detecting degradation in dna samples keith inman forensic analytical specialties, inc dayton, ohio...

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)

unsuccessful analysis using routine methods i.e., partial or null typing results

• inefficient use of analyst time

Degradation of DNA

Random breaking of DNA molecule into numerous fragments of varying sizes

Can speak of “average fragment size”

Loss of signal at high MW loci

Potential causes Uneven

amplification

• Preferential (allele)

• Differential (locus)

Loss of signal at high MW loci

Potential causes Uneven

amplification

• Preferential (allele)

Loss of signal at high MW loci

Potential causes Uneven

amplification

• Differential (locus)

Uneven signal response

Differential dye sensitivity

Fewer intact molecules - degradation

• Exposure to environmental insult

• Time

• Heat

• Moisture

• Chemicals; microorganisms

• UV light

Loss of signal at high MW loci

Effect of Heat on DNA

Solutions Detection

• Prior to amplification

• Knowledge of sample

• Age

• Condition

• Substrate

Solutions

Adjustment of primer concentrations and amp conditions Done by mfg during developmental validation

Solves problem of uneven amplification and dye sensitivity

Solutions

Detection Prior to amplification

• Differential quantitation

• Use of two primers, one for long and one for short molecules

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

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

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 0 1 2 3 4 5 15 30 45 60 LD LH

10 kbp

1.5 kb

1 kbp 800 bp

600 bp

400 bp

200 bp

Minutes of DNase Treatment

~67 bpnuCSFar

nuTH01

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 0 1 2 3 4 5 15 30 45 60 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

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

Interpreting the qPCR Degradation Ratio

Degradation Ratio

STR Implications

1 – 3 none

3 – 5 “wedge” 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

Solutions

Post amplification• Yield gel

Solutions

Post Typing

•Assessment of PHR’s between loci

•At this point, a visual assessment

Solutions Increase injection time

• Increases likelihood of saturated data

• Artifacts created

• Doesn’t really work with degraded samples

Saturated data and artifacts

Solutions Amplify more DNA

Increases likelihood of saturated data

• Frequently must combine data from two amps to get full profile

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

Solutions

Consideration of PHR’s between loci Use of positive

controls

• Likely undegraded

• Establishes a baseline for good samples

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

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)

Strategy

Calculation of slope by best fit linear regression

Intercompare slopes between dye colors using correlation coefficients (r2) and paired-T tests

Results

Distribution of slopes is approximately normally distributed

A

0

5

10

15

20

25

30

35

40

-12 -11 -10-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5

Slope (RFU/data collection point)

Count

B

0

5

10

15

20

25

30

35

40

-12 -11 -10-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5

Slope (RFU/data collection point)

Count

C

0

5

10

15

20

25

30

35

40

-12 -11 -10-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5

Slope (RFU/data collection point)

Count

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

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)

0

5

10

15

20

25

0

0.2 0.4 0.6 0.8

1

1.2 1.4 1.6 1.8

2

2.2 2.4 2.6 2.8

3

Normalized Sum

Count

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.

Threshold levels and significance levels

Avg PH Avg PH Std Dev Slope Avg

Slope Std Dev = 0.05 = 0.01

Blue 1320 515 -3.82 2.26 -7.54 -9.08 Green 1790 682 -1.00 1.67 -3.75 -4.88 Yellow 1570 592 -4.53 1.98 -7.79 -9.14 Normalized Sum 1.49 0.42 0.80 0.51

Threshold levels and significance levels

Avg PH Avg PH Std Dev Slope Avg

Slope Std Dev = 0.05 = 0.01

Blue 1320 515 -3.82 2.26 -7.54 -9.08 Green 1790 682 -1.00 1.67 -3.75 -4.88 Yellow 1570 592 -4.53 1.98 -7.79 -9.14 Normalized Sum 1.49 0.42 0.80 0.51

Avg PH Avg PH Std Dev Slope Avg

Slope Std Dev = 0.05 = 0.01

Blue 1320 515 -3.82 2.26 -7.54 -9.08 Green 1790 682 -1.00 1.67 -3.75 -4.88 Yellow 1570 592 -4.53 1.98 -7.79 -9.14 Normalized Sum 1.49 0.42 0.80 0.51

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

Next Step

Amplify and type the samples Amplify normal

amounts (1.5 – 2 ng)

Amplify larger amounts to bring up larger, more degraded loci

Acknowledgements

Dan Krane Jason Gilder Cristian Orrego Zach Gaskin