Inferring Ethnicity from Mitochondrial DNA Sequence

1. Inferring Ethnicity from Mitochondrial DNA Sequence Chih Lee1, Ion Mandoiu1 and Craig E. Nelson2 chih.lee@uconn.edu ion@engr.uconn.edu craig.nelson@uconn.edu 1Department of Computer Science and Engineering 2Department of Molecular and Cell Biology University of Connecticut

2. Outline • Introduction • Methods • Results and Discussions • Conclusions

3. Outline • Introduction • Methods • Results and Discussions • Conclusions

4. Ethnicity in Forensics • Ethnicity information assists forensic investigators. • Investigator-assigned ethnicity: based on genetic and non-genetic markers. • Genetic information enhances inference accuracy when accessto most informative markers (e.g. skin/hair) is limited. • Autosomal markers: • Excellent accuracy assigning samples to clades [Phi07, Shr97] • May not survivedegradation

5. Mitochondrial DNA • Circular • 16,569 bps • Maternally inherited • High copy number Recoverable from degraded samples • Coding region • SNPs define haplogroups [Beh07] • Hypervariable Region

6. Hypervariable Region • High mutation rate compared to the coding region • Haplogroup inference [Beh07] • 23 groups • 96.7% accuracy rate with 1NN • Geographic origin inference [Ege04] • SE Africa, Germany and Icelandic • 66.8% accuracy rate with PCA-QDA

7. Ethnicity Inference from HVR • The problem: • Given a set of HVR sequences tagged with ethnicities • Predict the ethnicities of new HVR sequences • A classification problem • Our contribution: • Assess the performance of 4 classification algorithms: SVM, LDA, QDA and 1NN.

8. Outline • Introduction • Methods • Results and Discussions • Conclusions

9. Encoding HVR • Align to rCRS (revised Cambridge reference sequence)  SNP profile • a SNP  a binary variable • Missing data (not typed regions) • Assume rCRS • Use mutation probability • Common region

10. Support Vector Machines • Binary classification algorithm • Map instances to high-D space (the feature space) • Optimal separating hyperplane with max margins • Kernel function k(x1,x2): similarity x1 and x2 between in the feature space • Radial basis kernel: exp(-γ||x1-x2||2) • Software: LIBSVM [Cha01]

11. Linear/Quadratic Discriminant Analysis • Find argmaxg P(G=g|X=x) • Assumptions: • X|G=g ~Np(μg, Σg) • P(G=g)’s are equal for all g • P(G=g|X=x) prop. to P(X=x|G=g) • μg and Σg are estimated by the training data • LDA: common dispersion matrix Σg = Σ for all g

12. 1-Nearest Neighbor • Assign a new sample to the dominating ethnicity among the nearest samples in the training data • Distance measure: the Hamming distance • Used by Behar et al. (2007) for haplogroup assignment

13. Principal Component Analysis • A dimension reduction technique • Used in conjunction with SVM, LDA and QDA • Denoted as: PCA-SVM, PCA-LDA and PCA-QDA

14. Outline • Introduction • Methods • Results and Discussions • Conclusions

15. The FBI mtDNA Population Database • Two tables: • forensic: typed by FBI • published: collected from literature • Retain only Caucasian, African, Asian and Hispanic samples

16. Data Coverage and Subsets • Variable sequence lengths • trimmed forensic dataset (4,426) • 16024-16365 • trimmed published dataset (1,904) • 16024-16365 • full-length forensic dataset (2,540) • 16024-16569, 1-576 forensic published

17. 5-fold Cross-Validation (trimmed forensic) • Macro-Accuracy: Average of ethnicity-wise accuracy rates • Micro-Accuracy: Weighted by # Samples • More accurate than Egeland et al. (2004) • Matches human experts depending on skull and large bones [Dib83, isc83]

18. Seq. Region Effect on Accuracy 100% 90% 80% full-length forensic dataset • Different primers result in different coverage. • PCA-LDA outperforms 1NN on long sequences. • PCA-SVM is consistently the best.

19. Seq. Region Effect on Accuracy 80% 100% 90% full-length forensic dataset • HVR 2 contains less information. • PCA-SVM is consistently the best.

20. Twenty 10% Windows 10% 10% 10% • Accuracy varies with region. • PCA-SVM remains the best. • 1NN is as good as PCA-SVM for short regions.

21. Independent Validation (1/2) • Training data: trimmed forensic dataset • Test data: trimmed published dataset • PCA-SVM • No Hispanic samples in the test data but samples can be mis-classified as Hispanic • Asian: ~17% lower than CV

22. Independent Validation (2/2) • Composition of the Asian samples in the training data: • China (356 profiles), Japan (163), Korea (182), Pakistan (8), and Thailand (52) • Strong bias towards East Asia • 145 Mis-classified Asian samples in the test data: • 10 samples of unknown country of origin • 90 samples from Kazakhstan and Kyrgyzstan • Both countries have significant Russian population. • Evidence of admixture with Caucasians.

23. Handling Missing Data • Mimic real-world scenario • Training: forensic dataset • Test: published dataset • rCRS and Probability are biased toward Caucasian. • Common Region is the best overall.

24. Posterior Probability Calibration • PCA-SVM on published dataset with “Common Region” • Accuracy rates are slightly higher than the estimated posterior probabilities.

25. Conclusions • SVM is the most accurate algorithm among those investigated, outperforming • Discriminant analysis employed by Egeland et al. (2004) • 1NN similar to that used by Behar et al. (2007) • Overall accuracy of 80%-90% in CV and independent testing • Matches the accuracy of human experts depending on measurements of skull and large bones [Dib83,isc83] • Approaches the accuracy by using ~60 autosomal loci [Bam04]

26. Questions? • Thank you for your attention.