1. Doug Raiford Lesson 15

2.  Every cell has identical DNA  If know the sequence of a suspect can compare to evidence  But wait… Do we have to sequence the entire genome of the individual? 9/14/20152DNA forensic evidence

3.  Only small portions  Portions where there are a small number of differing alleles  What was an allele, again? 9/14/20153DNA forensic evidence One of a series of different forms of a gene But not really a gene—more of a location Locus (plural: loci) One of a series of different forms of a gene But not really a gene—more of a location Locus (plural: loci)

4.  One that varies in the number of Short Tandem Repeats  Describes a type of DNA polymorphism that:  Repeats  And has a short (usually 4 base pair) repeat unit  Length polymorphism: alleles differ in their number of repeats 9/14/20154DNA forensic evidence 5 repeats: AATG AATG AATG AATG AATG 6 repeats: AATG AATG AATG AATG AATG AATG 4 repeats: AATG AATG AATG AATG 3 repeats: AATG AATG AATG

5.  If given a locus: say, Chromosome 3, sequence 1358 (D3S1358)  And given the following four alleles  3,4,5,6  And given that there are two chromatids 9/14/2015DNA forensic evidence5 5 repeats: AATG AATG AATG AATG AATG 6 repeats: AATG AATG AATG AATG AATG AATG 4 repeats: AATG AATG AATG AATG 3 repeats: AATG AATG AATG A person might exhibit 2 different alleles (for instance, the 4 and 5 alleles of the D3S1358 Locus) Alleles 4 and 5

6.  What if a test indicated a person only had one allele? 9/14/2015DNA forensic evidence6 5 repeats: AATG AATG AATG AATG AATG 6 repeats: AATG AATG AATG AATG AATG AATG 4 repeats: AATG AATG AATG AATG 3 repeats: AATG AATG AATG Just Allele 4

7. DQ-alpha (specific gene) TEST STRIP Allele = BLUE DOT DQ-alpha (specific gene) TEST STRIP Allele = BLUE DOT Restriction fragment length polymorphism (RFLP) AUTORAD Allele = BAND Restriction fragment length polymorphism (RFLP) AUTORAD Allele = BAND Automated STR ELECTROPHEROGRAM Allele = PEAK Automated STR ELECTROPHEROGRAM Allele = PEAK

8. Differential extraction in sex assault cases separates out DNA from sperm cells Extract and purify DNA If have a suspect get Reference Sample 9/14/20158DNA forensic evidence

9.  Know the regions upstream and downstream of the STRs  DNA regions flanked by primers are amplified  Groups of amplified STR products are labeled with different colored dyes (blue, green, yellow)

11. Amplified STR DNA injected onto column Electric current applied DNA separated out by size: –Large STRs travel slower –Small STRs travel faster DNA pulled towards the positive electrode Color of STR detected and recorded as it passes the detector Detector Window

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14. 0.222x x2 = 0.1 9/14/201514DNA forensic evidence

15. = 0.1 1 in 79,531,528,960,000,000 1 in 80 quadrillion 1 in 101 in 1111 in 20 1 in 22,200 xx 1 in 1001 in 141 in 81 1 in 113,400 xx 1 in 1161 in 171 in 16 1 in 31,552 xx 9/14/201515DNA forensic evidence

16. 9/14/2015DNA forensic evidence16 “The chance of a coincidental match is one in 80 quadrillion?” Random Match Probability

17.  Combined DNA Index System (CODIS)  FBI Database of profiles  Very compact  Each entry has information about the individual and which alleles at each locus 9/14/2015DNA forensic evidence17

18.  Usually, sample from crime scene and sample from suspect are sent to the crime lab at the same time 9/14/2015DNA forensic evidence18

19.  Many samples are in the form of mixtures  E.g. multiple assailants in a rape case  Usually state that “can’t rule out the suspect”  Sometimes still publish the random match probability  Is this right? 9/14/2015DNA forensic evidence19

20. 9/14/201520Expression Prediction with CUB

21. Two samples really do have the same source  Samples match coincidentally  An error has occurred 9/14/201521DNA forensic evidence

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23. Can “Tom” be excluded? SuspectD3vWAFGA Tom17, 1715, 1725, 25 9/14/201523DNA forensic evidence

24. Can “Tom” be excluded? SuspectD3vWAFGA Tom17, 1715, 1725, 25 No -- the additional alleles at D3 and FGA are “technical artifacts.” 9/14/201524DNA forensic evidence

