Helfer, B. S. | bioRxiv Science

bioRxiv2018

11 Million SNP Reference Profiles for Identity Searching Across Ethnicities, Kinship, and Admixture

High throughput sequencing (HTS) of single nucleotide polymorphisms (SNPs) provides additional applications for DNA forensics including identification, mixture analysis, kinship prediction, and biogeographic ancestry prediction. Public repositories of human genetic data are being rapidly generated and released, but the majorities of these samples are de-identified to protect privacy, and have little or no individual metadata such as appearance (photos), ethnicity, relatives, etc. A reference in silico dataset has been generated to enable development and testing of new DNA forensics algorithms. This dataset provides 11 million SNP profiles for individuals with defined ethnicities and family relationships spanning eight generations with admixture for a panel with 39,108 SNPs.

bioinformatics↗

bioRxiv2017

The Genetic Chain Rule for Probabilistic Kinship Estimation

Accurate kinship predictions using DNA forensic samples has utility for investigative leads, remains identification, identifying relationships between individuals of interest, etc. High throughput sequencing (HTS) of STRs and single nucleotide polymorphisms (SNPs) is enabling the characterization of larger numbers of loci. Large panels of SNP loci have been proposed for improved mixture analysis of forensic samples. While multiple kinship prediction approaches have been established, we present an approach focusing on these large HTS SNP panels for predicting degree of kinship predictions. Formulas for first degree relatives can be multiplied (chained) together to model extended kinship relationships. Predictions are made using these formulations by calculating log likelihood ratios and selecting the maximum likelihood across the possible relationships. With a panel of 30,000 SNPs evaluated on an in silico dataset, this method can resolve parents from siblings and distinguish 1st, 2nd, and 3rd degree relatives from each other and unrelated individuals.

bioinformatics↗