An improved burden-test pipeline for identifying associations from rare germline and somatic variants

Abstract

BackgroundIdentifying rare germline and somatic variants associated with cancer progression is an important research topic in cancer genomics. Although many approaches are proposed for rare variant association study, they are not fit for cancer sequencing data due to multiple issues, such as overly relying on pre-selection, losing sight of interacting hotspots, etc.ResultsIn this article, we propose an improved pipeline to identify germline variant and somatic mutation interactions influencing cancer susceptibility from pair-wise cancer sequencing data. The proposed pipeline, RareProb-C performs an algorithmic selection on the given variants by incorporating the variant allelic frequencies. The interactions among the variants are considered within the regions which are limited by a four-gamete test. Then it filters singular cases according to the posterior probability at each site. Finally, it outputs the selected candidates that pass a collapse test.ConclusionsWe apply RareProb-C on a series of carefully constructed simulation cases and it outperforms six existing genetic model-free approaches. We also test RareProb-C on 429 TCGA ovarian cancer cases, and RareProb-C successfully identifies the known highlighted variants which are considered increasing disease susceptibilities.

InvBFM: finding genomic inversions from high-throughput sequence data based on feature mining

“…deletion) have been very actively studied (e.g. [2][3][4][5][6]), other types of SVs such as inversion are less studied. Different from deletion calling where there are a growing list of deletion calling tools, there are less tools for finding inversions.…”