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Forensic Human Identification With Targeted Microbiome Markers Using Nearest Neighbor Classification

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Since it remains an open question as to whether microbial human identification that relies on quantifying which taxa are present and their respective abundance levels is more individualizing than estimates of the degree of genetic relatedness between microbial samples, the current study addressed this question by contrasting two prediction strategies.
The first approach uses phylogenetic distance to predict the host individual; thus, it operates under the premise that microbes within individuals are more closely related than microbes between/among individuals. The second approach uses population genetic measures of diversity at clade-specific markers, serving as a fine-grained assessment of microbial composition and quantification. Both assessments were performed using targeted sequencing of 286 markers from 22 microbial taxa sampled in 51 individuals across three body sites measured in triplicate. Nearest neighbor and reverse nearest neighbor classifiers were constructed based on the pooled data and yielded 71 percent and 78 percent accuracy, respectively, when diversity was considered, and performed significantly worse when a phylogenetic distance was used (54 percent and 63 percent accuracy, respectively); however, empirical estimates of classification accuracy were 100 percent when conditioned on a maximum nearest neighbor distance when diversity was used, and identification based on a phylogenetic distance failed to reach saturation. These findings suggest that microbial strain composition is more individualizing than that of a phylogeny, perhaps indicating that microbial composition may be more individualizing than recent common ancestry. One inference that may be drawn from these findings is that host-environment interactions may maintain the targeted microbial profile and that this maintenance may not necessarily be repopulated by intra-individual microbial strains. (publisher abstract modified)
Date Created: January 28, 2021