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Y_Workshop_WI_planz (3).ppt12345789999987543
1. Applications in Forensic Genetics
John V. Planz, Ph.D.
UNT Center for Human Identification
Wisconsin Department of Justice
Madison, WI
January 4, 2008
Science and Statistics Behind
Y STR Systems
2. Assessing the Significance of Y
STR Data
• Characteristics of Y chromosome and loci
• Population structure and distribution of Y
haplogroups
• Forensic applications
• Powerplex Y characteristics
• Working with haplotype statistics
• What about mixtures
3. Classic View of Y-Chromosome
• TDF master gene
• patrilineal inheritance
• no recombination in NRY
• recombination in PAR
• junk-rich, gene poor
4. Characteristics of the Human Y Chromosome
• size: ~ 60 Mb
• ~ 35 Mb euchromatic (transcribed)
• ~ 25 Mb heterochromatic (non-transcribed)
• 95% non-recombining (NRY)
• 5% X-recombining (2 pseudoautosomal regions at telomeres)
• shape: acrocentric - very short p-arm, long q-arm (“Y” name)
• rich in different kinds of repetitive DNA sequences
• lack of recombination
• relatively poor in gene content
5. Genes on the Human Y Chromosome
• 23 Mb of the euchromatic region determined
• 156 transcription units
• 78 encode proteins (genes)
• 27 distinct Y-specific protein-coding genes (gene families)
• 16 ubiquitously expressed genes = housekeeping genes
– e.g. RPS4Y, ZFY, AMELY, SMCY, DBY
• 9 testis-specific genes = male sex determination,
spermatogenesis
– e.g. SRY, TSPY, CDY, RBMY, DAZ
8. Genes on the Human Y Chromosome
• 23 Mb of the euchromatic region determined
• 156 transcription units
• 78 encode proteins (genes)
• 27 distinct Y-specific protein-coding genes (gene families)
• 16 ubiquitously expressed genes = housekeeping genes
• 9 testis-specific genes = male sex determination, spermatogenesis
origin of NRY genes:
– derived / preserved from the proto-sex chromosomes
(X-homology)
– specialization in male-specific function
9. Evolution of Mammalian Sex Chromosomes
Lahn, Pearson & Jegalian 2001
Some homology with X – need to consider in validation
10.
11. Polymorphisms of the Human Y Chromosome
Mutations create DNA polymorphisms and
these may serve as genetic markers
• Repetitive DNA – e.g., STRs
• Single-Copy DNA – e.g., SNPs, indels
20. -From J.M. Butler (2003) Forensic Sci. Rev. 15:91-111
Forensic Y STR Systems
21. • Haplogroup: set of haplotypes defined
by slowly mutating markers (mainly
SNPs) which have more phylogenetic
stability.
• Haplotype: combination of allelic states
of a set of polymorphic markers lying on
the same DNA molecule.
Definitions
Unique event polymorphisms (UEP) record
history of Y chromosome
22. Why are our Y haplotypes so different?
• Many markers to choose from
• The selected loci are physically linked
• Markers have both SNPs and STRs
Infinite Sites Model
Stepwise Mutation
Model
Infinite Alleles Model
23. Population Differentiation
• Effective population size of Y chromosome is 1/4 of
autosomes or 1/3 of X
– lower sequence diversity on Y
– more susceptible to genetic drift
• random changes in frequency of haplotypes
due to sampling bias from one generation to
next
• accelerates differences between populations
• Variance of offspring further reduces Ne (effective
population size)
24. Population Differentiation
• Geographical clustering due to patrilocal behavior
of men
– women move closer to man’s birthplace
– local geographical differentiation enhanced
– Conquest effect
From Zerjal et al. Am. J. Hum. Genet. 72:717–721, 2003
25. Population Differentiation
• Geographical clustering due to patrilocal behavior
of men
– women move closer to man’s birthplace
– local geographical differentiation enhanced
– Conquest effect
You must consider that we are not talking
about contemporary populations when
discussing this!
