A Neighborhood-Wide Association Study (NWAS): Example of prostate cancer aggressiveness
Lynch, Shannon M.
Zeigler-Johnson, Charnita M.
Branas, Charles C.
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CitationLynch, Shannon M., Nandita Mitra, Michelle Ross, Craig Newcomb, Karl Dailey, Tara Jackson, Charnita M. Zeigler-Johnson, Harold Riethman, Charles C. Branas, and Timothy R. Rebbeck. 2017. “A Neighborhood-Wide Association Study (NWAS): Example of prostate cancer aggressiveness.” PLoS ONE 12 (3): e0174548. doi:10.1371/journal.pone.0174548. http://dx.doi.org/10.1371/journal.pone.0174548.
AbstractPurpose Cancer results from complex interactions of multiple variables at the biologic, individual, and social levels. Compared to other levels, social effects that occur geospatially in neighborhoods are not as well-studied, and empiric methods to assess these effects are limited. We propose a novel Neighborhood-Wide Association Study(NWAS), analogous to genome-wide association studies(GWAS), that utilizes high-dimensional computing approaches from biology to comprehensively and empirically identify neighborhood factors associated with disease. Methods: Pennsylvania Cancer Registry data were linked to U.S. Census data. In a successively more stringent multiphase approach, we evaluated the association between neighborhood (n = 14,663 census variables) and prostate cancer aggressiveness(PCA) with n = 6,416 aggressive (Stage≥3/Gleason grade≥7 cases) vs. n = 70,670 non-aggressive (Stage<3/Gleason grade<7) cases in White men. Analyses accounted for age, year of diagnosis, spatial correlation, and multiple-testing. We used generalized estimating equations in Phase 1 and Bayesian mixed effects models in Phase 2 to calculate odds ratios(OR) and confidence/credible intervals(CI). In Phase 3, principal components analysis grouped correlated variables. Results: We identified 17 new neighborhood variables associated with PCA. These variables represented income, housing, employment, immigration, access to care, and social support. The top hits or most significant variables related to transportation (OR = 1.05;CI = 1.001–1.09) and poverty (OR = 1.07;CI = 1.01–1.12). Conclusions: This study introduces the application of high-dimensional, computational methods to large-scale, publically-available geospatial data. Although NWAS requires further testing, it is hypothesis-generating and addresses gaps in geospatial analysis related to empiric assessment. Further, NWAS could have broad implications for many diseases and future precision medicine studies focused on multilevel risk factors of disease.
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