25. Can “Dick” be excluded? SuspectD3vWAFGA Tom17, 1715, 1725, 25 Dick12, 1715, 1720, 25 9/14/201525DNA forensic evidence

26. Can “Dick” be excluded? SuspectD3vWAFGA Tom17, 1715, 1725, 25 Dick12, 1715, 1720, 25 No -- stochastic effects explain peak height disparity in D3; blob in FGA masks 20 allele. 9/14/201526DNA forensic evidence

27. Can “Harry” be excluded? SuspectD3vWAFGA Tom17, 1715, 1725, 25 Dick12, 1715, 1720, 25 Harry14, 1715, 1720, 25 No -- the 14 allele at D3 may be missing due to “allelic drop out”; FGA blob masks the 20 allele. 9/14/201527DNA forensic evidence

28. Can “Sally” be excluded? SuspectD3vWAFGA Tom17, 1715, 1725, 25 Dick12, 1715, 1720, 25 Harry14, 1715, 1720, 25 Sally12, 1715, 1520, 22 No -- there must be a second contributor; degradation explains the “missing” FGA allele. 9/14/201528DNA forensic evidence

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30. What is signal and what is noise? Distinguish between “real peaks” and technical artifacts  Deducing the number of contributors to mixtures  Accounting for relatives Determine measurement variability 9/14/201530DNA forensic evidence

31. “Conservative” thresholds established during validation studies Eliminate noise (even at the cost of eliminating signal) Can arbitrarily remove legitimate signal Contributions to noise vary over time (e.g. polymer and capillary age/condition)  Analytical chemists use LOD and LOQ 9/14/201531DNA forensic evidence

32. μbμb μ b + 3σ b μ b + 10σ b Mean background Signal Detection limit Quantification limit Measured signal (In Volts/RFUS/etc) Saturation 0 9/14/201532DNA forensic evidence

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35. Average (  b ) and standard deviation (  b ) values with corresponding LODs and LOQs from positive, negative and reagent blank controls in 50 different runs. BatchExtract: ftp://ftp.ncbi.nlm.nih.gov/pub/forensics/ 9/14/201535DNA forensic evidence

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37. Two reference samples in a 1:10 ratio (male:female). Three different thresholds are shown: 150 RFU (red); LOQ at 77 RFU (blue); and LOD at 29 RFU (green). From Gilder et al., J. For. Sci, 2007, 52:97-101. 9/14/201537DNA forensic evidence

38. What is signal and what is noise? Distinguish between “real peaks” and technical artifacts  Deducing the number of contributors to mixtures  Accounting for relatives Determine measurement variability 9/14/201538DNA forensic evidence

39. Stutter peaks  Pull-up (bleed through)  Spikes and blobs 9/14/201539DNA forensic evidence

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41. Primary peak height vs. n+4 stutter peak height. Evaluation of 37 data points, R 2 =0.293, p=0.0005. From 224 reference samples in 52 different cases. A filter of 5.9% would be conservative. Rowland and Krane, accepted with revision by JFS. 9/14/201541DNA forensic evidence

42. Advanced Classic 9/14/201542DNA forensic evidence

43.  89 samples (references, pos controls, neg controls)  1010 “good” peaks  55 peaks associated with 24 spike events  95% boundaries shown 9/14/201543DNA forensic evidence

44. What is signal and what is noise? Distinguish between “real peaks” and technical artifacts Deducing the number of contributors to mixtures  Accounting for relatives Determine measurement variability 9/14/201544DNA forensic evidence

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46. How many contributors to a mixture if analysts can discard a locus? Maximum # of alleles observed in a 3-person mixture # of occurrencesPercent of cases 200.00 3780.00 44,967,0343.39 593,037,01063.49 648,532,03733.12 There are 146,536,159 possible different 3-person mixtures of the 959 individuals in the FB I database (Paoletti et al., November 2005 JFS). 3,398 7,274,823 112,469,398 26,788,540 0.00 4.96 76.75 18.28

47. How many contributors to a mixture if analysts can discard a locus? Maximum # of alleles observed in a 3-person mixture # of occurrencesPercent of cases 200.00 33100.00 42,498,1395.53 529,938,77766.32 612,702,67028.14 There are 45,139,896 possible different 3-person mixtures of the 648 individuals in the MN BCI database (genotyped at only 12 loci). 8,151 1,526,550 32,078,976 11,526,219 0.02 3.38 71.07 25.53

48. Maximum # of alleles observed in a 4-person mixture # of occurrencesPercent of cases 413,4800.02 58,596,32015.03 635,068,04061.30 712,637,10122.09 8896,4351.57 There are 57,211,376 possible different 4-way mixtures of the 194 individuals in the FB I Caucasian database (Paoletti et al., November 2005 JFS). (35,022,142,001 4-person mixtures with 959 individuals.)