Converse seen with mtDNA in Native American
Populations
26. Forensic Y-STR Applications
– Detect male DNA in a sample containing
male and female DNA (Huge background
of female DNA)
– Aspermic males
– Fingernail Scrapings
– Additional Power of Discrimination
– Multiple male donors
– Limits of differential extraction/ tissues
– Gender clarification (amelogenin)
27. Finger Nail Scraping Case
• Victim was found strangled to death
• Suspect had scratches on his face
• Based on STR results, suspect could not be
excluded; many alleles were below
interpretation threshold (inconclusive
result)
A Forensic Application
29. Identification of Male Contributor DNA in
Crime Scene Material
Autosomal STR profile
Female Victim DNA:
Male Suspect DNA:
Large Female DNA:
Perpetrator Male DNA
- See only female DNA profile
- Or partial DNA profile
- no female DNA
- no profile overlap
- only male component
Y STR profile
33. Y-STR Haplotype Analysis in Deficiency Paternity Case
DYS19 DYS389I DYS389II DYS390 DYS391 DYS392 DYS393 DYS385 DYS413 YCAII
Nephew 14 13 30(16) 25 11 13 12 11-14 22-22 3-7
Son 14 12 29 (16) 24 10 15 12 11-14 22-22 3-7
Exclusion
If true biological nephew, then alleged father is excluded as father of child in question
?
Kayser et al. Progress in Forensic Genetics (1998), 7: 494-496
34. • For effective use, guidelines are needed
• ISFG Recommendations
• Combine with existing recommendations (NRC II
Report)
• Nomenclature, Allelic Ladders, Population Genetics,
Statistical Issues
35. • Similar to autosomal STRs
• Thresholds for detection and interpretation
• Stutter
• Mixtures – what constitutes a mixture
• Validation studies in concert with guidelines
• Interpret evidence before knowns
Basic Interpretation Guidelines
36.
37. Y STR LOCI
• DYS19
• DYS398 I
• DYS398 II
• DYS390
• DYS391
• DYS392
• DYS393
• DYS385 I/II
“Minimal Haplotype” – defined for research only
40. • Commercially available Y-STR multiplex kits ---
allow for standard markers and QA/QC
• Most have EMH and SWGDAM recommended
loci
• Extra loci added to enhance discrimination
Kits
46. AmpFlSTR® Yfiler™ Kit
1 ng Male Control DNA 007
DYS458 DYS389 I DYS390 DYS389 II
DYS438 DYS19 DYS385 a/b
DYS393 DYS391 DYS439 DYS635 DYS392
Y GATA H4 DYS456 DYS437 DYS448
47. What can we expect?
Powerplex® Y
• Sensitivity
• Mixtures
• Anomalies
55. Of course, with Male:Male mixtures you will get
more peaks at each locus.
Sensitivities down to about 5% minor contributor
are typical.
You cannot bank on peak height differences to
remain consistent across the dyes or loci, so be
careful when trying to physically deconvolute
these mixtures… this may not be a valid practice!
i.e. If target input DNA is 0.5 ng…
5% minor contributor is only 0.025 ng
56. These types of issues should raise some
operational questions:
Input DNA:
• Total Genomic?
• Y specific?
• Increase to bring up minor?
• Impact of stutter?
Valid lab policies and interpretation
guidelines must be based on empirical data!
59. DYS392
N-1, N+1 stutter is
commonly seen at all
input template
amounts… this is
common among
trinucleotide repeat
loci.
1.0 ng
0.5 ng
0.25 ng
60. As with all typing systems, there are anomalies
that you should be aware of !
The majority of female samples will not
produce typing results with the Y STR kits…
But remember…the Y and X are functional
homologues and recombination IS possible.
always run a female “victim” known
when using Y STR kits in the male –
female context!
61. Other observed Powerplex® Y anomalies
DYS19 Primer binding mutation
This was in an Asian (Hong Kong) Chinese sample
62. Other observed Powerplex® Y anomalies
DYS385
“Gene” duplication at DYS385
DYS385 a/b
a b
F primer
R primer
F primer
R primer
Multiple mutation steps
in the lineage are needed
to explain this one!!
64. Population studies with Powerplex® Y
Before we can approach interpretive or
statistical understanding of the system we need
to understand what we are dealing with as a
locus…and yes, the whole set of markers in
Powerplex Y are just that…a single locus.
Several typical validation issues just don’t
matter with a Y haplotype system:
• Peak height ratio
• Hardy-Weinberg Equilibrium
But other things do!
