Literature DB >> 34379455

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

Allison J Sherier1,2, August E Woerner1,2, Bruce Budowle1,2.   

Abstract

Microbial DNA, shed from human skin, can be distinctive to its host and, thus, help individualize donors of forensic biological evidence. Previous studies have utilized 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. Additionally, genetic differentiation, rather than taxonomic distinction, may be more individualizing. To this end, the nondominant hands of 51 individuals were sampled in triplicate (n = 153). They were analyzed for markers in the hidSkinPlex, a multiplex panel comprising candidate markers for skin microbiome profiling. Single-nucleotide polymorphisms (SNPs) with the highest Wright's fixation index (FST) estimates were then selected for predicting donor identity using a support vector machine (SVM) learning model. FST is 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%, 94.77%, and 88.00%, respectively. The results support that estimates of FST, combined with SVM, can notably improve forensic HID via skin microbiome profiling. IMPORTANCE There is a need for additional genetic information to help identify the source of biological evidence found at a crime scene. The human skin microbiome is a potentially abundant source of DNA that can enable the identification of a donor of biological evidence. With microbial profiling for human identification, there will be an additional source of DNA to identify individuals as well as to exclude individuals wrongly associated with biological evidence, thereby improving the utility of forensic DNA profiling to support criminal investigations.

Entities:  

Keywords:  hidSkinPlex; human identification; massive parallel sequencing; microbial forensics; skin microbiome

Mesh:

Year:  2021        PMID: 34379455      PMCID: PMC8478459          DOI: 10.1128/AEM.01208-21

Source DB:  PubMed          Journal:  Appl Environ Microbiol        ISSN: 0099-2240            Impact factor:   4.792


  33 in total

1.  16S rRNA sequencing in routine bacterial identification: a 30-month experiment.

Authors:  S Mignard; J P Flandrois
Journal:  J Microbiol Methods       Date:  2006-07-21       Impact factor: 2.363

2.  Building a forensic ancestry panel from the ground up: The EUROFORGEN Global AIM-SNP set.

Authors:  C Phillips; W Parson; B Lundsberg; C Santos; A Freire-Aradas; M Torres; M Eduardoff; C Børsting; P Johansen; M Fondevila; N Morling; P Schneider; A Carracedo; M V Lareu
Journal:  Forensic Sci Int Genet       Date:  2014-02-25       Impact factor: 4.882

3.  Investigation of direct and indirect transfer of microbiomes between individuals.

Authors:  Ana Neckovic; Roland A H van Oorschot; Bianca Szkuta; Annalisa Durdle
Journal:  Forensic Sci Int Genet       Date:  2019-11-27       Impact factor: 4.882

4.  Targeted sequencing of clade-specific markers from skin microbiomes for forensic human identification.

Authors:  Sarah E Schmedes; August E Woerner; Nicole M M Novroski; Frank R Wendt; Jonathan L King; Kathryn M Stephens; Bruce Budowle
Journal:  Forensic Sci Int Genet       Date:  2017-10-18       Impact factor: 4.882

5.  Forensic Human Identification Using Skin Microbiomes.

Authors:  Sarah E Schmedes; August E Woerner; Bruce Budowle
Journal:  Appl Environ Microbiol       Date:  2017-10-31       Impact factor: 4.792

6.  Forensic analysis of the microbiome of phones and shoes.

Authors:  Simon Lax; Jarrad T Hampton-Marcell; Sean M Gibbons; Geórgia Barguil Colares; Daniel Smith; Jonathan A Eisen; Jack A Gilbert
Journal:  Microbiome       Date:  2015-05-12       Impact factor: 14.650

7.  Mobile phones carry the personal microbiome of their owners.

Authors:  James F Meadow; Adam E Altrichter; Jessica L Green
Journal:  PeerJ       Date:  2014-06-24       Impact factor: 2.984

8.  Microbiota of the indoor environment: a meta-analysis.

Authors:  Rachel I Adams; Ashley C Bateman; Holly M Bik; James F Meadow
Journal:  Microbiome       Date:  2015-10-13       Impact factor: 14.650

9.  Revised Estimates for the Number of Human and Bacteria Cells in the Body.

Authors:  Ron Sender; Shai Fuchs; Ron Milo
Journal:  PLoS Biol       Date:  2016-08-19       Impact factor: 8.029

10.  The Skin Microbiome of Cohabiting Couples.

Authors:  Ashley A Ross; Andrew C Doxey; Josh D Neufeld
Journal:  mSystems       Date:  2017-07-20       Impact factor: 6.496

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  1 in total

1.  Determining Informative Microbial Single Nucleotide Polymorphisms for Human Identification.

Authors:  Allison J Sherier; August E Woerner; Bruce Budowle
Journal:  Appl Environ Microbiol       Date:  2022-03-14       Impact factor: 5.005

  1 in total

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