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Context-dependent effects of genome-wide association study genotypes and macroenvironment on time...

Timothy R Rebbeck, Anita L Weber, Amy H Walker, Klara Stefflova, Teo V Tran, Elaine Spangler, Bao-Li Chang, Charnita M Zeigler-Johnson


First published: Sep 2010

PMID: 20826827 PMCID: PMC2972664 DOI: 10.1158/1055-9965.EPI-10-0173


Abstract

Background: Disparities in cancer defined by race, age, or gender are well established. However, demographic metrics are surrogates for the complex contributions of genotypes, exposures, health care, socioeconomic and sociocultural environment, and many other factors. Macroenvironmental factors represent novel surrogates for exposures, lifestyle, and other factors that are difficult to measure but might influence cancer outcomes.


Methods: We applied a "multilevel molecular epidemiology" approach using a prospective cohort of 444 White prostate cancer cases who underwent prostatectomy and were followed until biochemical failure (BF) or censoring without BF. We applied Cox regression models to test for joint effects of 86 genome-wide association study-identified genotypes and macroenvironment contextual effects after geocoding all cases to their residential census tracts. All analyses were adjusted for age at diagnosis and tumor aggressiveness.


Results: Residents living in census tracts with a high proportion of older single heads of household, high rates of vacant housing, or high unemployment had shorter time until BF postsurgery after adjustment for patient age and tumor aggressiveness. After correction for multiple testing, genotypes alone did not predict time to BF, but interactions predicting time to BF were observed for MSMB (rs10993994) and percentage of older single heads of households (P = 0.0004), and for HNF1B/TCF2 (rs4430796) and census tract per capita income (P = 0.0002).


Conclusions: The context-specific macroenvironmental effects of genotype might improve the ability to identify groups that might experience poor prostate cancer outcomes.


Impact: Risk estimation and clinical translation of genotype information might require an understanding of both individual- and macroenvironment-level context.


(c) 2010 AACR.



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