| Institution: | 1. Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, P. R. China
Guangdong Province Translational Forensic Medicine Engineering Technology Research Center, Sun Yat-sen University, Guangzhou, P. R. China;2. Fetal Medicine Department, The First Affiliated Hospital of Jinan University, Guangzhou, P. R. China;3. Fetal Medicine Center, Department of Obstetrics and Gynecology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, P. R. China;4. Reproductive Medicine Center, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, P. R. China;5. Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, P. R. China |
| Abstract: | The need to identify a missing person (MP) through kinship analysis of DNA samples found at a crime scene has become increasingly prevalent. DNA samples from MPs can be severely degraded, contain little DNA and mixed with other contributors, which often makes it difficult to apply conventional methods in practice. This study developed a massively parallel sequencing–based panel that contains 1661 single-nucleotide polymorphisms (SNPs) with low minor allele frequencies (MAFs) (averaged at 0.0613) in the Chinese Han population, and the strategy for relationship inference from DNA mixtures comprising different numbers of contributors (NOCs) and of varying allele dropout probabilities. Based on the simulated dataset and genotyping results of 42 artificial DNA mixtures (NOC = 2–4), it was observed that the present SNP panel was sufficient for balanced mixtures when referenced to the closest relatives (parents/offspring and full siblings). When the mixture profiles suffered from dropout, incorrect assignments were markedly associated with relatedness, NOC and the dropout level. We, therefore, indicate that SNPs with low MAFs could be reliably interpreted for MP identification through the kinship analysis of complex DNA mixtures. Further studies should be extended to more possible scenarios to test the feasibility of this present approach. |
