Online Program Home
My Program

Abstract Details

Activity Number: 652 - Genomics, Metabolomics, Microbiome and NextGen Sequencing
Type: Contributed
Date/Time: Thursday, August 1, 2019 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #301801
Title: Multivariate Association Analysis with Somatic Mutation Data
Author(s): Chad He* and Yang Liu and Ulrike Peters and Li Hsu
Companies: Fred Hutchinson Cancer Research Center and Wright State University and Fred Hutchinson Cancer Research Center and Fred Hutchinson Cancer Research Center, USA
Keywords: Association test; Mixture of traits; Multivariate traits; Somatic mutations

Somatic mutations are the driving forces for tumor development, and recent advances in cancer genome sequencing have made it feasible to evaluate the association between somatic mutations and cancer-related traits. However, it is challenging to conduct statistical analysis for somatic mutations because of their low frequencies. Furthermore, cancer is a complex disease and it is often accompanied by multiple traits that reflect various aspects of cancer; how to combine the information of these traits to identify important somatic mutations poses additional challenges. We introduce a statistical approach, named as SOMAT, for detecting somatic mutations associated with multiple cancer-related traits. Our approach provides a flexible framework for analyzing multiple traits, including a mixture of continuous and binary traits. In addition, we propose a data-adaptive procedure for effectively combining test statistics to enhance statistical power. Simulations show that the proposed approach works well in the considered situations. We also apply our approach to an exome-sequencing tumor dataset for illustration.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2019 program