Rothman, Nat

Mixture analysis is an emerging statistical tool in epidemiological research that seeks to estimate the health effects associated with mixtures of several exposures. This approach acknowledges that individuals experience many simultaneous exposures and it can estimate the relative importance of components in the mixture. Health effects due to mixtures may vary over space driven by to political, demographic, environmental, or other differences. In such cases, estimating a global mixture effect without accounting for spatial variation would induce bias in effect estimates and potentially lower statistical power. To date, no methods have been developed to estimate spatially varying chemical mixture effects. We developed a Bayesian spatially varying mixture model that estimates spatially varying mixture effects and the importance weights of components in the mixture, while adjusting for covariates. We demonstrate the efficacy of the model through a simulation study that varies the number of mixtures (one and two) and spatial pattern (global, one‐dimensional, radial) and magnitude of mixture effects, showing that the model is able to accurately reproduce the spatial pattern of mixture effects across a diverse set of scenarios. Finally, we apply our model to a multi‐center case‐control study of non‐Hodgkin lymphoma (NHL) in Detroit, Iowa, Los Angeles, and Seattle. We identify significant spatially varying positive and inverse associations with NHL for two mixtures of pesticides in Iowa and do not find strong spatial effects at the other three centers. In conclusion, the Bayesian spatially varying mixture model represents a novel method for modeling spatial variation in mixture effects.

The exposome is an ideal in public health research that posits that individuals experience risk for adverse health outcomes from a wide variety of sources over their lifecourse. There have been increases in data collection in the various components of the exposome, but novel statistical methods are needed that capture multiple dimensions of risk at once. We introduce a Bayesian index low‐rank kriging (LRK) multiple membership model (MMM) to simultaneously estimate the health effects of one or more groups of exposures, the relative importance of exposure components, and cumulative spatial risk over time using residential histories. The model employs an MMM to consider all residential locations for subjects weighted by duration and LRK to increase computational efficiency. We demonstrate the performance of the Bayesian index LRK‐MMM through a simulation study, showing that the model accurately and consistently estimates the health effects of one or several group indices and has high power to identify a region of elevated spatial risk due to unmeasured environmental exposures. Finally, we apply our model to data from a multicenter case‐control study of non‐Hodgkin lymphoma (NHL), finding a significant positive association between one index of pesticides and risk for NHL in Iowa. Additionally, we find an area of significantly elevated spatial risk for NHL in Los Angeles. In conclusion, our Bayesian index LRK‐MMM represents a step forward toward bringing the ideals of the exposome into practice for environmental risk analyzes.

Abstract Purpose: We have previously mapped a major susceptibility locus influencing familial lung cancer risk to chromosome 6q23-25. However, the causal gene at this locus remains undetermined. In this study, we further refined this locus to identify a single candidate gene, by fine mapping using microsatellite markers and association studies using high-density single nucleotide polymorphisms (SNP). Experimental Design: Six multigenerational families with five or more affected members were chosen for fine-mapping the 6q linkage region using microsatellite markers. For association mapping, we genotyped 24 6q-linked cases and 72 unrelated noncancer controls from the Genetic Epidemiology of Lung Cancer Consortium resources using the Affymetrix 500K chipset. Significant associations were validated in two independent familial lung cancer populations: 226 familial lung cases and 313 controls from the Genetic Epidemiology of Lung Cancer Consortium, and 154 familial cases and 325 controls from Mayo Clinic. Each familial case was chosen from one high-risk lung cancer family that has three or more affected members. Results: A region-wide scan across 6q23-25 found significant association between lung cancer susceptibility and three single nucleotide polymorphisms in the first intron of the RGS17 gene. This association was further confirmed in two independent familial lung cancer populations. By quantitative real-time PCR analysis of matched tumor and normal human tissues, we found that RGS17 transcript accumulation is highly and consistently increased in sporadic lung cancers. Human lung tumor cell proliferation and tumorigenesis in nude mice are inhibited upon knockdown of RGS17 levels. Conclusion: RGS17 is a major candidate for the familial lung cancer susceptibility locus on chromosome 6q23-25.