1. The new Y Chromosome Haplotype Reference Database (YHRD) and optimized approaches for the forensic Y-STR analysis Sascha Willuweit & Lutz Roewer Institut für Rechtsmedizin und Forensische Wissenschaften Charité – Universitätsmedizin Berlin 20002004 2008 2014 ©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015

2. Workshop schedule 2015, September 1st, 2:30 pm – 6:30 pm Different frequency estimation methods implemented in the YHRD Mixture analysis using the YHRD Kinship analysis using the YHRD Ancestry information retrievable from YHRD Subpopulation analysis (AMOVA) using YHRD Discussion of casework examples ©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015

3. YHRD - Increasing numbers

5. Frequency estimation ©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015

6. Frequency estimation methods Constant estimators Augmented counting (1/n+1) Counting with database inflation (Brenner‘s κ) Variable estimators Surveying method (Krawczak) Coalescence based estimation (Caliebe) Discrete Laplace method (Andersen) ©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015 Enabled in YHRD

7. Frequency estimation for Y-STR profiles ©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015

9. 23 loci 17 loci 9 loci Frequency estimation for rare haplotypes with „Kappa inflation“ (0 observations) Count Singletons with kappa ©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015 19.593 71.246 55.675 3.0 x 10 -6 19.593 n.a. 125.700 30.450 6.0 x 10 -6 K=0.78 K=0.24 (1.4 x 10 -5 )* (7.9 x 10 -6 )* * counting  - proportion of singletons estimator of the proportion of not sampled rare haplotypes in the database

10. ©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015

11. Comparison of estimators for rare haplotypes Discrete Laplace vs. counting, kappa and surveying methods using a simulated population of 1 million, with a database size of 1000 and a kappa proportion of singletons of  =0.864 ©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015 Courtesy of M.M. Andersen (Copenhagen)

12. Fig. 1 Comparison of (1) the relative frequency of a haplotype (number of times it has been observed divided by the database size) and (2) the estimated haplotype frequency using the discrete Laplace method. Note, that for frequently observed haplotypes, t... Mikkel Meyer Andersen, Poul Svante Eriksen, Niels Morling Cluster analysis of European Y-chromosomal STR haplotypes using the discrete Laplace method Forensic Science International: Genetics, Volume 11, 2014, 182 - 194 ©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015

13. Interpretation tools implemented in YHRD Mixture analysis (Frequency and LR based) Kinship calculation (Frequency and LR based) Population substructure (AMOVA, Fst/R st, MDS) Ancestry information (AI) ©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015

14. Mixture analysis ©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015

15. male mixture (major, minor component) only ♀ component no ♂ admixture in AMELOGENIN Autosomal analysisY chromosomal analysis Casework example Delict: sexual assault Evidence: contact stain on clothing ©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015

16. Analyse with Mixture analysis tool (partial Y23 profiles) ©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015

17. Result for PowerPlex Y23 (20 loci) ©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015

18. Reanalysis using reduced PPY12 profiles ©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015

19. Result for PowerPlex Y12 (10 loci) ©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015

20. Reanalysis using further reduced 9-locus minHt profiles ©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015

21. Result for minHt (7 loci) ©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015

22. Kinship ©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015

23. The Y chromosom - a linearly inherited, haploid marker system ©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015

24. For which cases? ©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015

25. Likelihood Calculation (LR) / Brotherhood (Probability for observing the haplotypes given same fathers vs. probability for observing the haplotypes given different fathers) L (X) = µ/2 x [f(A) + f(B)] L (Y) = f(A) x f(B) µ = mutation rate f = haplotype frequency (YHRD) Locus-spezific µ for one-step-mutations, see YHRD For the X hypothesis for each locus the probability of „non-mutation“ (1- µ) is also considered Rolf et al. (Int J. Legal Med. 2001); Buckleton et al. (CRC Press, 2005) AB Same or different fathers? ©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015

26. Brothers? Related: L(X) = 1.4 x 10 -4 x 1 x µ/2 + 2.3 x 10 -5 x 1 x µ/2 = 2.9 x 10 -7 Unrelated: L(Y) = 1.4 x 10 -4 x 2.3 x 10 -5 = 3.2 x 10 -9 LR (X/Y) = 91 14, 13, 31, 24, 11, 13, 14, 11-11, 14, 13 14, 13, 31, 25, 11, 13, 14, 11-11, 14, 13 µ = 3.6 x 10 -3 * f B = 2.3 x10 -5 * Meioses * YHRD f A = 1.4 x 10 -4 * AB Same or different fathers ? ©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015

27. L(X) = 1.4 x 10 -4 x 1 x µ/2 = 1.5 x 10 -7 L(Y) = 1.4 x 10 -4 x 2.3 x 10 -5 = 3.2 x 10 -9 LR (X/Y) = 46.8 14, 13, 31, 24, 11, 13, 14, 11-11, 14, 13 14, 13, 31, 24, 11, 14, 14, 11-11, 14, 13 µ = 2.1 x 10 -3 (moderate)* f B = 1.4 x 10 -4 * f A = 2.3 x10 -5 * * YHRD B A Father – son or unrelated ? Influence of the local mutation rate on LR ©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015

28. L(X) = 1.4 x 10 -4 x 1 x µ/2 = 8.4 x 10 -7 L(Y) = 1.4 x 10 -4 x 2.3 x 10 -5 = 3.2 x 10 -9 LR (X/Y) = 262.5 14, 13, 30, 24, 11, 13, 14, 11-12, 14, 13 14, 13, 30, 24, 11, 14, 14, 11-12, 14, 13 µ = 1.2 x 10 -2 (rapid)* B A f B = 1.4 x 10 -4 * f A = 2.3 x10 -5 * * YHRD Father – son or unrelated ? Influence of the local mutation rate on LR ©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015

