Volume 2, Issue 1 , Pages 5-7, December 2009
A review of low template STR analysis in casework using the DNA SenCE post-PCR purification technique
Article Outline
- Abstract
- 1. Introduction
- 2. Casework used for investigation
- 3. Results
- 4. Conclusions
- Conflict of interest
- Reference
- Copyright
Abstract
LGC has developed a method for analysing low-level DNA samples called DNA SenCE (Sensitive Capillary Electrophoresis) based on post-PCR treatment of standard 28-cycle SGMplus PCR product and demonstrated to be equally effective at enhancing profiles as 34-cycle PCR. The method has been validated and accredited and used in casework since July 2007. Inherent in the method is the initial generation of a standard 28-cycle SGMplus profile so a direct comparison of standard and DNA SenCE results for all casework is possible. Here we review DNA SenCE casework, reporting the magnitude of peak enhancement and stochastic effects seen in the DNA SenCE profiles.
Keywords: Low template, DNA SenCE, Low copy number
1. Introduction
The interpretation of STR profiles generated from 100
pg or less of DNA template in forensic casework is challenging. At this level, stochastic effects are common and allele drop-out, increased imbalance in heterozygotes, increased stutter and allele drop-in are all observed.
Since 2007, LGC Forensics has analysed low template samples using a combination of post-PCR purification and concentration, and modified capillary electrophoresis conditions collectively termed DNA SenCE (Sensitive Capillary Electrophoresis).
We have previously characterised DNA SenCE profiles in mock casework trials [1], and in reproducibility studies on known DNA. Here, we extend our studies to look at real casework profile characteristics.
2. Casework used for investigation
All cases analysed using the DNA SenCE methods at LGC Forensics over a five-month period (January–May 2009) were reviewed.
3. Results
3.1. Allele drop-in
In casework studies where we do not know the true donors it is impossible to accurately identify drop-in in profiles from evidential samples. Instead we have reviewed 1398 control samples processed alongside casework samples. This provides the best estimate possible of the actual levels of allele drop-in occurring in DNA SenCE casework (Fig. 1).
Fig. 1.
Frequency of observed drop-in peaks in positive and negative controls associated with low template casework.
Drop-in in control samples was only observed under DNA SenCE conditions; no instances were seen under standard SGMplus conditions in 595 control samples. Under “Clean SenCE” (purification/concentration of PCR product only), 48/285 (17%) of control samples had one or more additional peaks >50RFU and in “Extra SenCE” profiles (PCR product purification plus modified electrophoresis conditions), 122/518 (24%) showed evidence of drop-in.
Overall, 170/1398 (12%) of all control samples had one or more drop-in peaks. All these were in negative control samples. Of these, 65% had only one non-donor peak, 23% had two and 12% had three or more. This totalled 255 peaks in 7040 loci (under DNA SenCE conditions) or a probability of drop-in, Pr[drop-in], at any particular locus of 0.036. This is higher than our previously published observations in mock casework validation where Pr[drop-in], was 0.007 per locus.
3.2. Randomness of allele drop-in
The designations and numbers of alleles observed as drop-in in control samples were investigated.
Where 4 or more alleles were present in a negative control sample, comparison with profiles held on local staff/visitor elimination databases was made. Although potential matches were found, no scientists directly associated with the sample processing were identified, and RMPs calculated for the partial profiles suggested that adventitious matches would be expected. This suggests that the use of local elimination databases is ineffective in identifying the source of low-level drop-in.
The numbers of drop-in alleles observed (between 1 and 5) were tested for randomness. The distributions correlated closely with Poisson distributions, suggesting that drop-in events are essentially random. Chi-square tests indicated no significant deviation between observed drop-in numbers and Poisson distributed numbers.
3.3. Allele drop-out and stutter peaks
To identify allele drop-out, it is necessary to know the true donor profile so instances of missing alleles can be identified. This was possible in our mock casework trial [1] and reproducibility studies, where variable drop-out rates inversely proportional to the levels of input DNA (or the peak height of surviving alleles) were calculated. However, in casework it is uncommon to have unequivocal knowledge of the donor profile so in this study, estimations of allele drop-out rates were not possible.
Even when the donor profile is not known, it is possible to identify which peaks in a profile are in stutter positions relative to other peaks present, but again, in casework scenarios where the presence of mixtures cannot be ignored, and the donor profile is not known, we cannot confidently assign any peaks as “true” stutter. Consequently no estimation of stutter peak magnitude was attempted in this study.
3.4. Comparative peak heights
Our previous published characterisation of the DNA SenCE methods on mock casework samples indicated that an approximately 60-fold increase in peak height (RFU) was observed relative to the standard conditions and approximately 13-fold when only the PCR purification/concentration steps were used.
The casework data was assessed and the corresponding figures found to be approximately 41-fold and 10-fold.
3.5. Relative performance of duplicate PCRs
Reproducibility studies on diluted DNA samples, and anecdotal evidence from reporting officers had suggested that profile quality deteriorated if low template DNA samples were stored (at 4°C) for significant time periods between replicate analyses. In casework, extended storage between PCRs is not uncommon as the necessity to treat samples as “low template” is not always evident at the outset.
We reviewed our casework profiles in this study to investigate relative performance of the two duplicate PCRs given differing time periods between them.
Fig. 2 shows the results from 75 profile pairs with non-zero numbers of alleles in both replicates, with time intervals of between 0 and 59 days between PCRs. This shows no systematic differences between the numbers of alleles detected in first and second PCRs, despite extended DNA storage, with approximately equal numbers of points above and below the equality line.
Fig. 2.
Comparison of relative numbers of alleles present in duplicate PCRs under varying time intervals. 35 profiles showed more alleles in the first PCR (above equality line); 27 had more alleles in 2nd PCR and 13 had identical allele numbers in both.
4. Conclusions
This study extends our knowledge of the characteristics of low-level profiles generated using LGC's DNA SenCE method to casework samples.
Our previous studies have been on mock case samples or purified DNA and it was important to investigate if real casework profiles behaved similarly. The study has highlighted the difficulty of measuring parameters such as allele drop-in, drop-out and stutter where the true donor is not known, but the results do support our previous observations that drop-in is essentially random and occurs at low levels not dissimilar to those previously observed.
We also demonstrated that a perceived problem of deterioration of profile quality if low-level DNA samples are stored before profiling was not borne out by the casework data, which showed no clear differences between profile quality of the first and second replicate PCRs.
Conflict of interest
None.
Reference
PII: S1875-1768(09)00124-3
doi:10.1016/j.fsigss.2009.08.111
© 2009 Elsevier Ireland Ltd. All rights reserved.
Volume 2, Issue 1 , Pages 5-7, December 2009
