IntroductionSomatic mutations play a fundamental role in the development and progression of cancer (Luzzatto, ). Recent progress in high‐throughput genome technologies has enabled sequencing of cancer genomes in hundreds of patients (Watson et al., ), making it possible to comprehensively evaluate the health impact of somatic mutations at a large scale. The study of somatic mutations with respect to cancer‐related traits provides novel insight into tumor biology, offers new paths to drug development, and potentially leads to better clinical treatment (Yu, O'Toole, and Trent, ).A major challenge in analyzing somatic mutation data lies in the low frequency of somatic mutations. The median non‐silent mutation rate in protein‐coding regions is estimated to be 1.5/Mb (Lawrence et al., ); for a typical gene with 1000bp of coding sequence, this translates to only 1.5e‐3 non‐silent mutations per gene. Besides the low mutation rate, somatic mutations are often distributed across many different sites of the gene, hence the frequency of somatic mutation at each individual site can be extremely low; for this reason, it is nearly impossible to conduct statistical inference for a single mutation site. A common strategy to alleviate this problem is to summarize somatic mutations within a gene into a
Biometrics – Wiley
Published: Jan 1, 2018
Keywords: ; ; ; ;
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