Somatic mutations play a critical role in the development and progression of cancer. Studying the association
between somatic mutations and cancer-related traits is essential for understanding the genetic basis of cancer,
and constitutes a fundamental step towards precision oncology. However, due to the low frequency of somatic
mutations and high complexity of cancer, development of statistical methods for analyzing these data has
severely fallen behind. In this project, we propose to develop powerful statistical approaches for the analysis of
somatic mutation data. In Specific Aim 1, we propose methods for investigating the association between somatic
mutations and multiple cancer-related traits, including continuous, binary, and survival traits. The joint analysis
of multiple traits will lead to better understanding of the etiology of cancer, and substantially improve statistical
power for detecting disease-associated mutations. In Specific Aim 2, we will develop novel methods for analyzing
cancer subtypes with respect to somatic mutations. The proposed methods will be essential for understanding
the cancer heterogeneity and for developing personalized therapy for cancer patients. In Specific Aim 3, we will
develop statistical approaches for conducting biological pathway analysis that includes both somatic and
germline mutations. The proposed approaches can handle pathways with large dimensions, and are statistically
more powerful than existing approaches. Our methods are motivated by the research projects of the Genetics
and Epidemiology of Colorectal Cancer Consortium (GECCO), which is the largest colorectal-cancer consortium
in the world. The PI and co-investigators of this proposal are directly involved in the GECCO project, and hence
can quickly translate statistical findings into public health benefits and new scientific explorations. The proposed
methods development will provide essential tools for studying somatic mutations and make significant
contributions to biomedical science.
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