Shotgun DNA sequencing for human identification: Dynamic SNP selection and likelihood ratio calculations accounting for errors

Abstract

Shotgun sequencing is a DNA analysis method that potentially determines the nucleotide sequence of every DNA fragment in a sample, unlike PCR-based genotyping methods that is widely used in forensic genetics and targets predefined short tandem repeats (STRs) or predefined single nucleotide polymorphisms (SNPs). Shotgun DNA sequencing is particularly useful for highly degraded low-quality DNA samples, such as ancient samples or those from crime scenes. Here, we developed a statistical model for human identification using shotgun sequencing data and developed formulas for calculating the evidential weight as a likelihood ratio (LR). The model uses a dynamic set of binary SNP loci and takes the error rate from shotgun sequencing into consideration in a probabilistic manner. To our knowledge, the method is the first to make this possible. Results from replicated shotgun sequencing of buccal swabs (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. Different genotype quality filters may be applied to account for genotyping errors. An error probability of zero resulted in the commonly used LR formula for the weight of evidence. Error probabilities above zero reduced the LR contribution of matching genotypes and increased the LR in the case of a mismatch between the genotypes of the trace and the person of interest. In the latter scenario, the LR increased from zero (occurring when the error probability was zero) to low positive values, which allow for the possibility that the mismatch may be due to genotyping errors. We developed an open-source R package, wgsLR, which implements the method, including estimation of the calling error probability and calculation of LR values. The R package includes all formulas used in this paper and the functionalities to generate the formulas.