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Deconvolution of mixed DNA samples
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  1. Deconvolution of mixed DNA samples Michael L. Raymer, Ph.D. Dept. of Computer Science & Engineering Wright State University

  2. Introduction • Mixture interpretation is not trivial. • Difficult tasks include: • Determining the number of possible contributors • Determining the genotypes (or possible genotypes) of each contributor Doom/Raymer

  3. Introduction to Mixtures • Determining if two genotypes could be contributors is relatively easy Possible contributors to a mixture: D3 locus genotype Individual #1: 15, 18 Individual #2: 14, 18 Mixture: 14, 15, 18 • But beware – the opposite is not true Doom/Raymer

  4. Introduction to Mixtures • Mixtures can exhibit up to two peaks per contributor at any given locus • Mixtures can exhibit as few as 1 peak at any given locus (regardless of the number of contributors) Doom/Raymer

  5. Introductionto Mixtures • Even determining the number of contributors is non-trivial D3 locus genotype Mixture: 14, 15, 18 Another Option Individual C: 15, 15 Individual D: 14, 15 Individual E: 18, 18 • There is no “hard” mathematical upper bound to the number of contributors possible Doom/Raymer

  6. Introductionto Mixtures • Determining what genotypes created the mixture is non-trivial D3 locus genotype Mixture: 14, 15, 18 Option #1Option #3 Individual A: 15, 18 Individual #D: 14, 15 Individual B: 14, 18 Individual #E: 18, 18 Option #2Option #4 Individual B: 14, 18 Individual #A: 15, 18 Individual C: 15, 15 Individual #F: 14, 14 Doom/Raymer

  7. Introductionto Mixtures • Often the victim’s genotype is known, but this does not always make the defendant’s genotype clear D3 locus genotype Mixture: 14, 15, 18 Victim: 14, 15 Possible genotypes for a single perpetrator: Individual C: 14, 18 Individual D: 15, 18 Individual E: 18, 18 Individual F: 14, 14 ? Doom/Raymer

  8. Making sense of mixtures • How many contributors were there? • What are the genotypes of each contributor? Doom/Raymer

  9. Number of contributors • D. Paoletti, T. Doom, C. Krane, M. Raymer, and D. Krane (2005) “Empirical Analysis of the STR Profiles Resulting from Conceptual Mixtures”, Journal of Forensic Sciences, Vol. 50, No. 6, Nov. 2005. • 13-loci genotypes from 959 individuals from six racial groups • Combine into hypothetical mixtures • 146,536,159 three-person mixtures considered Doom/Raymer

  10. All 3-way FBI mixtures Maximum # of alleles observed # of occurrences As Percent 2 0 0.00% 3 78 0.00% 4 4,967,034 3.39% 5 93,037,010 63.49% 6 48,532,037 33.12% • 3.4% of three contributors mixtures “look like” two contributors Doom/Raymer

  11. 4-way FBI mixtures Maximum # of alleles observed # of occurrences As Percent 1, 2, 3 0 0.00% 4 13,480 0.02% 5 8,596,320 15.03% 6 35,068,040 61.30% 7 12,637,101 22.09% 8 896,435 1.57% • ~76% of four contributors mixtures “look like” three contributors Doom/Raymer

  12. Contrib 1Contrib 2 16,16 17,17 16,17 16,17 16,16 16,17 17,17 16,17 Mixture Deconvolution • Even when the number of contributorsis known (or assumed), separating mixtures into their components can be difficult Doom/Raymer

  13. High peak avg. Low peak avg. Simple example: All loci heterozygous, two contributors Current Methods • Most methods start by inferring themixture ratio: Doom/Raymer

  14. Minimal Basic Assumptions • A primary assumption of all methods is peak additivity • Consistency: Most labs assume peaks from the same source will vary by  30% Doom/Raymer

  15. Evidence of consistency Relationship between the smaller and larger peaks in heterozygous loci of reference samples. Doom/Raymer

  16. Homozygous peak additivity • y = 1.865x + 208.5 => Hom = 1.9 * Het • R2 = 0.845 Doom/Raymer

  17. Objectives • Start with simple assumptions: • Additivity with constant variance: c • Peaks below a minimum threshold (often 50 or 150 RFU) are not observable • Peaks above the saturation threshold (often 4000 RFU) are not measurable • Obtain provably correct deconvolution where possible • Identify when this is not possible Doom/Raymer

  18. Method • Assume the number of contributors • Enumerate all possible mixture contributor combinations • Determine which pairs of profiles contain peaks in balance Doom/Raymer

  19. 2080 RFU 2030 RFU 210 RFU 180 RFU P3 P4 P1 P2 Peak Balance • Example: assume two contributors, four peaks: • For this locus, and c = 1.3, the combination (P1,P3) is out of balance because: Peaks are numbered by height Doom/Raymer

  20. Example: Mixture of four peaks • P4 >= P3 >= P2 >= P1 >= Min. Threshold Doom/Raymer

  21. Sweet Spot • If only one row is satisfied, then the genotypes can be unambiguously and provably determined Doom/Raymer

  22. 2080 RFU 2030 RFU 210 RFU 180 RFU P3 P4 P1 P2 Example: In the sweet spot • P4 > cP2so we can’t have (P4,P2) • P4 > cP1so we can’t have (P4, P1) Doom/Raymer

  23. 245 RFU 230 RFU 190 RFU 180 RFU P3 P4 P1 P2 Example: Ambiguous Locus • P2 is within c of both P1 and P4, so we can have • (P1,P3) (P2,P4), or • (P1,P2) (P3,P4) • P4 cannot pair with P1 Doom/Raymer

  24. 700 RFU 500 RFU 300 RFU 200 RFU P3 P4 P1 P2 Example: No row satisfied • P4 (for example) cannot pair with any other peak • One of our assumptions (c or the number of contributors) is incorrect Doom/Raymer

  25. Dealing with homozygotes • If we assume two contributors and there are only three peaks, then one of several possibilities exists: • One contributor is homozygous • One of the peaks is there, but not observable because it is below the minimum threshold • The contributors share an allele at this locus Doom/Raymer

  26. Three Peaks Doom/Raymer

  27. Advantages of the method • If you accept the simple assumptions, the resulting mixture interpretations directly follow • Interprets mixtures on a locus by locus basis • Does not interpret ambiguous loci Doom/Raymer

  28. Future work • Mixture ratio can be inferred only from unambiguous loci, and then applied to perform an more aggressive interpretation of the ambiguous loci when desired • Confidence values can be applied to the more aggressively interpreted possitions Doom/Raymer

  29. Acknowledgements • Research Students • David Paoletti (analysis of allele sharing) • Jason Gilder (data collection, additivity study, mixture deconvolution) • Faculty • Travis Doom • Dan Krane • Michael Raymer Doom/Raymer