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Common Genetic Polymorphisms Modify the Effect of Smoking on Absolute Risk of Bladder Cancer

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TLDR
The potential impact of eliminating smoking on the number of bladder cancer cases prevented is larger for individuals at higher than lower genetic risk, which could have implications for targeted prevention strategies.
Abstract
Bladder cancer results from the combined effects of environmental and genetic factors, smoking being the strongest risk factor. Evaluating absolute risks resulting from the joint effects of smoking and genetic factors is critical to assess the public health relevance of genetic information. Analyses included up to 3,942 cases and 5,680 controls of European background in seven studies. We tested for multiplicative and additive interactions between smoking and 12 susceptibility loci, individually and combined as a polygenic risk score (PRS). Thirty-year absolute risks and risk differences by levels of the PRS were estimated for U.S. males aged 50 years. Six of 12 variants showed significant additive gene-environment interactions, most notably NAT2 (P = 7 × 10(-4)) and UGT1A6 (P = 8 × 10(-4)). The 30-year absolute risk of bladder cancer in U.S. males was 6.2% for all current smokers. This risk ranged from 2.9% for current smokers in the lowest quartile of the PRS to 9.9% for current smokers in the upper quartile. Risk difference estimates indicated that 8,200 cases would be prevented if elimination of smoking occurred in 100,000 men in the upper PRS quartile compared with 2,000 cases prevented by a similar effort in the lowest PRS quartile (P(additive) = 1 × 10(-4)). Thus, the potential impact of eliminating smoking on the number of bladder cancer cases prevented is larger for individuals at higher than lower genetic risk. Our findings could have implications for targeted prevention strategies. However, other smoking-related diseases, as well as practical and ethical considerations, need to be considered before any recommendations could be made.

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Citations
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Journal ArticleDOI

Developing and evaluating polygenic risk prediction models for stratified disease prevention

TL;DR: This Review provides a summary of the methodologies used for building, evaluating and applying risk prediction models that include information from genetic testing and environmental risk factors.
Journal ArticleDOI

From GWAS to Function: Using Functional Genomics to Identify the Mechanisms Underlying Complex Diseases.

TL;DR: A review of how challenges of integrating GWAS results with single-cell sequencing read-outs, designing functionally informed polygenic risk scores (PRS), and validating disease associated genes using genetic engineering have been addressed over the last decade are summarized.
Journal ArticleDOI

Genome-wide association studies of cancer: current insights and future perspectives

TL;DR: Genome-wide association studies have shown that common genetic variation contributes substantially to the heritable risk of many common cancers, and deciphering the functional and biological basis of associations is challenging and is in part a barrier to fully unlocking the potential of GWAS.
Journal ArticleDOI

Genetic Epidemiology and Insights into Interactive Genetic and Environmental Effects in Autism Spectrum Disorders

TL;DR: Use of autism spectrum disorder (ASD) as a paradigmatic neurodevelopmental disorder and how genetic epidemiology approaches including gene-environment interactions (G×E) can play a role in identifying factors associated with ASD etiology are reviewed.
References
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Inference of population structure using multilocus genotype data

TL;DR: Pritch et al. as discussed by the authors proposed a model-based clustering method for using multilocus genotype data to infer population structure and assign individuals to populations, which can be applied to most of the commonly used genetic markers, provided that they are not closely linked.
Journal ArticleDOI

genalex 6: genetic analysis in Excel. Population genetic software for teaching and research

TL;DR: Genalex is a user-friendly cross-platform package that runs within Microsoft Excel, enabling population genetic analyses of codominant, haploid and binary data.
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TL;DR: This is an account of cancer epidemiology has been expanded and contains new material on cancer biology, molecular epidemiology, preventive strategies and specific types and sites of cancer.
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TL;DR: In this paper, the authors present a formal justification for the use of the Bootstrap in statistical inference. But they do not discuss future limitations of the bootstrap and their application in the statistical verification of confidence intervals.
Journal ArticleDOI

Estimating the population attributable risk for multiple risk factors using case-control data

TL;DR: A straightforward and unified approach is presented for the calculation of the population attributable risk per cent in the general multivariate setting, with emphasis on using data from case-control studies, so that risks need not be estimated separately in a large number of strata.
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