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DNA samples crime cases analysed in forensic genetics, frequently contain DNAfrom multiple contributors. These occur as convolutions of the DNA profiles ofthe individual contributors to the DNA sample. Thus, in cases where one or moreof the contributors were unknown, an objective of interest would be theseparation, often called deconvolution, of these unknown profiles. In order toobtain deconvolutions of the unknown DNA profiles, we introduced a multiplepopulation evolutionary algorithm (MEA). We allowed the mutation operator ofthe MEA to utilise that the fitness is based on a probabilistic model and guideit by using the deviations between the observed and the expected value forevery element of the encoded individual. This guided mutation operator (GM) wasdesigned such that the larger the deviation the higher probability of mutation.Furthermore, the GM was inhomogeneous in time, decreasing to a specified lowerbound as the number of iterations increased. We analysed 102 two-person DNAmixture samples in varying mixture proportions. The samples were quantifiedusing two different DNA prep. kits: (1) Illumina ForenSeq Panel B (30 samples),and (2) Applied Biosystems Precision ID Globalfiler NGS STR panel (72 samples).The DNA mixtures were deconvoluted by the MEA and compared to the true DNAprofiles of the sample. We analysed three scenarios where we assumed: (1) theDNA profile of the major contributor was unknown, (2) DNA profile of the minorwas unknown, and (3) both DNA profiles were unknown. Furthermore, we conducteda series of sensitivity experiments on the ForenSeq panel by varying thesub-population size, comparing a completely random homogeneous mutationoperator to the guided operator with varying mutation decay rates, and allowingfor hill-climbing of the parent population.

DNA mixture deconvolution using an evolutionary algorithm with multiple populations, hill-climbing, and guided mutation