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.

翻译：在法医遗传学中，短串联重复序列（STRs）被用于人类个体识别（HID）。对于含有少量短DNA片段（低质量DNA样本）的降解生物微量样本，STR分型面临挑战。预定义的单核苷酸多态性（SNPs）可在短PCR片段上扩增，并用于从低质量DNA样本中生成SNP图谱。然而，低质量DNA样本的随机性结果可能导致频繁的基因座丢失和SNP基因型数量不足，从而难以对个体进行可靠识别。与基于靶向PCR的测序方法不同，鸟枪法DNA测序可潜在分析样本中的所有DNA片段，并可应用于极低质量的DNA样本，如严重受损的犯罪现场样本和古DNA样本。本文中，我们为鸟枪法测序、序列比对和基因型判定开发了一个统计模型。通过对口腔拭子（高质量样本）和毛发样本（低质量样本）进行重复鸟枪法测序，将结果整理为基因型判定混淆矩阵，以通过最大似然估计和贝叶斯推断来估计判定错误概率。我们开发了基于鸟枪法DNA测序中动态选择的SNPs数据，将证据权重计算为似然比（LR）的公式。该方法考虑了潜在的基因分型错误。可应用不同的基因型质量过滤器来处理基因分型错误。当错误概率为零时，即得到法医学中常用的LR公式。在考虑单个SNP标记对LR的贡献时，大于零的错误概率会降低匹配基因型的LR贡献，并在不匹配的情况下增加LR。我们开发了一个开源R软件包wgsLR，用于实现该方法，包括估计判定错误概率和计算LR值。