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Clustering by genetic ancestry using genome-wide SNP data

Nadia Solovieff1*, Stephen W Hartley1, Clinton T Baldwin2, Thomas T Perls3, Martin H Steinberg4 and Paola Sebastiani1

Author Affiliations

1 Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA

2 Center for Human Genetics, Boston University School of Medicine, Boston, MA, 02118, USA

3 Geriatrics Division, Department of Medicine, Boston University School of Medicine, Boston, MA, 02118, USA

4 Department of Medicine, Boston University School of Medicine, Boston, MA, 02118, USA

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BMC Genetics 2010, 11:108  doi:10.1186/1471-2156-11-108

Published: 9 December 2010



Population stratification can cause spurious associations in a genome-wide association study (GWAS), and occurs when differences in allele frequencies of single nucleotide polymorphisms (SNPs) are due to ancestral differences between cases and controls rather than the trait of interest. Principal components analysis (PCA) is the established approach to detect population substructure using genome-wide data and to adjust the genetic association for stratification by including the top principal components in the analysis. An alternative solution is genetic matching of cases and controls that requires, however, well defined population strata for appropriate selection of cases and controls.


We developed a novel algorithm to cluster individuals into groups with similar ancestral backgrounds based on the principal components computed by PCA. We demonstrate the effectiveness of our algorithm in real and simulated data, and show that matching cases and controls using the clusters assigned by the algorithm substantially reduces population stratification bias. Through simulation we show that the power of our method is higher than adjustment for PCs in certain situations.


In addition to reducing population stratification bias and improving power, matching creates a clean dataset free of population stratification which can then be used to build prediction models without including variables to adjust for ancestry. The cluster assignments also allow for the estimation of genetic heterogeneity by examining cluster specific effects.