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Ricke, D.

Publications and source records attributed to Ricke, D..

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Estimating Individual Contributions to Complex DNA SNP Mixtures

Mixture analysis and deconvolution methods can identify both known and unknown individuals contributing to DNA mixtures. These methods may not identify all DNA contributors with the remaining fraction of the mixture being contributed by one or more unknown individuals. The proportion of DNA contributed by individuals to a forensic sample can be estimated using their quantified mixture alleles. For short tandem repeats (STRs), methods to estimate individual contribution concentrations compare capillary electrophoresis peak heights and or peak areas within a mixture. For single nucleotide polymorphisms (SNPs), the major:minor allele ratios or counts, unique to each contributor, can be compared to estimate contributor proportion within the mixture. This article introduces three approaches (mean, median, and slope methods) for estimating individual DNA contributions to forensic mixtures for high throughput sequencing (HTS)/massively parallel sequencing (MPS) SNP panels.

bioinformatics

Fast P(RMNE): Fast Forensic DNA Probability of Random Man Not Excluded Calculation

High throughput sequencing (HTS) of DNA forensic samples is expanding from the sizing of short tandem repeats (STRs) to massively parallel sequencing (MPS). HTS panels are expanding from the FBI 20 core Combined DNA Index System (CODIS) loci to include SNPs. The calculation of random man not excluded, P(RMNE), is used in DNA mixture analysis to estimate the probability that a person is present in a DNA mixture. This calculation encounters calculation artifacts with expansion to larger panel sizes. Increasing the floating-point precision of the calculations allows for increased panel sizes but with a corresponding increase in computation time. The Taylor series higher precision libraries used fail on some input data sets leading to algorithm unreliability. Herein, a new formula is introduced for calculating P(RMNE) that scales to larger SNP panel sizes while being computationally efficient (patent pending)[1].

bioinformatics