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Population Informative Markers Selected Using Wright's Fixation Index and Machine Learning Improves Human Identification Using the Skin Microbiome

NCJ Number
Applied and Environmental Microbiology Volume: 87 Issue: 20 Dated: September 2021
Date Published
September 2021

In this study, the nondominant hands of 51 individuals were sampled in triplicate (n = 153) and they were analyzed for markers in the hid SkinPlex, a multiplex panel comprising candidate markers for skin microbiome profiling. 


Microbial DNA shed from human skin can be distinctive to its host, thus helping to individualize donors of forensic biological evidence. Previous studies have used single-locus microbial DNA markers (e.g., 16S rRNA) to assess the presence/absence of personal microbiota to profile human hosts; however, since the taxonomic composition of the microbiome is in constant fluctuation, this approach may not be sufficiently robust for human identification (HID). Multimarker approaches may be more powerful. In addition, genetic differentiation, rather than taxonomic distinction, may be more individualizing. In the current study, FST was an estimate of the genetic differences within and between populations. Three different SNP selection criteria were employed: SNPs with the highest-ranking FST estimates (i) common between any two samples regardless of markers present (termed overall); (ii) each marker common between samples (termed per marker); and (iii) common to all samples used to train the SVM algorithm for HID (termed selected). The SNPs chosen based on criteria for overall, per marker, and selected methods resulted in an accuracy of 92.00 percent, 94.77 percent, and 88.00 percent. respectively. The results support that estimates of FST, combined with SVM, can notably improve forensic HID via skin microbiome profiling. (publisher abstract modified)

Date Published: September 1, 2021