A graphical simulation model of the entire DNA process associated with the analysis of short tandem repeat loci

Abstract

The use of expert systems to interpret short tandem repeat DNA profiles in forensic, medical and ancient DNA applications is becoming increasingly prevalent as high-throughput analytical systems generate large amounts of data that are time-consuming to process. With special reference to low copy number (LCN) applications, we use a graphical model to simulate stochastic variation associated with the entire DNA process starting with extraction of sample, followed by the processing associated with the preparation of a PCR reaction mixture and PCR itself. Each part of the process is modelled with input efficiency parameters. Then, the key output parameters that define the characteristics of a DNA profile are derived, namely heterozygote balance (Hb) and the probability of allelic drop-out p(D). The model can be used to estimate the unknown efficiency parameters, such as πextraction. ‘What-if’ scenarios can be used to improve and optimize the entire process, e.g. by increasing the aliquot forwarded to PCR, the improvement expected to a given DNA profile can be reliably predicted. We demonstrate that Hb and drop-out are mainly a function of stochastic effect of pre-PCR molecular selection. Whole genome amplification is unlikely to give any benefit over conventional PCR for LCN.

Probabilistic expert systems for handling artifacts in complex DNA mixtures

“…The allele will not be selected and hence drop out when N = 0, which happens with probability P (N = 0) = (1 − π e π a ) n 0 . Gill et al (2005) estimated π e = 0.6 and π a = 20/66 for their laboratory procedures, giving π e π a = 0.182, and hence a dropout of 0.818 n 0 ≈ exp(−0.2n 0 ).…”

“…Recently, Cowell (2009) has carried out an analysis of the validity of the gamma model by comparing with simulations of mixtures using a stochastic procedure presented by Gill et al (2005). He found that the gamma model gives a good description of the distribution of peak area values arising from the PCR process in amplifying moderate to large quantities of (simulated) DNA, but becomes inadequate in the low-copy-template regime where dropout becomes a significant factor.…”

“…The differential selection of alleles is also a factor in the stochastic variability of peak size values. Before presenting our dropout model, we summarize how dropout arises in the simulation model of Gill et al (2005).…”

“…This all-or-nothing selection of the maternal contribution of an individual of a given marker is assumed to be independent of the selection or otherwise of the individual's paternal contribution from the marker, and of the selection or otherwise of any of the individual's other marker alleles; more precisely, this independence is conditional on the total amount of DNA from the individual in the mixture. Our model can thus be viewed as a crude approximation to the Gill et al (2005) model, which models the partial selection of alleles in the aliquot. The remaining variation in the number of amplified alleles is modelled by the gamma distribution of peak heights.…”

“…The remaining variation in the number of amplified alleles is modelled by the gamma distribution of peak heights. Note, however, the difference that we are modelling allelic selection at an individual contributor level, whereas in the Gill et al (2005) model the selection is from the combined contributions from all of the mixture contributors.…”