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Open Access Highly Accessed Research article

Geography and genography: prediction of continental origin using randomly selected single nucleotide polymorphisms

Dominic J Allocco12*, Qing Song3, Gary H Gibbons3, Marco F Ramoni14 and Isaac S Kohane14

Author Affiliations

1 Children's Hospital Informatics Program at Harvard-MIT Division of Health Sciences and Technology, Boston, MA, USA

2 Division of Cardiology, Beth Israel Deaconess Medical Center, Boston, MA, USA

3 Cardiovascular Research Institute, Morehouse School of Medicine, Atlanta, GA, USA

4 Harvard Partners Center for Genetics and Genomics, Boston, MA, USA

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BMC Genomics 2007, 8:68  doi:10.1186/1471-2164-8-68

Published: 10 March 2007

Abstract

Background

Recent studies have shown that when individuals are grouped on the basis of genetic similarity, group membership corresponds closely to continental origin. There has been considerable debate about the implications of these findings in the context of larger debates about race and the extent of genetic variation between groups. Some have argued that clustering according to continental origin demonstrates the existence of significant genetic differences between groups and that these differences may have important implications for differences in health and disease. Others argue that clustering according to continental origin requires the use of large amounts of genetic data or specifically chosen markers and is indicative only of very subtle genetic differences that are unlikely to have biomedical significance.

Results

We used small numbers of randomly selected single nucleotide polymorphisms (SNPs) from the International HapMap Project to train naïve Bayes classifiers for prediction of ancestral continent of origin. Predictive accuracy was tested on two independent data sets. Genetically similar groups should be difficult to distinguish, especially if only a small number of genetic markers are used. The genetic differences between continentally defined groups are sufficiently large that one can accurately predict ancestral continent of origin using only a minute, randomly selected fraction of the genetic variation present in the human genome. Genotype data from only 50 random SNPs was sufficient to predict ancestral continent of origin in our primary test data set with an average accuracy of 95%. Genetic variations informative about ancestry were common and widely distributed throughout the genome.

Conclusion

Accurate characterization of ancestry is possible using small numbers of randomly selected SNPs. The results presented here show how investigators conducting genetic association studies can use small numbers of arbitrarily chosen SNPs to identify stratification in study subjects and avoid false positive genotype-phenotype associations. Our findings also demonstrate the extent of variation between continentally defined groups and argue strongly against the contention that genetic differences between groups are too small to have biomedical significance.