Gene Environment Interactions and Predictors of Colorectal Cancer in Family-Based, Multi-Ethnic Groups

• Main Authors: S. Pamela K. Shiao, James Grayson, Chong Ho Yu, Brandi Wasek, Teodoro Bottiglieri
• Format: Article
• Language: English
• Published: MDPI AG 2018-02-01
• Series: Journal of Personalized Medicine
• Subjects: gene–environment interaction; colorectal cancer; predictor; multi-ethnic groups
• Online Access: http://www.mdpi.com/2075-4426/8/1/10

For the personalization of polygenic/omics-based health care, the purpose of this study was to examine the gene–environment interactions and predictors of colorectal cancer (CRC) by including five key genes in the one-carbon metabolism pathways. In this proof-of-concept study, we included a total of 54 families and 108 participants, 54 CRC cases and 54 matched family friends representing four major racial ethnic groups in southern California (White, Asian, Hispanics, and Black). We used three phases of data analytics, including exploratory, family-based analyses adjusting for the dependence within the family for sharing genetic heritage, the ensemble method, and generalized regression models for predictive modeling with a machine learning validation procedure to validate the results for enhanced prediction and reproducibility. The results revealed that despite the family members sharing genetic heritage, the CRC group had greater combined gene polymorphism rates than the family controls (p < 0.05), on MTHFR C677T, MTR A2756G, MTRR A66G, and DHFR 19 bp except MTHFR A1298C. Four racial groups presented different polymorphism rates for four genes (all p < 0.05) except MTHFR A1298C. Following the ensemble method, the most influential factors were identified, and the best predictive models were generated by using the generalized regression models, with Akaike’s information criterion and leave-one-out cross validation methods. Body mass index (BMI) and gender were consistent predictors of CRC for both models when individual genes versus total polymorphism counts were used, and alcohol use was interactive with BMI status. Body mass index status was also interactive with both gender and MTHFR C677T gene polymorphism, and the exposure to environmental pollutants was an additional predictor. These results point to the important roles of environmental and modifiable factors in relation to gene–environment interactions in the prevention of CRC.