65. CFS AFR
CT AFR
MI AFR
NYC AFR
TX AFR
CFS CAU
CT CAU
MI CAU
NYC CAU
TX CAU
CT HIS
Population
37
182
86
80
192
57
164
97
83
194
160
N
MI HIS
MN HIS
NYC HIS
TX HIS
Apache
Navajo
CFS ASN
MN ASN
NYC ASN
TX ASN
CFS EI
Population
97
101
80
192
138
219
28
101
45
73
37
N
Y STR Population Data
Promega Study
Total = 2443
67. fi = frequency of each haplotype n = # haplotypes
h = n(1-fi
2)/ (n-1)
Haplotype Diversity
P = fi
2
Haplotype Random Match Probability
Population Parameters
72. What about Linkage Equilibrium?
Intuitively, since these markers are all on a
single chromosome we’d predict strong
linkage and a lack of independence between
the loci.
Although this is very different from what
we are used to with our beloved CODIS
loci…this is what we expect with a
haplotype
What do we get?
73. CFS AFR
CT AFR
MI AFR
NYC AFR
TX AFR
CFS CAU
CT CAU
MI CAU
NYC CAU
TX CAU
CT HIS
MI HIS
MN HIS
NYC HIS
TX HIS
Population
37
182
86
80
193
57
163
97
83
194
158
97
100
80
192
N # Equilibrium
35
23
27
34
21
30
26
30
22
11
26
33
42
35
37
Y STR Loci Pairwise Tests
12 Loci – 66 tests
74. Apache
Navajo
CFS ASN
MN ASN
NYC ASN
TX ASN
CFS EI
Y STR Loci Pairwise Tests
12 Loci – 66 tests
Population
138
219
28
100
45
70
37
N
9
12
60
50
47
50
35
Fewest – Native American
Most – Asian (sample size; but Minnesota and Texas)
# Equilibrium
78. • There is evidence of “independence” between
some of the loci in some of the populations
• A combination of mutation rate, subdivision
and random drift can cause this.
• One of the biggest factors is Haplogroup
Diversity
• The marker selection for increasing haplotype
diversity is not directly correlated to gene
diversity.
What do we see?
79. Approaching Analysis
• Some may suggest - “Use the set of Core Y STRs
and add more as needed to resolve matches”
• First question – when do you stop?
• If you get a match, you would have to continue on
ad infinitum!
• Is this a sensible policy?
How much power is needed???
83. So…the logic does work…
More loci… better resolution…
But…doesn’t the size of the database matter?
84. European Minimal
Haplotype
0
# of different
haplotypes 20
YfilerTM
6
PP® Y
Individuals
sharing
haplotypes
20 4, 4, 2, 6 0
Point
Estimate
(N = 3561)
0.0056 0.00028
0.0011
0.0011
0.0006
0.0017
N= 1000 0.02
0.004
0.004
0.002
0.006
0.001
85. Approaching Analysis
• Unlikely approach because information gain is low
• Many samples will already be very limited
• Community will rely on commercially available kits
not in-house designer systems
• QC/ Proficiency Testing
• Better to increase size of database(s) to gain power
• We will re-visit substructure issues later
86. Approaching Analysis
• Some may suggest - “A reference database should
contain related individuals” – to better define the
population
• Probability of paternal relative having the same
haplotype is usually 1
• Databases are typically comprised of unrelated
individuals
• Although a small unknown number of related
individuals may be in a database
• Able to address significance of a very closely related
profile
87. Exclusion with 1 mismatch among 12 analyzed Y-
STRs
Evidence 14 12 28 25 11 11 13 14,14 11 11 15
Known 14 12 28 24 11 11 13 14,14 11 11 15
By having a database of unrelated males one can
assess weight of relative (with mutation) versus rarity
of haplotype in population
88. Qualitative Conclusions of Y-STR
Haplotype Comparison
Exclusion
- The two haplotypes are dissimilar; i.e, the
reference person is excluded as the contributor
of Y-specific DNA of the evidence sample
Inclusion/Match
- The Y haplotypes from two samples are
sufficiently similar and potentially could have
originated from the same source, or from a
common paternal lineage
Inconclusive
- Exclusion/Inclusion cannot be definitively
inferred due to insufficient data from one or
both of the DNA samples
89. Calculation to Convey to the Court
• Frequency estimate not possible
• Court desires a frequency estimate
• Point Estimate (Counting Method)
• Confidence Interval
• Approach the same as mtDNA
90. • The vast majority of possible haplotypes
will not be observed in any database
• The counting method is likely to be
conservative
• A correction for sampling
• A correction for substructure
Calculation to Convey to the Court
??
