UNCLASSIFIED Defense Forensic Science Center DNA Mixture Interpretation Study: Inter- and Intra-laboratory Variation Roman Aranda IV 1, Emily Rogers 1,

Slides:



Advertisements
Similar presentations
Overcoming DNA Stochastic Effects 2010 NEAFS & NEDIAI Meeting November, 2010 Manchester, VT Mark W Perlin, PhD, MD, PhD Cybergenetics, Pittsburgh, PA Cybergenetics.
Advertisements

Attaching statistical weight to DNA test results 1.Single source samples 2.Relatives 3.Substructure 4.Error rates 5.Mixtures/allelic drop out 6.Database.
3 Person Example #2 Suspect Boxer Shorts (The Ladies Man)
Infrasound Station Ambient Noise Estimates and Models: J. Roger Bowman, Gordon Shields, and Michael S. O’Brien Science Applications International.
Stat-JR: eBooks Richard Parker. Quick overview To recap… Stat-JR uses templates to perform specific functions on datasets, e.g.: – 1LevelMod fits 1-level.
DTIC Overview Mr. Al Astley Defense Technical Information Center Director, Information Science & Technology June 3, 2010 Approved for Public Release U.S.
Chapter 1, Section 2 Answers to review for worksheet pages
RESEARCH STUDY FOR THE RELIABILITY OF THE ACE-V PROCESS:
Defense Travel Management Office Office of the Under Secretary of Defense (Personnel and Readiness) Defense Travel Management Office Office of the Under.
1 CS 430 / INFO 430 Information Retrieval Lecture 24 Usability 2.
TrueAllele ® Casework Validation on PowerPlex ® 21 Mixture Data Australian and New Zealand Forensic Science Society September, 2014 Adelaide, South Australia.
Evaluation Entry System Overview NCOER (SGT)
The Forensic Laboratory. K-Fed sez: Quiz on Friday.
Ronald Reinstein Chairman, Arizona Forensic Services Advisory Committee.
International Business and Technology Consultants AMS confidential & proprietary SPS Help Desk Presentation Army User’s Conference June 2002.
IResults TBI Crime Laboratory.
DTIC Discovery Tools 28 March 2012 Moderator: Kapin L. Ferguson.
Ben Livelsberger NIST Information Technology Laboratory, CFTT Program
Qualitative Business Surveys: Signal or Noise? Silvia Lui, James Mitchell & Martin Weale Presented at the Third International Seminar on Early Warning.
Computer Interpretation of Uncertain DNA Evidence National Institute of Justice Computer v. Human June, 2011 Arlington, VA Mark W Perlin, PhD, MD, PhD.
Requirements Specification for Lab3 COP4331 and EEL4884 OO Processes for Software Development © Dr. David A. Workman School of Computer Science University.
Optimal Food Safety Sampling Under a Budget Constraint Mark Powell U.S. Department of Agriculture, Office of Risk Assessment and Cost-Benefit Analysis.
Using a Comprehensive Occupational Exposure Database to Integrate Members of the Occupational Health Team and Improve Your Occupational Health Program.
Disclaimer Certain trade names and company products are mentioned in the text or identified. In no case does such identification imply recommendation or.
DNA and CODIS CSI UMMC From
1 Peter Fox GIS for Science ERTH 4750 (98271) Week 8, Tuesday, March 20, 2012 Analysis and propagation of errors.
The Mathematical Laboratory: using Mathematica with Science Students Phil Ramsden METRIC Project Imperial College, London.
You don’t know what you don’t know But does it matter? Or is everything inconclusive?
Mr. Gopi Nair Defense Technical Information Center Briefing at Board on Research Data and Information (BRDI) Meeting September 24, 2009 Approved for Public.
Research Problem In one sentence, describe the problem that is the focus of your classroom research project about student learning: That students do not.
