Abstract

Peaks in an electropherogram could represent alleles, stutter product, or a combination of allele and stutter. Continuous probabilistic genotyping (PG) systems model the heights of peaks in an additive manner: for a shared or composite peak, PG models assume that the peak height is the sum of the allelic component and the stutter component. In this work we examine the assumption that the heights of overlapping alleles from a minor contributor and stutter peaks from a major contributor are additive. Any peak below the analytical threshold is considered unobserved; hence, in any dataset and particularly in low-template DNA profiles, some or many peaks may be unobserved or missing. Using simulation and empirical data, we show that an additive model can explain the heights of overlapping alleles from a minor contributor and stutter peaks from a major contributor as long as missing data are carefully considered. We use a naive method of imputation for the missing data which appears to perform adequately in this case. If missing data are ignored then the sum of stutter and allelic peaks is expected to be an overestimate of the average height of the composite peaks, as was observed in this study.

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