Optimization of computational ancestry inference for use in cancer cell lines.

Cancer cell lines have provided invaluable preclinical mechanistic data for cancer health disparities research. Although there are several studies that detail ancestry inference methods using microarray data, there are none that provide investigators with documentation of ancestry inference methods using sequencing data. Here, we describe our computational workflow for inferring genetic ancestry using either whole genome sequencing (WGS) or RNA-sequencing (RNA-seq) data from cancer cell lines. RNA-seq and WGS datasets were generated from four head and neck cancer cell lines with self-identified race/ethnicity (SIRE) as either White or Black. Our workflow included variant calling and genotype imputation via Illumina DRAGEN pipelines, merging genotyping datasets with the 1000 Genomes Project (1KGP), single nucleotide polymorphism (SNP) filtering via PLINK, and ancestry inference with ADMIXTURE. We encountered challenges in workflow development with SNP filtering and clustering of 1KGP superpopulations. Adjusting stringency of filtering parameters to a window size of 100 kb and