49. Five simulations are shown with each data point representing 57,211,376 4- person mixtures (average shown in black). (Paoletti et al., November 2005 JFS). Mischaracterization rate of 76.34% for original 13 loci. 9/14/201549DNA forensic evidence

50. What is signal and what is noise? Distinguish between “real peaks” and technical artifacts  Deducing the number of contributors to mixtures Accounting for relatives Determine measurement variability 9/14/201550DNA forensic evidence

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52. Original FBI dataset’s mischaracterization rate for 3- person mixtures (3.39%) is more than two  above the average observed in five sets of randomized individuals Original FBI dataset has more shared allele counts above 19 than five sets of randomized individuals (3 vs. an average of 1.4) 9/14/201552DNA forensic evidence

53. Maximum allele count by itself is not a reliable predictor of the number of contributors to mixed forensic DNA samples. Simply reporting that a sample “arises from two or more individuals” is reasonable and appropriate. Analysts should exercise great caution when invoking discretion. Excess allele sharing observed in the FBI allele frequency database is most easily explained by the presence of relatives in that database. 9/14/201553DNA forensic evidence

54.  Database search yields a close but imperfect DNA match  Can suggest a relative is the true perpetrator  Great Britain performs them routinely  Reluctance to perform them in US since 1992 NRC report  Current CODIS software cannot perform effective searches 9/14/201554DNA forensic evidence

55.  Search for rare alleles (inefficient)  Count matching alleles (arbitrary)  Likelihood ratios with kinship analyses 9/14/201555DNA forensic evidence

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57.  Given a closely matching profile, who is more likely to match, a relative or a randomly chosen, unrelated individual?  Use a likelihood ratio 9/14/201557DNA forensic evidence

58. HF = 1 for homozygous loci and 2 for heterozygous loci; P a is the frequency of the allele shared by the evidence sample and the individual in a database. 9/14/201558DNA forensic evidence

59. HF = 1 for homozygous loci and 2 for heterozygous loci; P a is the frequency of the allele shared by the evidence sample and the individual in a database. 9/14/201559DNA forensic evidence

60. Cousins: Grandparent-grandchild; aunt/uncle-nephew-neice;half- sibings: HF = 1 for homozygous loci and 2 for heterozygous loci; P a is the frequency of the allele shared by the evidence sample and the individual in a database. 9/14/201560DNA forensic evidence

61. What is signal and what is noise? Distinguish between “real peaks” and technical artifacts  Deducing the number of contributors to mixtures  Accounting for relatives Determine measurement variability 9/14/201561DNA forensic evidence

62. Can “Sally” be excluded? SuspectD3vWAFGA Tom17, 1715, 1725, 25 Dick12, 1715, 1720, 25 Harry14, 1715, 1720, 25 Sally12, 1715, 1520, 22 Is the 12 allele at the D3 locus really 47 RFUs tall? 9/14/201562DNA forensic evidence

63.  Data from 18 samples, each amplified twice and with each amplification product injected two times (n = 1,316). 9/14/201563DNA forensic evidence

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66.  --the tendency to interpret data in a manner consistent with expectations or prior theories (sometimes called “examiner bias”)  Most influential when:  Data being evaluated are ambiguous or subject to alternate interpretations  Analyst is motivated to find a particular result 9/14/201566DNA forensic evidence

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70. DNA Lab Notes (Commonwealth v. Davis)  “I asked how they got their suspect. He is a convicted rapist and the MO matches the former rape…The suspect was recently released from prison and works in the same building as the victim…She was afraid of him. Also his demeanor was suspicious when they brought him in for questioning…He also fits the general description of the man witnesses saw leaving the area on the night they think she died…So, I said, you basically have nothing to connect him directly with the murder (unless we find his DNA). He said yes.” 9/14/201570DNA forensic evidence

