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The standard forensic DNA analysis workflow typically encompasses DNA extraction, quantification, STR-PCR amplification, and CE detection. A direct PCR amplification method eliminates the extraction and quantification steps, shortening the turnaround time for DNA profiling. However, a limitation to the direct PCR amplification method lies in its inability to allow for additional PCR amplification on the same sample. We found that replicate PCR amplifications can be afforded with the following modification to the direct PCR amplification method: after sample collection, the microFLOQ™ Direct swabs were incubated in low TE buffer prior to PCR amplification of the lysate. With replicate amplifications, the impact of stochastic effects during STR-PCR amplification on DNA profile interpretation would be reduced. Additionally, adjustments can be made to template volumes in subsequent amplifications, preventing oversaturated PCR reactions. Our results showed that this modified direct amplification method gave comparable median peak heights, allele recovery and intra-locus peak-height-ratio to those of the standard workflow, while maintaining the advantage of minimal evidence consumption.

The ability to predict bio-geographic ancestry can be valuable to generate investigative leads towards solving crimes. Ancestry informative marker (AIM) sets include large numbers of SNPs to predict an ancestral population. Massively parallel sequencing has enabled forensic laboratories to genotype a large number of such markers in a single assay. Illumina's ForenSeq DNA Signature Kit includes the ancestry informative SNPs reported by Kidd et al. In this study, the ancestry prediction capabilities of the ForenSeq kit through sequencing on the MiSeq FGx were evaluated in 1030 unrelated Singapore population samples of Chinese, Malay and Indian origin. A total of 59 ancestry SNPs and phenotypic SNPs with AIM properties were selected. The bio-geographic ancestry of the 1030 samples, as predicted by Illumina's ForenSeq Universal Analysis Software (UAS), was determined. 712 of the genotyped samples were used as a training sample set for the generation of an ancestry prediction model using STRUCTURE and Snipper. The performance of the prediction model was tested by both methods with the remaining 318 samples. Ancestry prediction in UAS was able to correctly classify the Singapore Chinese as part of the East Asian cluster, while Indians clustered with Ad-mixed Americans and Malays clustered in-between these two reference populations. Principal component analyses showed that the 59 SNPs were only able to account for 26% of the variation between the Singapore sub-populations. Their discriminatory potential was also found to be lower (GST=0.085) than that reported in ALFRED (FST=0.357). The Snipper algorithm was able to correctly predict bio-geographic ancestry in 91% of Chinese and Indian, and 88% of Malay individuals, while the success rates for the STRUCTURE algorithm were 94% in Chinese, 80% in Malay, and 91% in Indian individuals. Both these algorithms were able to provide admixture proportions when present. Ancestry prediction accuracy (in terms of likelihood ratio) was generally high in the absence of admixture. Misclassification occurred in admixed individuals, who were likely offspring of inter-ethnic marriages, and hence whose self-reported bio-geographic ancestries were dependent on that of their fathers, and in individuals of minority sub-populations with inter-ethnic beliefs. The ancestry prediction capabilities of the 59 SNPs on the ForenSeq kit were reasonably effective in differentiating the Singapore Chinese, Malay and Indian sub-populations, and will be of use for investigative purposes. However, there is potential for more accurate prediction through the evaluation of other AIM sets.

The Kidd set of ancestry informative SNPs are included in Illumina’s ForenSeq DNA Signature Kit. We had previously reported on the capability of these SNPs together with some phenotypic SNPs with ancestry informative properties in differentiating individuals from the Chinese, Malay and Indian populations in Singapore. The Singapore population is primarily made up of Chinese, Malays and Indians, with individuals from other Asian countries making up the rest. In this study, we evaluated the ancestry prediction capabilities of the ForenSeq kit in 484 unrelated individuals of self-declared Bangladeshi, Burmese, Filipino, Indonesian and Vietnamese origin. 750 Chinese, Malay and Indian individuals previously reported were included in this study. 48 ancestry SNPs and 12 phenotypic SNPs with ancestry informative properties were selected for analyses. Ancestry modelling in STRUCTURE showed that the eight tested populations could be better classified as five. Principal component analysis also showed that the eight populations clustered in five groups based on general geographic location within Asia; with Chinese clustering with Vietnamese, Malays clustering with Indonesians, Indians clustering with Bangladeshi, and the Burmese and Filipino populations clustering in-between and overlapping with the Chinese and Malay populations. The 60 SNPs analysed could account for only 23 % of the variation between the populations. The lack of distinction between the populations resulted in poor (43 % correct self-classification) cross-validation using Snipper. While this was improved by merging the co-clustering populations into five groups (East, South-East, South Asian, Burmese & Filipino), successful self-classification was still relatively low (69 %). While the 60 tested ancestry informative markers were able to differentiate between individuals of East, South-East and South Asian origin, they are not sufficiently informative to effectively discriminate between Chinese, Malays and Indians, and Bangladeshi, Burmese, Filipino, Indonesian and Filipino populations in the country.