Mining Whole Genome Sequence data to efficiently attribute individuals to source populations

Abstract

Whole genome sequence (WGS) data could transform our ability to attribute individuals to source populations. However, methods that effectively mine these data are yet to be developed. We present a minimal multilocus distance (MMD) method which rapidly deals with these large data sets as well as methods for optimally selecting loci. This was applied on WGS data to determine the source of human campylobacteriosis, the geographical origin of diverse biological species including humans and proteomic data to classify breast cancer tumours. The MMD method provides a highly accurate attribution which is computationally efficient for extended genotypes. These methods are generic, easy to implement for WGS and proteomic data and have wide application.