91. Calculation to Convey to the Court
Approaches
• It is more likely that the counting method will be
employed by the U.S. laboratories and courts
because of its operational simplicity
92. Limitations of the Counting Method
• Non-matched sites of the haplotype are given
weight equal to that of different origin (but
may have some extra value for substructure)
• Mutations are not weighted
• Haplotypes of the same paternal lineage can be
excluded, when they are subject to mutations
• Does not recognize evolutionary changes,
and/or effect of convergent mutations
93. CI = p ± 1.96 p(1-p)/N
For Y haplotype observed,
count the number of times
the profile is observed (X)
p = X/N
95% Upper bound on frequency
Where
• N is the size of the database
94. What about for Y haplotype that is not
observed in your database??
The upper bound of the CI is
1-1/N
Where
• is the confidence level (0.05 for a 95% CI)
• N is the size of the database
Following: W. E. Ricker, 1937. Journal of the American Statistical Association, Vol. 32, No. 198: 349-356.
95. Maximum haplotype frequency
• If a Y-haplotype is not seen in a sample of N males
then at the level of significance:
• Maximum frequency = 1 - 1/N
• Confidence level = 1-
• As N becomes larger, maximum frequency becomes
closer to point estimate
This is why databases will drive our
statistical strength
96. N frequency
• 100 3/100 (0.03)
• 500 3/500 (0.006)
• 1,000 3/1,000 (0.003)
• 10,000 3/10,000 (0.0003)
Haplotype frequency
97. Calculation to Convey to the Court
Confidence Interval
• In many instances, the evidentiary haplotype may
not be observed in the reference database
• As a consequence, the usual assumption of a
Normal distribution may not apply for Y-STR
haplotype frequency estimates
• Ricker’s theory (1937) accommodates this
requirement
• The counts as well as the confidence bounds are
divided by the number of haplotypes sampled in the
entire database to estimate the probability of a
match
98. Online available Y-STR haplotype
reference databases
How de we actually get our Haplotype frequencies?
http://www.appliedbiosystems.com/yfilerdatabase/
Applied Biosystems Yfiler
http://www.promega.com/techserv/tools/pplexy/default.htm
Promega Powerplex Y
114. We can evaluate these
matches by looking at
the distribution of
matches among the
various population
groups.
115.
116.
117. CI = p ± 1.96 p(1-p)/N
We can do like we did before…looking at the
frequency in the whole database:
CI = 0.0052 + 1.96√ (0.0052(0.9948))/4004
Upper bound would be:
0.00743
118. CI = p ± 1.96 p(1-p)/N
Or we could do specific to the population group
in which the match was found:
CI = 0.0076 + 1.96√ (0.0076(0.9924))/1311
Upper bound would be:
0.0123
Caucasians
119. CI = p ± 1.96 p(1-p)/N
Or we could do specific to the population group
in which the match was found:
CI = 0.0067 + 1.96√ (0.0067(0.9933))/894
Upper bound would be:
0.01205
Hispanics
120. CI = p ± 1.96 p(1-p)/N
Or we could do specific to the population group
in which the match was found:
CI = 0.0036 + 1.96√ (0.0036(0.9964))/1108
Upper bound would be:
0.00712
African American
121. • Correction for population structure may be considered
• Effective population size ¼ of autosomal loci
• May actually be a little lower
• Substructure effects less in US than ancestral
populations
• Use when reference database considered not
representative
Calculation to Convey to the Court
Population Substructure
122. Problems created by population subdivision
Haplotype frequencies calculated
from population average
frequencies couldlead to:
–Wrong estimates!
