Relationship Testing Flashcards

1
Q

What can relationship testing also be known as?

A

Kinship analysis

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2
Q

What is relationship testing used for?

A
  • Used to assess whether (and how) people may be genetically related to one another
  • Relatedness calculations are carried out based on number of alleles shared
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3
Q

What are some applications for relationship testing?

A

o Paternity testing
o Immigration cases
o Missing person identification (disaster victim identification)
o Inheritance disputes

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4
Q

In relationship testing, what is it that is being investigated?

A

Genetic relatedness

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5
Q

What evidence are we searching for with relationship testing?

A
  • We are looking for evidence to suggest that one individual is direction descended from the other
    o Children are separated from their parents by **one generation **
    o Grandchildren and separated from their grandparents by **two generations **
  • Or that individuals share a very **recent common ancestor **
    o Full siblings share two parents
    o Half-siblings share one parent
    o First cousins share two grandparents
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6
Q

Whats recombination?

A
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7
Q

How was relationship testing first performed?

A
  • First performed **using blood groups **
    o Very useful for excluding a man as the potential father as mutations do not occur
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8
Q

Why are mutations more common in STR than in blood groups?

A

STR in bit of DNA non-coding and blood groups are proteins, there for a purpose, for survival and function

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9
Q

What was the first application of ‘DNA fingerprinting’?

A

**First application of ‘DNA fingerprinting’ (RFLP testing) was an immigration case (maternity) **
o A boy born in Britain to a Ghanian mother had just returned to the UK after living with his father in Ghana for a short while
o About 20 blood groups- one exclusion (Duffy)
o Mother Christiana phenotype Fy(a+)
o Child Andrew phenotype Fy(b+)

  • Certainly, closely related, but was Christiana his aunt?
    o Bands shared on average 26% of the time
  • Mother v aunt more than one billion times more likely
  • Currently routinely carried out using STR typing
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10
Q

In what situations would you want to determine paternity (maternity)?
Give subgroup examples for each

A
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11
Q

What are the non-paternity rates?

A
  • Quoted rates vary 1%-30%
    o Methodology often not validated
    o Prevalent in testing laboratories- 15%
     Paternity questioned
    o Recent studies
     **1-3% estimated in the UK **
  • Large variation seen between countries, cultures and different social statis
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12
Q

How is the assessment of paternity done in the following case?

  • Mother names a man as the father of her child
  • If the ‘mother’ is the true mother, then mother and child must have an allele in common
  • Tested man is not excluded from paternity
A
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13
Q

What is the equation for likelihood ratio?

A
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14
Q

What is the chance of the tested man being the father?

A
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15
Q

How would you work out the probability of independent events?

A
  • When both A and B occur together
    A n B= p(A) x p(B)
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16
Q

What do indepent events allow in forensic genetic situations?

What are some limitations to this?

A
  • Allows likelihood ratios from independent genetic systems to be combined into a total LR by multiplication
    o 1.89 x 1.45 x ….. = 10,0000 (minimum)
    o The **‘combined paternity index (CPI)’ **

*** Caveats **
o Same chromosome- genes may be linked (VWA and D12S391 are linked)
o Different chromosomes- non-random assortment due to population structure

17
Q

Tell me about the paternity index (likelihood of paternity)

A

**Paternity index (likelihood of paternity)= 10,000 **
* The tested man is 10,000 times more likely to be the father than an unrelated man (no)

  • The types observed are10,000 time more likely to be seen in a trio of true father, mother, and child than in a trio of an unrelated man, mother and child
  • A laboratory should define a minimum PI- 100 standard in US- most labs look for at least 10,000
18
Q

What does the report day regarding PI?

A

**Some paternity reports may say: **
o Paternity index (LR)= 10,000
 LR= likelihood ratio

o Probability of paternity (W)= 99.99%
 W (Wahrscheinlichkeit)- probability

o Implies that these are two ways of expressing the same thing, which they are NOT

19
Q

Given the evidence, what are the offs that the man we have tested IS the father?
How is this caluclated and what is the equation for this?

20
Q

Tell me what Prior odds and what they depend on

21
Q

A range of priors?

A
  • Assuming that the CPI is 10,000
  • ‘Assuming a prior of between 10% and 90% the paternity index is somewhere between 1,111 and 900,000’
    o 10% prior= 0.1/0.9 x 10,000 = 1,111
    o 90% prior= 0.9/0.1 x 10,000= 90,000
  • A pragmatic approach
    o We say- 10,000, ‘assuming no other relevant evidence’
22
Q

But is that 99.99%?

A
  • Probability of paternity (W)
    = **(Pi LR) / (Pi LR + 1) – (Essen-Möller equation) **

Where Pi is the prior odds
Assuming a 50% prior
o **Probability (W) = LR / LR+1 **
o If paternity index = 10,000
 Probability of paternity= 10,000 / 10,001
= 0.9999
= 99.99%
o Can never reach 100% and always include non-genetic evidence- unlike the PI

23
Q

What are the pros/cons of paternity of a diagnositc test?

