DNA Short Tandem Repeats. Organism

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1 DNA Short Tandem Repeats Organism

2 DNA Short Tandem Repeats Organ

3 DNA Short Tandem Repeats Cell

4 Weights 1kg a bag of sugar 1g paper clip 1mg (milligram) 0.001g brain of a bee 1µg (microgram) g weight of a bacterium 1ng (nanogram) g a millionth of a grain of salt - recommended input to profiling 1pg (picogram) g 6pg of DNA from each cell

5 Cells We lose about 30,000-40,000 skin cells an hour In a year, you lose about 8lbs of cells Where do they all go? The dust that collects on your tables, TV, windowsills and on those picture frames that are so hard to get clean is made mostly from dead human skin cells. In other words, your house is filled with former bits of yourself. About 10,000 will fit on the head of a pin Current DNA technology can profile one cell

6 DNA Short Tandem Repeats Nucleus

7 DNA Short Tandem Repeats Chromosomes

8 DNA Short Tandem Repeats DNA

9 DNA Short Tandem Repeats Locus

10 DNA Short Tandem Repeats STR

11 DNA Short Tandem Repeats

12 DNA Short Tandem Repeats Allele

13 DNA Short Tandem Repeats 5 3 Allele

14 DNA Short Tandem Repeats FGA 3 D3 3 Locus is important

15 DNA Short Tandem Repeats A D3 vwa D16 D2 D8 D21 D18 D19 THO1 X Y DNA profile Homozygote Locus Allele Heterozygote

16 The process Extraction Quantitation Amplification Separation Interpretation Evaluation

17 Amplification = Multiplication

18 Raw data

19 Single source profile

20 Area of DNA tested Names of DNA components One DNA component from mother, another from father

21 Why statistics? DNA is NOT unique We look at only a few areas Need to know what the probability of finding the profile by chance is (i.e. to give an idea of how many other people may have been the source of the profile)

22 Statistical estimates x = in 10 1 in in 20 x 1 in 22,200 x 1 in in 14 1 in 81 x x x 1 in in 17 1 in 16 1 in 1,400 1 in 31,552 1 in a billion

23 Probability Black hair Blue eyes Beard Gold tooth Probability= 0.6 x 0.25 x 0.01 x = = 1 in 666,666

24 Random Match Probability R B f RB = 0.1 x 0.1 x 2 = 0.02 = 2 in 100 = 1 in 50

25 Mixtures

26 Mixtures

27 ? Mixtures

28 Mixtures?

29 ? Mixtures

30 Mixtures?

31 Mixtures RB RY RG BY BG GY = 6 suspect profiles that cannot be excluded as contributors

32 How many suspects? With 6 possibilities at each of 15 areas There are 6x6x6x6x6x6x6x6x6x6x6x6x6x6x6= More than 60 million suspect profiles

33 Alleles observed on outside D8 D21 D7 CSF D3 THO1 D D16 D2 D19 vwa TPOX D18 D5 FGA

34 Alleles observed on outside D8 D21 D7 CSF D3 THO1 D D16 D2 D19 vwa TPOX D18 D5 FGA

35 No. of alleles at each locus D8 D21 D7 CSF D3 THO1 D D16 D2 D19 vwa TPOX D18 D5 FGA

36 No of suspect profiles D8 D21 D7 CSF D3 THO1 D D16 D2 D19 vwa TPOX D18 D5 FGA

37 No of suspect profiles D8 D21 D7 CSF D3 THO1 D D16 D2 D19 vwa TPOX D18 D5 FGA x3 x6 x3 x3 x10 x3 x10 x3 x1 x6 x3 x3 x1 x10

38 No of suspect profiles D8 D21 D7 CSF D3 THO1 D D16 D2 D19 vwa TPOX D18 D5 FGA x3 x6 x3 x3 x10 x3 x10 x3 x1 x6 x3 x3 x1 x10 = 78,732,000 suspect profiles

