Deconvoluting Mixtures Using Proportional Allele Sharing What does it mean and how do you do it?

Slides:



Advertisements
Similar presentations
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.
Advertisements

3 Person Example #2 Suspect Boxer Shorts (The Ladies Man)
Introduction To 2 and 3 Person Mixtures How the RMP Can Help With Complex Mixtures.
2 Person Mixture #3 Questioned samples from bomb remains, no references.
Random Match Probability Statistics
1 BI3010H08 Population genetics Halliburton chapter 9 Population subdivision and gene flow If populations are reproductible isolated their genepools tend.
Automated Regression Modeling Descriptive vs. Predictive Regression Models Four common automated modeling procedures Forward Modeling Backward Modeling.
The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL Chapter 3. Conditional Probability and Independence Section 3.1. Conditional Probability Section 3.2 Independence.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. Chapter 18 Sampling Distribution Models.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. Chapter 18 Sampling Distribution Models.
Copyright © 2010 Pearson Education, Inc. Chapter 18 Sampling Distribution Models.
Chapter 11 Contingency Table Analysis. Nonparametric Systems Another method of examining the relationship between independent (X) and dependant (Y) variables.
Chapter 19 Confidence Intervals for Proportions.
1-1 Copyright © 2015, 2010, 2007 Pearson Education, Inc. Chapter 25, Slide 1 Chapter 25 Comparing Counts.
Chapter 8 Introduction to Hypothesis Testing
Quantitative Genetics
 Read Chapter 6 of text  We saw in chapter 5 that a cross between two individuals heterozygous for a dominant allele produces a 3:1 ratio of individuals.
Copyright © 2012 Pearson Education. All rights reserved Copyright © 2012 Pearson Education. All rights reserved. Chapter 10 Sampling Distributions.
Confidence Intervals and Hypothesis Testing - II
Chapter 8 Hypothesis testing 1. ▪Along with estimation, hypothesis testing is one of the major fields of statistical inference ▪In estimation, we: –don’t.
2 Person Mixture #2 Vaginal swab of Victim. Case Scenario Assault occurred in dorm room Suspect says it was consensual No other parties heard or saw anything.
3 Person Mixture #4 (Or is it 2?) The Hardest Mixture I Know.
More informative DNA identification: Computer reinterpretation of existing data Ria David, PhD Cybergenetics, Pittsburgh, PA Cybergenetics ©
F22H1 Logic and Proof Week 6 Reasoning. How can we show that this is a tautology (section 11.2): The hard way: “logical calculation” The “easy” way: “reasoning”
Variables, sampling, and sample size. Overview  Variables  Types of variables  Sampling  Types of samples  Why specific sampling methods are used.
Copyright © 2009 Pearson Education, Inc. Chapter 18 Sampling Distribution Models.
1 Chapter 18 Sampling Distribution Models. 2 Suppose we had a barrel of jelly beans … this barrel has 75% red jelly beans and 25% blue jelly beans.
Accuracy Assessment Having produced a map with classification is only 50% of the work, we need to quantify how good the map is. This step is called the.
You don’t know what you don’t know But does it matter? Or is everything inconclusive?
Statistics (cont.) Psych 231: Research Methods in Psychology.
9.3 PAGES Mendel’s Inheritance. Introduction To understand how Mendel’s laws can be used, you first need to know about probability.
Copyright © 2008 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Chapter 18 Sampling Distribution Models.
1 Psych 5500/6500 Introduction to the F Statistic (Segue to ANOVA) Fall, 2008.
Topics for our first Seminar The readings are Chapters 1 and 2 of your textbook. Chapter 1 contains a lot of terminology with which you should be familiar.
TrueAllele ® Genetic Calculator: Implementation in the NYSP Crime Laboratory NYS DNA Subcommittee May 19, 2010 Barry Duceman, Ph.D New York State Police.
Chapter 7 Forensic Issues: Degraded DNA, PCR Inhibition, Contamination, and Mixed Samples ©2002 Academic Press.
Central Tendency & Dispersion
Lesson Overview 11.2 Applying Mendel’s Principles.
Section 11-2 Interest Grabber Tossing Coins
Lesson Overview Lesson Overview Applying Mendel’s Principles Probability and Punnett Squares Whenever Mendel performed a cross with pea plants, he carefully.
Statistical Analysis of DNA Simple Repeats –Identical length and sequence agat agat agat agat agat Compound Repeats –Two or more adjacent simple repeats.
HAWKES LEARNING SYSTEMS Students Matter. Success Counts. Copyright © 2013 by Hawkes Learning Systems/Quant Systems, Inc. All rights reserved. Section 8.1.
By: Maisha Loveday 8C Maths Reflection: Binomial Expansion.
Level 3 Practical Investigation Where to start?. Aim This is the purpose of your practical i.e. what it is that you want to find out This is the purpose.
Genetics Tutorial Introduction Punnett Square – 1 Trait Punnett Square – 2 Traits Product Rule.
Comparing Counts Chapter 26. Goodness-of-Fit A test of whether the distribution of counts in one categorical variable matches the distribution predicted.
Statistics (cont.) Psych 231: Research Methods in Psychology.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. Chapter 18 Sampling Distribution Models.
CHAPTER 11 Mean and Standard Deviation. BOX AND WHISKER PLOTS  Worksheet on Interpreting and making a box and whisker plot in the calculator.
Algebra The greatest mathematical tool of all!!. This is a course in basic introductory algebra. Essential Prerequisites: Ability to work with directed.
I. Allelic, Genic, and Environmental Interactions
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.
AP Statistics From Randomness to Probability Chapter 14.
Introduction to Mendelian Genetics
11.2 Applying Mendel’s Principles
The greatest mathematical tool of all!!
Validating TrueAllele® genotyping on ten contributor DNA mixtures
Simulation-Based Approach for Comparing Two Means
Chapter 25 Comparing Counts.
On the threshold of injustice: manipulating DNA evidence
Fundamentals of Data Representation
Segregation of gametes
11.2 Applying Mendel’s Principles
11.2 Applying Mendel’s Principles
Psych 231: Research Methods in Psychology
Chapter 26 Comparing Counts.
Psych 231: Research Methods in Psychology
11-2 Probability & Punnett Squares
DNA Identification: Mixture Interpretation
Presentation transcript:

