Somalier: rapid relatedness estimation for cancer and germline studies using efficient genome sketches

Abstract

When interpreting sequencing data from multiple spatial or longitudinal biopsies, detecting sample mix-ups is essential yet more difficult than in studies of germline variation. In most genomic studies of tumors, genetic variation is frequently detected through pairwise comparisons of the tumor and a matched normal tissue from the sample donor, and in many cases, only somatic variants are reported. The disjoint genotype information that results hinders the use of existing tools that detect sample swaps solely based on genotypes of germline variants. To address this problem, we have developed somalier, which can operate directly on the alignments, so as not to require jointly-called germline variants. Instead, somalier extracts a small sketch of informative genetic variation for each sample. Sketches from hundreds of biopsies and normal tissues can then be compared in under a second. This speed also makes it useful for checking relatedness in large cohorts of germline samples. Somalier produces both text output and an interactive visual report that facilitates the detection and correction of sample swaps using multiple relatedness metrics. We introduce the tool and demonstrate its utility on a cohort of five glioma samples each with a normal, tumor, and cell-free DNA sample. Applying somalier to high-coverage sequence data from the 1000 Genomes Project also identifies several related samples. Somalier can be applied to diverse sequencing data types and genome builds, and is freely available for academic use at github.com/brentp/somalier.

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* https://github.com/brentp/somalier