PopInf: An approach for reproducibly visualizing and assigning population affiliation in genomic samples of uncertain origin

ABSTRACT Germline genetic variation contributes to cancer etiology, but self-reported race is not always consistent with genetic ancestry, and samples may not have identifying ancestry information. Here we describe a flexible computational pipeline, PopInf, to visualize principal components analysis output and assign ancestry to samples with unknown genetic ancestry, given a reference population panel of known origins. PopInf is implemented as a reproducible workflow in Snakemake with a tutorial on GitHub. We provide a pre-processed reference population panel that can be quickly and efficiently implemented in cancer genetics studies. We ran PopInf on TCGA liver cancer data and identify discrepancies between reported race and inferred genetic ancestry. Significance The PopInf pipeline facilitates visualization and identification of genetic ancestry across samples, so that this ancestry can be accounted for in studies of disease risk. All code and a tutorial are available on Github: https://github.com/SexChrLab/PopInf .