123. Employ a Theta (q ) Correction
q is used as a measure of the effects of
population substructure
(inbreeding, coancestry)
124. NRCII q recommendation was pragmatically set
Empirical values are much less for autosomal
loci
National Academy of Sciences
May 1996
125. Still need to calculate substructure effects
But likely to be low for most major
populations, if evaluated under a
forensic model vs that of an
evolutionary model
126. U.S. Y-STR Haplotype Reference Database
www.ystr.org/usa
AA CAU HIS Total
Number of populations
sampled
10 11 9 30
Number of individuals
sampled
599 628 478 1,705
Number of Y-STR loci typed
(EMH)
9 9 9 9
Number of different
haplotypes
454
76%
437
70%
354
74%
1116
65%
Haplotype diversity 99.8% 99.6% 99.5% 99.7%
Most frequent haplotype 12
2.0%
25
3.98%
19
3.97%
53
3.1%
Kayser et al. J. Forensic Sci. (2002), 47(3): 5513-519
127. Hispanic
Structure of U.S. Populations with Y-STR Haplotypes
Florida EA
European-American
African-American
Virginia AA
Florida AA
Maryland AA
Texas AA
New York AA
Pennsylvania AA
Missouri AA
Oregon AA
Indiana AA
Lousiana AA
Pennsylvania EA
New York EA
Indiana EA
Missouri EA
Lousiana EA
Maryland EA
Pennsylvania H
Florida H
New York H
Connecticut H
Texas EA
Cajun EA
Virginia EA
Oregon EA
Oregon H
Maryland H
Lousiana H
Texas H
Virginia H
RST = 0.1
RST: measure for population differentiation
Kayser et al. J. Forensic Sci. (2002), 47(3): 5513-519
128. African American
Asian
Caucasian
Hispanic
Native American
Afr-Cau-His
All 5
Population
Partition (%) of genetic variance
(AMOVA)
98.96
98.69
98.45
99.08
96.98
87.19
83.40
A
A = within sample population
1.04
1.31
1.55
0.92
3.02
1.02
1.25
B
B= among sample populations within major population group (or regional variation)
---
---
---
---
---
11.79
15.35
C
C = among major population components for North American populations
130. FST
(AMOVA)
African American
Asian
Caucasian
Hispanic
Native American
Afr-Cau-His
All 5
Population
0.0051
0.0148
0.0071
0.0061
0.0188
0.0745
0.1001
FST
Note: Asian is likely inflated and more data are needed to assess FST
0.0104
0.0131
0.0155
0.0092
0.0302
0.1179
0.1535
ФST
AMOVA routine (without the option of allele size difference) of Arlequin 2.0
132. f (haplotype) = pi + q (1- pi)
Formula
Note: θ is the limiting factor!
With q of 0.01 and our p of 0.00028:
0.00028 + (0.01 x (1 – 0.00028))
0.00028 + 0.0099
≈ 0.0103
133. q
Pool populations ---
Impact
• US populations
• Intra-individual variation
• Most common haplotypes the same
• What is the frequency of unknown or uncommon
haplotypes in different datasets?
• Even if there is substructure
Most frequent
134. Y STR haplotype is one locus with many alleles
A1
A2
.
.
.
.
A100
Population 1
A101
A102
.
.
.
.
A200
Population 2
Databases with reasonable size
approximate this model
θ is almost 0
136. So the more loci typed,
the more haplotypes/alleles will be in the database
Thus, multi-locus kits are valuable for this aspect
q approaches 0
In the process of calculating q under forensic model***
Y STR haplotype is one locus with many alleles
137. Forensic Model
Population Substructure
DYS19 DYS389I DYS389II DYS390 DYS391 DYS392 DYS393 DYS385
A --- 14 12 29 24 10 15 12 11-14
E --- 18 12 25 24 10 15 15 11-18
B --- 14 13 29 24 10 15 12 11-14
C --- 14 13 29 24 12 15 12 10-14
D --- 18 11 25 24 10 13 15 12-18
Which haplotypes might be more closely related?
138. Forensic Model
Population Substructure
DYS19 DYS389I DYS389II DYS390 DYS391 DYS392 DYS393 DYS385
A --- 14 12 29 24 10 15 12 11-14
C --- 14 13 29 24 12 15 12 10-14
Are such evolutionary differences
considered in forensic evaluation?
E --- 18 12 25 24 10 15 15 11-18
A --- 14 12 29 24 10 15 12 11-14
Exclusion
Exclusion
140. • 692 confirmed father-son pairs (probability > 99.9%)
• 14 mutation events were observed
• Average rate of 1.57 x 10-3/locus /generation (13/8304)
• With a 95% confidence bound of 0.83 x 10-3 to 2.69 x 10-3
• This rate is a little smaller than that of the Kayser, et al.
• Estimate (2.80 x 10-3/locus)
• But the difference is not statistically significant (P > 0.05).
one Asian father-son pair at the DYS389I/II loci complex (12,29) (13, 30)
appears as a double mutation, but likely is a single original event.
Y STR mutations (father:son allele transmission)
141. Mutation??
Likelihood calculation
14, 12, 28, 22, 10, 11, 14, 13-14, 19-21
14
14, 12, 28, 22, 10, 11, 13, 13-14, 19-21
13
Mutation: µ (DYS393) = 3.2 x 10-3
?
f obs= 0.001
fobs = 0.007
7
5
Paternal Relatives share the same haplotype
Are they related?
