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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 WissenschaftenCharité – Universitätsmedizin Berlin
2000 2004 2008
2014©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
Workshop schedule2015, 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
YHRD - Increasing numbers
Frequency estimation
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
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
Frequency estimation for Y-STR profiles
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
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
1N
1)hS|hT(P̂ 00
k- proportion of singletons estimator of the proportion of not sampled rare haplotypes in the database
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
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)
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
Interpretation tools implemented in YHRD
• Mixture analysis (Frequency and LR based)• Kinship calculation (Frequency and LR based)• Population substructure (AMOVA, Fst/Rst, MDS) • Ancestry information (AI)
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
Mixture analysis
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
• male mixture (major, minor component)• only component♀
• no admixture in AMELOGENIN♂
Autosomal analysis Y chromosomal analysis
Casework exampleDelict: sexual assault
Evidence: contact stain on clothing
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
Analyse with Mixture analysis tool (partial Y23 profiles)
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
Result for PowerPlex Y23 (20 loci)
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
Reanalysis using reduced PPY12 profiles
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
Result for PowerPlex Y12 (10 loci)
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
Reanalysis using further reduced 9-locus minHt profiles
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
Result for minHt (7 loci)
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
Kinship
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
The Y chromosom - a linearly inherited, haploid marker system
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
For which cases?
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
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 ratef = 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)
A B
Same ordifferent fathers?
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
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*
A B
Same ordifferent fathers ?
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
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
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
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*
fobs = 2.3 x10-5*
Meioses * YHRD
A
B
7
5
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
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*
fobs = 2.3 x10-5*
Meioses * YHRD
A
B
7
5
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
Loci Mutation Rate [95% CI] Meioses Position[MutRate] Group[MutRate]dys438 2,96E-04 10122 1 slowdys392 4,04E-04 14867 2 slowdys393 1,09E-03 13713 3 slowdys437 1,19E-03 10101 4 slowdys448 1,65E-03 6678 5 slowdys390 2,06E-03 15061 6 medium
dys385 mc 2,30E-03 25620 7 mediumdys19 2,32E-03 15539 8 medium
ygatah4 2,47E-03 7709 9 mediumdys391 2,54E-03 14935 10 mediumdys389i 2,68E-03 13788 11 mediumdys635 3,72E-03 7525 12 medium
dys389ii 3,78E-03 13759 13 mediumdys456 4,19E-03 6678 14 mediumdys481 4,97E-03 1744 15 mediumdys533 5.01E-03 1730 16 mediumdys439 5,35E-03 10096 17 mediumdys460 6,22E-03 1717 18 mediumdys458 6,74E-03 6677 19 mediumdys518 1,84E-02 1556 20 fast
dyf387S1ab mc 1,59E-02 1804 21 fastdys576 1,43E-02 1727 22 fastdys570 1,24E-02 1426 23 fastdys627 1,23E-02 1766 24 fastdys449 1,22E-02 1617 25 fast
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
Ranking of Y-STR mutation rates
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
D
Af (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)
Population analysis(AMOVA)
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
YHRD: Test on population substructure (Fst, Rst)(Example: 17,278 Chinese individuals in 52 populations)
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
Ancestry information
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
Fast and slowly mutating Y markers
• Y-SNPsµ = 10-9 - 10-12
irreversible stable phylogeny
• Y-STRsµ = 10-3
recurrent networks
TCGAGGTATTAACTCTAGGTATTAACTCGAGGCATTAACTCTAGGTGTTAACTCGAGGTATTAGCTCTAGGTATCAAC * ** * *
17,13,30,25,10,11,13,10-1416,13,30,25,10,11,13,10-1417,13,31,25,10,11,13,10-1417,13,30,24,10,11,13,10-1417,13,30,25,10,11,13,11-1417,13,29,25,10,11,13,10-1517,13,30,26,10,11,13,10-1417,13,30,25,10,11,14,10-1417,13,30,25,11,11,13,10-14
Time
5
2
1
3
2
4
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
Roewer et al. Hum Genet 2005©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
Y-STR gradients (7 loci)
Y-SNP gradients (R1a)
Fechner et al., Am J Phys Anthropology 2008©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
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 predictnationalityresidency orphenotype
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)
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
Unknown skeletonized person – extract, type, search and add „ancestry information“
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
Ancestry information – three features and heat map
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
Heat Map (searched haplotypes are reduced to the most representatively sampled minHt)
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
Searched haplotype is compared with a database of STR+SNP typed samples
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
„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!
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
Part II: Casework examples
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
• Frequency estimation• Mixture• Kinship• Ancestry