An initial exploration of machine learning for establishing associations between genetic markers and THC levels in Cannabis sativa samples.

Cannabis sativa, a globally commercialized plant used for medicinal, food, fiber production, and recreation, necessitates effective identification to distinguish legal and illegal varieties in forensic contexts. This research utilizes multivariate statistical models and Machine Learning approaches to establish correlations between specific genotypes and tetrahydrocannabinol (Δ9-THC) content (%) in C. sativa samples. 132 cannabis leaves samples were obtained from legal growers in Piedmont, Italy, and illegal drug seizures in Turin. Samples were genetically profiled using a 13-loci STR multiplex and their Δ9-THC content was detected through quantitative GC-MS analysis. This study aims to assess the use of supervised classification modelling on genetic data to distinguish cannabis samples into legal and illegal categories, revealing distinct clusters characterized by unique allele profiles and THC content. t-distributed Stochastic Neighbor Embedding (t-SNE), Random Forest (RF) and Partial Least Squares Regression (PLS-R) were executed for the machine learning modelling. All the tested models resulted effective discriminating between legal samples and illegal. Although further validation is necessary, this study presents a novel forensic investigative approach, potentially aiding law enforcement in significant marijuana seizures or tracking illicit drug trafficking routes.

Correction to: Mixture deconvolution by massively parallel sequencing of microhaplotypes.

The original version of this article contained an author name error. In this article, Katrina Madella has been corrected to Katrina Maddela.

Mixture deconvolution by massively parallel sequencing of microhaplotypes.

Short tandem repeat polymorphisms (STRs) are the standard markers for forensic human identification. STRs are highly polymorphic loci analyzed using a direct PCR-to-CE (capillary electrophoresis) approach. However, STRs have limitations particularly when dealing with complex mixtures. These include slippage of the polymerase during amplification causing stutter fragments that can be indistinguishable from minor contributor alleles, preferential amplification of shorter alleles, and limited number of loci that can be effectively co-amplified with CE. Massively parallel sequencing (MPS), by enabling a higher level of multiplexing and actual sequencing of the DNA, provides forensic practitioners an increased power of discrimination offered by the sequence of STR alleles and access to new sequence-based markers. Microhaplotypes (i.e., microhaps or MHs) are emerging multi-allelic loci of two or more SNPs within < 300 bp that are highly polymorphic, have alleles all of the same length, and do not generate stutter fragments. The growing number of loci described in the literature along with initial mixture investigations supports the potential for microhaps to aid in mixture interpretation and the purpose of this study was to demonstrate that practically. A panel of 36 microhaplotypes, selected from a set of over 130 loci, was tested with the Ion S5™ MPS platform (Thermo Fisher Scientific) on single-source samples, synthetic two-to-six person mixtures at different concentrations/contributor ratios, and on crime scene-like samples. The panel was tested both in multiplex with STRs and SNPs and individually. The analysis of single-source samples showed that the allele coverage ratio across all loci was 0.88 ± 0.08 which is in line with the peak height ratio of STR alleles in CE. In mixture studies, results showed that the input DNA can be much higher than with conventional CE, without the risk of oversaturating the detection system, enabling an increased sensitivity for the minor contributor in imbalanced mixtures with abundant amounts of DNA. Furthermore, the absence of stutter fragments simplifies the interpretation. On casework-like samples, MPS of MHs enabled the detection of a higher number of alleles from minor donors than MPS and CE of STRs. These results demonstrated that MPS of microhaplotypes can complement STRs and enhance human identification practices when dealing with complex imbalanced mixtures.

Correction to: Population data of 21 autosomal STR loci in the Hausa, Igbo and Yoruba people of Nigeria.

In the original paper author Alani Sulaimon Akanmu was erroneously omitted from the author list. Prof. Akanmu has now been added as 4th author. Prof. Akanmu acted as an academic supervisor of the study and additionally contributed to the publication by reading, commenting and editing the manuscript.

Population data of 21 autosomal STR loci in the Hausa, Igbo and Yoruba people of Nigeria.

The three major ethnic groups of Nigerian population namely the Hausa, Igbo and Yoruba make up 29, 21 and 18% of the total population, respectively. To provide genetic information necessary for forensic analysis, this study was carried out to determine STR allele frequencies in 102 Hausa, 128 Igbo and 134 Yoruba individuals in Nigeria using 21 STR loci including the 20 CODIS (Combined DNA Index System) loci plus SE33.

PowerPlex® Fusion 6C System: evaluation study for analysis of casework and database samples.

AIM: To report on the successful analysis of amplicons obtained with PowerPlex® Fusion 6C System, a highly robust 27-plex genotyping kit developed for human identification laboratories, on the Applied Biosystems® 3500 Genetic Analyzer. METHOD: We performed characterization and evaluation studies following the Scientific Working Group on DNA Analysis Methods (SWGDAM) validation guidelines, examining several critical areas of kit performance. We report the results of sensitivity, robustness, heterozygous peak height ratio, precision, concordance, caseworks, and mixture interpretations. We tested sensitivity, using serial dilutions of control DNA. RESULTS: The minimum amount of input DNA resulting in a full profile was 125 pg. Inhibition, inducted by urea, showsed a progressively fragmentation of DNA and a full profile was obtained until 1M of inhibitor factor. To test the profile quality, casework samples were extracted with different extraction methods: Chelex®100, QIAmp DNA Micro Kit and Phenol-Chloroform extraction. The results demonstrated that extraction chemistries do not have affect on amplification performance. Concordance check was performed by typing some casework samples and comparing the typing results with those obtained with other available kits. Thus, concordance was expected and supported by the data. CONCLUSION: Reliable DNA typing results can be obtained using this new kit, demonstrating its effectiveness and utility in forensic analysis.