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

Abstract

27Germline genetic variation contributes to cancer etiology, but self-reported race is not 28 always consistent with genetic ancestry, and samples may not have identifying ancestry 29 information. Here we describe a flexible computational pipeline, PopInf, to visualize principal 30 components analysis output and assign ancestry to samples with unknown genetic ancestry, 31given a reference population panel of known origins. PopInf is implemented as a reproducible 32 workflow in Snakemake with a tutorial on GitHub. We provide a pre-processed reference 33 population panel that can be quickly and efficiently implemented in cancer genetics studies. We 34 ran PopInf on TCGA liver cancer data and identify discrepancies between reported race and 35 inferred genetic ancestry. Significance. The PopInf pipeline facilitates visualization and 36 identification of genetic ancestry across samples, so that this ancestry can be accounted for in 37 studies of disease risk. All code and a tutorial are available on Github: 38 https://github.com/SexChrLab/PopInf. 39 40 Keywords: population ancestry, principal components analysis, visualization, computational 41 pipeline, cancer GWAS 42 43 44 45 46 47 48