Association analysis using somatic mutations.

ABSTRACT: Somatic mutations drive the growth of tumor cells and are pivotal biomarkers for many cancer treatments. Genetic association analysis using somatic mutations is an effective approach to study the functional impact of somatic mutations. However, standard regression methods are not appropriate for somatic mutation association studies because somatic mutation calls often have non-ignorable false positive rate and/or false negative rate. While large scale association analysis using somatic mutations becomes feasible recently-thanks for the improvement of sequencing techniques and the reduction of sequencing cost-there is an urgent need for a new statistical method designed for somatic mutation association analysis. We propose such a method with computationally efficient software implementation

SUBMITTER: Liu Y 

PROVIDER: S-EPMC6235399 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature