""""""""Incorporating intermediate biomarkers of folate with colorectal cancer"""""""" The goal of this proposal is to measure intermediate biomarkers and to evaluate these with statistical methods designed to elucidate the underlying etiologic mechanism of colorectal cancer. The proposal leverages existing genetic data from studies conducted within the Colon Cancer Family Registry (Colon CFR), an NCI-supported consortium initiated in 1997 and dedicated to the establishment of a comprehensive collaborative infrastructure for interdisciplinary studies in the genetics and genetic epidemiology of colorectal cancer. The subjects include 1,531 controls genotyped in a candidate gene study of FOCM pathway genes (RO1CA112237), and 999 controls genotyped in a genome wide association study of colon CFR cases (U01CA122839). In these subjects, we will evaluate plasma measures within one carbon metabolism (plasma folate, vitamins B2, B6, B12, methionine, methyl malonic acid, creatinine, plasma total Hcy (tHcy), and DNA methylation in circulating lymphocytes (PBL)). In addition, we build upon our previous work in developing statistical methods for modeling genetic associations in putative disease pathways. These models integrate various levels of data, e.g. genotypes, gene expression, biomarkers, and exogenous exposures, with prior information to build more comprehensive statistical models for better prioritization, estimation, and characterization of genetic effects.

Public Health Relevance

The overall goal of this proposal is to gain further insight into the underlying etiologic mechanism of colorectal cancer. We will accomplish this by first measuring intermediate biomarkers relevant to folate associated one- carbon metabolism in subjects with existing genotype data from a related candidate gene pathway-based and genome-wide association studies (GWAS). Then using novel statistical methods, we will investigate SNP- biomarker, SNP-disease, and pathway-disease associations to provide more insight into the complex effects of folate on the colorectal carcinogenic process.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA140561-04
Application #
8707212
Study Section
Epidemiology of Cancer Study Section (EPIC)
Program Officer
Zhu, Claire
Project Start
2011-09-07
Project End
2016-07-31
Budget Start
2014-08-01
Budget End
2015-07-31
Support Year
4
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of Southern California
Department
Public Health & Prev Medicine
Type
Schools of Medicine
DUNS #
City
Los Angeles
State
CA
Country
United States
Zip Code
90089
Asante, Isaac; Pei, Hua; Zhou, Eugene et al. (2018) Exploratory metabolomic study to identify blood-based biomarkers as a potential screen for colorectal cancer. Mol Omics :
Moss, Lilit C; Gauderman, William J; Lewinger, Juan Pablo et al. (2018) Using Bayes model averaging to leverage both gene main effects and G?×? E interactions to identify genomic regions in genome-wide association studies. Genet Epidemiol :
Gauderman, W James; Mukherjee, Bhramar; Aschard, Hugues et al. (2017) Update on the State of the Science for Analytical Methods for Gene-Environment Interactions. Am J Epidemiol 186:762-770
McAllister, Kimberly; Mechanic, Leah E; Amos, Christopher et al. (2017) Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases. Am J Epidemiol 186:753-761
Ritchie, Marylyn D; Davis, Joe R; Aschard, Hugues et al. (2017) Incorporation of Biological Knowledge Into the Study of Gene-Environment Interactions. Am J Epidemiol 186:771-777
Wang, Hansong; Schmit, Stephanie L; Haiman, Christopher A et al. (2017) Novel colon cancer susceptibility variants identified from a genome-wide association study in African Americans. Int J Cancer 140:2728-2733
Schmit, Stephanie L; Schumacher, Fredrick R; Edlund, Christopher K et al. (2016) Genome-wide association study of colorectal cancer in Hispanics. Carcinogenesis 37:547-556
Newcombe, Paul J; Conti, David V; Richardson, Sylvia (2016) JAM: A Scalable Bayesian Framework for Joint Analysis of Marginal SNP Effects. Genet Epidemiol 40:188-201
Bough, K J; Lerman, C; Rose, J E et al. (2013) Biomarkers for smoking cessation. Clin Pharmacol Ther 93:526-38
Quintana, M A; Conti, D V (2013) Integrative variable selection via Bayesian model uncertainty. Stat Med 32:4938-53

Showing the most recent 10 out of 14 publications