structured association approach for the detection and correction of population stratification.


METHODS
A method is developed, within a latent class analysis framework, similar to the methods proposed by Satten et al. (2001) and Pritchard et al. (2000). A series of simulations illustrate the relative impact of number and type of loci, sample size and population structure.


RESULTS
The ability to detect stratification and assign individuals to population strata is determined for a number of different scenarios.


CONCLUSIONS
The results underline the importance of careful marker selection.
Purcell
Shaun
S
Whitehead Institute, Nine Cambridge Center, Cambridge, MA 02129, USA. soyrcekk@pngu.mgh.harvard.edu
Sham
Pak
P
D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005787:Gene Frequency; D005828:Genetics, Population / Q000706:statistics & numerical data*; D006801:Humans; D015810:Linkage Disequilibrium