Data Mining of Rare Alleles to Assess Biogeographic Ancestry

Abstract

The United States Department of Defense (DoD) routinely seeks more efficient ways to examine genetic data applied to cases of foreign or domestic crime. The process of identifying biogeographic ancestry groups using forensic DNA data to provide investigative leads is currently performed on Single Nucleotide Polymorphisms (SNP). The motivation for this project was to determine whether SNP assessment of biogeographic ancestry can be replicated using analysis of autosomal Short Tandem Repeats (STR) while preserving predictive accuracy. Replacing SNP analysis with STR analysis is theoretically more efficient. STR data can be generated from a significantly smaller amount of DNA. Additionally, readily available genetic data can be analyzed well after collection. Moreover, in contrast to SNP analysis, STR analysis is more cost effective per sample. Several considerations for this paper were necessary: 1) Whether or not STR profiles at 24 loci can be distinguished into distinct clusters using microvariants and off-ladder alleles. 2) Given that there is identifiable clustering, whether or not these clusters can be probabilistically identified as members biogeographic ancestry groups. STR profiles consisting of 24 loci from N=2,348 subjects were analyzed. The present analysis employed multidimensional scaling (MDS), which provides a measure of dissimilarity between STR profiles and reduces the tabular profiles into two latent dimensions. Using the scaled MDS coordinates, a Gaussian Mixture Model (GMM) was constructed which provides probabilities of belongingness for every data point to each cluster. Results from the model indicated separations between certain biogeographic ancestry groups with the probabilities generated from the GMM providing a posteriori confidence levels for group membership. Such analyses may be of benefit for efforts in future crime investigation where biogeographic ancestry identification is needed.