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2017 ; 83
(22
): ä Nephropedia Template TP
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Forensic Human Identification Using Skin Microbiomes
#MMPMID28887423
Schmedes SE
; Woerner AE
; Budowle B
Appl Environ Microbiol
2017[Nov]; 83
(22
): ä PMID28887423
show ga
The human microbiome contributes significantly to the genetic content of the
human body. Genetic and environmental factors help shape the microbiome, and as
such, the microbiome can be unique to an individual. Previous studies have
demonstrated the potential to use microbiome profiling for forensic applications;
however, a method has yet to identify stable features of skin microbiomes that
produce high classification accuracies for samples collected over reasonably long
time intervals. A novel approach is described here to classify skin microbiomes
to their donors by comparing two feature types: Propionibacterium acnes pangenome
presence/absence features and nucleotide diversities of stable clade-specific
markers. Supervised learning was used to attribute skin microbiomes from 14 skin
body sites from 12 healthy individuals sampled at three time points over a
>2.5-year period with accuracies of up to 100% for three body sites. Feature
selection identified a reduced subset of markers from each body site that are
highly individualizing, identifying 187 markers from 12 clades. Classification
accuracies were compared in a formal model testing framework, and the results of
this analysis indicate that learners trained on nucleotide diversity perform
significantly better than those trained on presence/absence encodings. This study
used supervised learning to identify individuals with high accuracy and
associated stable features from skin microbiomes over a period of up to almost 3
years. These selected features provide a preliminary marker panel for future
development of a robust and reproducible method for skin microbiome profiling for
forensic human identification.IMPORTANCE A novel approach is described to
attribute skin microbiomes, collected over a period of >2.5 years, to their
individual hosts with a high degree of accuracy. Nucleotide diversities of stable
clade-specific markers with supervised learning were used to classify skin
microbiomes from a particular individual with up to 100% classification accuracy
for three body sites. Attribute selection was used to identify 187 genetic
markers from 12 clades which provide the greatest differentiation of individual
skin microbiomes from 14 skin sites. This study performs skin microbiome
profiling from a supervised learning approach and obtains high classification
accuracy for samples collected from individuals over a relatively long time
period for potential application to forensic human identification.
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