Database Searches Non-random samples of N individuals Typically individuals convicted of some crime Maryland, people arrested but not convicted.

Slides:



Advertisements
Similar presentations
Copyright Pearson Prentice Hall
Advertisements

TWO STEP EQUATIONS 1. SOLVE FOR X 2. DO THE ADDITION STEP FIRST
Chapter 4 Sampling Distributions and Data Descriptions.
Chapter 5 One- and Two-Sample Estimation Problems.
Overcoming DNA Stochastic Effects 2010 NEAFS & NEDIAI Meeting November, 2010 Manchester, VT Mark W Perlin, PhD, MD, PhD Cybergenetics, Pittsburgh, PA Cybergenetics.
Solving Systems of Equations by Substitution Objectives: Solve Systems of Equations using substitution. Solve Real World problems involving systems of.
Familial searches and cold hit statistics Forensic Bioinformatics ( Dan Krane Wright State University, Dayton, OH
Elementary Statistics for Lawyers References Evett and Weir, Interpreting DNA evidence. Balding, Weight-of-evidence for forensic DNA profiles.
The statistical weight of mixed samples with allelic drop out First serious attempt by Gill et al. 2006, Forensic Science International 160:90 An important.
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.
Database Searches Evidence profiles are compared to a list of up to 3,528,903 profiles (FBI CODIS) References Balding, Weight-of- evidence for forensic.
Inferring the Number of Contributors to Mixed DNA Profiles David Paoletti.
Introductory Mathematics & Statistics for Business
1 Copyright © 2010, Elsevier Inc. All rights Reserved Fig 2.1 Chapter 2.
Business Transaction Management Software for Application Coordination 1 Business Processes and Coordination.
Summary of Convergence Tests for Series and Solved Problems
Jeopardy Q 1 Q 6 Q 11 Q 16 Q 21 Q 2 Q 7 Q 12 Q 17 Q 22 Q 3 Q 8 Q 13
Jeopardy Q 1 Q 6 Q 11 Q 16 Q 21 Q 2 Q 7 Q 12 Q 17 Q 22 Q 3 Q 8 Q 13
Title Subtitle.
Determine Eligibility Chapter 4. Determine Eligibility 4-2 Objectives Search for Customer on database Enter application signed date and eligibility determination.
0 - 0.
ALGEBRAIC EXPRESSIONS
DIVIDING INTEGERS 1. IF THE SIGNS ARE THE SAME THE ANSWER IS POSITIVE 2. IF THE SIGNS ARE DIFFERENT THE ANSWER IS NEGATIVE.
ADDING INTEGERS 1. POS. + POS. = POS. 2. NEG. + NEG. = NEG. 3. POS. + NEG. OR NEG. + POS. SUBTRACT TAKE SIGN OF BIGGER ABSOLUTE VALUE.
SUBTRACTING INTEGERS 1. CHANGE THE SUBTRACTION SIGN TO ADDITION
MULT. INTEGERS 1. IF THE SIGNS ARE THE SAME THE ANSWER IS POSITIVE 2. IF THE SIGNS ARE DIFFERENT THE ANSWER IS NEGATIVE.
FACTORING ax2 + bx + c Think “unfoil” Work down, Show all steps.
Addition Facts
Year 6 mental test 5 second questions
Overview of Lecture Parametric vs Non-Parametric Statistical Tests.
C82MST Statistical Methods 2 - Lecture 2 1 Overview of Lecture Variability and Averages The Normal Distribution Comparing Population Variances Experimental.
Around the World AdditionSubtraction MultiplicationDivision AdditionSubtraction MultiplicationDivision.
SADC Course in Statistics Estimating population characteristics with simple random sampling (Session 06)
ZMQS ZMQS
© Richard A. Medeiros 2004 x y Function Machine Function Machine next.
On Comparing Classifiers : Pitfalls to Avoid and Recommended Approach
ABC Technology Project
MAT 103 Probability In this chapter, we will study the topic of probability which is used in many different areas including insurance, science, marketing,
Page Replacement Algorithms
5-1 Chapter 5 Theory & Problems of Probability & Statistics Murray R. Spiegel Sampling Theory.
CHAPTER 6 Introduction to Graphing and Statistics Slide 2Copyright 2012, 2008, 2004, 2000 Pearson Education, Inc. 6.1Tables and Pictographs 6.2Bar Graphs.
Chapter 2.3 Counting Sample Points Combination In many problems we are interested in the number of ways of selecting r objects from n without regard to.
© S Haughton more than 3?
1 Directed Depth First Search Adjacency Lists A: F G B: A H C: A D D: C F E: C D G F: E: G: : H: B: I: H: F A B C G D E H I.
Factor P 16 8(8-5ab) 4(d² + 4) 3rs(2r – s) 15cd(1 + 2cd) 8(4a² + 3b²)
Squares and Square Root WALK. Solve each problem REVIEW:
Created by Susan Neal $100 Fractions Addition Fractions Subtraction Fractions Multiplication Fractions Division General $200 $300 $400 $500 $100 $200.
SYSTEMS OF EQUATIONS.
© 2012 National Heart Foundation of Australia. Slide 2.
Absolute-Value Equations and Inequalities
Past Tense Probe. Past Tense Probe Past Tense Probe – Practice 1.
Understanding Generalist Practice, 5e, Kirst-Ashman/Hull
Chapter 5 Test Review Sections 5-1 through 5-4.
GG Consulting, LLC I-SUITE. Source: TEA SHARS Frequently asked questions 2.
What You Should Learn • Represent and classify real numbers.
Addition 1’s to 20.
25 seconds left…...
Test B, 100 Subtraction Facts
Week 1.
We will resume in: 25 Minutes.
©Brooks/Cole, 2001 Chapter 12 Derived Types-- Enumerated, Structure and Union.
Solving Addition and Subtraction Inequalities
Bottoms Up Factoring. Start with the X-box 3-9 Product Sum
PSSA Preparation.
Chapter 11: The t Test for Two Related Samples
Multiple Regression and Model Building
One sample means Testing a sample mean against a population mean.
Rules for DNA Comparison Analysis
2018 AAFS Annual Scientific Meeting February 22, 2018
Presentation transcript:

