Currently, there is no global consensus about the essentiality of dietary chromium. To provide evidence to this debate, an examination of blood chromium levels and common chronic health conditions was undertaken. Using a subsample from the 2015–2016 US National Health and Nutrition Examination Survey (n = 2894; 40 years+), chi-square and binary logistic regression analyses were conducted to examine blood chromium levels (0.7–28.0 vs. <0.7 µg/L) and their associations with cardiovascular diseases (CVDs; self-report), diabetes mellitus (DM; glycohemoglobin ≥5.7%), and depression (Patient Health Questionnaire-9 score ≥5), while controlling for socio-demographic (age/sex/income/education/relationship status) and health-related (red blood cell folate/medications/co-morbidities/body mass index (BMI)/substance use) factors. The sample was almost evenly distributed between men and women (n = 1391, 48.1% (men); n = 1503, 51.9% (women)). The prevalence estimates of low blood chromium levels tended to be higher among those with CVDs (47.4–47.6%) and DM (50.0–51.6%). Comparisons between those with low vs. normal blood chromium levels indicate men have increased odds of CVDs (adjusted odds ratio (aOR) = 1.86, 95% confidence interval (CI): 1.22–2.85, p < 0.001) and DM (aOR = 1.93, 95% CI: 1.32–2.83, p < 0.001) and lower odds of depression (aOR = 0.42, 95% CI: 0.22–0.77, p < 0.05). Dietary chromium may be important in the prevention and management of CVDs and DM for men. Continued exploration of chromium’s role in chronic diseases, including differences by biological factors, is needed.

We investigate the impact of confounding on the results of a genome-wide association analysis by Beaty et al., which identified multiple single nucleotide polymorphisms that appeared to modify the effect of maternal smoking, alcohol consumption, or multivitamin supplementation on risk of cleft palate. The study sample of case-parent trios was primarily of European and East Asian ancestry, and the distribution of all three exposures differed by ancestral group. Such differences raise the possibility that confounders, rather than the exposures, are the risk modifiers and hence that the inference of gene-environment (G×E) interaction may be spurious. Our analyses generally confirmed the result of Beaty et al. and suggest the interaction G×E is driven by the European trios, whereas the East Asian trios were less informative.

Introduction: In genetic epidemiology, log-linear models of population risk may be used to study the effect of genotypes and exposures on the relative risk of a disease. Such models may also include gene-environment interaction terms that allow the genotypes to modify the effect of the exposure, or equivalently, the exposure to modify the effect of genotypes on the relative risk. When a measured test locus is in linkage disequilibrium with an unmeasured causal locus, exposure-related genetic structure in the population can lead to spurious gene-environment interaction; that is, to apparent gene-environment interaction at the test locus in the absence of true gene-environment interaction at the causal locus. Exposure-related genetic structure occurs when the distributions of exposures and of haplotypes at the test and causal locus both differ across population strata. A case-parent trio design can protect inference of genetic main effects from confounding bias due to genetic structure in the population. Unfortunately, when the genetic structure is exposure-related, the protection against confounding bias for the genetic main effect does not extend to the gene-environment interaction term.Methods: We show that current methods to reduce the bias in estimated gene-environment interactions from case-parent trio data can only account for simple population structure involving two strata. To fill this gap, we propose to directly accommodate multiple population strata by adjusting for genetic principal components (PCs).Results and Discussion: Through simulations, we show that our PC adjustment maintains the nominal type-1 error rate and has nearly identical power to detect gene-environment interaction as an oracle approach based directly on population strata. We also apply the PC-adjustment approach to data from a study of genetic modifiers of cleft palate comprised primarily of case-parent trios of European and East Asian ancestry. Consistent with earlier analyses, our results suggest that the gene-environment interaction signal in these data is due to the self-reported European trios.

In genetic epidemiology, log-linear models of population risk may be used to study the effect of genotypes and exposures on the relative risk of a disease. Such models may also include gene environment interaction terms that allow the genotypes to modify the effect of the exposure, or equivalently, the exposure to modify the effect of genotypes on the relative risk. When a measured test locus is in linkage disequilibrium with a causal locus, exposure-related genetic population structure can lead to spurious gene-environment interaction; that is, to apparent gene-environment interaction at the test locus in the absence of true gene-environment interaction at the causal locus. Exposure-related genetic population structure occurs when the distributions of exposures and of haplotypes at the test and causal locus both differ across population strata (i.e., population substructure). In a case-parent trio design researchers collect genotypes and exposures on affected children and genotypes on their parents. The design permits inference of genetic main effects while avoiding confounding bias due to population stratification. Unfortunately, when there is exposure-related genetic population structure, the protection against confounding bias for the main effect does not extend to the interaction term. We show that current methods to reduce the bias in estimated gene-environment interactions can only account for simple population structure involving two strata. To fill this gap, we propose to directly accommodate multiple population strata by adjusting for genetic principal components. We evaluate our approach through simulation and illustrate it on data from a study of genetic modifiers of cleft palate.