39 D8

40 D8

41 D8

42 Adding new alleles at D8 D8 D21 D7 CSF D3 THO1 D D16 D2 D19 vwa TPOX D18 D5 FGA ,392,000 (470m) suspect profiles

43 D21

44 D21 zoom

45 D21

46 Adding new alleles at D21 D8 D21 D7 CSF D3 THO1 D D16 D2 D19 vwa TPOX D18 D5 FGA D8 D21 D7 CSF D3 THO1 D D16 D2 D19 vwa TPOX D18 D5 FGA ,574,640,000 (1.5 billion) suspect profiles

47 Alleles on inside & outside D8 D21 CSF D3 THO1 D D19 TPOX D18 D5 IN OUT

48 times more likely The Likelihood Ratio = LR Probability of this evidence if the DNA came from Mr X + unknown Probability of this evidence if it came from 2 unknowns LR = Probability of E given Hpros Probability of E given Hdef e.g. LR = 1/10 1/100 = = 10

49 For single source profiles LR = 1 (1/frequency) =frequency e.g. 1/(1/10) = 10

50 Mixtures

51 Mr X + unknown rather than two unknowns R B Y G f X p(hp) p(hd) LR RB RY RG BY BG YG

52 Mr X + unknown rather than two unknowns R B Y G f Mr X p(hp) p(hd) LR RB RY RG BY BG YG

53 Mr X + unknown rather than two unknowns R B Y G f Mr X p(hp) p(hd) LR RB RY RG BY BG YG

54 Mr X + unknown rather than two unknowns RG 33.33

55 Mr X + unknown rather than two unknowns R B Y G f Mr X p(hp) p(hd) LR RB RY RG BY BG YG

56 Mr X + unknown rather than two unknowns R B Y G f Mr X p(hp) p(hd) LR RB RY RG BY BG YG

57 More complicated mixture

58 Second area

59 Second area

60 Second area (locus) A B C D A B C D B D A C

61 Second area only AB AC AD BC BD CD = 6 suspect profiles that cannot be excluded as contributors

62 AB AC AD BC BD CD RB RY RG BY BG YG 444

63 AB AC AD BC BD CD RB RY RG BY BG YG

64 X + unknown rather than two unknowns AB AC AD BC BD CD RB RY RG BY BG YG 1, ,

65 X + unknown rather than two unknowns AB AC AD BC BD CD RB RY RG BY BG 44,444 22,222 44,444 11,111 22,222 11,111 YG 1, ,

66 X + unknown rather than two unknowns AB AC AD BC BD CD RB 88,889 44,444 88,889 22,222 44,444 22,222 RY RG BY BG 44,444 22,222 44,444 11,111 22,222 11,111 YG 1, ,

67 X + unknown rather than two unknowns AB AC AD BC BD CD RB 88,889 44,444 88,889 22,222 44,444 22,222 RY 3,556 1,778 3, , RG 8,889 4,444 8,889 2,222 4,444 2,222 BY 17,778 8,889 17,778 4,444 8,889 4,444 BG 44,444 22,222 44,444 11,111 22,222 11,111 YG 1, ,

68 Stochastic variation Examples so far assume allele calls are certain, but low template samples cause new problems because of stochastic variation. Stochastic variation is random variation Failure to reproduce results Leads to uncertainty

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74 The crimestain

75 Standard technique Enough sample so that no dropout is expected and peak height represents amount of DNA present (i.e. not variable)

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83 Low Template Sample Stochastic variation is random variation Failure to reproduce results Leads to uncertainty

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91 A B C D E F G I H

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98 A B C D E F

99 Dropout or dropin? D8 D21 D7 CSF D3 THO1 D D16 D2 D19 vwa TPOX D18 D5 FGA

100 Probability of dropout and dropin p(d) Is the probability that an allele is really there but you have not detected it. p(c) Is the probability that an allele you have detected is not from the crimestain it is contamination

101 FST statistic FST is the programme used to calculate the LR in this case Statistic depends on Probability of dropout which is Dependent usually on the weight of DNA Which is unknown for the minor contributors And the validation data do not support any p(d) for any weight of DNA The LR being correct

102 Low Template Sample Identified by variable results, NOT the amount of DNA Causes problems in; Identifying true sample alleles Using peak height information Inclusion/exclusion of people Number of contributors