KINSHIP%20ANALYSIS%20BY%20DNA%20WHEN%20THERE%20ARE%20MANY%20POSSIBILITIES - PowerPoint PPT Presentation

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KINSHIP%20ANALYSIS%20BY%20DNA%20WHEN%20THERE%20ARE%20MANY%20POSSIBILITIES

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missing person (Is this corpse the missing relative? Kinship II (advanced) ... missing person. disaster. inheritance. immigration. Swissair flight 111 crash ... – PowerPoint PPT presentation

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Title: KINSHIP%20ANALYSIS%20BY%20DNA%20WHEN%20THERE%20ARE%20MANY%20POSSIBILITIES


1
KINSHIP ANALYSIS BY DNA WHEN THERE ARE MANY
POSSIBILITIES
  • Charles Brenner
  • visiting Dept of Genetics, University of
    Leicester, UK
  • forensic mathematics

2
Kinship analysis
  • Q How are these people related?
  • Genetic evidence
  • Likelihood ratio
  • Kinship program
  • ref Brenner, CH Symbolic Kinship Program,
    Genetics 145535-542, 1997 Feb

3
What a likelihood ratio is
  • Compares two explanations for data
  • Example man child both have Q allele
  • explanations
  • paternity some coincidence
  • non-paternity lots of coincidence

4
Likelihood ratio for Paternity (PI)
  • Data MotherPS, ChildPQ, ManRQ
  • explanation 1 man is father
  • (2ps)(2qs)(1/4) event
  • LR1/(2q)
  • If q1/20, data 10 times more characteristic of
    father explanation

5
Paternity Index exegesis
  • PI X/Y, where
  • XP(genetic types manfather)
  • YP(genetic types man not father)
  • Interpretations
  • Odds favoring paternity over non-paternity
    assuming all other evidence is equally divided
  • Evidence is PI times more characteristic of
    paternity

6
Kinship I (basic)
  • paternity (Is this man the father?)
  • avuncular (Is this man the uncle?)
  • (Latin avunculus uncle)
  • missing person (Is this corpse the missing
    relative?

7
Kinship II (advanced)
  • More than two scenarios
  • Three
  • Many
  • disaster
  • inheritance
  • immigration
  • Can always compare two at a time.
  • The trick is to organize the work.

8
Three scenarios
  • Father?
  • Uncle?
  • Unrelated?

9
Father/Uncle/Unrelated analysis
So, LR for tested man being father, vs uncle, is
53
10
Likelihood ratios are multiplicative
  • means that if explanation father is 2 times
    better than explanation uncle
  • and uncle is 10 times better than unrelated
  • then father explains data 20 times better than
    unrelated.

11
Many-scenario kinship cases
  • missing person
  • disaster
  • inheritance
  • immigration

12
Swissair flight 111 crash
13
Swissair example
  • DNA data
  • crash victims (unknowns)
  • relatives effects (references)
  • Tentative families
  • per Benoit Leclair program
  • Too many possibilities!
  • Bottom-up approach
  • Top-down approach

14
Five of the X family are lost
  • Living reference Albon E
  • Body parts G,F,D,C,M share DNA with Albon
  • (of which G,D,M are female, F,C are male)

G F M
D C
?
15
Too many possibilities!
GF M DCE
DF M GCE
?F M ?CE
?? M DFE
?F M DCE
?C G DFE
?? M ??E
...
Note G, D, M are female F, C are male. E is
living reference.
16
Bottom-up approach
  • MJöelle vs. Munknown

X
X
M
M
?? M ??E
Albon
Albon
?? ? ??E
Biggest objection Doesnt use all the
information (e.g. other people similar to both
M and Albon)
17
Lattice
A diagram showing that some things are better
than others.
Arrow better than
Dot hypothesis/explanation
18
Kinship lattice principle of design
  • heuristic assumption any consistent explanation
    is weakened when a person is removed

GF M DC
19
Top-down approach
GF M DC
(?lt1)
Lattice
20
X family conclusion
  • GF(DC)M explains the data at least ten million
    times better than any other arrangement of some
    or all of the DNA profiles G,F,D,C,M
  • except ?F(DC)M is only 300-fold inferior
  • Practically speaking, the identifications are
    proven.

21
Summary
  • Likelihood ratios are the way to quantify
    evidence
  • Kinship with multiple scenarios
  • Individual likelihoods for several scenarios
  • Lattice approach for the most complicated
    situations

22
Acknowledgements
  • Ron Fourney, George Carmody, Benoit Leclair,
    Chantal Frégeau
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