29. Common ancestor? L(X) = 1.4 x 10 -4 x 7 x µ/2 + 2.3 x 10 -5 x 5 x µ/2 = 1.1 x 10 -6 L(Y) = 1.4 x 10 -4 x 2.3 x 10 -5 = 3.2 x 10 -9 LR (X/Y) = 343 14, 13, 31, 24, 11, 13, 13, 11-12, 14, 13 14, 13, 31, 24, 11, 14, 13, 11-12, 14, 13 µ = 2.1 x 10 -3 (moderate)* f obs = 1.4 x 10 -4 * f obs = 2.3 x10 -5 * Meioses * YHRD A B 7 5 ©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015

30. Common ancestor? L(X) = 1.4 x 10 -4 x 7 x µ/2 + 2.3 x 10 -5 x 5 x µ/2 = 6.6 x 10 -6 L(Y) = 1.4 x 10 -4 x 2.3 x 10 -5 = 3.2 x 10 -9 LR (X/Y) = 2053 14, 13, 31, 24, 11, 13, 13, 11-12, 14, 13 14, 13, 31, 24, 11, 14, 13, 11-12, 14, 13 µ = 1.2 x 10 -2 (rapid)* f obs = 1.4 x 10 -4 * f obs = 2.3 x10 -5 * Meioses * YHRD A B 7 5 ©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015

31. LociMutation Rate [95% CI]MeiosesPosition[MutRate]Group[MutRate] dys438 2,96E-04101221slow dys392 4,04E-04148672slow dys393 1,09E-03137133slow dys437 1,19E-03101014slow dys448 1,65E-0366785slow dys390 2,06E-03150616medium dys385 mc 2,30E-03256207medium dys19 2,32E-03155398medium ygatah4 2,47E-0377099medium dys391 2,54E-031493510medium dys389i 2,68E-031378811medium dys635 3,72E-03752512medium dys389ii 3,78E-031375913medium dys456 4,19E-03667814medium dys481 4,97E-03174415medium dys533 5.01E-03173016medium dys439 5,35E-031009617medium dys460 6,22E-03171718medium dys458 6,74E-03667719medium dys518 1,84E-02155620fast dyf387S1ab mc 1,59E-02180421fast dys576 1,43E-02172722fast dys570 1,24E-02142623fast dys627 1,23E-02176624fast dys449 1,22E-02161725fast ©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015 Ranking of Y-STR mutation rates

32. ©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015

34. D A f (A) = 1/123* f (D) = 1/388** Likelihood Ratio (LR, KI) calculation for Y-STRs * Program uses counting (Discrete Laplace extrapolation: 1/311) ** Program uses counting (Discrete Laplace extrapolation: 1/821)

35. Population analysis (AMOVA) ©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015

36. YHRD: Test on population substructure (Fst, Rst) (Example: 17,278 Chinese individuals in 52 populations) ©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015

37. Ancestry information ©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015

38. Fast and slowly mutating Y markers Y-SNPs µ = 10 -9 - 10 -12 irreversible  stable phylogeny Y-STRs µ = 10 -3 recurrent  networks TCGAGGTATTAAC TCTAGGTATTAAC TCGAGGCATTAAC TCTAGGTGTTAAC TCGAGGTATTAGC TCTAGGTATCAAC * ** * * 17,13,30,25,10,11,13,10-14 16,13,30,25,10,11,13,10-14 17,13,31,25,10,11,13,10-14 17,13,30,24,10,11,13,10-14 17,13,30,25,10,11,13,11-14 17,13,29,25,10,11,13,10-15 17,13,30,26,10,11,13,10-14 17,13,30,25,10,11,14,10-14 17,13,30,25,11,11,13,10-14 Time 521521 324324 ©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015

39. Roewer et al. Hum Genet 2005 ©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015 Y-STR gradients (7 loci)

40. Y-SNP gradients (R1a) Fechner et al., Am J Phys Anthropology 2008 ©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015

41. Semino et al. 2004 (n = 2400) Haplogroup J2a Haplotype: 14,13,30,22,10,11,12,13-16,... Is ancestry prediction possible? Biogeographical analysis using Y doesn‘t predict nationality residency or phenotype Y markers infer very useful information the deep ancestry of a paternal lineage and its proliferation (radiation) over time until today Skeleton in a trolley, 5g femur extracted ©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015 37 Y marker analysis (Geppert et al. 2010)

42. ©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015 Unknown skeletonized person – extract, type, search and add „ancestry information“

43. ©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015 Ancestry information – three features and heat map

44. ©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015 Heat Map (searched haplotypes are reduced to the most representatively sampled minHt)

45. ©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015 Searched haplotype is compared with a database of STR+SNP typed samples

46. Hg prediction is prone to IBS errors (as evidenced by YHRD)! Mandatory: Y-SNP analysis using (mini)sequencing SNaPshot method (Hierarchical Multiplex Analysis) Geppert M & Roewer L (2012) SNaPshot® minisequencing analysis of multiple ancestry- informative Y-SNPs using capillary electrophoresis. Methods Mol Biol. 830:127-40.  J2a  Turkey, Fertile Crescent, Caucasus, Mediterranean ©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015

47. „Most frequent neighbour“ - 15,13,29,22,10,11,12,15-16 – 22 matches 15,13,30,22,10,11,12,15-16 – 2 matches to YHRD Legende: Each dot is one population sample (on average 120 individuals) with matching populations marked in red ©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015 CAVE!

48. But: SNaPshot analysis Haplogroup E-M2 highest frequency in West Africa (~ 80%) and Central Africa (~ 60%), not India Discrepancy between YSTR and YSNP distribution! ©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015

49. Part II: Casework examples ©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015 Frequency estimation Mixture Kinship Ancestry