Deconvoluting Mixtures Using Proportional Allele Sharing What does it mean and how do you do it?

What is a mixture? If you start with two single source profiles and combine them, you have a mixture Two sources combined and looks like a two person mixture Male Ref Female Ref Mixture

Same references mixed differently Two sources combined that looks like a single source profile Her birthday is at Thanksgiving Dad Mom Ellie

Start with the mixture, and then go backwards to the single source profiles. What is deconvolution?

Deconvolution Some loci are easy to deconvolute Major/minor contributors

Deconvolution Some loci are harder to deconvolute But if you can assume a contributor… It can become easier Mixture - Victim= Foreign

How do you deal with shared alleles? Common to find shared alleles in any mixture of two people. How is the shared allele distributed between the two contributors? Lots of papers published and various software programs deal with this issue.

AB BC Sharing Donor 1 is AB (10,12) Donor 2 is BC (12,13) Mixture is ABC (10,12,13)

AB BC Sharing But how do you work backwards from this To this?

AB BC Sharing Validation studies give PHR expectations – These expectations show up in protocols for interpretation – Used in setting stochastic thresholds – Used in determining number of contributors At times you may have a major contributor that helps At times you can assume a contributor

AB BC Sharing – Test 1 Assume 50% PHR rule for AB (10,12) Can you then assume 250 rfu from the 12 goes with the 10? So for 10,12 type: ÷= 0.5 PHR = 0.27 P (proportion)

AB BC Sharing – Test 1 So for 12,13 type: ÷= 0.63 PHR = 0.73 P

AB BC Sharing – Test 2 Or Assume 1000 rfu of the 12 goes with the 10 So for 10,12 type: Note the PHR is the same, but P is double ÷ 1000 = 0.5 PHR = 0.54 P

AB BC Sharing – Test 2 12,13 type is now: Note that PHR is the same as Test 1 Proportion is about 1/3 less than Test ÷ 790 = 0.5 PHR = 0.46 P

AB BC Sharing Summary Test 1 AB PHR = 0.5 AB portion = 0.27 BC PHR = 0.63 BC portion = :4 mixture Test 2 AB PHR = 0.5 AB portion = 0.54 BC PHR = 0.63 BC portion = :1 mixture

Time to Vote 1.Test 1 is correct 2.Test 2 is correct 3.The truth is somewhere in between 4.You cannot make a determination at all 5.I just want lunch

AA AB Sharing Donor 1 is AA (8,8) Donor 2 is AB (8,12) Mixture is AB (8,12)

AA AB Sharing How do you work backward from this To this

AA AB Sharing – Test 1 Assume 50% PHR rule for AB (8,12) Can you then assume 250 rfu from the 8 goes with the 12? So for 8,12 type: ÷ 500 = 0.5 PHR = 0.42 P

