Evaluating probabilistic genotyping for low-pass DNA sequencing

Abstract

Most genomic methods consider the sample genotype. Data are evaluated at some location, and if the signal strength is sufficient, a genotype call is made. Conversely, sites that lack sufficient signal are treated as missing data. Such methods for genotype calling are binary, and this dichotomy limits genomic analyses to relatively high-coverage (and high-cost) massively parallel sequencing (MPS) data. It follows that bioinformatic methods that rely on genotypes may not be ideal for trace DNA samples, such as those sometimes encountered in forensic investigations, but even when applicable such analyses can be expensive. However, there are some genomic analyses where having many uncertain genotypes (with measured uncertainty) assayed over the entirety of the genome may be more powerful than current multi-locus approaches that consider a limited number of well-characterized markers. Methods for such problems may rely on genotype likelihood, which expresses the likelihood of alternative genotype calls in addition to the most likely call. One application that can benefit from genotype likelihoods is kinship analysis. NgsRelate is a bioinformatic tool that infers pairwise relatedness using a probabilistic genotyping framework, which accommodates the uncertainty associated with genotype calls for low-pass MPS data. Here, NgsRelate was used to infer kinship coefficients from low-pass whole genome sequencing data from a known pedigree. Multiple samples in a titration series (ranging from 50 ng to 0.5 ng) on a single MPS S4 flow cell were assessed. A reproducible scientific bioinformatic workflow was developed to evaluate kinship coefficients considering up to 3rd degree relatives. NgsRelate was found to provide robust assessments of kinship. Further, the use of low-pass MPS data provides a more cost-effective way to conduct forensic investigations.