Ulrike Peters

Fat, fiber, fruits, vegetables, and risk of colorectal adenomas

A case‐control study was conducted at the National Naval Medical Center (Maryland, USA) from 1994 to 1996 to investigate the possible association between dietary factors and colorectal adenomas. Cases (n = 239) were subjects diagnosed with adenomas (146 new and 93 recurrent) by sigmoidoscopy or colonoscopy. Those with no evidence of adenomas found by sigmoidoscopy were recruited as controls (n = 228). Dietary variables, assessed by a 100‐item food frequency questionnaire, were analyzed by the logistic regression model, which was adjusted for age, gender and total energy intake. Variables of fat intake were further adjusted for red meat intake. An increased risk of 7% [odds ratio (OR): 1.07; 95% confidence interval (95% CI): 0.94–1.22] per 5% energy/day from total fat was observed. Every additional 5% unit of oleic acid intake/day significantly increased the adenoma risk by 115% (OR: 2.15; 95% CI: 1.05–4.39). Red meat fat increased the risk by 20% (OR: 1.20; 95% CI: 0.71–2.04), and white meat fat decreased the risk by 67% (OR: 0.33; 95% CI: 0.19–0.95) for every additional 5% unit of respective intake/day. Risk decreased by 41% (OR: 0.59; 95% CI: 0.41–0.86) for every additional 5% unit of fiber intake/day. Vegetable [OR per 100 g of vegetable intake/day: 0.83, 95% CI: 0.67–1.04] and fruit (OR per 100 g of fruit intake/day: 0.92, 95% CI: 0.82–1.03) intake showed an inverse association, and the results are suggestive of an association with the risk for adenomas. In conclusion, a strong positive association between oleic acid intake and colorectal adenoma risk was observed. This is likely to be an indicator of “unhealthy” food (meat, dairy, margarine, mayonnaise, sweet baked food) consumption in this population. Increased intake of dietary fiber was associated with a moderately decreased risk of adenomas.

Powerful Cocktail Methods for Detecting Genome‐Wide Gene‐Environment Interaction

Identifying gene and environment interaction (G × E) can provide insights into biological networks of complex diseases, identify novel genes that act synergistically with environmental factors, and inform risk prediction. However, despite the fact that hundreds of novel disease‐associated loci have been identified from genome‐wide association studies (GWAS), few G × Es have been discovered. One reason is that most studies are underpowered for detecting these interactions. Several new methods have been proposed to improve power for G × E analysis, but performance varies with scenario. In this article, we present a module‐based approach to integrating various methods that exploits each methods most appealing aspects. There are three modules in our approach: (1) a screening module for prioritizing Single Nucleotide Polymorphisms (SNPs); (2) a multiple comparison module for testing G × E; and (3) a G × E testing module. We combine all three of these modules and develop two novel “cocktail” methods. We demonstrate that the proposed cocktail methods maintain the type I error, and that the power tracks well with the best existing methods, despite that the best methods may be different under various scenarios and interaction models. For GWAS, where the true interaction models are unknown, methods like our “cocktail” methods that are powerful under a wide range of situations are particularly appealing. Broadly speaking, the modular approach is conceptually straightforward and computationally simple. It builds on common test statistics and is easily implemented without additional computational efforts. It also allows for an easy incorporation of new methods as they are developed. Our work provides a comprehensive and powerful tool for devising effective strategies for genome‐wide detection of gene‐environment interactions.

Mendelian randomization study of height and risk of colorectal cancer

BACKGROUNDnFor men and women, taller height is associated with increased risk of all cancers combined. For colorectal cancer (CRC), it is unclear whether the differential association of height by sex is real or is due to confounding or bias inherent in observational studies. We performed a Mendelian randomization study to examine the association between height and CRC risk.nnnMETHODSnTo minimize confounding and bias, we derived a weighted genetic risk score predicting height (using 696 genetic variants associated with height) in 10,226 CRC cases and 10,286 controls. Logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (95% CI) for associations between height, genetically predicted height and CRC.nnnRESULTSnUsing conventional methods, increased height (per 10-cm increment) was associated with increased CRC risk (OR = 1.08, 95% CI = 1.02-1.15). In sex-specific analyses, height was associated with CRC risk for women (OR = 1.15, 95% CI = 1.05-1.26), but not men (OR = 0.98, 95% CI = 0.92-1.05). Consistent with these results, carrying greater numbers of (weighted) height-increasing alleles (per 1-unit increase) was associated with higher CRC risk for women and men combined (OR = 1.07, 95% CI = 1.01-1.14) and for women (OR = 1.09, 95% CI =u2009 .01-1.19). There was weaker evidence of an association for men (OR = 1.05, 95% CI = 0.96-1.15).nnnCONCLUSIONnWe provide evidence for a causal association between height and CRC for women. The CRC-height association for men remains unclear and warrants further investigation in other large studies.

A Haplotype-Based Test of Association Using Data from Cohort and Nested Case-Control Epidemiologic Studies

Haplotype-based risk models can lead to powerful methods for detecting the association of a disease with a genomic region of interest. In population-based studies of unrelated individuals, however, the haplotype status of some subjects may not be discernible without ambiguity from available locus-specific genotype data. A score test for detecting haplotype-based association using genotype data has been developed in the context of generalized linear models for analysis of data from cross-sectional and retrospective studies [1]. In this article, we develop a test for association using genotype data from cohort and nested case-control studies where subjects are prospectively followed until disease incidence or censoring (end of follow-up) occurs. Assuming a proportional hazard model for the haplotype effects, we derive an induced hazard function of the disease given the genotype data, and hence propose a test statistic based on the associated partial likelihood. The proposed test procedure can account for differential follow-up of subjects, can adjust for possibly time-dependent environmental co-factors and can make efficient use of valuable age-at-onset information that is available on cases. We provide an algorithm for computing the test statistic using readily available statistical software. Utilizing simulated data in the context of two genomic regions GPX1 and GPX3, we evaluate the validity of the proposed test for small sample sizes and study its power in the presence and absence of missing genotype data.

Gene-environment interactions in cancer epidemiology

Cancer risk is determined by a complex interplay of genetic and environmental factors. Genome‐wide association studies (GWAS) have identified hundreds of common (minor allele frequency [MAF] > 0.05) and less common (0.01 < MAF < 0.05) genetic variants associated with cancer. The marginal effects of most of these variants have been small (odds ratios: 1.1–1.4). There remain unanswered questions on how best to incorporate the joint effects of genes and environment, including gene‐environment (G × E) interactions, into epidemiologic studies of cancer. To help address these questions, and to better inform research priorities and allocation of resources, the National Cancer Institute sponsored a “Gene‐Environment Think Tank” on January 10–11, 2012. The objective of the Think Tank was to facilitate discussions on (1) the state of the science, (2) the goals of G × E interaction studies in cancer epidemiology, and (3) opportunities for developing novel study designs and analysis tools. This report summarizes the Think Tank discussion, with a focus on contemporary approaches to the analysis of G × E interactions. Selecting the appropriate methods requires first identifying the relevant scientific question and rationale, with an important distinction made between analyses aiming to characterize the joint effects of putative or established genetic and environmental factors and analyses aiming to discover novel risk factors or novel interaction effects. Other discussion items include measurement error, statistical power, significance, and replication. Additional designs, exposure assessments, and analytical approaches need to be considered as we move from the current small number of success stories to a fuller understanding of the interplay of genetic and environmental factors.