  In forensic genetics, short tandem repeats (STRs) are used for human
identification (HID). Degraded biological trace samples with low amounts of
short DNA fragments (low-quality DNA samples) pose a challenge for STR typing.
Predefined single nucleotide polymorphisms (SNPs) can be amplified on short PCR
fragments and used to generate SNP profiles from low-quality DNA samples.
However, the stochastic results from low-quality DNA samples may result in
frequent locus drop-outs and insufficient numbers of SNP genotypes for
convincing identification of individuals. Shotgun DNA sequencing potentially
analyses all DNA fragments in a sample in contrast to the targeted PCR-based
sequencing methods and may be applied to DNA samples of very low quality, like
heavily compromised crime-scene samples and ancient DNA samples. Here, we
developed a statistical model for shotgun sequencing, sequence alignment, and
genotype calling. Results from replicated shotgun sequencing of buccal swab
(high-quality samples) and hair samples (low-quality samples) were arranged in
a genotype-call confusion matrix to estimate the calling error probability by
maximum likelihood and Bayesian inference. We developed formulas for
calculating the evidential weight as a likelihood ratio (LR) based on data from
dynamically selected SNPs from shotgun DNA sequencing. The method accounts for
potential genotyping errors. Different genotype quality filters may be applied
to account for genotyping errors. An error probability of zero resulted in the
forensically commonly used LR formula. When considering a single SNP marker's
contribution to the LR, error probabilities larger than zero reduced the LR
contribution of matching genotypes and increased the LR in the case of a
mismatch. We developed an open-source R package, wgsLR, which implements the
method, including estimating the calling error probability and calculating LR
values.