AA AB Sharing – Test 1 So for 8,8 type: No PHR = 0.58 P

AA AB Sharing – Test 2 Or Assume 1000 rfu from the 8 goes with the 12? So for 8,12 type: Note PHR is the same, but P doubles = 0.5 PHR÷ = 0.83 P

AA AB Sharing – Test 2 So for 8,8 type: Note that P is smaller by a factor of ~3 ½ No PHR = 0.17 P

AA AB Sharing Summary Test 1 AB PHR = 0.5 AB portion = 0.42 AA portion = 0.58 ≈1:1 mixture Test 2 AB PHR = 0.5 AB portion = 0.83 AA portion = 0.17 ≈1:6 mixture

Time to Vote 1.Test 1 is correct 2.Test 2 is correct 3.The truth is out there 4.Who cares, a 1:1 mixture looks like a 1:6 mixture anyway 5.I said I wanted lunch

How do you deal with shared alleles? Don’t rely on a major or assumed contributor – That inserts the analyst into the amp tube – The enzyme certainly doesn’t care whose DNA it amps Calculate the PHR and P without bias – Use the same set of rules every time – Calculate every possible combination, then see what fits

Should we do this? Section 3.5 – SWGDAM 3.5. Interpretation of DNA Typing Results for Mixed Samples An individual’s contribution to a mixed biological sample is generally proportional to their quantitative representation within the DNA typing results. Accordingly, depending on the relative contribution of the various contributors to a mixture, the DNA typing results may potentially be further refined.

Should we do this? Section – SWGDAM The laboratory should define and document what, if any, assumptions are used in a particular mixture deconvolution If no assumptions are made as to the number of contributors, at a minimum, the laboratory should assign to a major contributor an allele (e.g., homozygous) or pair of alleles (e.g., heterozygous) of greater amplitude at a given locus that do not meet peak height ratio expectations with any other allelic peak(s) If assumptions are made as to the number of contributors, additional information such as the number of alleles at a given locus and the relative peak heights can be used to distinguish major and minor contributors.

Should we do this? Section – SWGDAM So don’t forget about proportion between contributors (mixture ratio) (PHR is proportion within a contributor) We’ll mention in a minute A laboratory may define other quantitative characteristics of mixtures (e.g., mixture ratios) to aid in further refining the contributors.

All combinations for 2 people Grouped by number of alleles Sub-grouped by homozygotes/heterzygotes and sharing/no-sharing

All combinations for 2 people Grouped as “family” or “category” of like types Highlighted top row is generic form Other possible combinations in white

All combinations for 2 people Some don’t have much to calculate

All combinations for 2 people Some have no sharing so easy to calculate

All combinations for 2 people We just saw two categories that take a bit more effort to calculate

Proportional Allele Sharing Method No crazy math Easy to follow the logic of the model Supported by numerous in-house studies It just works All models are wrong, but some are wrong more often than others

Rule 1 – AB BC Sharing Whenever possible, shared alleles are shared proportionately

Rule 1 – AB BC Sharing First, consider the alleles that are unshared to get the proportion of the two donors Essentially, you calculate the proportion for two homozygotes

Rule 1 – AB BC Sharing Proportion = = 0.39 for 10 (,12) = 0.61 for (12,)

Rule 1 – AB BC Sharing So based 39%/61% ratio: For 10, 12 donor For 12, 13 donor .39 = 585 rfu 500 ÷ 585 = 0.85 PHR  ÷ = 915 rfu = 0.85 PHR

Rule 1 – AB BC Sharing PHR = 0.85 for both contributors PHR is always the same for proportional sharing model The enzyme doesn’t arbitrarily give one person a good PHR and the other a bad PHR

AB BC Sharing Summary Test 1 AB PHR = 0.5 AB portion = 0.27 BC PHR = 0.63 BC portion = :4 mixture Test 2 AB PHR = 0.5 AB portion = 0.54 BC PHR = 0.63 BC portion = :1 mixture Proportional AB PHR = 0.85 AB portion = 0.39 BC PHR = 0.85 BC portion = :2.5 mixture

Rule 2 – AA AB Sharing Whenever possible, PHRs are assumed to be 1.0

Rule 2 – AA AB Sharing Which way? 50% “down” 50% “up”

Rule 2 – AA AB Sharing PHR = 1.0 is the middle ground

Rule 2 – AA AB Sharing PHR = 1.0 is the middle ground Replicate amps Calculate the PHR Not very close to /1320 = /1013 = /1031 = /894 = 0.68 Ave PHR = 0.805Ave PHR = 0.74

