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Detecting Degradation in DNA samples

Detecting Degradation in DNA samples. Keith Inman Forensic Analytical Specialties, Inc Dayton, Ohio August 11, 2006. Intact and degraded DNA. “Wedge” effect. How To Identify Challenging Samples?. experience (analyst, intra-lab, inter-lab, literature)

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Detecting Degradation in DNA samples

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

  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. probe STRs 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

  16. probe STR 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

  17. nuTH01 nuCSFar Using Short and Long Nuclear Targets to Assess DNA Fragmentation Minutes of DNase Treatment LH 0 1 2 3 4 5 15 30 45 60 LD LH • nuCSF assay – detects and quantifies DNA fragments larger than ~67bp • nuTH01 assay – detects and quantifies DNA fragments larger than ~180bp 10 kbp 1.5 kb 1 kbp 800 bp 600 bp 400 bp 200 bp ~67 bp

  18. Minutes of DNase Treatment LH 0 1 2 3 4 5 15 30 45 60 LD LH 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. 10 kbp 1.5 kb 1 kbp 800 bp 600 bp 400 bp 200 bp nuTH01 ~67 bp nuCSFar

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

  20. Interpreting the qPCR Degradation Ratio

  21. Solutions • Post amplification • Yield gel

  22. Solutions • Post Typing • Assessment of PHR’s between loci • At this point, a visual assessment

  23. Solutions • Increase injection time • Increases likelihood of saturated data • Artifacts created • Doesn’t 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 PHR’s 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 (r2) 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. 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 mnorm = (m – mmin)/(mmax – mmin)

  34. 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.

  35. Threshold levels and significance levels

  36. Threshold levels and significance levels

  37. 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

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

  39. Acknowledgements • Dan Krane • Jason Gilder • Cristian Orrego • Zach Gaskin

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