Statistical Genetics and Genomics for Epidemiologic Research

点击翻译按钮获取中文摘要

Project Summary/Abstract Genome-wide association studies have identified an unprecedented number of genetic variants associated with disease risk, yet molecular mechanisms and clinical implications of genetic risk alleles are largely unknown. Epidemiologic research is extending its reach to more translational and mechanistic studies, integrating genetic risk variants with environmental exposures, interventions, gene expression and epigenetics. Motivated by population-based studies for prostate cancer research, we will develop statistical methods for emerging translational topics: how to search for genotypes that predict individual and subgroup intervention effects? how to identify epigenetic alterations that may be an interface of the environment and the genome? how to assess causal mediation effect of a modifiable risk factor or a molecular alteration in relation to disease outcomes? These topics present unmet statistical challenges because of high dimensionality and complex modeling. Specific statistical methods to be developed include high-dimensional gene- treatment interaction, multi-locus regional association, mediation analyses, instrumental variable analyses, Mendelian randomization, and shrinkage and regularization. The methodological research in this project is driven primarily by prostate cancer, the most common noncutaneous cancer and the second leading cause of cancer death in American men, a ecting one in six in his lifetime. This project nests in highly-accomplished consortium studies (PCPT/SELECT/PRACTICAL/PCPS), all of which have generated far-reaching impact on prostate cancer research. The unique feature of this project is that methodological development will be seamlessly integrated with ongoing analyses, ensuring immediate translation. Our transdisciplinary research team has been actively engaged in statistical genetics and genomics, and conducting molecular epidemiological studies. The PI brings a wealth of expertise in high-dimensional methods, molecular biomarkers, and genetic epidemiology. This project will have a far-reaching impact on methodologies in cancer etiology, prevention, and treatment outcomes.