Kern Regional Crime Laboratory Laboratory Director: Dr. Kevin W. P. Miller TRUEALLELE WORK AND WORKFLOW: KERN COUNTYS FIRST CASES APRIL 23, 2014 Presenters Kelly Woolard Garett Jerry Sugimoto Garza Overview
Validation study Procedures/ Casework Workflow Case examples: Sexual assault case Making something of nothing. Soda Ax can case Using all available information. case Defense gets 1 upd a million times over.
Q&A The Validation Reasons for validating the TrueAllele system: Is it more informative than manual interpretation? Is it a more consistent method of mixture interpretation? Validation set up: Mixtures were set up consisting of 2, 3, 4 and 5 contributors.
10 mixtures were prepared per mixture group using 5 known references. Each of the mixtures within each mixture group were amplified with 1ng of DNA template and with 200 pg of DNA template. The known references were chosen randomly as were the mixture ratios to better approximate casework samples. 40 total mixtures. The Validation Results: TrueAllele was a more informative, reproducible and consistent method of mixture interpretation.
TrueAllele had little interpretation variance across the mixture samples containing 2-5 contributors and 200pg-1.0ng of DNA template. There were no significant differences in the regression line slopes between all the samples regardless of the number of contributors and target amount of DNA for amplification. The Validation Mixture range (%) Inclusion rate % (1 ng) Inclusion rate % (200 pg) 50-100 100
100 25-50 100 100 10-25 100 91 5-10 82 24 1-5 40
0 0-1 0 0 TrueAllele is a very robust system with regards to attaining match statistics with contributors that represent 10%+ of a mixture. Even at 1-5% of mixtures with 1 ng starting DNA template, TrueAllele is able to calculate a match statistic 40% of the time.
The Validation 8.4 million total comparisons were made between 10,000 randomly generated profiles using the three FBI ethnic databases. False exclusions were relatively rare with most coming from low template samples (200pg) and samples with a higher number of contributors (4-5). False inclusions were also very rare. When a false match did occur, it was rarely a match score of more than 3 log(LR) units. Only six out of 8.4 million false inclusions were greater than 3 log(LR) unit match scores but none were more than 4 log(LR) which is why we set our cannot exclude limit at 4 log(LR) units. Additional validation studies by NY State and Virginia crime labs also support our 4 log(LR) unit limit. Specificity (1ng mixture samples) TrueAllele Analysis Workflow
Analysts manually review profiles using GeneMapper ID-X Single source profiles Two person mixtures Mixture with at least three contributors Low-level mixtures that are uninterpretable using manual interpretation (below thresholds) Partial profiles Manual interpretation and statistical calculations using Popstats TrueAllele analysis TrueAllele Analysis Workflow Performed by trained casework operators Upload raw data to system
Create requests based on number of contributors and condition of profiles (degraded or non-degraded) Infer contributors in requests (known reference samples in the case) Kern Protocol for interpretation of STR profiles using the TrueAllele Casework System- when to infer contributors Uncertainty of genotype inference is reduced for some mixture profiles when samples from known contributors are inferred. Individuals can be inferred for intimate samples or for samples where it is reasonable to assume their presence based on case-specific information.
In addition, for the non-intimate samples, the request must be made with and without known reference profiles and log(LR) values must be >4. Kern Regional Crime Laboratory Interpretation Protocol (Naming Requests) Example: 12CL12345_D9947X_Q_ncon3_D_100K+D9948X_copy When copy requests are made, leave the _copy at the end of the request name YYCL##### Laboratory Number X####X Unique ID number (DNA number)
Q/K Questioned or known item Ncon# Number of assumed contributors N/D X-degraded option off D- degraded option on ###K Number of cycles, K= thousand +X####X DNA number(s) of known reference profiles inferred to the mixture Processing Time
Depends on several factors Number of processors Type of request (degraded vs. non-degraded) Number of cycles Number of contributors in the mixture Nature of the evidence sample
Total number of samples and requests made in the case Approximate Request Time 500 cycles ~15 minutes (known reference samples) 50,000 cycles ~6 to 12 hours (two person mixtures) 100,000 cycles ~1 day (three person mixtures and above) 200,000 cycles ~2+ days (nasty three person mixtures and above or difficult samples) Review of the results from the first round requests
Mixture weights (% contributions for each contributor) Make adjustments to requests Convergence (how well the system has been able to separate each contributor in the sample) Genotype probability distributions Increase cycle time Results are not good Match scores Infer contributors Results look good Change degraded option Put in duplicate requests to check for concordance between requests(similar results with match scores within two
log(LR) units of each other using the same request parameters) Increase or decrease number of contributors in request Conclusions made After all sample requests have been completed and concordance between duplicate requests is achieved, compare to known references. Conclusions: Cannot Exclude - Positive match scores greater than 4 log (LR) units (>10,000) Inconclusive - Match scores between -4 and +4 log(LR) units (-10,000 to 10,000)
