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1.
Fa Yi Xue Za Zhi ; 38(5): 625-639, 2022 Oct 25.
Article in English, Chinese | MEDLINE | ID: mdl-36727180

ABSTRACT

The succession of microbiota is closely associated with several essential factors, including race, sex, health condition, lifestyle, postmortem interval, etc., and it has great potential application value in forensic medicine. This paper summarizes recent studies on the forensic applications of the microbiome, including individual identification, geographical feature identification, origin identification of the tissue or body fluid, and postmortem interval estimation, and introduces the current machine learning algorithms for microbiology research based on next-generation sequencing data. In addition, the current problems facing forensic microbiomics such as the extraction and preservation of samples, construction of standardization and database, ethical review and practical applicability are discussed. Future multi-omics studies are expected to explore micro ecosystems from a comprehensive and dynamic perspective, to promote the development of forensic microbiomics application.


Subject(s)
Forensic Medicine , Microbiota , Humans , Autopsy , Microbiota/genetics , Algorithms , High-Throughput Nucleotide Sequencing , Postmortem Changes
2.
Leg Med (Tokyo) ; 49: 101848, 2021 Mar.
Article in English | MEDLINE | ID: mdl-33517135

ABSTRACT

Co-separation studies between surnames and Y chromosome genetic markers are beneficial to revealing population migrations, surname origins, population formation histories and forensic familial searching. Genetic distributions of 27 Y-STRs in Chinese four surnames (Li, Lin, Chen and Huang) from Zhanjiang Han population were investigated. Meanwhile, we tried to develop a decision tree model for surname predictions based on Y-STR haplotypes. Allelic frequencies of 27 Y-STRs showed that unique alleles were only observed in a certain surname; besides, some alleles displayed higher frequencies in a certain surname than those in other surnames, implying these alleles might be employed as the useful indicators for surname predictions. Haplotype match probability values of 27 Y-STRs in these surnames revealed that the system could be used as a valuable tool for forensic male identification. The developed decision tree model performed well for the training set with the accuracy of 0.9860 and obtained the relatively high accuracy (>0.70) for surname predictions of the testing set. To sum up, we explored the power of the machine learning to the surname predictions based on obtained Y-STR haplotypes, which showed promising application values in forensic familial searching.


Subject(s)
Asian People/genetics , Chromosomes, Human, Y/genetics , Decision Trees , Forensic Genetics/methods , Genetic Markers/genetics , Genetics, Population/methods , Haplotypes/genetics , Microsatellite Repeats/genetics , Names , China , Gene Frequency/genetics , Humans , Male , Pedigree
3.
PeerJ ; 7: e6508, 2019.
Article in English | MEDLINE | ID: mdl-30956897

ABSTRACT

In recent years, forensic geneticists have begun to develop some ancestry informative marker (AIM) panels for ancestry analysis of regional populations. In this study, we chose 48 single nucleotide polymorphisms (SNPs) from SPSmart database to infer ancestry origins of continental populations and Chinese subpopulations. Based on the genetic data of four continental populations (African, American, East Asian and European) from the CEPH-HGDP database, the power of these SNPs for differentiating continental populations was assessed. Population genetic structure revealed that distinct ancestry components among these continental populations could be discerned by these SNPs. Another novel population set from 1000 Genomes Phase 3 was treated as testing populations to further validate the efficiency of the selected SNPs. Twenty-two populations from CEPH-HGDP database were classified into three known populations (African, East Asian, and European) based on their biogeographical regions. Principal component analysis and Bayes analysis of testing populations and three known populations indicated these testing populations could be correctly assigned to their corresponding biogeographical origins. For three Chinese populations (Han, Mongolian, and Uygur), multinomial logistic regression analyses indicated that these 48 SNPs could be used to estimate ancestry origins of these populations. Therefore, these SNPs possessed the promising potency in ancestry analysis among continental populations and some Chinese populations, and they could be used in population genetics and forensic research.

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