Biology Faculty Publications

Document Type

Article

Journal/Book/Conference Title Title

BMC Genomics

Volume

12

Issue

622

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.

Department

Department of Biology

Comments

First published in BMC Genomics, v. 12 n.622 (2011), 18 pages, published by BioMed Central Ltd. DOI: https://doi.org/10.1186/1471-2164-12-622

Original Publication Date

2011

DOI of published version

10.1186/1471-2164-12-622

Repository

UNI ScholarWorks, University of Northern Iowa, Rod Library

Date Digital

2011

Copyright

©2011 Lili Ding, Howard Wiener, Tilahun Abebe, Mekbib Altaye, Rodney CP Go, Carolyn Kercsmar, Greg Grabowski, Lisa J. Martin, Gurjit K. Khurana Hershey, Ranajit Chakorborty, and Tesfaye M. Baye. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Language

EN

File Format

application/pdf

Included in

Biology Commons

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