A
  • A good diagnostic test is one which will minimise the number of false positive and false negative results
  • **False positive- **telling someone that they have a disease, when in fact they do not
    o Significant false positive rates for mammograms
    * False positive- telling someone that they are the father of a child when they are not
    o Not tolerable- an easy mistake in some circumstances

* False negatives- telling someone that they do not have the disease, when in fact they do
o Significant false negative rates for cervical cytology
o Significant false negative rates for COVID19
* False negatives- telling someone that they are not the father of a child, when they are
o Not tolerable- but an easy mistake
* Unlike diagnostic tests- the ‘one off’

24
Q

False positive paternity

A
  • Man, mother, and child tested
  • CPI= 21,000
  • Probability of paternity= 99.995%
  • PI is the genetic odds in favour of paternity
  • Man is the brother of the true father
  • “Offers very strong evidence paternity providing a close relative of the tested man is not a possible father”
25
Example case study
*** Disputed paternity ** o CPI 87,000 o Probability of paternity 99.998% *** What were the prior odds?** o Intercourse acknowledged with mother o Fertility undisputed- 4 children o Vasectomy 5 years previously o Regular un-protected sex with wife **Vasectomy ** *** Failure rate ** o Literature report 6 in 14,000 (1 in 2400) 0.04%  Failure defined as sperm seen o Literature report (1 in 85,000) 0.00118%  Failure defined as child born to vasectomised man (6 men) **Very low prior odds** * 0.0000118 (very low prior odds) x PI 87,000 (Very strong evidence towards paternity) = 1.0266 (? Paternity) * Judgement **o He is the father **
26
Whats the first rule of inheritance leading to exclusion of paternity?
* When something is found in a child that is not in the child’s mother it must come from the father * If the tested man does not have it then he normally cannot be the father * Why ‘normally’ o We can’t test the sperm that fertilised the egg
27
Why do I need to be tested? I am his mother?
* Tested man is VWA (16,19), child is VWA (13,16) o Tested man is not excluded from paternity * Tested man is VWA (16,19), child is VWA (13,16) and mother is VWA (16,18) o Tested man is very probably excluded from paternity
28
Is this man excluded?
29
Including mutations in paternity
30
What is the second rule of inheritance?
Second rule of inheritance leading to exclusion of paternity (does not involve the mother) * When something is found in the alleged father that should be inherited by his children, if a child does not have it he may not be the father
31
Is this man excluded?
32
What is the estimated incidence of ‘silent’ alleles?
33
How would you deal with silent alleles in paternity? What could they be due to?
*** Could be a primer mutation ** o Try to repeat system using different primers o Check the height of the allele peaks in comparison with others in the profile *** Could be a deletion of part of the chromosome ** o Very rare (other than the Y chromosome)
34
Reporting non-paternity
Laboratory needs to determine rules o Greater than 2 or 3 exclusions o Number will depend on chance of a mutation- more tests more chances
35
Tested man is excluded from paternity, UNLESS what?
*** Mutation ** * **Man is chimera** (an organism containing a mixture of genetically different tissues, formed by processes such as fusion of early embryos, grafting or mutations ie., sperm may be different to blood) * **Wrong sample ** o Wrong man presented (identity error) o Sample wrongly labelled (sampler error) o Wrong sample tested (laboratory error) * **Wrong results** o Laboratory error (interpretation, technical) o Laboratory error (clerical)
36
Is this man excluded?
37
Is this man excluded?
38
Special cases
* Deficient family- deceased parent o 2-person test * Deficient family- deceased parent o Use a sibling of deceased- * Deficient family- deceased parent o 2-person test * Deficient family- deceased parent o Use a sibling of deceased- **avuncular index (AI)- An avuncular index is calculated using these genetic profiles. If the avuncular DNA index is less than 1.00 it indicates that the tested persons are not related** o = (locus PI + 1)/2 * Immigration case- is a close relative the father? o Evaluate the ratio combined PI/ combined AI * Deficient family- deceased parent o Use parent(s) of deceased  Substitute each grandparent as putative father and take the mean of the two PIs for each test  If only one grandparent, use PI=1 for the other grandparent * Y chromosome STRs and MtDNA o Must use haplotype frequencies o Can be combined with autosomal data * Need to caution against the chance match probabilities because of population substructure * ISFG recommendations on biostatistics in paternity testing (2007) FSI genetics 1: 223- 231
39
Familias- a programme for relationships
* Choose a population frequency * Check database choice * Import your data * Import family * Import data * Set up hypothesis * Hypothesis 1 (tested man is the father) * Hypothesis 2 (an unrelated man is the father) * Result o Look at LR, anything over 1x109 is billion o So this would mean the change that, that male is the father is over 1 billion