Database Searches Non-random samples of N individuals Typically individuals convicted of some crime Maryland, people arrested but not convicted

What does the scientific community think happens? Balding and Donnelly (1996):..a search is made through the database with the result that exactly one of the profiles in the database is found to match the crime scene profile. NRCII:..the suspect is initially identified by searching a database to find a DNA profile matching that left at a crime scene. Stockmarr (1999):..exactly one profile in D is found to match TP Evett and Weir (1998):..the profile from the vaginal swab was searched against the database and his profile was found to match.

So whats a match? Evett and Weir (1998): They suppose that there are two samples, a crime and suspect sample that are typed by DNA techniques. The samples match when, The two samples are found to be of the same type.

What really happens? Target profile and database, candidate, profiles may be compared by two different criteria: high stringency and moderate stringency High stringency has the usual meaning of a match, that every allele in the target must be seen in the candidate and there can be no extra alleles in the candidate profile

Moderate Stringency If the target is a mixture with three or more alleles then matching candidates would be any of the possible pairs of samples Target = 9,11,14 Moderate matches = 9/9, 9/11, 9/14, 11/11, 11/14,14/14 This is similar to computing included genotypes in mixtures

Moderate Stringency: one allele If either the target or the candidate profile has only one allele, then moderate stringency matches are all genotypes with at least one copy of the single allele Target profile = 12 Matching candidates = 12/X, where X is any other allele This criteria is more generous than the typical definitions for mixture inclusions

Implications Vague protocols for matches by labs creates substantial liability on what constitutes a match. Example: single source target profile 13/13, would normally not be called a match to a 12/13 candidate Since labs insist on invoking allelic drop out the possibility of this type of match must always be considered relevant

Statistical Implications The class of matching genotypes to a 13/13 profile is then 13/X, which is greater than the frequency of the 13 allele but less than twice that frequency Apparently in California the moderate criteria is turned on in all searches Even if the search identifies a candidate that matches at high stringency the statistical penalty for the moderate search must be paid (see Venegas)

Are Balding, Donnelly, Evett, Weir, NRCII, and Stockmarr idiots? NO! However, because almost all forensic labs do not allow access to their software or databases, the scientific community is unaware of the real match criteria If scientific evidence is not yet ready for both scientific scrutiny and public re- evaluation by others, it is not yet ready for court. (NRC I)

Typing Errors Evett and Weir assume that the chance of not finding a match if the true perpetrator is in the database is zero. The difference at THO1 is a typing error FBI Bahamian [2118] 15 /16 17 /20 23 /24 10 /12 30 / /16 11 /13 8 /11 10 /12 9 /12 11 /11 8 /9 9 /11 [2163] 15 /16 17 /20 23 /24 10 /12 30 / /16 11 /13 8 /11 10 /12 9 /12 11 /11 8 /10 9 /11

Solutions Any lab following NRCII or likelihood ratios would have to compute random match probabilities adding up all potential matching profiles Avoid these headaches and follow the NRC I recommendations