Rule 2 – AA AB Sharing PHR is (typically) smallest/tallest Do it again with first/second as the smallest and tallest switched Pretty close to 1.0 with only 2 replicates (Some folks do HMW/LMW) 1093/1320 = /797 = /823 = /894 = 0.68 Ave = 1.04Ave = 0.965

Rule 2 – AA AB Sharing So two reasons for Rule 2 – PHR = 1.0 is the middle ground – PHR = 1.0 fits replicate amps May not fit quite as well at large loci and/or big steps between alleles – eg: 11,23 at D18

Rule 2 – AA AB Sharing For AB (heterozygote): For AA (homozygote): = 1.0 PHR÷ + = 0.56 P 500 = 0.44 P No PHR PHR Defined as 1.0!

AA AB Sharing Summary Test 1 AB PHR = 0.5 AB portion = 0.42 AA portion = 0.58 ≈1:1 mixture Test 2 AB PHR = 0.5 AB portion = 0.83 AA portion = 0.17 ≈1:6 mixture PHR = 1.0 AB PHR = 0.5 AB portion = 0.56 AA portion = 0.44 ≈1:1 mixture Similar ratio, but major/minor (sort of) has flipped

An advantage of this approach The proportion of contributors calculated at a specific locus is not dependent upon something calculated at some other locus This allows for consideration of degradation – (When we look at degraded samples using this approach, they kind of “self-correct” meaning with known mixtures, the true types are still usually the best fit.)

An advantage of this approach Section – SWGDAM But you can’t predict from one locus how degradation will affect the next This approach helps, as each locus is independent A laboratory may define other quantitative characteristics of mixtures (e.g., mixture ratios) to aid in further refining the contributors Differential degradation of the contributors to a mixture may impact the mixture ratio across the entire profile.

Rule 3 – Minimum Peak Height Minimum peak heights (mph) are always maintained and supersede Rules 1 and 2. We won’t discuss this much now Analogous to your peak calling threshold (75 rfu or 100 rfu, etc) Or based on the mixture ratio (proportions)

Rule 3 – Minimum Peak Height Comes into play for certain combinations Think of looking for an AB and BB contributor – 12,12 homozygote? How many RFU? – We just saw this example – The other homozygote option MPH gives a starting point

Three Person Mixtures These simple rules work for three person mixtures also Most (well, lots anyway) 3 person mixtures break down into simple patterns that we just discussed for 2 person mixtures – Rule 1 – Rule 2

Three Person Mixtures PHR and P for Donor 1 is straight forward – 6,7 Donors 2 and 3 is AA AB pattern (Rule 2) – 9,9.3 – 9.3,9.3 Donor 1 Donors 2 and 3

Three Person Mixtures PHR and P for Donor 1 is straight forward – 24,26 Donors 2 and 3 is AB BC pattern (Rule 1) – 21,22 – 22,25 Donor 1 = 24,26 Donors 2 and 3 are 21,22 and 22,25

Three Person Mixtures You just have to realize some calculated PHR and P results have two contributors added together – Victim ≈ 15%, Consensual ≈ 35%, Foreign ≈ 50% – AB AB CD locus (V and C are both AB) P for AB = 48% (combined known V and C) P for CD = 52% for F

Three Person Mixtures This is where it gets a bit tricky for three person mixtures

Three Person Mixtures 4 types where we cannot calculate PHR and P B CA A B A CB C A BA CB D B A A B

Three Person Mixtures Two alleles shared by two people (twice) – “Circular” sharing – “Double” sharing In these cases, upper and lower boundaries can be calculated based on PHR to determine if viable – “Not Excluded” result – Increase PHR stringency to “test” fit – If type with defined PHR and P dropout but not the “Not Excluded” option…

A computer can help Calculate the PHR and P for every possible combination using Rules 1, 2, and 3 – 3 Contributors in a 4 allele pattern: 6 “families” of types 52 total combinations – 3 Contributors in a 5 allele pattern Only 2 “families” But one contains 30 combinations

A computer can help Filter the possibilities shown to the analyst: – Don’t show combinations with low PHR (eg: <50%) – Don’t show combinations with proportions of 5% when you know your minor is at least 20% (4-fold difference) – Don’t show combinations that do not include a known donor (V on own panties) Starts to become fairly manageable

A computer can help Assumes good data – Can’t do much with a 3 person mixture that only had 100pg of DNA in the first place – Deconvolution works best when you are in a range that your validation says PHR’s are robust Even if you can’t deconvolute the mixture, you may be able to limit the possible types present to a manageable number – (Statistics…)