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Open Access Research article

Comparison of measures of marker informativeness for ancestry and admixture mapping

Lili Ding1, Howard Wiener2, Tilahun Abebe3, Mekbib Altaye1, Rodney CP Go2, Carolyn Kercsmar1, Greg Grabowski1, Lisa J Martin1, Gurjit K Khurana Hershey1, Ranajit Chakorborty4 and Tesfaye M Baye1*

Author Affiliations

1 Cincinnati Children's Hospital Medical Center, Department of Pediatrics, University of Cincinnati, Cincinnati, OH, USA

2 Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA

3 Department of Biology, University of Northern Iowa, Cedar Falls, IA, USA

4 Center for Computational Genomics, Institute of Applied Genetics, Department of Forensic and Investigative Genetics, University of North Texas Health Science Center, Fort Worth, TX, USA

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BMC Genomics 2011, 12:622  doi:10.1186/1471-2164-12-622

Published: 20 December 2011

Abstract

Background

Admixture mapping is a powerful gene mapping approach for an admixed population formed from ancestral populations with different allele frequencies. The power of this method relies on the ability of ancestry informative markers (AIMs) to infer ancestry along the chromosomes of admixed individuals. In this study, more than one million SNPs from HapMap databases and simulated data have been interrogated in admixed populations using various measures of ancestry informativeness: Fisher Information Content (FIC), Shannon Information Content (SIC), F statistics (FST), Informativeness for Assignment Measure (In), and the Absolute Allele Frequency Differences (delta, δ). The objectives are to compare these measures of informativeness to select SNP markers for ancestry inference, and to determine the accuracy of AIM panels selected by each measure in estimating the contributions of the ancestors to the admixed population.

Results

FST and In had the highest Spearman correlation and the best agreement as measured by Kappa statistics based on deciles. Although the different measures of marker informativeness performed comparably well, analyses based on the top 1 to 10% ranked informative markers of simulated data showed that In was better in estimating ancestry for an admixed population.

Conclusions

Although millions of SNPs have been identified, only a small subset needs to be genotyped in order to accurately predict ancestry with a minimal error rate in a cost-effective manner. In this article, we compared various methods for selecting ancestry informative SNPs using simulations as well as SNP genotype data from samples of admixed populations and showed that the In measure estimates ancestry proportion (in an admixed population) with lower bias and mean square error.