Creating a Virtual Library in the Age of Closings Jane Killian Defense Forensic Science Center December 10, 2013 SLA Military Library Division Workshop.
TrueAllele ® Genetic Calculator: Implementation in the NYSP Crime Laboratory NYS DNA Subcommittee May 19, 2010 Barry Duceman, Ph.D New York State Police.
Primer Briefing “Brand Name or Equal” Purchase Descriptions Ask a Professor - # Date:
Rapid DNA Response: On the Wings of TrueAllele Mid-Atlantic Association of Forensic Scientists May, 2015 Cambridge, Maryland Martin Bowkley, Matthew Legler,
Getting Past First Bayes with DNA Mixtures American Academy of Forensic Sciences February, 2014 Seattle, WA Mark W Perlin, PhD, MD, PhD Cybergenetics,
Lab 2: Descriptive Statistics. Today’s Activities Complete Brief Personality Inventory – Must put PID on Scantron Calculate Scales and Compute Descriptive.
Desmond Foley, Richard Wilkerson and Pollie Rueda Division of Entomology, Walter Reed Army Institute of Research, 503 Robert Grant Avenue, Silver Spring.
Knowledge Translation Conference KT Solutions for Overcoming Barriers to Research Use Hosted by SEDL’s Center on Knowledge Translation for Disability and.
Jerry Reiter Department of Statistical Science and the Information Initiative at Duke Duke University.
Exploring Forensic Scenarios with TrueAllele ® Mixture Automation 59th Annual Meeting American Academy of Forensic Sciences February, 2007 Mark W Perlin,
Measured Progress ©2012 Combined Human and Automated Scoring of Writing Stuart Kahl Measured Progress.
National Institute of Food and Agriculture – NIFA ASAP Enrollment Process- From A Grantee Point of View by Eric D. Still-Branch Chief FMB1.
UNCLASSIFIED Defense Forensic Science Center Evaluating Probabilistic Genotyping Results Joel Sutton, Technical Leader USACIL DNA Casework Branch 7 th.
Disputed DNA Stats for a Low-level Sample: A Case Study By Dan Krane – Carrie Rowland –
Seventh Annual Prescriptions for Criminal Justice Forensics Program Fordham University School of Law June 3, 2016 DNA Panel.
Department of Defense Voluntary Protection Programs Center of Excellence Development, Validation, Implementation and Enhancement for a Voluntary Protection.
UNCLASSIFIED Defense Forensic Science Center Development and Evaluation of a Model to Quantify the Weight of Fingerprint Evidence *Henry Swofford; Koertner.
Chapter 1, Section 2 Answers to review for worksheet pages
Negotiation 201: Military Kathy L. Ryan, PhD
BAE 5333 Applied Water Resources Statistics
Forensic Stasis in a World of Flux
Recapping: Distribution of data.
Microsoft Office Illustrated
IET 603 Quality Assurance in Science & Technology
DNA identification pathway
Success Criteria: I will be able to analyze data about my classmates.
September Workshop and Advisory Board Meeting Presenter Affiliation
September Workshop and Advisory Board Meeting Presenter Affiliation
Dynamic Cyber Training with Moodle
Question What is a threshold? Cybergenetics ©
DNA identification pathway
2018 AAFS Annual Scientific Meeting February 22, 2018
Box plot showing the distribution of pH by sample type, including: median (midline); mean (◊); 25th and 75th percentiles (box); and the range, excluding.
Statistics---SPSS.
Actively Learning Ontology Matching via User Interaction
Chapter 6.4 Box and Whisker Plots
A Sensitive Denaturing Gradient-Gel Electrophoresis Assay Reveals a High Frequency of Heteroplasmy in Hypervariable Region 1 of the Human mtDNA Control.
Overview of Minimum Group Size Business Rules for State Accreditation
STAT 515 Statistical Methods I Sections
Presentation transcript:

