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Detecting Degradation in DNA samples Keith Inman Forensic Analytical Specialties, Inc Dayton, Ohio August 11, 2006.

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Presentation on theme: "Detecting Degradation in DNA samples Keith Inman Forensic Analytical Specialties, Inc Dayton, Ohio August 11, 2006."— Presentation transcript:

1 Detecting Degradation in DNA samples Keith Inman Forensic Analytical Specialties, Inc Dayton, Ohio August 11, 2006

2 Intact and degraded DNA

3 Wedge effect

4 How To Identify Challenging Samples? experience (analyst, intra-lab, inter-lab, literature) unsuccessful analysis using routine methods i.e., partial or null typing results inefficient use of analyst time

5 Degradation of DNA Random breaking of DNA molecule into numerous fragments of varying sizes Can speak of average fragment size

6 Loss of signal at high MW loci Potential causes Uneven amplification Preferential (allele) Differential (locus)

7 Loss of signal at high MW loci Potential causes Uneven amplification Preferential (allele)

8 Loss of signal at high MW loci Potential causes Uneven amplification Differential (locus)

9 Uneven signal response Differential dye sensitivity

10 Fewer intact molecules - degradation Exposure to environmental insult Time Heat Moisture Chemicals; microorganisms UV light Loss of signal at high MW loci

11 Effect of Heat on DNA

12 Solutions Detection Prior to amplification Knowledge of sample Age Condition Substrate

13 Solutions Adjustment of primer concentrations and amp conditions Done by mfg during developmental validation Solves problem of uneven amplification and dye sensitivity

14 Solutions Detection Prior to amplification Differential quantitation Use of two primers, one for long and one for short molecules

15 Nuclear nuTH01 qPCR Target target sequence spans TH01 CODIS STR locus (2 copies/diploid genome) FAM-labeled TaqMan detection probe target sequence length: ~170 – 190 bp STRs probe

16 Nuclear nuCSF qPCR Target target sequence flanks the CODIS CSF STR region (2 copies/diploid genome) VIC-labeled TaqManMGB detection probe target sequence length: 67 bp probe STR

17 Using Short and Long Nuclear Targets to Assess DNA Fragmentation nuCSF assay – detects and quantifies DNA fragments larger than ~67bp nuTH01 assay – detects and quantifies DNA fragments larger than ~180bp LH LD LH 10 kbp 1.5 kb 1 kbp 800 bp 600 bp 400 bp 200 bp Minutes of DNase Treatment ~67 bp nuCSFar nuTH01

18 qPCR Degradation Ratio = nuCSF Quantity (ng) nuTH01 Quantity (ng) For high-molecular weight DNA, expect the Degradation Ratio to be ~ 1. For highly-degraded DNA, expect the Degradation Ratio to be > 1. The bigger the qPCR Degradation Ratio, the more fragmented the DNA. LH LD LH 10 kbp 1.5 kb 1 kbp 800 bp 600 bp 400 bp 200 bp Minutes of DNase Treatment nuCSFar nuTH01 ~67 bp

19 qPCR Degradation Ratio ~ 25: 1 ng (nuTH01) Identifiler STR Results

20 Interpreting the qPCR Degradation Ratio Degradation Ratio STR Implications 1 – 3none 3 – 5wedge effect, possible cross-dye pull-up >5 (>10 artifacts expected to be significant) increasing wedge effect, pull-up, dropped-out alleles at larger loci, off-scale peaks, called stutter peaks, -A shouldering

21 Solutions Post amplification Yield gel

22 Solutions Post Typing Assessment of PHRs between loci At this point, a visual assessment

23 Solutions Increase injection time Increases likelihood of saturated data Artifacts created Doesnt really work with degraded samples

24 Saturated data and artifacts

25 Solutions Amplify more DNA Increases likelihood of saturated data Frequently must combine data from two amps to get full profile

26 New (Non-Routine) Analysis Tools for Challenging and Compromised Samples * miniSTRs * SNPs * mitochondrial sequencing/linear-array typing enhanced PCR conditions (e.g., extra Taq, BSA) Y-STR analysis for male/female mixtures low-volume PCR amplifications increased PCR cycle numbers

27 Solutions Consideration of PHRs between loci Use of positive controls Likely undegraded Establishes a baseline for good samples

28 Strategy for post-typing diagnosis of degradation Consider the slope between loci as indicator of drop-off of signal within colors Calculate a single summary value from the three normalized slopes as another parameter of normal undegraded sample

29 For each dye color, 6 data points were used to calculate the slope Y coordinate is RFU X coordinate is peak data collection point (as determined by Genescan)

30 Strategy Calculation of slope by best fit linear regression Intercompare slopes between dye colors using correlation coefficients (r 2 ) and paired-T tests

31 Results Distribution of slopes is approximately normally distributed

32 All slopes are negative Due to differential dye sensitivity and multiplex complexities summarized earlier Slopes between the three colors are not correlated Each color shows a different pattern of drop- off in intensity between the loci

33

34 One number for evaluation Slopes for each samples were normalized against the max and min slopes for each dye, then added to give a single normalized sum of slopes value m norm = (m – m min )/(m max – m min )

35

36 Results The average and standard deviation of the samples can be used to calculate thresholds of departure from normal at both the 5% and 1% levels for each color The same statistic can be used with the normalized sum to determine departures from normal at the 5% and 1% level for a single sample Can now determine if, post typing, a sample deviates from our expectation of a normal, undegraded sample.

37 Threshold levels and significance levels

38

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40 Next step Prepare degraded samples and apply the same analysis Artificially degrade samples with DNAse Monitor level of degradation via a yield gel Gives information about average base pair size when compared to a standard ladder

41 Next Step Amplify and type the samples Amplify normal amounts (1.5 – 2 ng) Amplify larger amounts to bring up larger, more degraded loci

42 Acknowledgements Dan Krane Jason Gilder Cristian Orrego Zach Gaskin


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