Sexual assault samples are some of the most common samples encountered in forensic analysis. These samples can require a significant time investment due to differential extraction processes. We report on the first record of successful direct amplification of semen for STR analysis. Neat seminal fluid, dilutions ranging from 1:5 to 1:160 and GEDNAP samples were successfully amplified using a direct method. A mild differential isolation technique to enrich spermatozoa was developed and successfully implemented to separate and directly amplify a mixture of semen and female epithelial cells. Aliquots of samples subjected to the differential isolation protocol were stained with Haemotoxylin and Eosin for sperm scoring. Samples stained after PCR showed a complete lack of intact spermatozoa demonstrating that the cells are lysed during the PCR process. This paper demonstrates the potential to incorporate direct PCR in cases of sexual assault to more rapidly obtain results and achieve a higher sensitivity.
Ancestry-informative markers (AIMs) can be used to infer the ancestry of an individual to minimize the inaccuracy of self-reported ethnicity in biomedical research. In this study, we describe three methods for selecting AIM SNPs for the Malay population (Malay AIM panel) using different approaches based on pairwise FST, informativeness for assignment (In), and PCA-correlated SNPs (PCAIMs). These Malay AIM panels were extracted from genotype data stored in SNP arrays hosted by the Malaysian node of the Human Variome Project (MyHVP) and the Singapore Genome Variation Project (SGVP). In particular, genotype data from a total of 165 Malay individuals were analyzed, comprising data on 117 individual genotypes from the Affymetrix SNP-6 SNP array platform and data on 48 individual genotypes from the OMNI 2.5 Illumina SNP array platform. The HapMap phase 3 database (1397 individuals from 11 populations) was used as a reference for comparison with the Malay genotype data. The accuracy of each resulting Malay AIM panel was evaluated using a machine learning "ancestry-predictive model" constructed by using WEKA, a comprehensive machine learning platform written in Java. A total of 1250 SNPs were finally selected, which successfully identified Malay individuals from other world populations with an accuracy of 90%, but the accuracy decreased to 80% using 157 SNPs according to the pairwise FST method, while a panel of 200 SNPs selected using In and PCAIMs could be used to identify Malay individuals with an accuracy of approximately 80%.
The applicability of the Willems et al. model was verified on a collected sample of Malay (Malaysian nationality) children. This sample was split in a reference sample to develop a Malay-specific prediction model based on the Willems et al. method and in a test sample to validate this new developed model. Next, the incorporation of third molars into this model was analyzed. Panoramic radiographs (n = 1,403) of Malay children aged between 4 and 14.99 years (n = 702) and subadults aged between 15 and 23.99 years (n = 701) were collected. The left mandibular seven permanent teeth of the children were scored based on the staging technique described by Demirjian and converted to age using the Willems et al. method. Third molar development of all individuals was staged based on the technique described by Gleiser and Hunt modified by Kohler. Differences between dental age and chronological age were calculated and expressed in mean error (ME), mean absolute error (MAE), and root mean square error (RMSE). The Willems et al. model verified on the collected Malay children overestimated chronological age with a ME around 0.45 year. Small differences in ME, MAE, and RMSE between the verified Malay-specific prediction model and the Willems et al. model were observed. An overall neglected decrease in RMSE was detected adding third molar stages to the developed permanent teeth model.