YMrCA: Improving Y-chromosomal ancestor time estimation for DNA kinship research.

The Y-chromosome is a valuable kinship indicator in family history and forensic research. To reconstruct genealogies, the time to the most recent common ancestor (tMRCA) between paternal relatives can be estimated through Y-STR analysis. Existing models are the stepwise mutation model (SMM, only one-step Y-STR changes) and the infinite allele model (IAM, new allele per Y-STR change). In this study, these mutation models and all existing tMRCA calculators were validated through a genetic-genealogy database containing 1,120 biologically related genealogical pairs confirmed by 46 Y-STRs with known tMRCA (18,109 generations). Consistent under- and overestimation and broad confidence intervals were observed, leading to dubious tMRCA estimates. This is because they do not include individual mutation rates or multi-step changes and ignore hidden multiple, back, or parallel modifications. To improve tMRCA estimation, we developed a user-friendly calculator, the "YMrCA", including all previously mentioned mutation characteristics. After extensive validation, we observed that the YMrCA calculator demonstrated a promising performance. The YMrCA yields a significantly higher tMRCA success rate (96%; +20%) and a lower tMRCA error (7; -3) compared to the mutation models and all online tMRCA calculators. Therefore, YMrCA offers the next step towards more objective tMRCA estimation for DNA kinship research.

Cell survival and DNA damage repair are promoted in the human blood thanatotranscriptome shortly after death.

RNA analysis of post-mortem tissues, or thanatotranscriptomics, has become a topic of interest in forensic science due to the essential information it can provide in forensic investigations. Several studies have previously investigated the effect of death on gene transcription, but it has never been conducted with samples of the same individual. For the first time, a longitudinal mRNA expression analysis study was performed with post-mortem human blood samples from individuals with a known time of death. The results reveal that, after death, two clearly differentiated groups of up- and down-regulated genes can be detected. Pathway analysis suggests active processes that promote cell survival and DNA damage repair, rather than passive degradation, are the source of early post-mortem changes of gene expression in blood. In addition, a generalized linear model with an elastic net restriction predicted post-mortem interval with a root mean square error of 4.75 h. In conclusion, we demonstrate that post-mortem gene expression data can be used as biomarkers to estimate the post-mortem interval though further validation using independent sample sets is required before use in forensic casework.