This article is part of the supplement: Selected Proceedings of the 6th International Symposium on Bioinformatics Research and Applications (ISBRA'10)
Inferring ethnicity from mitochondrial DNA sequence
1 Computer Science and Engineering Department, University of Connecticut, Storrs, CT, USA
2 Molecular and Cell Biology Department, University of Connecticut, Storrs, CT, USA
BMC Proceedings 2011, 5(Suppl 2):S11 doi:10.1186/1753-6561-5-S2-S11Published: 28 April 2011
The assignment of DNA samples to coarse population groups can be a useful but difficult task. One such example is the inference of coarse ethnic groupings for forensic applications. Ethnicity plays an important role in forensic investigation and can be inferred with the help of genetic markers. Being maternally inherited, of high copy number, and robust persistence in degraded samples, mitochondrial DNA may be useful for inferring coarse ethnicity. In this study, we compare the performance of methods for inferring ethnicity from the sequence of the hypervariable region of the mitochondrial genome.
We present the results of comprehensive experiments conducted on datasets extracted from the mtDNA population database, showing that ethnicity inference based on support vector machines (SVM) achieves an overall accuracy of 80-90%, consistently outperforming nearest neighbor and discriminant analysis methods previously proposed in the literature. We also evaluate methods of handling missing data and characterize the most informative segments of the hypervariable region of the mitochondrial genome.
Support vector machines can be used to infer coarse ethnicity from a small region of mitochondrial DNA sequence with surprisingly high accuracy. In the presence of missing data, utilizing only the regions common to the training sequences and a test sequence proves to be the best strategy. Given these results, SVM algorithms are likely to also be useful in other DNA sequence classification applications.