The Genetic Chain Rule for Probabilistic Kinship Estimation

Abstract

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.