Excluded - Negative match scores less than -4 log (LR) units (<-10,000) Reporting exclusions and inconclusive results Excluded Inconclusive (Name) is excluded as a potential contributor to the DNA profile obtained from this item. No conclusion can be drawn as to whether or not (name) could be excluded as a potential contributor to this DNA profile obtained from this item. Reporting non-exclusions Cannot Exclude (without individuals inferred) Cannot Exclude (with individuals
inferred) When a likelihood ratio was When a likelihood ratio was calculated using the TrueAllele calculated using the TrueAllele Casework system, it was assumed Casework System, it was assumed that the evidence sample contained that the evidence sample contained a (single source profile)/(mixture of X a mixture of X unknown contributors, unknown contributors). A match was and contained DNA from known identified between this evidence contributor(s) (name(s)) (item #(s)). item and (name) (item #). A match A match was identified between this between this evidence item and evidence item and name (item #). A (name) is X times more probable match between this evidence item than a coincidental match to an and (name) is X times more probable unrelated person relative to the
than a coincidental match to an reference populations listed (see unrelated person relative to the statement #) reference populations listed (see statement #). Casework Documentation Request files (.req) Report file (.txt and .zip) Match Table (.xls) All raw data files (.fsa)
All notes are documented in LIMS (JusticeTrax) JusticeTrax Meets TrueAllele Sexual Assault Case Scenario Unknown male subject Sexual assault case Case consisted of 10+ known references and dozens of forensic samples. Most were challenging samples (i.e., touch DNA, low level mixtures)
All items processed with TrueAllele Prior to TrueAllele analysis, one sample was eligible for upload to CODIS Sexual Assault Case After getting a hit, the offender profile was compared to all evidence items in the case. Prior to using TrueAllele Casework, only one sample yielded a profile eligible for a probative manual statistical calculation. After TrueAllele analysis, five additional samples yielded
reportable matches to the offender. Six matches linked the subject to multiple cases. Sexual Assault Case All of the case samples and known references were compared to each other and there were no non-probative matches other than to the subject. Case took approximately 2 months to report TrueAllele results with only 4 TrueAllele processors. These cases are currently pending trial. The Soda Can Case
Scenario Drinking vessel Subject (individual #1) in a homicide drank out of can prior to shooting victim Elimination sample (individual #2) owned can and drank out of can (per case information provided) Question- is subject (individual #1) a contributor to the DNA profile from the swabbing of mouth of can? Individual #1
Compare to knowns Individual #2 TrueAllele Requests Initial requests (round 1) Number of unknown contributors = 3 Degraded option = on 100K burn in/ 100K read out + copy
150K burn in/150K read out Additional Requests (round 2) Our protocol states.. Individuals can be inferred for intimate samples or for samples where it is reasonable to assume their presence based on case-specific information In addition, for the non-intimate samples, the request must be made with and without known reference profiles and log(LR) values must be >4. Therefore, individual 2 could be an inferred as a contributor Additional requests Infer contributor 2 Number of unknown contributors = 2 Degraded option = on 100K burn in/ 100K read out + copy Evidence N Contributor Contrib Weight Std Dev KL
5.624 13CL00000_C0000X_Q_ncon3_D_100K+C1111X_copy 3 3 0.222 0.124 6.412 -13.411 5.533 The Ax Case Scenario: Request for re-analysis of samples previously analyzed
and reported out by another laboratory (using manual interpretation methods) Raw data files were submitted and uploaded into the system Sample details Questioned item: 1 Ax (three separate swabs were sampled from the item on the handle and blade areas All samples were at least three person mixtures Known reference samples:
1 Subject Requests Three person requests at 100,000 cycles non-degraded Three person requests at 100,000 cycles degraded* Four person requests at 100,000 cycles non-degraded Four person requests at 100,000 cycles degraded
Results of TrueAllele analysis Positive match scores for the subject in the minor portion of one of the samples (excluded from the major portion) Subject was excluded from the other two samples Sample details: ~ 1ng DNA was amplified Approximate % contribution for each contributor 83%, 9%, 8% Comparison of results Manual Interpretation Best statistic in a population group: 1 in 8 TrueAllele Analysis Best statistic in a population group: 2.4 Million
Whats Next? TrueAllele Database Evidence profile category (all requests run at 5000 cycles, 3 unknown contributors) EVI- Forensic Unknowns Reference profile categories (All requests run at 500 cycles) SUB VIC- Subjects/suspects named in case file Victims named in case file Individuals identified for use as
elimination knowns and or not specifically named as victims or subjects within the laboratory documents Match rule Usage (when they are searched) EVI to EVI ALWAYS EVI to SUB ALWAYS EVI to STF ALWAYS EVI to POI Upon request from the DA or ADA of Kern County
EVI to VIC NEVER POI- STF- Staff and law enforcement profiles Acknowledgement s Cybergenetics Attendees Dr. Kevin Miller Questions?
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