71. DNA Lab Notes  “Suspect-known crip gang member--keeps ‘skating’ on charges-never serves time. This robbery he gets hit in head with bar stool--left blood trail. Miller [deputy DA] wants to connect this guy to scene w/DNA …”  “Death penalty case! Need to eliminate Item #57 [name of individual] as a possible suspect” 9/14/201571DNA forensic evidence

72.  Resolve ambiguous data in a manner consistent with expectations  Miss or disregard evidence of problems  Miss or disregard alternative interpretations of the data  Thereby undermining the scientific validity of conclusions  See, Risinger, Saks, Thompson, & Rosenthal, The Daubert/Kumho Implications of Observer Effects in Forensic Science: Hidden Problems of Expectation and Suggestion. 93 California Law Review 1 (2002). 9/14/201572DNA forensic evidence

73. Simply interpret evidence with no knowledge of reference samples  Minimizes subjectivity of interpretations  Forces analysts to be truly conservative in their interpretations  See, Krane et al., Sequential unmasking: a solution for context effects in DNA profiling. June, 2008 issue of the Journal of Forensic Sciences. 9/14/201573DNA forensic evidence

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75. What we do: Review DNA testing results Typically work with defendants Rely heavily upon Genophiler™ Incorporated April 2, 2002 25 reviews in 2002, more than 250 reviews in 2007 to date 9/14/201575DNA forensic evidence

76.  Publications  Forensic Bioinformatics Website: http://www.bioforensics.com/http://www.bioforensics.com/  Collaborators  Larry Mueller and Bill Thompson (UC Irvine)  Simon Ford (Lexigen Inc., San Francisco, CA)  William Shields (SUNY, Syracuse, NY)  Sandy Zabell (Northwestern University, Chicago, IL)  Travis Doom (Wright State, Dayton, OH)  Marc Taylor (Technical Associates, Ventura, CA)  Keith Inman (Forensic Analytical, Hayword, CA)  D. Michael Risinger (Seton Hall University, South Orange, NJ)  Allan Jamieson (The Forensics Institute, Glasgow, UK)  Testing laboratories  Technical Associates (Ventura, CA)  Forensic Analytical (Hayword, CA)  Indiana State Police Laboratory (Indianapolis, IN) 9/14/201576DNA forensic evidence

77.  Toddler disappears in bizarre circumstances: found dead six months later  Mother’s boy friend is tried and acquitted.  Unknown female profile on clothing.  Cold hit to a rape victim.  RMP: 1 in 227 million.  Lab claims “adventitious match.” 9/14/201577DNA forensic evidence

78.  Condom with rape victim’s DNA was processed in the same lab 1 or 2 days prior to Leskie samples.  Additional tests find matches at 5 to 7 more loci.  Review of electronic data reveals low level contributions at even more loci.  Degradation study further suggests contamination. 9/14/201578DNA forensic evidence

79.  When biological samples are exposed to adverse environmental conditions, they can become degraded  Warm, moist, sunlight, time  Degradation breaks the DNA at random  Larger amplified regions are affected first  Classic ‘ski-slope’ electropherogram  Degradation and inhibition are unusual and noteworthy. LARGELARGE SMALLSMALL 9/14/201579DNA forensic evidence

80. The Leskie Inquest, a practical application  Undegraded samples can have “ski-slopes” too.  How negative does a slope have to be to an indication of degradation?  Experience, training and expertise.  Positive controls should not be degraded. 9/14/201580DNA forensic evidence

81. The Leskie Inquest  DNA profiles in a rape and a murder investigation match.  Everyone agrees that the murder samples are degraded.  If the rape sample is degraded, it could have contaminated the murder samples.  Is the rape sample degraded? 9/14/201581DNA forensic evidence

82. The Leskie Inquest 9/14/201582DNA forensic evidence

83. “8. During the conduct of the preliminary investigation (before it was decided to undertake an inquest) the female DNA allegedly taken from the bib that was discovered with the body was matched with a DNA profile in the Victorian Police Forensic Science database. This profile was from a rape victim who was subsequently found to be unrelated to the Leskie case.” 9/14/201583DNA forensic evidence

84. “8. The match to the bib occurred as a result of contamination in the laboratory and was not an adventitious match. The samples from the two cases were examined by the same scientist within a close time frame.” www.bioforensics.com/articles/Leskie _decision.pdf 9/14/201584DNA forensic evidence

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