Overlapping association signals in the genetics of hair-related phenotypes in humans and their relevance to predictive DNA analysis

Pośpiech, Ewelina; Karłowska-Pik, Joanna; Kukla-Bartoszek, Magdalena; Woźniak, Anna; Boroń, Michał; Zubańska, Magdalena; Jarosz, Agata; Bronikowska, Agnieszka; Grzybowski, Tomasz; Płoski, Rafał; Spólnicka, Magdalena; Branicki, Wojciech

doi:10.1016/j.fsigen.2022.102693

Cited by 5 publications

(1 citation statement)

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“…This has led to significant interest in using molecular markers, specifically forensic DNA phenotypic inference, to identify human body characteristics. Examples include estimating age through DNA methylation (Xu et al, 2019 ; Naue, 2023 ) or telomeric DNA (Elmadawy et al, 2021 ), characterizing facial features using single nucleotide polymorphism (SNP) (Pospiech et al, 2022 ), and inferring age based on RNA (Rodriques et al, 2021 ; Wang et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Preliminary exploratory research on the application value of oral and intestinal meta-genomics in predicting subjects' occupations–A case study of the distinction between students and migrant workers

Dou,

Ma,

Liang

et al. 2024

Front. Microbiol.

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BackgroundIn the field of forensic science, accurately determining occupation of an individual can greatly assist in resolving cases such as criminal investigations or disaster victim identifications. However, estimating occupation can be challenging due to the intricate relationship between occupation and various factors, including gender, age, living environment, health status, medication use, and lifestyle habits such as alcohol consumption and smoking. All of these factors can impact the composition of oral or gut microbial community of an individual.Methods and resultsIn this study, we collected saliva and feces samples from individuals representing different occupational sectors, specifically students and manual laborers. We then performed metagenomic sequencing on the DNA extracted from these samples to obtain data that could be analyzed for taxonomic and functional annotations in five different databases. The correlation between occupation with microbial information was assisted from the perspective of α and β diversity, showing that individuals belonging to the two occupations hold significantly different oral and gut microbial communities, and that this correlation is basically not affected by gender, drinking, and smoking in our datasets. Finally, random forest (RF) models were built with recursive feature elimination (RFE) processes. Models with 100% accuracy in both training and testing sets were constructed based on three species in saliva samples or on a single pathway annotated by the KEGG database in fecal samples, namely, “ko04145” or Phagosome.ConclusionAlthough this study may have limited representativeness due to its small sample size, it provides preliminary evidence of the potential of using microbiome information for occupational inference.

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