title: Inferring ethnicity from mitochondrial DNA sequence.
abstract: BACKGROUND
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Computer Science and Engineering Department, University of Connecticut, Storrs, CT, USA. chihlee@engr.uconn.edu.
authors.forename: Chih
authors.initials: C
authors.lastname: Lee
authors.forename: Ion I
authors.initials: II
authors.lastname: Măndoiu
authors.forename: Craig E
authors.initials: CE
authors.lastname: Nelson