Determining the number of contributors to DNA mixtures in the low-template regime: Exploring the impacts of sampling and detection effects

Norsworthy, Sarah; Lun, Desmond S.; Grgicak, Catherine M.

doi:10.1016/j.legalmed.2018.02.001

Cited by 19 publications

(13 citation statements)

References 26 publications

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“…In concordance with a previous study 19 , our results showed a greater underestimation of NOC when there is a 30% allele dropout rate than would be observed with no allele dropouts 19 . Since the SE33 locus 20 , 21 was able to reduce the NOC underestimation risk in a no-allele dropout scenario 2 , we investigated whether SE33 locus can similarly reduce NOC underestimation risk in a mixture profile with 30% allele dropout.…”

Section: Discussionsupporting

confidence: 93%

RETRACTED ARTICLE: Uncertainty in estimating the number of contributors from simulated DNA mixture profiles, with and without allele dropout, from Chinese, Malay, Indian, and Caucasian ethnic populations

Chong

Syn

2021

Sci Rep

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Determining the number of contributors (NOC) accurately in a forensic DNA mixture profile can be challenging. To address this issue, there have been various studies that examined the uncertainty in estimating the NOC in a DNA mixture profile. However, the focus of these studies lies primarily on dominant populations residing within Europe and North America. Thus, there is limited representation of Asian populations in these studies. Further, the effects of allele dropout on the NOC estimation has not been explored. As such, this study assesses the uncertainty of NOC in simulated DNA mixture profiles of Chinese, Malay, and Indian populations, which are the predominant ethnic populations in Asia. The Caucasian ethnic population was also included to provide a basis of comparison with other similar studies. Our results showed that without considering allele dropout, the NOC from DNA mixture profiles derived from up to four contributors of the same ethnic population could be estimated with confidence in the Chinese, Malay, Indian and Caucasian populations. The same results can be observed on DNA mixture profiles originating from a combination of differing ethnic populations. The inclusion of an overall 30% allele dropout rate increased the probability (risk) of underestimating the NOC in a DNA mixture profile; even a 3-person DNA mixture profile has a > 99% risk of underestimating the NOC as two or fewer contributors. However, such risks could be mitigated when the highly polymorphic SE33 locus was included in the dataset. Lastly there was a negligible level of risk in misinterpreting the NOC in a mixture profile as deriving from a single source profile. In summary, our studies showcased novel results representative of the Chinese, Malay, and Indian ethnic populations when examining the uncertainty in NOC estimation in a DNA mixture profile. Our results would be useful in the estimation of NOC in a DNA mixture profile in the Asian context.

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Section: Discussionsupporting

confidence: 93%

RETRACTED ARTICLE: Uncertainty in estimating the number of contributors from simulated DNA mixture profiles, with and without allele dropout, from Chinese, Malay, Indian, and Caucasian ethnic populations

Chong

Syn

2021

Sci Rep

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“…With such a method, the true NOC is uncertain, especially with high order mixtures (three or more) and/or low levels of DNA [64][65][66]. It is difficult to refer to the true NOC even in mock samples, but we will define it here as the number of donors that have left some signal above the analytical threshold.…”

Section: Number Of Contributors (Noc)mentioning

confidence: 99%

A Review of Probabilistic Genotyping Systems: EuroForMix, DNAStatistX and STRmix™

Gill

Benschop

Buckleton

et al. 2021

Genes

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Probabilistic genotyping has become widespread. EuroForMix and DNAStatistX are both based upon maximum likelihood estimation using a γ model, whereas STRmix™ is a Bayesian approach that specifies prior distributions on the unknown model parameters. A general overview is provided of the historical development of probabilistic genotyping. Some general principles of interpretation are described, including: the application to investigative vs. evaluative reporting; detection of contamination events; inter and intra laboratory studies; numbers of contributors; proposition setting and validation of software and its performance. This is followed by details of the evolution, utility, practice and adoption of the software discussed.

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“…Some suggestions were made for validation of PGS systems [ 87 ], and several validation studies were published for the PGS system STRmix including a developmental validation by the developers [ 88 ], an FBI Laboratory internal validation [ 89 ], and a 31-laboratory compilation of 2825 mixture results [ 90 ]. A machine learning-based assessment for estimating the number of contributors was described [ 91 ], and the challenges of estimating the number of contributors with low levels of DNA were explored [ 92 ]. Variation of results with four different continuous PGS models were studied [ 93 ] and responses to court admissibility challenges with STRmix were provided [ 94 ].…”

Section: Dna Mixture Interpretation and Probabilistic Genotyping Softmentioning

confidence: 99%

Interpol review of forensic biology and forensic DNA typing 2016-2019

Butler

Willis

2020

Forensic Science International: Synergy

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Determining the number of contributors to DNA mixtures in the low-template regime: Exploring the impacts of sampling and detection effects

Cited by 19 publications

References 26 publications

RETRACTED ARTICLE: Uncertainty in estimating the number of contributors from simulated DNA mixture profiles, with and without allele dropout, from Chinese, Malay, Indian, and Caucasian ethnic populations

RETRACTED ARTICLE: Uncertainty in estimating the number of contributors from simulated DNA mixture profiles, with and without allele dropout, from Chinese, Malay, Indian, and Caucasian ethnic populations

A Review of Probabilistic Genotyping Systems: EuroForMix, DNAStatistX and STRmix™

Interpol review of forensic biology and forensic DNA typing 2016-2019

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