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
2000
2004
2008
2014
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
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
YHRD - Increasing numbers
Frequency estimation
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
Frequency estimation methods
Constant estimators
Variable estimators
• Augmented counting
(1/n+1)
• Counting with database
inflation (Brenner‘s κ)
• Surveying method (Krawczak)
• Coalescence based
estimation (Caliebe)
• Discrete Laplace method
(Andersen)
Enabled in YHRD
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
Frequency estimation for Y-STR profiles
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
Frequency estimation for rare haplotypes with „Kappa inflation“
(0 observations)
Count
19.593
23 loci
Singletons
with kappa
19.593
n.a.
17 loci
71.246
55.675
K=0.78
3.0 x 10-6
(1.4 x 10-5)*
9 loci
125.700
30.450
K=0.24
6.0 x 10-6
(7.9 x 10-6)*
1 
P̂(T  h0 | S  h0 ) 
N1
* counting
- proportion of singletons
estimator of the proportion of not sampled rare haplotypes in the database
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
©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
Courtesy of M.M. Andersen (Copenhagen)
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
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
Casework example
Delict: sexual assault
Evidence: contact stain on clothing
Autosomal analysis
• only ♀ component
• no ♂ admixture in AMELOGENIN
Y chromosomal analysis
• male mixture (major, minor component)
©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 rate
f = haplotype frequency (YHRD)
A
B
Same or
different
fathers?
• 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)
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
Brothers?
A
B
Same or
different
fathers ?
µ = 3.6 x 10-3 *
14, 13, 31, 24, 11, 13, 14, 11-11, 14, 13
f A = 1.4 x 10-4*
14, 13, 31, 25, 11, 13, 14, 11-11, 14, 13
f B = 2.3 x10-5*
* YHRD
Meioses
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
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
A
Influence of the local mutation rate on LR
B
Father – son
or unrelated ?
µ = 2.1 x 10-3 (moderate)*
14, 13, 31, 24, 11, 13, 14, 11-11, 14, 13
f B = 1.4 x 10-4*
14, 13, 31, 24, 11, 14, 14, 11-11, 14, 13
f A = 2.3 x10-5*
* YHRD
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
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
A
Influence of the local mutation rate on LR
B
Father – son
or unrelated ?
µ = 1.2 x 10-2 (rapid)*
14, 13, 30, 24, 11, 13, 14, 11-12, 14, 13
f B = 1.4 x 10-4*
14, 13, 30, 24, 11, 14, 14, 11-12, 14, 13
f A = 2.3 x10-5*
* YHRD
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
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
Common ancestor?
5
7
µ = 2.1 x 10-3 (moderate)*
A
B
14, 13, 31, 24, 11, 13, 13, 11-12, 14, 13
f obs= 1.4 x 10-4*
14, 13, 31, 24, 11, 14, 13, 11-12, 14, 13
fobs = 2.3 x10-5*
* YHRD
Meioses
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
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
Common ancestor?
5
7
µ = 1.2 x 10-2 (rapid)*
A
B
14, 13, 31, 24, 11, 13, 13, 11-12, 14, 13
f obs= 1.4 x 10-4*
14, 13, 31, 24, 11, 14, 13, 11-12, 14, 13
fobs = 2.3 x10-5*
* YHRD
Meioses
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
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
Ranking of Y-STR mutation rates
Loci
dys438
dys392
dys393
dys437
dys448
dys390
dys385 mc
dys19
ygatah4
dys391
dys389i
dys635
dys389ii
dys456
dys481
dys533
dys439
dys460
dys458
dys518
dyf387S1ab mc
dys576
dys570
dys627
dys449
Mutation Rate [95% CI]
2,96E-04
4,04E-04
1,09E-03
1,19E-03
1,65E-03
2,06E-03
2,30E-03
2,32E-03
2,47E-03
2,54E-03
2,68E-03
3,72E-03
3,78E-03
4,19E-03
4,97E-03
5.01E-03
5,35E-03
6,22E-03
6,74E-03
1,84E-02
1,59E-02
1,43E-02
1,24E-02
1,23E-02
1,22E-02
Meioses
10122
14867
13713
10101
6678
15061
25620
15539
7709
14935
13788
7525
13759
6678
1744
1730
10096
1717
6677
1556
1804
1727
1426
1766
1617
Position[MutRate]
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Group[MutRate]
slow
slow
slow
slow
slow
medium
medium
medium
medium
medium
medium
medium
medium
medium
medium
medium
medium
medium
medium
fast
fast
fast
fast
fast
fast
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
Likelihood Ratio (LR, KI) calculation for Y-STRs
A
f (A) = 1/123*
f (D) = 1/388**
* Program uses counting (Discrete Laplace extrapolation: 1/311)
** Program uses counting (Discrete Laplace extrapolation: 1/821)
D
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
TCGAGGTATTAAC
TCTAGGTATTAAC
TCGAGGCATTAAC
TCTAGGTGTTAAC
TCGAGGTATTAGC
TCTAGGTATCAAC
*
** * *
• Y-SNPs
µ = 10-9 - 10-12
irreversible
 stable phylogeny
Time
• Y-STRs
µ = 10-3
recurrent
 networks
5
3
2
2
1
4
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
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
Y-STR gradients (7 loci)
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
Roewer et al. Hum Genet 2005
Y-SNP gradients (R1a)
Fechner et al., Am J Phys Anthropology 2008
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
Is ancestry prediction possible?
Haplogroup J2a
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
Semino et al. 2004 (n = 2400)
37 Y marker analysis (Geppert et al. 2010)
Skeleton in a trolley, 5g femur extracted
Haplotype: 14,13,30,22,10,11,12,13-16,...
©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
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
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 ancestryinformative Y-SNPs using capillary
electrophoresis. Methods Mol Biol. 830:127-40.
 J2a
 Turkey, Fertile Crescent, Caucasus,
Mediterranean
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
CAVE!
15,13,30,22,10,11,12,15-16 – 2 matches to YHRD
„Most frequent neighbour“ - 15,13,29,22,10,11,12,15-16 – 22 matches
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015
Legende: Each dot is one population sample (on average 120 individuals) with matching
populations marked in red
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
•
•
•
•
Frequency estimation
Mixture
Kinship
Ancestry
©Charité – Universitätsmedizin Berlin, Dept. Forensic Genetics 2015