The Genetic Chain Rule for Probabilistic Kinship Estimation

Abstract

Accurate kinship predictions using DNA forensic samples is limited to first degree relatives. High throughput sequencing of single nucleotide polymorphisms and short tandem repeats (STRs) can be used to expand DNA forensics kinship prediction capabilities. Current kinship identification models incorporate STR size profiles to statistical models that do not adequately depict genetic inheritance beyond the first degree, or machine learning algorithms that are prone to over optimization and requiring similar training data. This work presents an alternative approach using a com- putational framework that incorporates the inheritance of single nucleotide polymorphisms (SNPs) between specific relationships(patent pending)[1]. The impact of SNP panel size on predictions is visualized in terms of the distribution of allelic differences between individuals. The confidence of predictions is made by calculating log likelihood ratios. With a panel of 39108 SNPs evaluated on an in silico dataset, this method can resolve parents from siblings and distinguish 1st, 2nd, 3rd, and 4th degree relatives from each other and unrelated individuals.