UNCLASSIFIED Defense Forensic Science Center DNA Mixture Interpretation Study: Inter- and Intra-laboratory Variation Roman Aranda IV 1, Emily Rogers 1, Karl Mereus 1, Phillippa Spencer 2, and Rick Tontarski 1 Office of the Chief Scientist ASCLD, 28 April

UNCLASSIFIED Disclaimer The opinions or assertions contained herein are the private views of the author and are not to be construed as official or as reflecting the views of the Department of the Army or the Department of Defense. Names of commercial manufacturers or products included are incidental only, and inclusion does not imply endorsement by the authors, DFSC, OPMG, DA or DoD. Unless otherwise noted, all figures, diagrams, media, and other materials used in this presentation are created by the respective author(s) and contributor(s) of the presentation and research. 2

UNCLASSIFIED Briefing Overview 3 Mixture Study Structure Preliminary Results Variation Illustrated via GIM and AM Path Forward: DNA Examiner Assessment Tool (DEAT)

UNCLASSIFIED Purpose: To assess the inter- and intra-laboratory variation in DNA examiners’ generated genotype interpretations To better understand the current state and potential limitations of mixture interpretation in the forensic community 4 DFSC Mixture Study

UNCLASSIFIED Initiated Summer examiners 55 forensic laboratories 5 Participation:

UNCLASSIFIED Examiners asked to interpret 6 mixtures: All examiners received identical 6 mixtures Used their laboratory’s SOP Stochastic and analytical thresholds set by DFSC Genotypes recorded on Excel-based worksheet provided User assessment form 6 Study Datasets:

UNCLASSIFIED 7 Metrics: GIM + AM How many answers did I provide at each locus? Did my genotypes include the “correct answer”? KnownGeneratedATMAFMInc 11, , Any100 11, , Inc.002 Genotype Interpretation Metric: GIM Allelic Match: InconclusiveExact Match Combinations + Anys

UNCLASSIFIED Automated program analyzes returned genotypes Uses worksheet template Calculates GIM/AM metrics 8 DEAT: DNA Examiner Assessment Tool

UNCLASSIFIED 9 GIM Box Plots = Median GIM Locus, Mixture, etc. = 25–75 Percentile = Statistical Max/Min Distribution = Outliers

UNCLASSIFIED 10 Mixture 1: 3.5:1, 2-person GIM Major and Minor Combined Region Local State Federal Int’l/Other

UNCLASSIFIED 11 Mixture 1: 2-person, 3.5:1 AF % AT % Median Method 1 Method 2 All Examiners Plotted Individually Major and Minor Combined

UNCLASSIFIED 12 Mixture 2 & 3: 2:1, 2-person GIM 2:1, w/ Ref2:1, w/o Ref B C D E F Method 1 2 B C D E F Method 1 2 Lab A Major and Minor Combined

UNCLASSIFIED 13 Inc % AT % Mixture 2 & 3: 2:1, 2-person 2:1, w/ Ref2:1, w/o Ref Major and Minor Combined Median Method 1 Method 2 Median Method 1 Method 2

UNCLASSIFIED 14 Mixture 5: 3-person, 4:1:1 w/ Ref GIM Method 1 Method 2 B C D E F Lab A

UNCLASSIFIED 15 Mixture 5: 4:1:1 w/ ref (Major) AF % AT % Method 2 Method 1 Major and Minors Combined

UNCLASSIFIED 16 Mixture 6: Box Plot GIM Method 1 Method 2 B C D E F Lab A

UNCLASSIFIED 17 Mixture 6: 3-person, 1:1:1 AF % AT % Method 2 Method 1 Major and Minors Combined

UNCLASSIFIED 18 Final Stat: 3.5:1, 2-person, Major Lab A Lab B 5 25 Examiner ~75% of examiners reported log (fs)

UNCLASSIFIED log (fs) 19 Final Stat: 2:1, 2-person, Major Contributor Lab A Lab B Lab A Lab B w/ Ref w/o Ref 20 Examiner ~75% of examiners reported ~50% of examiners reported

UNCLASSIFIED 20 Final Stat: 4:1:1, 3-person w/ Ref, Major Lab A Lab B 0 25 ~55% of examiners reported Examiner log (fs)

UNCLASSIFIED Laboratory Use of DEAT 21

UNCLASSIFIED 22 Benchmarking GIM Mixture Interpretation rate 2.Variation 3.Deconvolution

UNCLASSIFIED 1.Within Δ range 2.Minor Δ: review/retest? 3.Major Δ: retrain? 23 Examiner Assessment GIM Training/Proficiency Tests Training of new and existing DNA examiners:

UNCLASSIFIED 24 Examiner Assessment AF% AT% or GIM 1.Within Δ range 2.Minor Δ: review/retest? 3.Major Δ: retrain? Simulate whether those errors would still produce a match/hit Training of new and existing DNA examiners:

UNCLASSIFIED Summary Large variation in examiner generated genotypes: Within and between laboratories Variation in interpretation methods Creation of DNA Examiner Assessment Tool: quantify and assess changes via GIM and AM Potential use of metrics in DNA laboratories Benchmarking, training, and proficiency tests 25

UNCLASSIFIED Roman Aranda IV Research Scientist Office of the Chief Scientist CTR, A-T Solutions 26