L(X) = 0.001 x 5 x µ/2 + 0.007 x 7 x µ/2 (related)
L(Y) = 0.001 x 0.007 (non-related)
LR (X/Y) ≈ 12 for patrilinear relationship
142. Next Task
• Test independence between
autosomal loci and Y haplotypes
143. Independence Testing of Y Haplotype
and 13 Autosomal CODIS STR Loci
(Autosomal Locus/ Y Haplotype Displaying Disequilibria* - 22 populations)
1. Apache
FGA, p-value = 0.03760000
D21S11, p-value = 0.03460000
D18S51, p-value = 0.02820000
D5S818, p-value = 0.02660000
2. Minnesota Asian
D8S1179, p < 10-3
3. Minnesota Hispanic
D16S539, p-value = 0.03340000
D18S51, p-value = 0.02100000
4. Canada African American
FGA, p-value = 0.00920000
5. Canada Asian Indian
D7S820, p-value = 0.02820000
6. Connecticut African American
FGA, p-value = 0.04300000
THO1, p-value = 0.00280000
7. Connecticut Caucasian
THO1, p-value = 0.02880000
8. Michigan Caucasian
D16S539, p-value = 0.04820000
144. 13. New York Caucasian
D7S820, p-value = 0.01660000
14. New York Hispanic
D21S11, p-value = 0.01340000
15. Texas African American
D13S317, p-value = 0.01200000
D18S51, p-value = 0.01420000
16. Texas Hispanic
D5S818, p-value = 0.01880000
9. Michigan Hispanic
vWA, p-value = 0.03160000
FGA, p-value = 0.02240000
10. Native American Total
D3S1358, p-value = 0.02680000
D21S11, p-value = 0.00060000
D18S51, p-value = 0.00840000
11. Navajo
D21S11, p-value = 0.02820000
12. New York Asian
D16S539, p-value = 0.00740000
Independence Testing of Y Haplotype
and 13 Autosomal CODIS STR Loci
(Autosomal Locus/ Y Haplotype Displaying Disequilibria* - 22 populations)
145. Next Task
• Mixtures
• Assume 2 alleles for 11 loci
• 211 possible haplotypes with PP Y – 2048
• Most haplotypes not observed in database
• Assumption of independence not correct
• Minimal haplotype frequency (minimum
allele frequency) not practical
146. Mixture
• Probability of Exclusion
• Binomial distribution - haplotypes excluded and
haplotypes not excluded
• Count number (m) not excluded; (PI = m/n)
• Estimate upper CI of PI
• PE = 1- PI
• Based on same principles used for autosomal loci (but
at haplotype level)
147. • Four scenarios for two contributor sample in example:
•Hp --- S1 and S2 are source
• Hd --- S1 and unknown are source (same as PI)
• Hd --- S2 and unknown are source (same as PI)
• Hd --- two unknowns are source
Mixture
Likelihood Ratio
148. • Assume three loci, two alleles at each locus, two
male suspects
•Total alleles the same as in evidence
• Equal contribution
• 8 possible haplotypes
Mixture
Likelihood Ratio
152. LR =
1
2[Pr(H1)Pr(H8) + Pr(H2)Pr(H7) + Pr(H3)Pr(H6) + Pr(H4)Pr(H5)]
Mixture
Likelihood Ratio
153. • Technically correct
• Can not estimate individual haplotype frequencies
• 217 (131,072) possible haplotypes (Yfiler)
211 (2048) possible haplotypes (PP Y)
• Not all combinations can explain the evidence
• Assuming independence is not correct
• Cannot place types in database, most never seen, too
many
Mixture
LR
154. Use same logic as PI
for the denominator in the LR
Haplotypes fall into either category
Only E/E can explain the evidence
and only a subset of these fit
E/E and E/E pairs can not explain the evidence
E = excluded E = not excluded
155. m*/n(n-1) and take upper CI as denominator
Mixture
LR
m* - those pairs (of E/E) that explain evidence
157. Online available Y-STR haplotype
reference databases
We still have the problem that none of these
(or PopStats) is designed to enable mixture
calculations!!
Calculation of reporting statistics is quite
straight-forward with the help of the searchable
databases
And doing this with 12 or 17 loci by hand…
…would be a bear!
158. John V. Planz, Ph.D
UNT Center for Human Identification
jplanz@hsc.unt.edu
